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CHAPTER

49

HISTORY AND THE QUALITY LANDSCAPE

Norbert Goldfield

Introduction

The extraordinary developments in the measurement and management of healthcare quality need to be placed into historical context. This chapter will first briefly highlight the many advances that have occurred in quality control in industry, as a whole, and in the healthcare system specifically between 1850 and 1960. As this discussion is purposely brief, readers wanting to learn more can refer to the numerous articles and books that have been written about this period. The remainder of this chapter focuses on the most fertile period of quality measurement and management in healthcare in the United States—the time since the enactment of Medicare and Medicaid in 1965.

Although many decry the profit motive that drives healthcare systems, the reality is that healthcare delivery is a critical part of the US economy, as it is in other countries. Many sectors—not just the insurance industry—enjoy substantial profits from the healthcare system. Healthcare spending represented 17.8 percent of the US gross domestic product (GDP) in 2015, and that figure is projected to rise to 19.9 percent by 2025—the highest of any country in the world (Advisory Board 2017). Financial incentives, such as those described in this chapter, are critical to the successful implementation of any quality mea- surement program. As numerous articles have documented, access to health insurance, the extent of its coverage, and socioeconomic disparities have a significant impact on quality of outcomes and important implications for the American healthcare economy. Typically, the people who suffer the most in terms of poor quality are the poor and nonwhite populations.

Perhaps the best way to counter the profit motive that exists in our pres- ent healthcare system is to tie transparent financial incentives of quality outcomes to organizational (as opposed to individual health professional) behavior. These financial incentives should be one leg of the “three-legged stool” of quality management. The other two legs are a focus on consumer empowerment, as discussed in detail later in this chapter, and payers’ regular release of transpar- ent, comparative outcomes data, which helps foster collaboration with payers.

If we were to implement this three-legged stool approach