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Chapter 7

Shinto

Copyright © 2017, 2014, 2011 Pearson Education, Inc. or its affiliates. All rights reserved.

Living Religions

Tenth Edition

Chapter 6

Daoism and Confucianism

Copyright © 2017, 2014, 2011 Pearson Education, Inc. or its affiliates. All rights reserved.

Living Religions

Tenth Edition

......................................................................................................... European Journal of Public Health, Vol. 26, No. 1, 60–64

� The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. doi:10.1093/eurpub/ckv122 Advance Access published on 1 July 2015

.........................................................................................................

The impact of electronic health records on healthcare quality: a systematic review and meta-analysis

Paolo Campanella, Emanuela Lovato, Claudio Marone, Lucia Fallacara, Agostino Mancuso, Walter Ricciardi, Maria Lucia Specchia

Department of Public Health, Catholic University of Sacred Heart, L.go F. Vito 1 00168, Rome, Italy

Correspondence: Paolo Campanella, Department of Public Health, Section of Hygiene, Catholic University of Sacred Heart, L.go F. Vito 1 00168, Rome, Italy, Tel: (+39) 0635019534, Fax: (+39) 0635019535, e-mail: [email protected]

Objective: To assess the impact of electronic health record (EHR) on healthcare quality, we hence carried out a systematic review and meta-analysis of published studies on this topic. Methods: PubMed, Web of Knowledge, Scopus and Cochrane Library databases were searched to identify studies that investigated the association between the EHR implementation and process or outcome indicators. Two reviewers screened identified citations and extracted data according to the PRISMA guidelines. Meta-analysis was performed using the random effects model for each indicator. Heterogeneity was quantified using the Cochran Q test and I2 statistics, and publication bias was assessed using the Egger’s test. Results: Of the 23 398 citations identified, 47 articles were included in the analysis. Meta-analysis showed an association between EHR use and a reduced documentation time with a difference in mean of �22.4% [95% confidence interval (CI) =�38.8 to �6.0%; P < 0.007]. EHR resulted also associated with a higher guideline adherence with a risk ratio (RR) of 1.33 (95% CI = 1.01 to 1.76; P = 0.049) and a lower number of medication errors with an overall RR of 0.46 (95% CI = 0.38 to 0.55; P < 0.001), and adverse drug effects (ADEs) with an overall RR of 0.66 (95% CI = 0.44 to 0.99; P = 0.045). No association with mortality was evident (P = 0.936). High heterogeneity among the studies was evident. Publication bias was not evident. Conclusions: EHR system, when properly implemented, can improve the quality of healthcare, increasing time efficiency and guideline adherence and reducing medication errors and ADEs. Strategies for EHR implementation should be therefore recommended and promoted. .........................................................................................................

Introduction

O ur world has been radically transformed through digital innovation. Information technologies play a growing role in

healthcare delivery and help address the health problems and challenges faced by clinicians and other health professionals.

An electronic health record (EHR) is a systematic electronic collection of health information about patients such as medical history, medication orders, vital signs, laboratory results, radiology reports, and physician and nurse notes. In healthcare institutions, it automates the medication, as well as exam, ordering process ensuring standardized, readable and complete orders.

An EHR may also include a decision support system (DSS) that provides up-to-date medical knowledge, reminders or other actions that aid health professionals in decision making.

1

Although several studies on the effects of EHR implementation have been published, evidence on EHR effects continues to be disputed. Even if most of the studies published seem to provide promising data, some reported different results, such as Han et al.

2 who reported an unexpected rise in mortality after the EHR

implementation in a tertiary care children’s hospital. To assess the impact of EHRs on healthcare quality, we hence carried

out a systematic review and meta-analysis of published studies on this topic that may provide a rational basis for recommendations.

Methods

This study was conducted and reported in accord with PRISMA guidelines for meta-analyzes and systematic reviews.3

Search strategy and study selection

A protocol was developed, and we searched in PubMed, Web of Knowledge, Scopus and Cochrane Library databases to identify studies that evaluated the benefits of EHR implementation using the following algorithm:#1 = ‘Electronic Medical Record’ OR

‘Electronic Health Record’ OR ‘Electronic Patient Record’. #2 = ‘Computerized Physician Order Entry’. #3 = ‘Decision Support Systems’. #4 = #1 OR #2 OR #3. #5 = value OR impact OR benefit OR improvement. #6 = quality OR efficiency OR risk OR safety. #7 = #5 OR #6. #8 = #4 AND #7.

Our search was restricted to English language studies published from 1994 to 2013.

Studies were considered eligible if they investigated the association between the EHR implementation and process or outcome indicators and if they had a control group who did not use the EHR.

One reviewer screened titles, and then, abstracts of relevant titles were identified. Full texts of potential citations were subsequently obtained; two reviewers independently screened them for inclusion, and disagreements were resolved through discussion. Additional relevant publications were identified from the references of the initially retrieved articles.

Data extraction

From each study, we extracted data on the first author’s last name, year of publication and process or outcome indicators evaluated.

For indicators represented by dichotomous variables, risk ratios (RRs) with their confidence intervals (CIs) (or data necessary to obtain them) were extracted. For indicators represented by

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continuous variables, sample sizes of both control and intervention groups and differences in mean (DMs) and their CIs (or data necessary to obtain them) were extracted.

All data extractions were conducted independently by two reviewers, and disagreements were resolved through discussion.

Data analysis

Meta-analysis was performed for each process or outcome indicators evaluated. Because of the significant heterogeneity expected among the studies performed in different settings, the random effects model was employed using the Der Simonian and Laird’s method.4

Heterogeneity was quantified using the Cochran Q test and I 2

statistics. 5

For indicators with available both studies including DSS and not subgroup analyzes were performed.

Sensitivity analyzes were conducted by excluding one study at a time from the meta-analysis to determine whether the results of the meta-analysis were influenced by individual studies and whether risk estimates and heterogeneity were substantially modified.

The presence of publication bias was assessed using a visual funnel plot inspection and Egger’s test.6

All statistical tests were performed with Comprehensive Meta- Analysis software version 2.2.064 (Biostat, Englewood, NJ).

Results

Search results and study characteristics

Searching the online databases resulted in 23 398 articles from PubMed, Web of Knowledge, Scopus and Cochrane Library. After the initial screening of titles and abstracts, 404 articles were considered for full text review. Twelve articles were excluded because full texts were not available, and 352 articles were excluded based on the full text review. After having identified seven additional articles by reviewing bibliographies, 47 articles were included in the analysis (figure 1).

Nine studies investigated the relationship between EHR use and a reduced documentation time spent by healthcare professionals. The association between EHR and guideline adherence, medication

Figure 1 Search flow for EHR literature

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Figure 2 Forest plot for the meta-analysis of studies reporting on (a) EHR and documentation time, (b) guideline adherence, (c) medication errors, (d) ADEs and (e) mortality. The overall, as well as subgroup, estimates of the effect are represented by diamonds in each plot

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errors, adverse drug effects (ADEs), and mortality were evaluated in 6, 24, 7 and 8 studies, respectively.

Meta-analysis

Meta-analysis showed an association between EHR use by healthcare professionals and a reduced documentation time with a difference in mean of �22.4% (95% CI =�38.8% to �6.0%; P < 0.007).

The EHR resulted also associated with a higher guideline adherence with an RR of 1.33 (95% CI = 1.01 to 1.76; P = 0.049) and a lower number of medication errors with an overall RR of 0.46 (95% CI = 0.38 to 0.55; P < 0.001) and ADEs with an overall RR of 0.66 (95% CI = 0.44 to 0.99; P = 0.045). No association with mortality was evident (P = 0.936) (figure 2).

High heterogeneity among the studies regarding documentation time (Q test P < 0.001 and I2 = 92.4%), guideline adherence (Q test P < 0.001 and I

2 = 91.9%), medication errors (Q test P < 0.001 and

I2 = 97.7%) and ADEs (Q test P < 0.001 and I2 = 80.8%) was evident. Moderate heterogeneity regarding mortality (Q test P = 0.012 and I2 = 61.0%) was also evident.

Sensitivity analysis and publication bias

Sensitivity analysis has shown the stability of the overall effect sizes with the withdrawal of any of the study from the analysis without a significant improvement of the heterogeneity. Publication bias was not evident from reviews of the funnel plot or Egger’s test for any process or outcome indicators considered.

Subgroup analysis

For medication errors, ADEs and mortality both studies including and excluding DSS were available. Subgroup analysis confirmed the association between EHR and a reduction of medication errors and showed a better outcome for EHR including DSS, RR of 0.33 (95% CI = 0.25 to 0.45), compared with software without DSS, RR of 0.60 (95% CI = 0.45 to 0.81). Regarding the association between EHR and ADEs reduction, subgroup analysis also showed a better significant association for EHR including DSS, RR of 0.40 (95% CI = 0.21 to 0.75), but it showed a non-significant association for software not including DSS, RR of 1.20 (95% CI = 0.79 to 1.82).

Moreover, regarding the absence of significant association between EHR and mortality, subgroup analysis confirmed this absence with a slightly better outcome for EHR using DSS, RR of 0.93 (95% CI = 0.58 to 1.49), compared with EHR not using DSS, RR of 1.06 (95% CI = 0.59 to 1.92).

Discussion

This meta-analysis provides evidence that the use of EHR can improve the quality of healthcare, increasing time efficiency and guideline adherence and reducing medication errors and ADEs.

Consequently, EHR can determine also a reduction of costs associated with medical errors, ADEs and time inefficiency. In effect, several studies focused on the economics of medical errors7–9 and ADEs10,11 point out that considerable cost reductions are achievable through improving quality of care and reducing harm to patients.

12

Guidelines adherence may have an impact on resource use and cost reduction, supporting specialists in their clinical choices by reducing errors and ADEs related to treatment and, consequently, unnecessary waste of resources, as some examples reported by scientific literature.13 In fact guidelines are promoted as a means to decrease inappropriate clinical practice variability and use of in- effective therapies and to reduce medical errors,14 thus resulting in improved patient outcomes and more cost-effective care.15

Moreover, several studies have reported that the use of appropriate information technology in the delivery of healthcare may also improve hospital efficiency, with benefits exceeding the costs of adoption16 and patient satisfaction rating.17

Subgroup analyzes for EHR with DSS compared with EHR without DSS provide also interesting results. EHR including DSS, that actively provides up-to-date medical knowledge, reminders or other actions that aid health professionals in decision making, showed in fact generally a better outcome.

So, even if in this review we are far from knowing how EHR generates these quality improvements, this may suggest that such dynamic components are ones of the most effective parts of EHRs.

Regarding the association between EHR and ADEs reduction, subgroup analysis showed a better significant association for EHR including DSS, but a non-significant association for software not including DSS. However, the absence of association with ADEs reduction for the subgroup of studies not using DSS is probably due to the limitation of having only three studies in this subgroup.

Despite the benefits that EHR can provide, a proper implemen- tation strategy is essential. In our opinion, it is likely that there are cases where the success of EHR was not reached because of a non- effective implementation strategy.

An example of an effective strategy may be identified through the WHO guidelines for EHR in developing countries18 and reassumed in six key actions:–review the current health record system, –try to emulate benchmark practices, –involve the anticipated users of the system from the onset of

discussions, –train the users to the EHR system, –evaluate the benefits of the implemented system, –update the system when needed.

We believe that such an implementation strategy or a similar one is crucial in effectively setting up an EHR system, reducing the resistance of medical practitioners and health professionals, ensuring that the system is used optimally, and obtaining clinical results.

Having used the tool of quantitative meta-analysis of several outcomes to synthesize the evidence on the EHR is definitely a strength of our study.

However, our study has also its limitations. In fact, we focused on different indicators and although we did a comprehensive search, we found only a limited number of articles with quantitative data among the articles identified and even less for each indicator and subgroup. High heterogeneity was also present and may have affected the robustness of the results. Possible source of such het- erogeneity includes difference in the software used, their quality and usability, and different settings of implementation.

Moreover, information on technical items and procedures that shape the EHR software was not included in most studies. Further research is therefore needed to determine the differences among the various system, the different items that shape an EHR software, and the different benefits of any of them. Health information technology systems are, in fact, healthcare interventions, and systems for evaluating their efficacy and safety should be as robust as those evaluating other healthcare technologies. Such evidence may provide healthcare providers with useful indication regarding the kind of EHR software and its proper implementation to improve the quality of health care provided and to generate value.

EHR is also often considered an ideal tool to be used to assess healthcare quality and monitor health providers’ performance because of the availability of stored computerized data. The last could allow automated quality assessment, avoiding manual chart review and medical record abstraction, both of which are expensive and time-consuming processes. This will require future research to focus on intervention strategies for improving both quality and comprehensiveness of clinical data stored in EHR and identifying the best process of data extraction.19,20

Cumulative evidence shows that EHR systems can improve the quality of healthcare by increasing time efficiency and guideline adherence and reducing medication errors and ADEs. Therefore, strategies for EHR implementation should be recommended and promoted.

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Further research on technical items and procedures that shape the EHR software is needed to identify the features that have value for both clinical results and quality monitoring.

Conflicts of interest: None declared.

Key points

� Health information technology systems are healthcare interventions. � EHR systems can improve the quality of healthcare. � Strategies for EHR implementation should be recommended

and promoted.

References

1 Murphy EV. Clinical decision support: effectiveness in improving quality processes

and clinical outcomes and factors that may influence success. Yale J Biol Med

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2 Han YY, Carcillo JA, Venkataraman ST, et al. Unexpected increased mortality after

implementation of a commercially sold computerized physician order entry system.

Pediatrics 2005;116:1506–12.

3 Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting

systematic reviews and meta-analyses of studies that evaluate healthcare interven-

tions: explanation and elaboration. BMJ 2009;339:b2700.

4 DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials

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5 Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med

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6 Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by

a simple, graphical test. BMJ 1997;315:629–34.

7 Andel C, Davidow SL, Hollander M, Moreno DA. The economics of health care

quality and medical errors. J Health Care Finance 2012;39:39–50.

8 David G, Gunnarsson CL, Waters HC, et al. Economic measurement of medical

errors using a hospital claims database. Value Heal 2013;16:305–10.

9 Goodman JC, Villarreal P, Jones B. The social cost of adverse medical events, and

what we can do about it. Health Aff 2011;30:590–5.

10 Agency for Healthcare Research and Quality. Reducing and Preventing

Adverse Drug Events To Decrease Hospital Costs: Research in Action,

Issue 1. March 2001. Agency for Healthcare Research and Quality, Rockville, MD.

http://archive.ahrq.gov/research/findings/factsheets/errors-safety/aderia/ade.html.

11 Pham JC, Aswani MS, Rosen M, et al. Reducing medical errors and adverse events.

Annu Rev Med 2012;63:447–63.

12 Swensen SJ, Kaplan GS, Meyer GS, et al. Controlling healthcare costs

by removing waste: what American doctors can do now. BMJ Q Saf

2011;20:534–7.

13 Akenroye AT, Baskin MN, Samnaliev M, Stack AM. Impact of a bronchiolitis

guideline on ED resource use and cost: a segmented time-series analysis. Pediatrics

2014;133:e227–34.

14 Codish S, Shiffman RN. A model of ambiguity and vagueness in clinical practice

guideline recommendations. AMIA Annu Symp Proc 2005:146–50.

15 Fritz JM, Cleland JA, Brennan GP. Does adherence to the guideline

recommendation for active treatments improve the quality of care for

patients with acute low back pain delivered by physical therapists? Med Care

2007;45:973–80.

16 Zhivan NA, Diana ML. U.S. hospital efficiency and adoption of health information

technology. Health Care Manag Sci 2012;15:37–47.

17 Zabada C, Singh S, Munchus G. The role of information technology in enhancing

patient satisfaction. Br J Clin Gov 2001;6:9–16.

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Countries. 2006. Geneva, Switzerland: World Health Organization.

19 Roth CP, Lim YW, Pevnick JM, et al. The challenge of measuring quality of care

from the electronic health record. Am J Med Q 2009;24:385–94.

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Chapter 2 Health Care Data Learning Objectives To be able to define health care data and information. To be able to understand the major purposes for maintaining patient records. To be able to discuss basic patient health records and claims content. To be able to discuss basic uses of health care data, including big and small data and analytics. To be able to identify common issues related to health care data quality. Central to health care information systems is the actual health care data that is collected and subsequently transformed into useful health care information. In this chapter we will examine key aspects of healthcare data. In particular, this chapter is divided into four main sections:

Health care data and information defined (What are health data and health information?) Health care data and information sources (Where does health data originate and why? When does health care data become health care information?) Health care data uses (How do healthcare organizations use data? What is the impact of the trend toward analytics and big data on health care data?) Health care data quality (How does the quality of health data affect its use?) Health Care Data and Information Defined Often the terms health care data and healthcare information are used interchangeably. However, there is a distinction, if somewhat blurred in current use. What, then, is the difference between health data and health information? The simple answer is that health information is processed health data. (We interpret processing broadly to cover everything from formal analysis to explanations supplied by the individual decision maker's brain.) Health care data are raw health care facts, generally stored as characters, words, symbols, measurements, or statistics. One thing apparent about health care data is that they are generally not very useful for decision making. Health care data may describe a particular event, but alone and unprocessed they are not particularly helpful. Take, for example, this figure: 79 percent. By itself, what does it mean? If we process this datum further by indicating that it represents the average bed occupancy for a hospital for the month of January, it takes on more meaning. With the additional facts attached, is this figure now information? That depends. If all a healthcare executive wants or needs to know is the bed occupancy rate for January, this could be considered information. However, for the hospital executive who is interested in knowing the trend of the bed occupancy rate over time or how the facility's bed occupancy rate compares to that of other, similar facilities, this is not yet the information he needs. A clinical example of raw data would be the lab value, hematocrit (HCT) = 32 or a diagnosis, such as diabetes. These are single facts, data at the most granular level. They take on meaning when assigned to particular patients in the context of their health care status or analyzed as components of population studies.

Knowledge is seen by some as the highest level in a hierarchy with data at the bottom and information in the middle (Figure 2.1). Knowledge is defined by Johns (1997, p. 53) as “a combination of rules, relationships, ideas, and experience.” Another way of thinking about knowledge is that it is information applied to rules, experiences, and relationships with the result that it can be used for decision making. Data analytics applied to healthcare information and

research studies based on health care information are examples of transforming health care information into new knowledge. To carry out our example from previous paragraphs, the 79 percent occupancy rate could be related to additional information to lead to knowledge that the health care facility's referral strategy is working.

Figure 2.1 Health care data to health care knowledge

Where do health care data end and where does health care information begin? Information is an extremely valuable asset at all levels of the health care community. Health care executives, clinical staff members, and others rely on information to get their jobs accomplished. The goal of this discussion is not to pinpoint where data ends and information begins but rather to further an understanding of the relationship between health care data and information—health care data are the beginnings of health care information. You cannot create information without data. Through the rest of this chapter the terms health care data and health care information will be used to describe either the most granular components of health care information or data that have been processed, respectively (Lee, 2002).

The first several sections of this chapter focus primarily on the health care data and information levels, but the content of the section on health care data quality takes on new importance when applied to processes for seeking knowledge from health care data. We will begin the chapter exploring where some of the most common health care data originate and describe some of the most common organizational and provider uses of health care information, including patient care, billing and reimbursement, and basic health care statistics. Please note there are many other uses for health information that go beyond these basics that will be explored throughout this text.

Health Care Data and Information Sources The majority of healthcare information created and used in healthcare information systems within and across organizations can be found as an entry in a patient's health record or claim, and this information is readily matched to a specific, identifiable patient.

The Health Insurance Portability and Accountability Act (HIPAA), the federal legislation that includes provisions to protect patients' health information from unauthorized disclosure, defines health information as any information, whether oral or recorded in any form or medium, that does the following:

Is created or received by a health care provider, health plan, public health authority, employer, life insurer, school or university, or health care clearinghouse Relates to the past, present, or future physical or mental health or condition of an individual, the provision of health care to an individual, or the past, present, or future payment for the provision of health care to an individual HIPAA refers to this type of identifiable information as protected health information (PHI).

The Joint Commission, the major accrediting agency for many types of healthcare organizations in the United States, has adopted the HIPAA definition of protected health information as the definition of “health information” listed in their accreditation manuals' glossary of terms (The Joint Commission, 2016). Creating, maintaining, and managing quality health information is a significant factor in health care organizations, such as hospitals, nursing homes, rehabilitation centers, and others, who want to achieve Joint Commission accreditation. The accreditation manuals for each type of facility contain dozens of standards that are devoted to the creation and management of health information. For example, the hospital accreditation manual contains two specific chapters, Record of Care, Treatment, and Services (RC) and Information Management (IM). The RC chapter outlines specific standards governing the components of a complete medical record, and the IM chapter outlines standards for managing information as an important organizational resource.

Medical Record versus Health Record The terms medical record and health record are often used interchangeably to describe a patient's clinical record. However, with the advent and subsequent evolution of electronic versions of patient records these terms actually describe different entities. The Office of the National Coordinator for Health Information Technology (ONC) distinguishes the electronic medical record and the electronic health record as follows.

Electronic medical records (EMRs) are a digital version of the paper charts. An EMR contains the medical and treatment history of the patients in one practice (or organization). EMRs have advantages over paper records. For example, EMRs enable clinicians (and others) to do the following:

Track data over time Easily identify which patients are due for preventive screenings or checkups Check how their patients are doing on certain parameters such as blood pressure readings or vaccinations Monitor and improve overall quality of care within the practice But the information in EMRs doesn't travel easily out of the practice (or organization). In fact, the patient's record might even have to be printed out and delivered by mail to specialists and other members of the care team. In that regard, EMRs are not much better than a paper record.

Electronic health records (EHRs) do all those things—and more. EHRs focus on the total health of the patient—going beyond standard clinical data collected in the provider's office (or during episodes of care)—and are inclusive of a broader view on a patient's care. EHRs are designed to reach out beyond the health organization that originally collects and compiles the information. They are built to share information with other health care providers (and organizations), such as laboratories and specialists, so they contain information from all the clinicians involved in the patient's care (Garrett & Seidman, 2011). Another distinguishing feature of the EHR (discussed in more detail in Chapter Three) is the inclusion of decision-support capabilities beyond those of the EMR.

Patient Record Purposes Health care organizations maintain patient clinical records for several key purposes. As we move into the discussion on clinical information systems in subsequent chapters, it will be important to remember these purposes, which remain constant regardless of the format or infrastructure supporting the records. In considering the purposes listed, the scope of care is also important. Records support not only managing a single episode of care but also a patient's continuum of care and population health. Episode of care generally refers to the services provided to a patient with a specific condition for a specific period Continuum of care, as defined by HIMSS (2014), is a concept involving a system that guides and tracks patients over time through a comprehensive array of health services spanning all levels and intensity of care. Population health is a relatively new term and definitions vary. However, the concept behind managing population health is to improve health outcomes within defined communities (Stoto, 2013). The following list comprises the most commonly recognized purposes for creating and maintaining patient records.

Patient care. Patient records provide the documented basis for planning patient care and treatment, for a single episode of care and across the care continuum. This purpose is considered the number-one reason for maintaining patient records. As our health care delivery system moves toward true population health management and patient-focused care, the patient record becomes a critical tool for documenting each provider's contribution to that care. Communication. Patient records are an important means by which physicians, nurses, and others, whether within a single organization or across organizations, can communicate with one another about patient needs. The members of the health care team generally interact with patients at different times during the day, week, or even month or year. Information from the patient's record plays an important role in facilitating communication among providers across the continuum of care. The patient record may be the only means of communication among various providers. It is important to note that patients also have a right to access their records, and their engagement in their own care is often reflected in today's records. Legal documentation. Patient records, because they describe and document care and treatment, are also legal records. In the event of a lawsuit or other legal action involving patient care, the record becomes the primary evidence for what actually took place during the care. An old but absolutely true adage about the legal importance of patient records says, “If it was not documented, it was not done.” Billing and reimbursement. Patient records provide the documentation patients and payers use to verify billed services. Insurance companies and other third-party payers insist on clear documentation to support any claims submitted. The federal programs Medicare and Medicaid have oversight and review processes in place that use patient records to confirm the accuracy of claims filed. Filing a claim for a service that is not clearly documented in the patient record may be construed as fraud. Research and quality management. Patient records are used in many facilities for research purposes and for monitoring the quality of care provided. Patient records can serve as source documents from which information about certain diseases or procedures can be taken, for

example. Although research is most prevalent in large academic medical centers, studies are conducted in other types of healthcare organizations as well. Population health. Information from patient records is used to monitor population health, assess health status, measure utilization of services, track quality outcomes, and evaluate adherence to evidence-based practice guidelines. Health care payers and consumers are increasingly demanding to know the cost-effectiveness and efficacy of different treatment options and modalities. Population health focuses on prevention as a means of achieving cost-effective care. Public health. Federal and state public health agencies use information from patient records to inform policies and procedures to ensure that they protect citizens from unhealthy conditions.

Patient Records as Legal Documents The importance of maintaining complete and accurate patient records cannot be underestimated. They serve not only as a basis for planning patient care but also as the legal record documenting the care that was provided to patients. The data captured in a patient record becomes a permanent record of that patient's diagnosis, treatments, response to treatments, and case management. Patient records provide much of the source data for health care information that is created, maintained, and managed within and across health care organizations.

When the patient record was a file folder full of paper housed in the health information management department of the hospital, identifying the legal health record (LHR) was fairly straightforward. Records kept in the usual course of business (in this case, providing care to patients) represent an exception to the hearsay rule, are generally admissible in a court, and therefore can be subpoenaed—they are legal documentation of the care provided to the patients. With the implementation of comprehensive EHR systems the definition of an LHR remains the same, but the identification of the boundaries for it may be harder to determine. In 2013, the ONC's National Learning Consortium published the Legal Health Record Policy Template to guide health care organizations and providers in defining which records and record sets constitute their legal health record for administrative, business, or evidentiary purposes. The media on which the records are maintained does not determine the legal status; rather, it is the purpose for which the record was created and is maintained. The complete template can be found at www.healthit.gov/sites/default/files/legal_health_policy_template.docx.

Because of the legal nature of patient records, the majority of states have specific retention requirements for information contained within them. These state requirements should be the basis for the health care organization's formal retention policy. (The Joint Commission and other accrediting agencies also address retention but generally refer organizations back to their own state regulations for specifics.) When no specific retention requirement is made by the state, all patient information that is a part of the LHR should be maintained for at least as long as the state's statute of limitations or other regulation requires. In the case of minor children the LHR should be retained until the child reaches the age of majority as defined by state law, usually eighteen or twenty-one. Health care executives should be aware that statutes of limitations may allow a patient to bring a case as long as ten years after the patient learns that his or her care

caused an injury (Lee, 2002). Although some specific retention requirements and general guidelines exist, it is becoming increasingly popular for health care organizations to keep all LHR information indefinitely, particularly if the information is stored in an electronic format. If an organization does decide to destroy LHR information, this destruction must be carried out in accordance with all applicable laws and regulations.

Another important aspect related to the legal nature of patient records is the need for them to be authenticated. State and federal laws and accreditation standards require that medical record entries be authenticated to ensure that the legal document shows the person or persons responsible for the care provided. Generally, authentication of an LHR entry is accomplished when the physician or other health care professional signs it, either with a handwritten signature or an electronic signature.

Personal Health Records An increasingly common type of patient record is maintained by the individual to track personal health care information: the personal health record (PHR). According to the American Health Information Management Association (AHIMA, 2016), a PHR “is a tool . . . to collect, track and share past and current information about your health or the health of someone in your care.” A PHR is not the same as a health record managed by a healthcare organization or provider, and it does not constitute a legal document of care, but it should contain all pertinent health care information contained in an individual's health records. PHRs are an effective tool enabling patients to be active members of their own health care teams (AHIMA, 2016).

Patient Record Content The following components are common to most patient records, regardless of facility type or record system (AHIMA, 2016). Specific patient record content is determined to a large extent by external requirements, standards, and regulations (discussed in Chapter Nine). Keep in mind, a patient record may contain some or all of the documentation listed. Depending on the patient's illness or injury and the type of treatment facility, he or she may need additional specialized health care services. These services may require specific documentation. For example, long-term care facilities and behavioral health facilities have special documentation requirements. Our list is intended to introduce the common components of patient records, not to provide a comprehensive list of all possible components. The following provides a general overview of record content and the person or persons responsible for capturing the content during a single episode of care. It reveals that the patient record is a repository for a variety of healthcare data and information that is captured by many different individuals involved in the care of the patient.

Identification screen. Information found on the identification screen of a health or medical record originates at the time of registration or admission. The identification data generally includes at least the patient name, address, telephone number, insurance carrier, and policy number, as well as the patient's diagnoses and disposition at discharge. These diagnoses are recorded by the physicians and coded by administrative personnel. (Diagnosis coding is discussed in this chapter.) The identification component of the data is used as a clinical and an administrative

document. It provides a quick view of the diagnoses that required care during the encounter. The codes and other demographic information are used for reimbursement and planning purposes. Problem list. Patient records frequently contain a comprehensive problem list, which identifies significant illnesses and operations the patient has experienced. This list is generally maintained over time. It is not specific to a single episode of care and may be maintained by the attending or primary care physician or collectively by all the health care providers involved in the patient's care. Medication record. Sometimes called a medication administration record (MAR), this record lists medicines prescribed for and subsequently administered to the patient. It often also lists any medication allergies the patient may have. Nursing personnel are generally responsible for documenting and maintaining medication information in acute care settings, because they are responsible for administering medications according to physicians' written or verbal orders. History and physics. The history component of the report describes any major illnesses and surgeries the patient has had, any significant family history of disease, patient health habits, and current medications. The information for the history is provided by the patient (or someone acting on his or her behalf) and is documented by the attending physician or other care provider at the beginning of or immediately prior to an encounter or treatment episode. The physical component of this report states what the physician found when he or she performed a hands-on examination of the patient. The history and physical together document the initial assessment of the patient for the particular care episode and provide the basis for diagnosis and subsequent treatment. They also provide a framework within which physicians and other care providers can document significant findings. Although obtaining the initial history and physical is a one-time activity during an episode of care, continued reassessment and documentation of that reassessment during the patient's course of treatment is critical. Results of reassessments are generally recorded in progress notes. Progress notes. Progress notes are made by the physicians, nurses, therapists, social workers, and other staff members caring for the patient. Each provider is responsible for the content of his or her notes. Progress notes should reflect the patient's response to treatment along with the provider's observations and plans for continued treatment. There are many formats for progress notes. In some organizations all care providers use the same note format; in others each provider type uses a customized format. A commonly used format for a progress note is the SOAP format. Providers are expected to enter notes divided into four components: Subjective findings Objective findings Assessment Plan Consultation. A consultation note or report records opinions about the patient's condition made by another health care provider at the request of the attending physician or primary care provider. Consultation reports may come from physicians and others inside or outside a particular health care organization, but this information is maintained as part of the patient record. Physician's orders. Physician's orders are a physician's directions, instructions, or prescriptions given to other members of the health care team regarding the patient's medications, tests, diets,

treatments, and so forth. In the current US healthcare system, procedures and treatments must be ordered by the appropriate licensed practitioner; in most cases this will be a physician. Imaging and X-ray reports. The radiologist is responsible for interpreting images produced through X-rays, mammograms, ultrasounds, scans, and the like and for documenting his or her interpretations or findings in the patient's record. These findings should be documented in a timely manner so they are available to the appropriate provider to facilitate the appropriate treatment. The actual digital images are generally maintained in the radiology or imaging departments in specialized computer systems. These images are typically not considered part of the legal patient record, per se, but in modern EHRs they are available through the same interface. Laboratory reports. Laboratory reports contain the results of tests conducted on body fluids, cells, and tissues. For example, a medical lab might perform a throat culture, urinalysis, cholesterol level, or complete blood count. There are hundreds of specific lab tests that can be run by health care organizations or specialized labs. Lab personnel are responsible for documenting the lab results into the patient record. Results of the lab work become part of the permanent patient record. However, lab results must also be available during treatment. Health care providers rely on accurate lab results in making clinical decisions, so there is a need for timely reporting of lab results and a system for ensuring that physicians and other appropriate care providers receive the results. Physicians or other primary care providers are responsible for documenting any findings and treatment plans based on the lab results. Consent and authorization forms. Copies of consents to admission, treatment, surgery, and release of information are an important component of the patient record related to its use as a legal document. The practitioner who actually provides the treatment must obtain informed consent for the treatment. Patients must sign informed consent documents before treatment takes place. Forms authorizing release of information must also be signed by patients before any patient-specific health care information is released to parties not directly involved in the care of the patient. Operative report. Operative reports describe any surgery performed and list the names of surgeons and assistants. The surgeon is responsible for documenting the information found in the operative report. Pathology report. Pathology reports describe tissue removed during any surgical procedure and the diagnosis based on examination of that tissue. The pathologist is responsible for documenting the information contained within the pathology report. Discharge summary. Each acute care patient record contains a discharge summary. The discharge summary summarizes the hospital stay, including the reason for admission, significant findings from tests, procedures performed, therapies provided, responses to treatments, condition at discharge, and instructions for medications, activity, diet, and follow-up care. The attending physician is responsible for documenting the discharge summary at the conclusion of the patient's stay in the hospital. With the passage of the Accountable Care Act (ACA) and other health care payment reform measures, organizations and communities have begun to shift focus from episodic care to population health. By definition, population health focuses on maintaining health and managing health care utilization for a defined population of patients or community with the goal of decreasing costs. Along with other key components, successful population health will require

extensive care coordination across care providers and community organizations. Care managers are needed to interact with patients on a regular basis during and in between clinical encounters (Institute for Health Technology Transformation, 2012). Needless to say, this will have a significant impact on the form and structure of the future EHRs. These care managers will document all plan findings, clinical and social, within the patient's record and rely on other providers' notes and findings to effectively coordinate care. Baker, Cronin, Conway, DeSalvo, Rajkumar, and Press (2016), for example, describes a new tool to support “person-centered care by a multidisciplinary team,” the comprehensive shared care plan (CSCP), which will rely on HIT to enable collaboration across settings. A stakeholder group organized by the US Department of Health and Human Services developed key goals for the CSCP as they envision it:

It should enable a clinician to electronically view information that is directly relevant to his or her role in the care of the person, to easily identify which clinician is doing what, and to update other members of an interdisciplinary team on new developments. It should put the person's goals (captured in his or her own words) at the center of decision making and give that individual direct access to his or her information in the CSCP. It should be holistic and describe clinical and nonclinical (including home- and community-based) needs and services. It should follow the person through high-need episodes (e.g., acute illness) as well as periods of health improvement and maintenance (Baker et al., 2016). Figures 2.2 through 2.5 display screens from one organization's EHR. extensive care coordination across care providers and community organizations. Care managers are needed to interact with patients on a regular basis during and in between clinical encounters (Institute for Health Technology Transformation, 2012). Needless to say, this will have a significant impact on the form and structure of the future EHRs. These care managers will document all plan findings, clinical and social, within the patient's record and rely on other providers' notes and findings to effectively coordinate care. Baker, Cronin, Conway, DeSalvo, Rajkumar, and Press (2016), for example, describes a new tool to support “person-centered care by a multidisciplinary team,” the comprehensive shared care plan (CSCP), which will rely on HIT to enable collaboration across settings. A stakeholder group organized by the US Department of Health and Human Services developed key goals for the CSCP as they envision it:

It should enable a clinician to electronically view information that is directly relevant to his or her role in the care of the person, to easily identify which clinician is doing what, and to update other members of an interdisciplinary team on new developments. It should put the person's goals (captured in his or her own words) at the center of decision making and give that individual direct access to his or her information in the CSCP. It should be holistic and describe clinical and nonclinical (including home- and community-based) needs and services. It should follow the person through high-need episodes (e.g., acute illness) as well as periods of health improvement and maintenance (Baker et al., 2016). Figures 2.2 through 2.5 display screens from one organization's EHR.

Claims Content As we have seen in the previous section, health care information is captured and stored as a part of the patient record. However, there is more to the story: health care organizations and providers must be paid for the care they provide. Generally, the health care organization's accounting or billing department is responsible for processing claims, an activity that includes verifying insurance coverage; billing third-party payers (private insurance companies, Medicare, or Medicaid); and processing the payments as they are received. Centers for Medicare and Medicaid Services (CMS) currently requires health care providers to submit claims electronically using a set of standard elements. As early as the 1970s the health care community strived to develop standard insurance claim forms to facilitate payment collection. With the nearly universal adoption of electronic billing and government-mandated transaction standards, standard claims content has become essential.

Figure 2.2 Sample EHR information screen

Source: Medical University of South Carolina; Epic.

Figure 2.3 Sample EHR problem list

Source: Epic.

Figure 2.4 Sample EHR progress notes

Source: Epic.

Figure 2.5 Sample EHR lab report

Source: Epic.

Depending on the type of service provided to the patient, one of two standard data sets will be submitted to the third-party payer. The UB-04, or CMS-1450, is submitted for inpatient, hospital-based outpatient, home health care, and long-term care services. The CMS-1500 is submitted for health care provider services, such as those provided by a physician's office. It is also used for billing by some Medicaid state agencies. The standard requirements for the parallel electronic counterparts to the CMS-1450 and CMS-1500 are defined by ANSI ASC X12N 837I (Institutional) and ANSI ASC X12N 837P (Professional), respectively. Therefore, the claims standards are frequently referred to as 837I and 837P.

UB-04/CMS-1450/837I

In 1975, the American Hospital Association (AHA) formed the National Uniform Billing Committee (NUBC), bringing the major national provider and identifying which clinician is doing what, and to update other members of an interdisciplinary team on new developments. It should put the person's goals (captured in his or her own words) at the center of decision making and give that individual direct access to his or her information in the CSCP. It should be holistic and describe clinical and nonclinical (including home- and community-based) needs and services. It should follow the person through high-need episodes (e.g., acute illness) as well as periods of health improvement and maintenance (Baker et al., 2016). Figures 2.2 through 2.5 display screens from one organization's EHR.

Claims Content As we have seen in the previous section, health care information is captured and stored as a part of the patient record. However, there is more to the story: health care organizations and providers must be paid for the care they provide. Generally, the health care organization's accounting or billing department is responsible for processing claims, an activity that includes verifying insurance coverage; billing third-party payers (private insurance companies, Medicare, or Medicaid); and processing the payments as they are received. Centers for Medicare and Medicaid Services (CMS) currently requires health care providers to submit claims electronically using a set of standard elements. As early as the 1970s the health care community strived to develop standard insurance claim forms to facilitate payment collection. With the nearly universal adoption of electronic billing and government-mandated transaction standards, standard claims content has become essential.

Figure 2.2 Sample EHR information screen

Source: Medical University of South Carolina; Epic.

Figure 2.3 Sample EHR problem list

Source: Epic.

Figure 2.4 Sample EHR progress notes

Source: Epic.

Figure 2.5 Sample EHR lab report Depending on the type of service provided to the patient, one of two standard data sets will be submitted to the third-party payer. The UB-04, or CMS-1450, is submitted for inpatient, hospital-based outpatient, home health care, and long-term care services. The CMS-1500 is

submitted for health care provider services, such as those provided by a physician's office. It is also used for billing by some Medicaid state agencies. The standard requirements for the parallel electronic counterparts to the CMS-1450 and CMS-1500 are defined by ANSI ASC X12N 837I (Institutional) and ANSI ASC X12N 837P (Professional), respectively. Therefore, the claims standards are frequently referred to as 837I and 837P.

UB-04/CMS-1450/837I In 1975, the American Hospital Association (AHA) formed the National Uniform Billing Committee (NUBC), bringing the major national provider and payer organizations together for the purpose of developing a single billing form and standard data set that could be used for processing health care claims by institutions nationwide. The first uniform bill was the UB-82. It has since been modified and improved on, resulting, first, in the UB-92 data set and now in the currently used UB-04, also known as CMS-1450. UB-04 is the de facto institutional provider claim standard. Its content is required by CMS and has been widely adopted by other government and private insurers. In addition to hospitals, UB-04 or 837I is used by skilled nursing facilities, end stage renal disease providers, home health agencies, hospices, rehabilitation clinics and facilities, community mental health centers, critical access hospitals, federally qualified health centers, and others to bill their third-party payers. The NUBC is responsible for maintaining and updating the specifications for the data elements and codes that are used for the UB-04/CMS-1450 and 837I. A full description of the elements required and the specifications manual can be found on the NUBC website, www.nubc.org (CMS 2016a; NUBC, 2016).

CMS-1500/837P The National Uniform Claim Committee (NUCC) was created by the American Medical Association (AMA) to develop a standardized data set for the noninstitutional or “professional” health care community to use in the submission of claims (much as the NUBC has done for institutional providers). Members of this committee represent key provider and payer organizations, with the AMA appointing the committee chair. The standardized claim form developed and overseen by NUCC is the CMS-1500 and its electronic counterpart is the 837P. This standard has been adopted by CMS to bill Medicare fee-for-service, and similar to UB-04 and 837I for institutional care, it has become the de facto standard for all types of noninstitutional provider claims, such as those for private physician services. NUCC maintains a crosswalk between the 837P and CMS-1500 explaining the specific data elements, which can be found on their website at www.nucc.org (CMS, 2013; NUCC, 2016).

It is important to recognize that the UB-04 and the CMS-1500 and their electronic counterparts incorporate standardized data sets. Regardless of a health care organization's location or a patient's insurance coverage, the same data elements are collected. In many states UB-04 data and CMS-1500 data must be reported to a central state agency responsible for aggregating and analyzing the state's health data. At the federal level the CMS aggregates the data from these claims forms for analyzing national health care reimbursement and clinical and population trends. Having uniform data sets means that data can be compared not only within organizations but also within states and across the country.

Diagnostic and Procedural Codes Diagnostic and procedural codes are captured during the patient encounter, not only to track clinical progress but also for billing, reimbursement, and other administrative purposes. This diagnostic and procedural information is initially captured in narrative form through physicians' and other health care providers' documentation in the patient record. This documentation is subsequently translated into numerical codes. Coding facilitates the classification of diagnoses and procedures for reimbursement purposes, clinical research, and comparative studies.

Two major coding systems are employed by healthcare providers today:

ICD-10 (International Classification of Diseases) CPT (Current Procedural Terminology), published by the American Medical Association Use of these systems is required by the federal government for reimbursement, and they are recognized by health care agencies nationally and internationally. The UB-04 and CMS-1500 have very specific coding requirements for claim submission, which include use of these coding sets.

ICD-10-CM The ICD-10 classification system used to code diseases and other health statuses in the United States is derived from the International Classification of Diseases, Tenth Revision, which was developed by the World Health Organization (WHO) (CDC, 2016) to capture disease data. The precursors to the current ICD system were developed to enable comparison of morbidity (illness) and mortality (death) statistics across nations. Over the years this basic purpose has evolved and today ICD-10-CM (Clinical Modification) coding plays a major role in reimbursement to hospitals and other health care institutions. ICD-10-CM codes used for determining the diagnosis related group (DRG) into which a patient is assigned. DRGs are in turn the basis for determining appropriate inpatient reimbursements for Medicare, Medicaid, and many other health care insurance beneficiaries. Accurate ICD coding has, as a consequence, become vital to accurate institutional reimbursement.

The National Center of Health Statistics (NVHS) is the federal agency responsible for publishing ICD-10-CM (Clinical Modification) in the United States. Procedure information is similarly coded using the ICD-10-PCS (Procedural Coding System). ICD-10-PCS was developed by CMS for US inpatient hospital settings only. The ICD-10-CM and ICD-10-PCS publications are considered federal government documents whose contents may be used freely by others. However, multiple companies republish this government document in easier-to-use, annotated, formally copyrighted versions. In general, the ICD-10-CM and ICD-10-PCS are updated on an annual basis (CMS, 2015, 2016b).

Exhibit 2.1 Excerpt from ICD-10-CM 2016 Malignant neoplasms (C00-C96)

Malignant neoplasms, stated or presumed to be primary (of specified sites), and certain specified histologies, except neuroendocrine, and of lymphoid, hematopoietic, and related tissue (C00-C75)

Malignant neoplasms of lip, oral cavity, and pharynx (C00-C14)

C00 Malignant neoplasm of lip Use additional code to identify: alcohol abuse and dependence (F10.-) history of tobacco use (Z87.891) tobacco dependence (F17.-) tobacco use (Z72.0) Excludes 1: malignant melanoma of lip (C43.0) Merkel cell carcinoma of lip (C4A.0) other and unspecified malignant neoplasm of skin of lip (C44.0-) C00.0 Malignant neoplasm of external upper lip Malignant neoplasm of lipstick area of upper lip Malignant neoplasm of upper lip NOS Malignant neoplasm of vermilion border of upper lip C00.1 Malignant neoplasm of external lower lip Malignant neoplasm of lower lip NOS Malignant neoplasm of lipstick area of lower lip Malignant neoplasm of vermilion border of lower lip C00.2 Malignant neoplasm of external lip, unspecified Malignant neoplasm of vermilion border of lip NOS C00.3 Malignant neoplasm of upper lip, inner aspect Malignant neoplasm of buccal aspect of upper lip Malignant neoplasm of frenulum of upper lip Malignant neoplasm of mucosa of upper lip Malignant neoplasm of oral aspect of upper lip C00.4 Malignant neoplasm of lower lip, inner aspect Malignant neoplasm of buccal aspect of lower lip Malignant neoplasm of frenulum of lower lip Malignant neoplasm of mucosa of lower lip Malignant neoplasm of oral aspect of lower lip C00.5 Malignant neoplasm of lip, unspecified, inner aspect Malignant neoplasm of buccal aspect of lip, unspecified Malignant neoplasm of frenulum of lip, unspecified Malignant neoplasm of mucosa of lip, unspecified Malignant neoplasm of oral aspect of lip, unspecified C00.6 Malignant neoplasm of commissure of lip, unspecified C00.7 Malignant neoplasm of overlapping sites of lip C00.8 Malignant neoplasm of lip, unspecified Source: CMS (2016b).

Exhibits 2.1 and 2.2 are excerpts from the ICD-10-CM and ICD-10-PCS classification systems. They show the system in its text form, but large health care organizations generally use encoders, computer applications that facilitate accurate coding. Whether a book or text file or encoder is used, the classification system follows the same structure.

CPT and HCPCS The American Medical Association (AMA) publishes an updated CPT each year. Unlike ICD-9-CM, CPT is copyrighted, with all rights to publication and distribution held by the AMA. CPT was first developed and published in 1966. The stated purpose for developing CPT was to provide a uniform language for describing medical and surgical services. In 1983, however, the government adopted CPT, in its entirety, as the major component (known as Level 1) of the Healthcare Common Procedure Coding System (HCPCS). Since then CPT has become the standard for physician's office, outpatient, and ambulatory care coding for reimbursement purposes. Exhibit 2.3 is a simplified example of a patient encounter form with HCPCS/CPT codes. Exhibit 2.2 Excerpt from ICD-10 PCS 2017 OCW Section 0 Medical and Surgical Body System C Mouth and Throat Operation W Revision: Correcting, to the extent possible, a portion of a malfunctioning device or the position of a displaced device Body Part Approach Device Qualifier A Salivary Gland 0 Open 3 Percutaneous X External 0 Drainage Device C Extraluminal Device Z No Qualifier S Larynx 0 Open 3 Percutaneous 7 Via Natural or Artificial Opening 8 Via Natural or Artificial Opening Endoscopic X External 0 Drainage Device 7 Autologous Tissue Substitute D Intraluminal Device J Synthetic Substitute K Nonautologous Tissue Substitute Z No Qualifier Y Mouth and Throat 0 Open 3 Percutaneous 7 Via Natural or Artificial Opening 8 Via Natural or Artificial Opening Endoscopic X External 0 Drainage Device 1 Radioactive Element 7 Autologous Tissue Substitute D Intraluminal Device J Synthetic Substitute

K Nonautologous Tissue Substitute Z No Qualifier Source: CMS (2016c).

Exhibit 2.3 Patient Encounter form Coding Standards Pediatric Associates P.A. 123 Children's Avenue, Anytown, USA

Office Visits 99211 Estab Pt—minimal Preventive Medicine—New 99212 Estab Pt—focused 99381 Prev Med 0–1 years 99213 Estab Pt—expanded 99382 Prev Med 1–4 years 99214 Estab Pt—detailed 99383 Prev Med 5–11 years 99215 Estab Pt—high complexity 99384 Prev Med 12–17 years 99385 Prev Med 18–39 years 99201 New Pt—problem focused 99202 New Pt—expanded Preventive Medicine—Established 99203 New Pt—detailed 99391 Prev Med 0–1 years 99204 New Pt—moderate complexity 99392 Prev Med 1–4 years 99205 New Pt—high complexity 99393 Prev Med 5–11 years 99394 Prev Med 12–17 years 99050 After Hours 99395 Prev Med 18–39 years 99052 After Hours—after 10 pm 99054 After Hours Sundays and Holidays 99070 10 Arm Sling 99070 11 Sterile Dressing Outpatient Consult 99070 45 Cervical Cap 99241 99242 99243 99244 99245 Immunizations, Injections, and Office Laboratory Services 90471 Adm of Vaccine 1 81000 Urinalysis w/ micro 90472 Adm of Vaccine > 1 81002 Urinalysis w/o micro 90648 HIB 82270 Hemoccult Stool 90658 Influenza 82948 Dextrostix 90669 Prevnar 83655 Lead Level 90701 DTP 84030 PKU 90702 DT 85018 Hemoglobin 90707 MMR 87086 Urine Culture 90713 Polio Injection 87081 Throat Culture 90720 DTP/HIB 87205 Gram Stain 90700 DTaP 87208 Ova Smear (pinworm) 90730 Hepatitis A 87210 Wet Prep 90733 Meningococcal 87880 Rapid Strep 90744 Hepatitis B 0–11 90746 Hepatitis B 18+ years Diagnosis Patient Name No.

Date Time Address DOB Name of Insured ID Insurance Company Return Appointment ___________________________________________________

As coding has become intimately linked to reimbursement, directly determining the amount of money a healthcare organization can receive for a claim from insurers, the government has increased its scrutiny of coding practices. There are official guidelines for accurate coding, and health care facilities that do not adhere to these guidelines are liable to charges of fraudulent coding practices. In addition, the Office of Inspector General of the Department of Health and Human Services (HHS OIG) publishes compliance guidelines to facilitate health care organizations' adherence to ethical and legal coding practices. The OIG is responsible for (among other duties) investigating fraud involving government health insurance programs. More specific information about compliance guidelines can be found on the OIG website (www.oig.hhs.gov) and will be more thoroughly discussed in Chapter Nine.

Health Care Data Uses The previous sections of this chapter examine how health care data is captured in patient records and billing claims. Even with this brief overview you can begin to see what a rich source of health care data these records could be. However, before health care data can be used, it must be stored and retrieved. How do we retrieve that data so that the information can be aggregated, manipulated, or analyzed for health care organizations to improve patient care and business operations? How do we combine this patient care data created and stored internally with other pertinent data from external sources?

As we discussed previously in the chapter, data needs to be processed to become information. We also noted that data and information may be considered along a continuum, one person's data may be another person's information depending on the level of processing required. In this section of the chapter we will focus on the use of data analysis to transform data into information. There is a lot of discussion about the current and future impact of so-called big data on the health care community. We will start the discussion of data analysis by looking at the basic elements required to perform effective health care data analysis, followed by a comparison of “small” data analysis examples to the emerging big data.

Regardless of the scope of the data or the tools used, health care data analysis requires basic elements. First, there must be a source of data, for example, the EHR, claims data, laboratory data, and so on. Second, these data must be stored in a retrievable manner, for example, in a database or data warehouse. Next, an analytical tool, such as mathematical statistics, probability models, predictive models, and so on, must be applied to the stored data. Finally, to be meaningful, the analyzed data must be reported in a usable manner.

Databases and Data Warehouses A database generally refers to any structured, accessible set of data stored electronically; it can be large or small. The back end of EHR and claims systems are examples of large databases. A data warehouse differs from a database in its structure and function. In health care, data warehouses that are derived from health care information systems may be referred to as clinical data repositories. The data in a data warehouse come from a variety of sources, such as the EHR, claims data, and ancillary health care information systems (laboratory, radiology, etc.). The data from the sources are extracted, “cleaned,” and stored in a structure that enables the data to be accessed along multiple dimensions, such as time (e.g., day, month, year); location; or diagnosis. Data warehouses help organizations transform large quantities of data from separate transactional files or other applications into a single decision-support database. The important concept to understand is that the database or data warehouse provides organized storage for data so that they can be retrieved and analyzed. Before useful information can be obtained, the data must be analyzed. In the most straightforward uses, the data from the data stores are aggregated and reported using simple reporting or statistical methods.

Small versus Big Data Data stores and data analytics are not new to health care. However, the scope and speed with which we are now capable of analyzing data and discovering new information has increased tremendously. Big data is not a data store (warehouse or database), nor is it a specific analytical tool, but rather it refers to a combination of the two. Experts describe big data as characterized by three Vs (the fourth V—veracity, or accuracy—is sometimes added). These characteristics are present in big but not small data:

Very large volume of data A variety (e.g., images, text, discrete) of types and sources (EHR, wearable fitness technology, social media, etc.) of data The velocity at which the data is accumulated and processed (Glaser, 2014; Macadamian, n.d.) Harris and Schneider (2015) describe a useful metaphor for explaining the difference between big data and traditional data storage and analysis systems. They tell us to consider “even enormous databases, such as the Medicare claims database as ‘filing cabinets,’ while big data is more like a ‘conveyor belt.’ The filing cabinet, no matter how large, is static, while the conveyor belt is constantly moving and presenting new data points and even data sources” (p. 53). They further provide the following examples of questions answered by big versus small data in health care:

What are the effects of our immunization programs? versus Is my child growing as expected? What are some of the healthiest regions? versus Is this medication improving my (or my patients') blood pressure? Small Data Examples Disease and Procedure Indexes Health care management often wants to know summary information about a particular disease or treatment. Examples of questions that might be asked are What is the most common diagnosis among patients treated in the facility? What percentage of patients with diabetes are

African American? What is the most common procedure performed on patients admitted with gastritis (or heart attack or any other diagnosis)? Traditionally, such questions have been answered by looking in disease and procedure indexes. Prior to EHRs and their resulting databases, disease and procedure indexes were large card catalogs or books that kept track of the numbers of diseases treated and procedures occurring in a facility by disease and procedure codes. Now that repositories of healthcare data are common, the disease and procedure index function is generally handled as a component of the EHR. The retrieval of information related to diseases and procedures is still based on ICD and CPT codes, but the queries are limitless. Users can search the disease and procedure database for general frequency statistics for any number of combinations of data. Figure 2.6 is an example of a screen resulting from a query for a specific patient, Iris Hale, who has been identified as a member of both the Heart Failure and Hypertension registries.

Many other types of aggregate clinical reports are used by healthcare providers and executives. Ad hoc reporting capability applied to clinical databases gives providers and executives access to any number of summary reports based on the data elements from patient health and claims records.

Health Care Statistics Utilization and performance statistics are routinely gathered for health care executives. This information is needed for facility and health care provision planning and improvement. Statistical reports can provide managers and executives a snapshot of their organization's performance.

Two categories of statistics directly related to inpatient stays are routinely captured and reported. Many variations of these reports and others that drill down to more granular levels of data also exist.

Census statistics. These data reveal the number of patients present at any one time in a facility. Several commonly computed rates are based on these census data, including the average daily census and bed occupancy rates. Discharge statistics. This group of statistics is calculated from data accumulated when patients are discharged. Some commonly computed rates based on discharge statistics are average length of stay, death rates, autopsy rates, infection rates, and consultation rates. Outpatient facilities and group practices, specialty providers, and so on also routinely collect utilization statistics. Some of the more common statistics are average patient visits per month (or year) and percentage of patients achieving a health status goal, such as immunizations or smoking cessation. The number of descriptive health care statistics that can be produced is limitless. Health care organizations also track a wide variety of financial performance, patient satisfaction, and employee satisfaction data. Patient and employee data generally come from surveys that are routinely administered. The body of data collected and analyzed is driven by the mission of the organization, along with reporting requirements from state, federal, and accrediting organizations. Figure 2.6 Sample heart failure and hypertension query screen

Source: Cerner Corporation (2016). Used with permission.

Health care organizations also look to data to guide improved performance and patient satisfaction. Performance data are essential to health care leaders; however, because they are generally managed within a quality or performance improvement department and are not derived from health care data, per se, they will not be discussed in depth in this chapter. A few significant external agencies that report performance data, however, will be discussed in Chapter Nine.

Although each organization will determine which daily, monthly, and yearly statistics they need to track based on their individual service missions, Rachel Fields (2010) in an article published by Becker's Hospital Review provides a list of ten common measures identified by a panel of five hospital leaders, as shown in Table 2.1.

Big Data Examples Health care organizations today contend with data from EHRs, internal databases, data warehouses, as well as the availability of data from the growing volume of other health-related sources, such as diagnostic imaging equipment, aggregated pharmaceutical research, social media, and personal devices such as Fitbits and other wearable technologies. No longer is the data needed to support health care decisions located within the organization or any single data source. As we begin to manage populations and care continuums we have to bring together data from hospitals, physician practices, long-term care facilities, the patient, and so on. These data needs are bigger than the data needs we had (and still have) when we focused primarily on inpatient care.

Big data is a practice that is applied to a wide range of uses across a wide range of industries and efforts, including health care. There is no single big data product, application, or technology, but big data is broadening the range of data that may be important in caring for patients. For instance, in the case of Alzheimer's and other chronic diseases such as diabetes and cancer, online social sites not only provide a support community for like-minded patients but also contain knowledge that can be mined for public health research, medication use monitoring, and other health-related activities. Moreover, popular social networks can be used to engage the public and monitor public perception and response during flu epidemics and other public health threats (Glaser, 2014). Table 2.1 Ten common hospital statistical measures

Source: Fields (2010).

Daily Monthly Yearly 1. Quality measures, such as Infection rates Patient falls Overall mortality 2. Patient census statistics

By physician By service line 3. Discharged but not final billed 4. Point-of-service cash collections 5. Percentage of charity care 6. Percentage of budget spent for each department 7. Door-to-discharge time 8. Patient satisfaction scores 9. Colleague satisfaction scores 10. Market share and service line development As important and perhaps more important than the data themselves are the novel analytics that are being developed to analyze these data. In health care we see an impressive range of analytics:

Post-market surveillance of medication and device safety Comparative effectiveness research (CER) Assignment of risk, for example, readmissions Novel diagnostic and therapeutic algorithms in areas such as oncology Real-time status and process surveillance to determine, for example, abnormal test follow-up performance and patient compliance with treatment regimes Determination of structure including intent, for example, identifying treatment patterns using a range of structured and unstructured and EHR and non-EHR data Machine correction of data-quality problems The potential impact of applying data analytics to big data is huge. McKinsey & Company (Kayyil, Knott, & Van Kuiken, 2013) estimates that big data initiatives could account for $300 to $450 billion in reduced health care spending, or 12 to 17 percent of the $2.6 trillion baseline in US health care costs. There are several early examples of possibly profound impact. For example, an analysis of the cumulative sum of monthly hospitalizations because of myocardial infarction, among other clinical and cost data, led to the discovery of arthritis drug Vioxx's adverse effects and its subsequent withdrawal from the market in 2004.

A Deloitte (2011) analysis identified five areas of analysis that will be crucial in the emerging era of providers being held more accountable for the care delivered to a patient and a population:

Population management analytics. Producing a variety of clinical indicator and quality measure dashboards and reports to help improve the health of a whole community, as well as help identify and manage at-risk populations Provider profiling/physician performance analytics. Normalizing (severity and case mix–adjusted profiling), evaluating, and reporting the performance of individual providers (PCPs and specialists) compared to established measures and goals Point of care (POC) health gap analytics. Identifying patient-specific health care gaps and issuing a specific set of actionable recommendations and notifications either to physicians at the point of care or to patients via a patient portal or PHR Disease management. Defining best practice care protocols over multiple care settings, enhancing the coordination of care, and monitoring and improving adherence to best practice care protocols

Cost modeling/performance risk management/comparative effectiveness. Managing aggregated costs and performance risk and integrating clinical information and clinical quality measures Health Care Data Quality Up to this point, this chapter has examined health care data and information with a focus on the origins and uses of such. Changes to the health care delivery system and payment reform are amending the ways in which we use health care information. Traditionally, patient clinical and claims records were used primarily to document episodic care or, at best, the care received by an individual across the continuum, as long as that care was provided through a single organization. In today's environment, care providers, care coordinators, analysts, and researchers are all looking to EHRs and electronic claims records as a source of data beyond the episodic scope. Any discussion of healthcare data analytics and big data include the EHR as a key data source. This expanded use of electronic records and the push for bigger and better data analytics has raised the bar for ensuring the quality of the health care data. Quality health care data has always been important, but the criteria for what constitutes high-quality data have shifted.

There are many operational definitions for quality. Two of the best known were developed by the well-known quality “gurus,” Philip B. Crosby and Joseph M. Juran. Crosby (1979) defines quality as “conformance to requirements” or conformance to standards. Juran (Juran & Gryna, 1988) defines quality as “fitness for use,” products or services must be free of deficiencies. What these definitions have in common is that the criteria against which quality is measured will change depending on the product, service, or use. Herein lies the problem with adopting a single standard for health care data quality—it depends on the use of the data.

EHRs evolved from patient medical records, whose central purpose was to document and communicate episodes of patient care. Today EHRs are being evaluated as source data for complex data analytics and clinical research. Before an organization can measure the quality of the information it produces and uses, it must establish data standards. And before it can establish data standards it must identify all endorsed uses of the EHR.

Consider this scenario. EHRs contain two basic types of data: structured data that is quantifiable or predefined and unstructured data that is narrative. Within a healthcare organization, the clinicians using the EHR for patient care prefer unstructured data, because it is easier to dictate a note than to follow a lengthy point and click pathway to create a structured note. The clinicians feel that the validation screens cost time that is too valuable for them to waste. The researchers within the organization, however, want as much of the data in the record as possible to be structured to avoid missing data and data entry errors. What should the organization adopt as its standard? Structured or unstructured data? Who will decide and based on what criteria? This discussion between the primary use of EHR data and secondary, or reuse, of data is likely to continue. However, to effectively use EHR data to create new knowledge, either through analytics or research, will require HIT leaders to adopt the more stringent data quality criteria posed by these uses. Wells, Nowacki, Chagin, and Kattan (2013) identify missing data as particularly problematic when using the EHR for research purposes. They further identify two main sources of missing EHR data:

Data was not collected. A patient was never asked about a condition. This is most likely directly related to the clinician's lack of interest in what would be considered irrelevant to the current episode of care. Few clinicians will take a full history, for example, at every encounter. Documentation was not complete. The patient was asked, but it was not noted in the record. This is common in the EHR when clinicians only note positive values and leave negative values blank. For example, if a patient states that he or she does not have a history of cancer, no note will be made, either positive or negative. For a researcher this creates issues. Is this missing data or a negative value? Although there is no single common standard against which health care data quality can be measured, there are useful frameworks for organizations to use to evaluate health care quality (once the purpose for the data is clearly determined).

The following section will examine two different frameworks for evaluating health care data quality. The first was developed by the American Health Information Management Association (AHIMA) (Davoudi et al., 2015), the second by Weiskopf and Weng (2013). The AHIMA framework is set in the context of managing health care data quality across the enterprise. The Weiskopf and Weng framework was delineated after in-depth research into the quality of data specifically found within an EHR, as currently used. Common health data quality issues will be examined using each framework. AHIMA Data Quality Characteristics AHIMA developed and published a set of healthcare data quality characteristics as a component of a comprehensive data quality management model. They define data quality management as “the business processes that ensure the integrity of an organization's data during collection, application (including aggregation), warehousing, and analysis” (Davoudi et al., 2015). These characteristics are to be measured for conformance during the entire data management process.

Data accuracy. Data that reflect correct, valid values are accurate. Typographical errors in discharge summaries and misspelled names are examples of inaccurate data. Data accessibility. Data that are not available to the decision makers needing them are of no value to those decision makers. Data comprehensiveness. All of the data required for a particular use must be present and available to the user. Even relevant data may not be useful when they are incomplete. Data consistency. Quality data is consistent. Use of an abbreviation that has two different meanings is a good example of how lack of consistency can lead to problems. For example, a nurse may use the abbreviation CPR to mean cardiopulmonary resuscitation at one time and computer-based patient record at another time, leading to confusion. Data currency. Many types of healthcare data become obsolete after a period of time. A patient's admitting diagnosis is often not the same as the diagnosis recorded on discharge. If a healthcare executive needs a report on the diagnoses treated during a particular time frame, which of these two diagnoses should be included?

Data definition. Clear definitions of data elements must be provided so that current and future data users will understand what the data mean. This issue is exacerbated in today's healthcare environment of collaboration across organizations. Data granularity. Data granularity is sometimes referred to as data atomicity. That is, individual data elements are “atomic” in the sense that they cannot be further subdivided. For example, a typical patient's name should generally be stored as three data elements (last name, first name, middle name—“Smith” and “John” and “Allen”), not as a single data element (“John Allen Smith”). Again, granularity is related to the purpose for which the data are collected. Although it is possible to subdivide a person's birth date into separate fields for the month, the date, and the year, this is usually not desirable. The birth date is at its lowest practical level of granularity when used as a patient identifier. Values for data should be defined at the correct level for their use. Data precision. Precision often relates to numerical data. Precision denotes how close to an actual size, weight, or other standard a particular measurement is. Some health care data must be very precise. For example, in figuring a drug dosage it is not all right to round up to the nearest gram when the drug is to be dosed in milligrams. Data relevancy. Data must be relevant to the purpose for which they are collected. We could collect very accurate, timely data about a patient's color preferences or choice of hairdresser, but are these matters relevant to the care of the patient? Data timeliness. Timeliness is a critical dimension in the quality of many types of healthcare data. For example, critical lab values must be available to the health care provider in a timely manner. Producing accurate results after the patient has been discharged may be of little or no value to the patient's care.

Table 2.2 Terms used in the literature to describe the five common dimensions of data quality

Source: Weiskopf and Weng (2013). Reproduced with permission of Oxford University Press.

Completeness Correctness Concordance Plausibility Currency Accessibility Accuracy Agreement Accuracy Recency Accuracy Corrections made Consistency Believability Timeliness Availability Errors Reliability Trustworthiness Missingness Misleading Variation Validity Omission Positive predictive value Presence Quality Quality Validity Rate of recording Sensitivity Validity

Weiskopf and Weng Data Quality Dimensions Weiskopf and Weng (2013) published a review article in the Journal of the American Medical Informatics Association that identified five dimensions of EHR data quality. They based their findings on a pool of ninety-five articles that examined EHR data quality. Their context was using

the EHR for research, that is, “reusing” the EHR data. Although different terms were used in the articles, the authors were able to map the terms to one of the five dimensions (see Table 2.2):

Completeness: Is the truth about a patient present? Correctness: Is an element that is in the EHR true? Concordance: Is there agreement between elements in the EHR or between the EHR and another data source? Plausibility: Does an element in the EHR make sense in light of other knowledge about what that element is measuring? Currency: Is an element in the EHR a relevant representation of the patient state at a given point in time?

Perspective Problems with Reusing EHR Data: Examples from the Literature Botsis, T., Hartvigsen, G., Chen, F., & Weng, C. (2010). Secondary use of EHR: Data quality issues and informatics opportunities. Summit on Translational Bioinformatics, 2010, 1–5.

The authors report on data quality issues they encountered when attempting to use data that originated in an EHR to conduct survival analysis of pancreatic cancer patients treated at a large medical center in New York City. They found that of 3,068 patients within the clinical data warehouse, only 1,589 had appropriate disease documentation within a pathology report. The sample size was further reduced to 522 when the researchers discovered incompleteness of key study variables. Other instances of incompleteness and inaccuracies were found within the remaining 522 subjects' documentation, causing the researchers to make inferences regarding some of the non-key study variables.

Bayley, K. B., Belnap, T., Savitz, L., Masica, A. L., Shah, N., & Fleming, N. S. (2013). Challenges in using electronic health record data for CER. Medical Care, 51(8 Suppl 3), S80–S86. doi:10.1097/mlr.0b013e31829b1d48

The authors conducted research to determine the “strengths and challenges” of using EHRs for CER across four major health care systems with mature EHR systems. They looked at comparing the effectiveness of antihypertensive medications on blood pressure control for a population of patients with hypertension who were being followed by primary care providers within the health systems. Data quality problems that were identified included the following:

Missing data Erroneous data Uninterpretable data Inconsistent data Text notes and non coded data The authors concluded that the potential for EHRs as a source of longitudinal data for comparative effectiveness studies in populations is high, but they note that “improving data

quality within the EHR in order to facilitate research will remain a challenge as long as research is seen as a separate activity from clinical care.” The authors further identify completeness, correctness, and currency as “fundamental,” stating that concordance and plausibility “appear to be proxies for the fundamental dimensions when it is not possible to assess them directly.”

Strategies for Minimizing Data Quality Issues As a beginning point, health care data standardization requires clear, consistent definitions. One essential tool for identifying and ensuring the use of standard data definitions is to use a data dictionary. AHIMA defines a data dictionary as “a descriptive list of names (also called ‘representations’ or ‘displays’), definitions, and attributes of data elements to be collected in an information system or database” (Dooling, Goyal, Hyde, Kadles, & White, 2014, p. 7) (see Table 2.3).

Regardless of how well data are defined, however, errors in entry will occur. These errors can be discussed in terms of two types of underlying cause: systematic errors and random errors. Systematic errors are errors that can be attributed to a flaw or discrepancy in the system or in adherence to standard operating procedures or systems. Random errors, however, are caused by carelessness, human error, or simply making a mistake.

Consider these scenarios:

A nurse is required to document vital signs into each patient's EHR at the beginning of each visit. However, the data entry screen is cumbersome and often the nurse must wait until the end of day and go back to update the vital signs. On occasion the EHR locks up and does not allow the nurse to update the information. This is an example of a systematic error. A physician uses the structured history and physical module of the EHR within her practice. However, to save time she cuts and pastes information from one visit to another. During cutting and pasting, she fails to reread her note and leaves in the wrong encounter date. Although there are some elements of systematic error in this situation (not following protocol), the error is primarily a random error. Effective systems are needed to ensure preventable errors are minimized and errors that are not preventable are easily detected and corrected. Clearly, there are multiple points during data collection and processing when the system design can reduce data errors.

The Markle Foundation (2006, p. 4) argues that comprehensive data quality programs are needed by healthcare organizations to prevent “dirty data” and subsequently improve the quality of patient care. They propose that a data quality program include “automated and human strategies'':

Standardizing data entry fields and processes for entering data Instituting real-time quality checking, including the use of validation and feedback loops Designing data elements to avoid errors (e.g., using check digits, algorithms, and well-designed user interfaces)

Developing and adhering to guidelines for documenting the care that was provided Building human capacity, including training, awareness-building, and organizational change Health care data quality problems are exacerbated by inter-facility collaborations and health information exchange. Imagine standardizing processes and definitions across multiple organizations.

Certainly, information technology has tremendous potential as a tool for improving health care data quality. Through the use of electronic data entry, users can be required to complete certain fields, prompted to add information, or warned when a value is out of prescribed range. When health care providers respond to a series of prompts, rather than dictating a free-form narrative, they are reminded to include all necessary elements of a health record entry. Data quality is improved when these systems also incorporate error checking. Structured data entry, drop-down lists, and templates can be incorporated to promote accuracy, consistency, and completeness (Wells et al., 2013). To date some of this potential for technology-enhanced improvements has been realized, but many opportunities remain. As noted in the Perspective many of the data in existing EHR systems are recorded in an unstructured format, rather than in data fields designated to contain specific pieces of information, which can lead to poor health care data quality. Natural language processing (NLP) is a promising, evolving technology that will enable efficient data extraction from the unstructured components of the EHR, but it is not yet commonplace with health care systems.

A clear example of data quality improvement achieved through information technology is the result seen from incorporating medication administration systems designed to prevent medication error. With structured data input and sophisticated error prevention, these systems can significantly reduce medication errors. The challenge for the foreseeable future is to balance the need for structured data with the associated costs (time and money). Further in the future, new challenges will appear as the breadth of data contained in patient records is likely to increase. Genomic and proteomic data, along with enhanced behavioral and social data, are likely to be captured (IOM, 2014). These added data will introduce new quality issues to be resolved.

Table 2.3 Excerpt from data dictionary used by AHRQ surgical site infection risk stratification/outcome detection

Source: Agency for Healthcare Research and Quality (2012). Table Field Datatype Description PATIENT Include patients who had surgery that meet inclusion CPT, SNOMED, or ICD-9 criteria between 1/1/2007 and 1/30/2009. PATIENT DOB Date The birthdate for the patient PATIENT PATIENT_ID Integer A unique ID for the patient PATIENT DATA_SOURCE_ID Varchar(10) An identifier for the source of the patient record data (UU, IHC, DH for example) DIAGNOSIS Include ICD-9 CM discharge codes within one month of surgery. A list of included codes is in table 2 of Stevenson et al. AJIC vol 36 (3) 155–164.

DIAGNOSIS DIAGNOSIS_ID Integer A unique ID for the diagnosis DIAGNOSIS DIAGNOSIS_CODE Varchar(64) The code for the patient's diagnosis DIAGNOSIS DIAGNOSIS_CODE_SOURCE Varchar(64) The nomenclature that the diagnosis code is taken from (ICD9, etc.) DIAGNOSIS CLINICAL_DTM Date The date and time of the diagnosis's onset or exacerbation MICROBIOLOGY Include all Microbiology specimens taken within one month before or after a surgery. (For risk, this might be expanded to one year or more.) MICROBIOLOGY MICRO_ID Integer A unique ID for the procedure MICROBIOLOGY SPECIMEN_CODE Varchar(64) The site that the specimen was collected from MICROBIOLOGY SPECIMEN_CODE_SOURCE Varchar(64) The nomenclature that the specimen code is taken from (SNOMED, LOINC, etc.) MICROBIOLOGY PATHOGEN_CODE Varchar(64) The code of the pathogen cultured from the collected specimen MICROBIOLOGY PATHOGEN_CODE_SOURCE Varchar(64) The nomenclature that the pathogen code is taken from (SNOWMAN, LOINC, etc.) MICROBIOLOGY COLLECT_DTM Date The date and time the specimen was collected ENCOUNTER Include all Encounters within one month before or after surgery. ENCOUNTER ENCOUNTER_ID Integer A unique ID for the visit. This will serve to tie all of the different data tables together via foreign key relationships. ENCOUNTER ADMIT_DTM Date The admission date and time for a patient's visit ENCOUNTER DISCH_DTM Date The discharge date and time for a patient's visit ENCOUNTER ENCOUNTER_TYPE Varchar(64) The type of patient encounter such as inpatient, outpatient, observation, etc.

Summary Without health care data and information, there would be no need for health care information systems. Health care data and information are valuable assets in health care organizations, and they must be managed similar to other assets. To that end, health care executives need an understanding of the sources of healthcare data and information and recognize the importance of ensuring the quality of health data and information. In this chapter, after defining health care data and information, we examined patient records and claims content as sources for health care data. We looked at disease and procedure indexes and health care statistics as examples of basic uses of the health care data. The emerging use of data analytics and big data were introduced and the chapter concluded with a discussion of two frameworks for examining health care data quality and a discussion of how information technology, in general, and the EHR, in particular, can be leveraged to improve the quality of healthcare data.

Chapter 3 Health Care Information Systems Learning Objectives To be able to identify the major types of administrative and clinical information systems used in health care. To be able to give a brief explanation of the history and evolution of health care information systems. To be able to discuss the key functions and capabilities of electronic health record systems and current adoption rates in hospitals, physician practices, and other settings. To be able to describe the use and adoption of personal health records and patient portals. To be able to discuss current issues pertaining to the use of HCIS systems including interoperability, usability, and health IT safety.

Review of Key Terms An information system (IS) is an arrangement of data (information), processes, people, and information technology that interact to collect, process, store, and provide as output the information needed to support the organization (Whitten & Bentley, 2007). Note that information technology is a component of every information system. Information technology (IT) is a contemporary term that describes the combination of computer technology (hardware and software) with data and telecommunications technology (data, image, and voice networks). Often in current management literature the terms information system (IS) and information technology (IT) are used interchangeably.

Within the health care sector, health care IS and IT include a broad range of applications and products and are used by a wide range of constituent groups such as payers, government, life sciences, and patients, as well as providers and provider organizations. For our purpose, however, we have chosen to focus on health care information systems from the provider organization's perspective. The provider organization is the hospital, health system, physician practice, integrated delivery system, nursing home, or rural health clinic. That is, it is any setting where health-related services are delivered. The organization (namely, the capacity, decisions about how health IT is applied, and incentives) and the external environment (regulations and public opinion) are important elements in how systems are used by clinicians and other users (IOM, 2011). We also examine the use of patient engagement tools such as PHRs and secure patient portals. Yet our focus is from an organization or provider perspective.

Major Health Care Information Systems There are two primary categories of health care information systems: administrative and clinical. A simple way to distinguish them is by purpose and the type of data they contain. An administrative information system (or an administrative application) contains primarily administrative or financial data and is generally used to support the management functions and general operations of the health care organization. For example, an administrative information system might contain information used to manage personnel, finances, materials, supplies, or equipment. It might be a system for human resource management, materials management,

patient accounting or billing, or staff scheduling. Revenue cycle management is increasingly important to health care organizations and generally includes the following:

Charge capture Coding and documentation review Managed care contracting Denial management of claims Payment posting Accounts receivable follow-up Patient collections Reporting and benchmarking By contrast, a clinical information system (or clinical application) contains clinical or health-related information used by providers in diagnosing and treating a patient and monitoring that patient's care. Clinical information systems may be departmental systems—such as radiology, pharmacy, or laboratory systems—or clinical decision support, medication administration, computerized provider order entry, or EHR systems, to name a few. They may be limited in their scope to a single area of clinical information (for example, radiology, pharmacy, or laboratory), or they may be comprehensive and cover virtually all aspects of patient care (as an EHR system does, for example). Table 3.1 lists common types of clinical and administrative health care information systems. Table 3.1. Common types of administrative and clinical information systems

Administrative Applications Clinical Applications Patient administration systems Admission, discharge, transfer (ADT) tracks the patient's movement of care in an inpatient setting Ancillary information systems Laboratory information supports collection, verification, and reporting of laboratory tests Registration may be coupled with ADT system; includes patient demographic and insurance information as well as date of visit(s), provider information Scheduling aids in the scheduling of patient visits; includes information on patients, providers, date and time of visit, rooms, equipment, other resources Patient billing or accounts receivable includes all information needed to submit claims and monitor submission and reimbursement status Utilization management tracks use and appropriateness of care Other administrative and financial systems Accounts payable monitors money owed to other organizations for purchased products and services General ledger monitors general financial management and reporting Radiology information supports digital image generation (picture archiving and communication systems [PACS]), image analysis, image management Pharmacy information supports medication ordering, dispensing, and inventory control; drug compatibility checks; allergy screening; medication administration Other clinical information systems

Nursing documentation facilitates nursing documentation from assessment to evaluation, patient care decision support (care planning, assessment, flow-sheet charting, patient acuity, patient education) Electronic health record (EHR) facilitates electronic capture and reporting of patient's health history, problem lists, treatment and outcomes; allows clinicians to document clinical findings, progress notes, and other patient information; provides decision-support tools and reminders and alerts Personnel management manages human resource information for staff, including salaries, benefits, education, and training Materials management monitors ordering and inventory of supplies, equipment needs, and maintenance Payroll manages information about staff salaries, payroll deductions, tax withholding, and pay status Staff scheduling assists in scheduling and monitoring staffing needs Staff time and attendance tracks employee work schedules and attendance Revenue cycle management monitors the entire flow of revenue generation from charge capture to patient collection; generally relies on integration of a host of administrative and financial applications Computerized provider order entry (CPOE) enables clinicians to directly enter orders electronically and access decision-support tools and clinical care guidelines and protocols Telemedicine and telehealth supports remote delivery of care; common features include image capture and transmission, voice and video conferencing, text messaging Rehabilitation service documentation supports the capturing and reporting of occupational therapy, physical therapy, and speech pathology services Medication administration is typically used by nurses to document medication given, dose, and time Health care organizations, particularly those that have implemented EHR systems, generally provide patients with access to their information electronically through a patient portal. A patient portal is a secure website through which patients may communicate with their provider, request refill on prescriptions, schedule appointments, review test results, or pay bills (Emont, 2011). Another term that is frequently used is personal health record (PHR). Different from an EHR or patient portal, which is managed by the provider or health care organization, the PHR is managed by the consumer. It may include health information and wellness information, such as an individual's exercise and diet. The consumer decides who has access to the information and controls the content of the record. The adoption and use of patient portals and PHRs are discussed further on in this chapter. For now, we begin with a brief historical overview of how these various clinical and administrative systems evolved in health care.

History and Evolution Since the 1960s, the development and use of health care information systems has changed dramatically with advances in technology and the impact of environmental influences and payment reform (see Figure 3.1). In the 1960s to 1970s, health care executives invested primarily in administrative and financial information systems that could automate the patient billing process and facilitate accurate Medicare cost reporting. The administrative applications

that were used were generally found in large hospitals, such as those affiliated with academic medical centers. These larger health care organizations were often the only ones with the resources and staff available to develop, implement, and support such systems. It was common for these facilities to develop their own administrative and financial applications in-house in what were then known as “data processing” departments. The systems themselves ran on large mainframe computers, which had to be housed in large, environmentally controlled settings. Recognizing that small, community-based hospitals could not bear the cost of an in-house, mainframe system, leading vendors began to offer shared systems, so called because they enabled hospitals to share the use of a mainframe with other hospitals. Vendors typically charged participating hospitals for computer time and storage, for the number of terminal connects, and for reports.

Figure 3.1 History and evolution of health care information systems (1960s to today)

By the 1970s, departmental systems such as clinical laboratory or pharmacy systems began to be developed, coinciding with the advent of minicomputers. Minicomputers were smaller and more powerful than some of the mainframe computers and available at a cost that could be justified by revenue-generating departments. Clinical applications including departmental systems such as laboratory, pharmacy, and radiology systems became more commonplace. Most systems were stand-alone and did not interface well with other clinical and administrative systems in the organization.

The 1980s brought a significant turning point in the use of health care information systems primarily because of the development of the microcomputer, also known as the personal computer (PC). Sweeping changes in reimbursement practices designed to rein in high costs of health care also had a significant impact. In 1982, Medicare shifted from a cost-based reimbursement system to a prospective payment system based on diagnosis related groups (DRGs). This new payment system had a profound effect on hospital billing practices. Reimbursement amounts were now dependent on the accuracy of the patient's diagnosis and procedures(s) and other information contained in the patient's record. With hospital reimbursement changes occurring, the advent of the microcomputer could not have been more timely. The microcomputer was smaller, often as or more powerful, and far more affordable than a mainframe computer. Additionally, the microcomputer was not confined to large hospitals. It brought computing capabilities to a host of smaller organizations including small community hospitals, physician practices, and other care delivery settings. Sharing information among microcomputers also became possible with the development of local area networks. The notion of best of breed systems was also common; individual clinical departments would select the best application or system for meeting their unique unit's needs and attempt to get the “systems to talk to each other” using interface engines.

Rapid technological advances continued into the 1990s, with the most profound being the evolution and widespread use of the Internet and electronic mail (e-mail). The Internet provided health care consumers, patients, providers, and industries with access to the World Wide Web and new and innovative opportunities to access care, promote services, and share information.

Concurrently, the Institute of Medicine (IOM, 1991) published its first landmark report The Computer-Based Patient Record: An Essential Technology for Health Care, which called for the widespread adoption of computerized patient records (CPRs) as the standard by the year 2001. CPRs were the precursor to what we refer to today as EHR systems. Numerous studies had revealed the problems with paper-based medical records (Burnum, 1989; Hershey, McAloon, & Bertram, 1989; IOM, 1991). Records are often illegible, incomplete, or unavailable when and where they are needed. They lack any type of active decision-support capability and make data collection and analysis very cumbersome. This passive role for the medical record was no longer sufficient. Health care providers needed access to active tools that afforded them clinical decision-support capabilities and access to the latest relevant research findings, reminders, alerts, and other knowledge aids. Along with patients, they needed access to systems that would support the integration of care across the continuum.

By the start of the new millennium, health care quality and patient safety emerged as top priorities. In 2000, the IOM published the report To Err Is Human: Building a Safer Health Care System, which brought national attention to research estimating that 44,000 to 98,000 patients die each year to medical errors. Since then, additional reports have indicated that these figures are grossly underestimated and the incidents of medical errors are much higher (Classen et al., 2011; James, 2013; Makary & Daniel, 2016;). A subsequent report, Patient Safety: Achieving a New Standard of Care (2004), called for health care providers to adopt information technology to help prevent and reduce errors because of illegible prescriptions, drug-to-drug interactions, and lost medical records, for example.

By 2009, the US government launched an “unprecedented effort to reengineer” the way we capture, store, and use health information (Blumenthal, 2011, p. 2323). This effort was realized in the Health Information Technology for Economic and Clinical Health (HITECH) Act. Nearly $30 billion was set aside over a ten-year period to support the adoption and Meaningful Use of EHRs and other types of health information technology with the goal of improving health and health care. Rarely, if ever, have we seen public investments in the advancement of health information technology of this magnitude (Blumenthal, 2011). Interest also grew in engaging patients more fully in providing access to their EHR through patient portals or the concept of a PHR. We have also seen significant advances in telemedicine and telehealth, cloud computing, and mobile applications that monitor and track a wide range of health data.

Electronic Health Records Features and Functions Let's first examine the features and functions of an EHR because it is core to patient care. An EHR can electronically collect and store patient data, supply that information to providers on request, permit clinicians to enter orders directly into a computerized provider order entry (CPOE) system, and advise health care practitioners by providing decision-support tools such as reminders, alerts, and access to the latest research findings or appropriate evidence-based guidelines. CPOE at its most basic level is a computer application that accepts provider orders electronically, replacing handwritten or verbal orders and prescriptions. Most CPOE systems provide physicians and other providers with decision-support capabilities at the point of ordering.

For example, an order for a laboratory test might trigger an alert to the provider that the test has already been ordered and the results are pending. An order for a drug to which the patient is allergic might trigger an alert warning to the provider of an alternative drug. These decision-support capabilities make the EHR far more robust than a digital version of the paper medical record.

Figure 3.2 illustrates an EHR alert reminding the clinician that the patient is allergic to certain medication or that two medications should not be taken in combination with each other. Reminders might also show that the patient is due for a health maintenance test such as a mammography or a cholesterol test or for an influenza vaccine (Figure 3.2).

Figure 3.2 Sample drug alert screen

Source: Epic. Used with permission.

Up until the passage of the HITECH Act of 2009, EHR adoption and use was fairly low. HITECH made available incentive money through the Medicare and Medicaid EHR Incentive Programs for eligible professionals and hospitals to adopt and become “meaningful users” of EHR. As mentioned in Chapter One, the Meaningful Use criteria were established and rolled out in three phases. Each phase built on the previous phase in an effort to further the advancement and use of EHR technology as a strategy to improve the nation's health outcome policy priorities:

Improve health care quality, safety, and efficiency and reduce health disparities. Engage patients and families in their health care. Improve care coordination. Improve population and public health. Ensure adequate privacy and security of personal health information. To accomplish these goals and facilitate patient engagement in managing their health and care, health care organizations provide patients with access to their records typically through a patient portal. A patient portal is a secure website through which patients can electronically access their medical records. Portals often also enable users to complete forms online, schedule appointments, communicate with providers, request refills on prescriptions, review test results, or pay bills (Emont, 2011) (see Figure 3.3). Some providers offer patients the opportunity to schedule e-visits for a limited number of nonurgent medical conditions such as allergic skin reactions, colds, and nosebleeds. Figure 3.3 Sample patient portal

Source: Epic.

EHR Adoption Rates in US Hospitals As of 2015, nearly 84 percent of US nonfederal acute care hospitals had adopted basic EHR systems representing a nine-fold increase from 2008 (Henry, Pylypchuck, Searcy, & Patel, 2016) (see Figure 3.4). Table 3.2 lists the difference functionality between a basic system and a fully functional system (DesRoches et al., 2008). A key distinguishing characteristic is fully

functional EHRs provide order entry capabilities (beyond ordering medications) and decision-support capabilities.

The Veterans Administration (VA) has used an EHR system for years, enabling any veteran treated at any VA hospital to have electronic access to his or her EHR. Likewise, the US Department of Defense is under contract with Cerner to replace its EHR system. EHR adoption among specialty hospitals such as children's (55 percent) and psychiatric hospitals (15 percent) is significantly lower than general medicine hospitals because these types of hospitals were not eligible for HITECH incentive payments. Small, rural, and critical access hospitals that have historically lagged behind in EHR adoption are now closing the gap with general acute care hospitals (Henry et al., 2016).

Figure 3.4 Percent of non-federal acute care hospitals with adoption of at least a basic EHR with notes system and position of a certified EHR: 2008–2015

Note: Basic EHR adoption requires the EHR system to have a set of EHR functions defined in Table 3.2. A certified EHR is EHR technology that meets the technological capability, functionality, and security requirements adopted by the Department of Health and Human Services. Possession means that the hospital has a legal agreement with the EHR vendor but is not equivalent to adoption. *Significantly different from previous year (p<0.05).

Source: ONC (2015a).

EHR Adoption in Office-Based Physician Practices In addition to EHR use in hospitals, we have also seen significant increases in the adoption and use of EHR systems among office-based physician practices. By 2014, 79 percent of primary care physicians had adopted a certified EHR system and 70 percent of medical and surgical specialties had as well (Heisey-Grove & Patel, 2015) (see Figure 3.5).

Ninety-eight percent of physicians in community health centers had adopted an EHR, three-quarters of them using a certified EHR. Not surprisingly, physicians in solo and small group practices were less likely to have adopted EHR systems (Heisey-Grove & Patel, 2015).

EHR Adoption in Other Settings Less is known nationally about EHR adoption rates in settings other than hospitals and physician practices. Among home health and hospice agencies, the latest national estimates based on data from the 2007 National Home and Hospice Care survey indicate that 44 percent of home health and hospice agencies have adopted EHR systems (16 percent EHRs only and 28 percent EHRs and mobile technologies such as tablets or hand-held devices used to gather information at the point of care) (Bercovitz, Park-Lee, & Jamoom, 2013). Table 3.2 Functions defining the use of EHRs

Basic System Fully Functional System

Health Information Data Patient demographics X X Patient problem lists X X Electronic lists of medications taken by patients X X Clinical notes X X Notes including medical history and follow-up X Order Entry Management Orders for prescriptions X X Orders for laboratory tests X Orders for radiology tests X Prescriptions sent electronically X Orders sent electronically X Results Management Viewing laboratory results X X Viewing imaging results X X Electronic images returned X Clinical Decision Support Warnings of drug interactions or contraindications provided X Out-of-range test levels highlighted X Reminders regarding guidelines-based interventions or screening X

Figure 3.5. Office-based physician practice EHR adoption since 2004

Source: ONC (2015a).

Some states, such as New York, have attempted to assess EHR adoption in long-term care facilities such as nursing homes. One study found that among 473 nursing homes in New York, 56.3 percent had implemented an EHR system (Abramson, Edwards, Silver, & Kaushal, 2014). Among the nursing homes that did not have EHRs, the majority had plans to implement one within two years. One-fifth had plans to implement one in more than two years, and 11.7 percent had no EHR implementation plans (Abramson et al., 2014). The majority of nursing homes indicated the biggest barriers to health IT investment were the initial cost, a lack of IT staff members, and the lack of fiscal incentives. National estimates on EHR adoption in long-term care are nearly nonexistent. Most are qualitative studies examining the experiences of early adopters (Cherry, Ford, & Peterson, 2011). Impact of EHR Systems Numerous studies over the years have demonstrated the value of using EHR systems and other types of clinical applications within health care organizations. The majority of benefits fall into three broad categories: (1) quality, outcomes, and safety; (2) efficiency, improved revenues, and cost reduction; and (3) provider and patient satisfaction. Following is a brief discussion of these major categories, along with several recent examples and reports illustrating the value of EHRs to the health care process. It is important to note, however, that despite the benefits, some studies have found mixed results or negative consequences.

Quality, outcomes, and safety. EHR systems can have a significant impact on patient quality, outcomes, and safety. Three major effects on quality are increased adherence to evidence-based care, enhanced surveillance and monitoring, and decreased medication errors. Banger and Graber (2015) recently conducted a review of the literature on the impact of health IT (including EHR systems) on patient quality and safety and found four major systematic reviews had been conducted from 2006 through 2014 each using a consistent methodology (Buntin, Burke, Hoaglin, & Blumenthal, 2011; Chaudhry et al., 2006; Goldzweig, Towfigh, Maglione, & Shekelle, 2009; Jones, Rudin, Perry, & Shekelle, 2014). Two of the reviews were published before the HITECH Act and two afterward. Collectively, 59 percent of the studies examined demonstrated positive effects on quality and safety, 25 percent had mixed-positive outcomes, 9 percent were neutral, and 8 percent were negative (Banger & Graber, 2015). Limitations of most of the earlier studies were based on the fact that they did not include many commercially available EHR systems. Since then, more than half of EHR evaluation studies involved commercially available EHR systems (Jones et al., 2014). Findings from the most recent systematic review conclude that CPOE effectively decreases medication errors. Hydari, Telang, and Marella (2014) studied the incidence of adverse patient safety events reported from 231 Pennsylvania hospitals from 2005 to 2012 in relation to their level of health IT use. After controlling for several possibly confounding factors, the authors found that hospitals adopting advanced EHRs (as defined by HIMSS) experienced a 27 percent overall reduction in reported patient safety events. Using advanced EHRs was associated with a 30 percent decline in medication errors and a 25 percent decline in procedure-related errors (Hydari et al., 2014). Efficiency, improved revenue, and cost reduction. In addition to improving quality and safety, some studies have shown that the EHR can improve efficiency, increase revenues, and lead to cost reductions (Barlow, Johnson, & Steck, 2004; Grieger, Cohen, & Krusch, 2007). A fairly recent study by Howley, Chou, Hansen, and Dalrymple (2014) examined the financial impact of EHRs on ambulatory practices by tracking the productivity (e.g., the number of patient visits) and reimbursement of thirty practices for two years after EHR implementation. They found that practice revenues increased during EHR implementation despite seeing fewer patients. Another study looked at seventeen primary care clinics that used EHR systems and found that the clinics recovered their EHR investments within an average period of ten months (95 percent CI 6.2–17.4 months), seeing more patients with an average increase of 27 percent in the active-patients-to-clinicians full-time equivalent ratio, and an increase in the clinic net revenue (p<.001) (Jang, Lortie, & Sanche, 2014). Provider and patient satisfaction. Provider and patient satisfaction are common factors to assess when implementing EHR systems. Results from satisfaction surveys are often mixed. In a 2008 national survey of physicians, 90 percent of providers using EHRs reported they were satisfied or very satisfied with them and a large majority could point to specific quality benefits (DesRoches et al., 2008). Those who had systems in place for two or more years were more likely to be satisfied (Menachemi, Powers, Au, & Brooks, 2010). A study that examined EHR satisfaction among obstetrics/gynecology (OB/GYN) physicians found that 63 percent reported being satisfied with their EHR system, and nearly 31 percent were not satisfied (Raglan, Margolis, Paulus, & Schulkin, 2014). Among study participants, younger OB/GYN physicians were more satisfied with their EHR than older physicians. A study by Rand (in collaboration with the AMA) found that although many physicians approved of EHRs in concept (for example, they

appreciated the fact that they could remotely access patient information and provide improved patient care), they expressed frustrations with usability and work flow (Friedberg et al., 2013). The time-consuming nature of data entry, interference with face-to-face patient care, inefficiency, and the inability to exchange health information between EHR products led to dissatisfaction. Physicians across the full range of specialties and practice models also described other concerns regarding the degradation of clinical documentation. Among US hospitals, a 2011 national study found that those with EHRs had significantly higher patient satisfaction scores on items such as “staff always giving patients information about what to do for the recovery at home,” “patients rating the hospital as a 9 or 10 overall,” and “patients would definitely recommend the hospital to others” than hospitals that did not (Kazley, Diana, Ford, & Menachemi, 2011, p. 26). Yet the same study found that the EHR use was not statistically associated with other patient satisfaction measures (such as having clean rooms) that one would not expect to be affected by EHR use. A more recent study by Jarvis and colleagues (2013) assessed the impact of using advanced EHRs (as defined as Stages 6 or 7 on the HIMSS Analytics EMR Adoption Model [EMRAM] level of health IT adoption) on hospital quality patient satisfaction using a composite score for measuring patient experience. (See the following Perspective.) They found that hospitals with the most advanced EHRs had the greatest gains in improving clinical clinical process of care scores, without negatively affecting the patient experience (Jarvis et al., 2013). Another study found that physicians using EHRS that met Meaningful Use criteria and had two or more years EHR experience were more likely to report clinical benefits (King, Patel, Jamoon, & Furukawa, 2014).

Limitations and Need for Further Research Not all studies have demonstrated positive outcomes from using EHR systems. For example, the same EHR or clinical information system can be implemented in different organizations and have different results. As example of variability, two children's hospitals implemented the same EHR (including CPOE) in their pediatric intensive care units. One hospital experienced a significant increase in mortality (Han et al., 2005), and the other did not (Del Beccaro, Jeffries, Eisenberg, & Harry, 2006). The hospital that experienced an increase in mortality noted that several implementation factors contributed to the deterioration in quality; specific order sets for critical care were not created, changes in workflow were not well executed, and orders for patients arriving via critical care transportation could not be written before the patient arrived at the hospital, delaying life-saving treatments. Many factors can influence the successful use and adoption of EHR systems. These are discussed more fully in Chapter Six.

Personal Health Records In addition to EHRs and patient portals, the broader concept of a personal health record has emerged in recent years. Initially, the PHR was envisioned as a tool to enable individuals to keep their own health records, and they could share information electronically with their physicians or other health care professionals and receive advice, reminders, test results, and alerts from them. Unlike the EHR and patient portal, which is managed by health care provider organizations, the PHR is managed by the consumer. It may include health and wellness information, such as an individual's exercise and diet. The consumer decides who has access

to the information and controls the content of the record. Personal data the consumer gathers through use of health apps such as My Fitness Pal or Fitbits may be included. What is the value of the PHR, and how does it relate to the EHR? Tang and Lansky (2005) believe the PHR enables individuals to serve as copilots in their own care. Patients can receive customized content based on their needs, values, and preferences. PHRs should be lifelong and comprehensive and should support information exchange and portability. Patients are often seen by multiple health care providers in different settings and locations over the course of a lifetime. In our fragmented health care system, this means patients are often left to consolidate information from the various participants in their care. A PHR that brings together important health information across an individual's lifetime and that is safe, secure, portable, and easily accessible can reduce costs by avoiding unnecessary duplicate tests and improving health care communications. The concept of patient portals and PHRs are also inherent in the CMS Meaningful Use program. Stage 3 Meaningful Use recommendations (originally scheduled for implementation in 2017 but now under policy reconsideration) state that patients should be able to (1) communicate electronically using secure messaging, (2) access patient education materials on the Internet, (3) generate health data into their providers' EHRs, and (4) view, download, and transmit their provider-managed EHRs. Taken together, Ford, Hesse, and Huerta (2016) argue that these requirements outline the basic functionalities of a consumer-managed PHR. Perspective HIMSS Analytics EHR Adoption Levels among US Hospitals Stage Cumulative Capabilities 2016—Q1 Stage 7 Complete EHR is used; data warehousing and data analytics is used to improve care; clinical information can be shared via standardized electronic transactions across continuum of care. 4.3% Stage 6 Physician documentation with structured templates and discrete data is implemented for at least one inpatient area. Full CCSS. The closed loop medication administration with bar coding is used. The five rights of medication administration are verified. 29.1% Stage 5 A full complement of radiology PACS system provides medical images to physicians via an intranet. 34.4% Stage 4 Computerized provider order entry (CPOE) used to create orders; CDSS is used with clinical protocols. 10.0% Stage 3 Nursing/clinical documentation has been implemented including electronic medication administration record (MAR); clinical decision support (CDS) capabilities allow for error checking with order entry. Medical image access from picture archive and communication systems (PACS) is available within organization. 15.3% Stage 2 Major clinical systems feed into clinical data repository (CDR) that enables viewing of orders and results. CDR contains a controlled medical vocabulary, and clinical decision support system (CDSS) capabilities. Hospital may have health information exchange (HIE) capabilities and can share CDR information with patient care stakeholders. 2.5% Stage 1 All three major ancillary clinical systems (laboratory, pharmacy, radiology) are installed. 1.8%

Stage 0 All three key ancillary department systems (laboratory, pharmacy, radiology) are not installed. 2.6% N=5,456

Ford and his colleagues (2016) examined US consumers PHR use over time, the factors that influence use, and projected the diffusion of PHR under three scenarios. Not surprisingly, they found that consumers were increasingly using electronic means for storing health data and communicating with their clinical providers. An estimated 5 percent of consumers used PHRs in 2008, and by 2013, this number had reached 17 percent (Ford et al., 2016), still relatively low. Using various prediction models, they estimate that PHR use will increase significantly within the next decade.

PHRs and personal health applications have the potential to positively affect medication adherence and quality of life for patients with chronic diseases. For example, a recent controlled study examined the impact of a text-based message reminder system on medication adherence among adolescents with asthma (Johnson et al., 2016). Compared to adolescents in the control group, they found improvements in self-reported medication adherence (p = .011), quality of life (p = .037), and self-efficacy (p = .016). System use varied considerably, however, with lower use among African American adolescents (Johnson et al., 2016).

Consumers are also increasingly capturing health, wellness, and clinical data about themselves using a wide range of mobile technologies and applications—everything from wrist-worn devices that track steps and sleep patterns to web-based food diaries, networked weight scales, and blood pressure machines (Rosenbloom, 2016). They also use social media networks to connect with others who share a similar health condition. Such approaches are referred to as person-generated health data (PGHD) technologies given that consumers may use these technologies independent of situations in which they are patients per se. According to Rosenbloom (2016) the field of PGHD and related technologies is in its infancy, particularly in studying the real value these technologies add to health care delivery. Shaw and his colleagues (2016) found, for example, that individuals with chronic illnesses (who may have the most to benefit from using mobile health devices) may be less likely to adopt and use these devices compared to healthy individuals. As health care organizations and providers move to managing population health and cohorts of patients under value-based payment models, the use of such technologies with certain populations of patients may be incredibly useful. Chapter Four discusses further the health IT tools needed to support population health management.

Key Issues and Challenges Despite the proliferation in the adoption and use of EHR systems, health care providers and organizations still face critical issues and challenges related to interoperability, usability, and health IT safety. Following is a brief discussion of each.

Interoperability In simple terms, interoperability is “the ability of a system to exchange electronic health information with and use electronic health information from other systems without special effort

on the part of the [user]” (Institute for Electrical and Electronics Engineering [IEEE], n.d.). The ONC's report Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap (ONC, 2015a) describes the importance of interoperability in a creating a “learning health system” in which “health information flows seamlessly and is available to the right people, at the right place, at the right time.” The overarching vision of a learning health system is to put patients at the center of their care—“where providers can easily access and use secure health information from different sources; where an individual's health information is not limited to what is stored in EHRs, but includes information from other sources (including technologies that individuals use) and portrays a longitudinal picture of their health, not just episodes of care; where diagnostic tests are only repeated when necessary, because the information is readily available; and where public health agencies and researchers can rapidly learn, develop and deliver cutting edge treatments” (ONC, 2015a, p. vi) (see Figure 3.6).

Today, providers are challenged to knit together multiple EHRs, financial systems, and analytic solutions in an effort to effectively manage population health and facilitate care coordination. As health care providers and organizations coalesce to manage performance and utilization risk in their communities, they need high degrees of interoperability among these systems (Glaser, 2015). The systems must also fit well into the clinical workflow and patient care process while ensuring patient safety and quality. Additionally, interoperability will enable data generated by personal fitness and wearable devices to be included in the patient's EHR (Glaser, 2015).

Figure 3.6 The ONC's roadmap to interoperability

Source: ONC (2015a).

True interoperability has yet to be realized. Several factors make interoperability among health care information systems complicated. EHR systems are often developed using different platforms with inconsistent use of standards, no universal patient identifier exists, and pulling together from a wide range of sources is complicated (Glaser, 2015). Moreover, historically there has not been a great deal of incentive for providers to share information, nor for health IT vendors to bridge together a number of different systems, giving rise to the concept of information blocking. According to the ONC, information blocking occurs “when persons or entities knowingly and unreasonably interfere with exchange or use of electronic health information” (ONC, 2015b). The concept of information blocking implies that the entity intentionally and knowingly interferes with sharing the data and is objectively unreasonable in light of public policy. The ONC has developed comprehensive strategies for identifying, deterring, and remedying information blocking and coordinating with other federal agencies that can investigate and take action against certain types of information blocking.

The ONC Roadmap to Interoperability postulates that work is needed in three critical areas: (1) requiring standards, (2) motivating the use of those standards through appropriate incentives, and (3) creating a trusted environment for collecting, sharing, and using electronic health information. Broad stakeholder involvement is critical to achieving interoperability. Stakeholders include those who receive or support care, those who deliver care, those who pay for care, and

people and organizations that support health IT capabilities, oversight of health care organizations, and those who develop and maintain standards (ONC, 2015b). (See the following Perspective.) In addition to the ONC, which resides in the Department of Health and Human Services, CMS and state governments also play key roles in advancing interoperability. Statewide health information exchanges can be found in Massachusetts, New York, and Delaware (Glaser, 2015). Interoperability efforts and standards development are discussed more fully in Chapter Ten.

Partnerships are also occurring within the private sector to advance interoperability among systems by creating standards and promoting the sharing of data. CommonWell Health Alliance has created and implemented patient identification and record-locating service capabilities, Carequality is developing an interoperability and governance framework, and the Argonaut Project is testing the next generation of interoperability standards. Glaser (2015) argues that we must focus on several important goals in making interoperability in health care a reality by doing the following:

Advancing standards development and pursuing new technical approaches to effecting standards-based interoperability Strengthening sanctions, perhaps through the certification process, to minimize business practices that thwart interoperability Increasing transparency of vendor and provider progress in achieving interoperability Developing a trust framework that balances the need for efficient exchange with the privacy rights of patients Promoting collaborative multi-stakeholder efforts, such as CommonWell Health Alliance, Carequality, and eHealth Initiative Encouraging provider-led activities within communities to broaden the range of interconnections and include stakeholders such as safety net providers Creating a governance mechanism that ensures an effective interchange across a wide range of health information exchanges Making reimbursement changes that emphasize care coordination and population health management, all of which must continue to evolve and be implemented Unfortunately, there is no silver bullet or easy road to achieving true interoperability. However, with collaboration among stakeholders, appropriate incentives, and keeping the patient at the center of our work and efforts, secure and efficient interoperability is certainly within reach.

Perspective The ONC Roadmap to Interoperability Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap (ONC, 2015b) was released by the Office of the National Coordinator for Health Information Technology in 2015. This document was published as a companion to the Connecting Health and Care for the Nation: A 10-Year Vision to Achieve an Interoperable Health IT Infrastructure. The following facts are taken from the Roadmap and its companion infographic, Shared Nationwide Interoperability Roadmap: The Journey to Better Health and Care. This outline lists progress toward interoperability since 2009, the current state of health care supporting the need

for interoperability, and the future goals and selected payer and outcome milestones for achieving the ultimate in interoperability, “learning health systems in which health information flows seamlessly and is available to the right people, at the right place, at the right time” (ONC, 2015a).

Selected Historical Interoperability Achievements 2009 16% of hospitals and 21% of providers adopted basic EHRs. 2011 27% of hospitals and 34% of providers adopted EHRs. 2013 94% of nonfederal acute care hospitals use a certified EHR. 78% of office-based physicians use an EHR. 62% of hospitals electronically exchanged health information with providers outside their system. 2014 80% of hospitals can electronically query other organizations for health information. 14% of office-based providers electronically share patient information with other providers. Current State of Health Care

One in three consumers must provide his or her own health information when seeking care for a medical problem. A typical Medicare beneficiary sees seven providers annually. A typical primary care physician has to coordinate care with 229 other physicians working in 117 practices. Eighty to ninety percent of health determinants are not related to health care. One in eight Americans tracks a health metric using technology. It takes seventeen years for evidence to go from research to practice. Barriers to Interoperability

States have different laws and regulations making it difficult to share health information across state lines. Health information is not sufficiently standardized. Payment incentives are not aligned to support interoperability. Privacy laws differ and are misinterpreted. There is a lack of trust among health care providers and consumers. 2015–2017 Goal and Milestones

Goal: Send, receive, find, and use priority data domains to improve health care quality and outcomes

Roadmap Milestones for a Supportive Payment and Regulatory Environment and Outcomes

CMS will aim to administer 30 percent of all Medicare payments to providers through alternative payment models that reward quality and value and encourage interoperability by the end of 2016.

A majority of individuals are able to securely access their electronic health information and direct it to the destination of their choice.

Providers evolve care processes and information reconciliation to ensure essential health information is sent, found, or received to support safe transitions in care.

ONC, federal partners, and stakeholders develop a set of measures assessing interoperable exchanges and the impact of interoperability on key processes that enable improved health and health care. 2018–2020 Goal and Milestones

Goal: Expand interoperable health IT and users to improve health and lower cost

Roadmap Milestones for a Supportive Payment and Regulatory Environment and Outcomes

CMS will administer 50 percent of all Medicare payments to providers through alternative payment models that reward quality and value by the end of 2018.

Individuals regularly access and contribute to their longitudinal electronic health information via health IT, send and receive that information through a variety of emerging technologies, and use that information to manage their health and participate in shared decision making with their care, support, and service teams.

Providers routinely and proactively seek outside information about individuals and can use it to coordinate care.

Public and private stakeholders report on progress toward interoperable exchange, including identifying barriers to interoperability, lessons learned, and impacts of interoperability on health outcomes and costs.

2020–2024 Goal and Milestones

Goal: Achieve nationwide interoperability to enable a learning health system

Roadmap Milestones for a Supportive Payment and Regulatory Environment and Outcomes

The federal government will use value-based payment models as the dominant mode of payment for providers.

Individuals are able to seamlessly integrate and compile longitudinal electronic health information across online tools, mobile platforms, and devices to participate in shared decision making with their care, support, and service teams.

Providers routinely use relevant info from a variety of sources, including environmental, occupational, genetic, human service, and cutting-edge research evidence, to tailor care to the individual.

Public and private stakeholders report on progress on key metrics identified to achieve a learning health system.

Source: ONC (2015a). Usability In addition to interoperability concerns, clinicians often express frustration with the usability of EHR systems and other clinical information systems. In fact, 55 percent of physicians reported that it was difficult or very difficult to use. Common frustrations include confusing displays, iconography that lacks consistency and intuitive meaning, and the feeling that systems do not support clinicians' cognitive workflow or inhibit them from easily drawing insights or conclusions from the data. Similarly, physicians who participated in a Rand study (Friedberg et al., 2013) felt that EHR data entry was time-consuming, interfered with face-to-face patient care, and was overall inefficient. They also reported that inability to exchange health information and the degradation of clinical documentation were of concern. Others argue that poor usability of EHR systems not only contributes to clinician frustration but also can lead to errors and patient safety concerns (Meeks, Smith, Taylor, Sittig, Scott, & Singh, 2014; Sittig & Singh, 2011). In essence, usability refers to “the effectiveness, efficiency, and satisfaction with which the intended users can achieve their tasks in the intended context of produce use” (Bevan, 2001). Smartphones are typically viewed as having high usability, because they require little training and are intuitive to use. In fact, we often see young children navigating them before they can even talk!

Given the importance of system usability, a task force was formed by the American Medical Informatics Association (Middleton et al., 2013) to study the issue. They identified key recommendations on critical usability issues, particularly those that may adversely affect patient safety and the quality of care. The recommendations fall into four categories: (1) usability and human factors research, (2) policy recommendations, (3) industry recommendations, and (4) clinical end user recommendations. (See the Perspective.)

As one can discern from AMIA's task force recommendations, usability is a multifaceted issue and one that requires thoughtful research, standardization and interoperability, a common user interface style guide, and systems for identifying best practices and monitoring use as well as adverse events that may affect patient safety. Perspective AMIA EHR Usability Recommendations Usability and human factors research agenda in health IT a. Prioritize standardized use cases. b. Develop a core set of measures for adverse events related to health IT use. c. Research and promote best practices for safe implementation of EHR. Policy recommendations

d. Standardization and interoperability across EHR systems should take account of usability concerns. e. Establish an adverse event reporting system for health IT and voluntary health IT event reporting. f. Develop and disseminate an educational campaign on the safe and effective use of EHR. Industry recommendations g. Develop a common user interface style guide for select EHR functionalities. h. Perform formal usability assessments on patient-safety sensitive EHR functionalities. Clinical end user recommendations i. Adopt best practices for EHR implementation and ongoing management. j. Monitor how IT systems are used and report IT-related adverse events.

Health IT Safety In 2011, the Institute of Medicine published a report titled Health IT and Patient Safety: Building Safer Systems for Better Care in which they outlined a number of recommendations to ensure health IT systems are safe. In brief, they suggest that safety is a shared responsibility between vendors and health care organizations and requires the following:

Building systems using user-centered design principles with adequate testing and simulation Embedding safety considerations throughout the implementation process Developing and publishing best practices Having accreditation agencies (such as the Joint Commission) assume a significant role in testing as part of their accreditation criteria Focusing on shared learning and transparency Creating a nonpunitive environment for reporting (IOM, 2011) Since then, the topic of health IT safety has grown in importance as more EHR systems have been deployed. Health IT patient safety concerns include adverse events that reached the patient, near misses that did not reach the patient, or unsafe conditions that increased the likelihood of a safety event (Meeks et al., 2014). Such events are often difficult to define and detect. Consequently, Singh and Sittig (2016) have developed a health IT safety measurement framework that takes into account eight technological and nontechnological dimensions or sociotechnical dimensions (see Table 3.3).

Table 3.3 Sociotechnical dimensions

Source: Reproduced from Measuring and Improving Patient Safety through Health Information Technology: The Health IT Safety Framework, Singh and Sittig, 25: p.228, 2016. With permission from BMJ Publishing Group Ltd. Dimension Description Hardware and software Computing infrastructure used to support and operate clinical applications and devices Clinical content The text, numeric data, and images that constitute the “language” of clinical applications, including clinical decision support

Human-computer interface All aspects of technology that users can see, touch, or hear as they interact with it People Everyone who is involved with patient care and/or interacts in some way with health care delivery (including technology). This would include patients, clinicians and other health care personnel, IT developers and other IT personnel, informaticians Workflow and communication Processes to ensure that patient care is carried out effectively, efficiently, and safely Internal organizational features Policies, procedures, the physical work environment, and the organizational culture that govern how the system is configured, who uses it, and where and how it is used External rules and regulations Federal or state rules (e.g., CMS's Physician Quality Reporting Initiative, HIPAA, and Meaningful Use program) and billing requirements that facilitate or constrain the other dimensions Measurement and monitoring Evaluating both intended and unintended consequences through a variety of prospective and retrospective, quantitative, and qualitative methods

The Health IT Safety Framework provides a conceptual framework for defining and measuring health IT–related patient safety issues. The framework is also built on continuous quality improvement methods that require stakeholders to ask themselves, How are we doing? Can we do better? How can we do better (Singh & Sittig, 2016)? In fact, Singh and Sittig (2016) argue that it is essential that clinicians and leaders make health IT patient safety an organizational priority by ensuring that the governance structure facilitates measuring and monitoring and creating an environment that is conducive to detecting, fixing, and learning from system vulnerabilities. Meeks and colleagues (2014) used a variation of the Health IT Safety Framework in analyzing one hundred different EHR-related safety concerns reported to and investigated by the VA's Informatics Patient Safety Office, which is a voluntary reporting system. The major categories of errors were because of (1) unmet display needs (mismatch between information needs and content display; (2) software modifications (concerns about upgrades, modifications, or configurations); (3) system-to-system interfacing (concerns about failure of interfacing between systems); and (4) hidden dependencies on distributed systems (one component of the EHR is unexpectedly or unknowingly affected by the state or condition of another component) (Meeks et al., 2014). They concluded that because EHR-related safety concerns have sociotechnical origins and are multifaceted, health care organizations should build a robust infrastructure to monitor and learn from them.

Numerous factors can affect the safety and effective use of health care information systems—everything from poor usability to software glitches to unexpected downtime or cyber attacks. Health care executives should be aware of these issues and vulnerabilities and ensure their organizations have in place mechanisms to prevent, detect, monitor, and address adverse events that may affect patient safety and quality of care.

Summary This chapter provided an overview of health care information systems including administrative and clinical information systems. We gave a brief history of the evolution of the use of

information systems in health care. Special attention was given to the adoption, use, and features of EHR systems, patient portals, and PHR systems. We also summarized recent literature on the value of EHR systems, which may be categorized into three main areas: (1) quality, outcomes, and safety; (2) efficiency, improved revenues, and cost reduction; and (3) provider and patient satisfaction. Limitations to research findings were noted along with the need for future research. Key issues related to the use of health care information systems were discussed including interoperability, usability, and health IT safety. The chapter concludes with a discussion of a health IT safety framework that may be useful to health care leaders in preventing, detecting, and monitoring health IT–related patient safety issues.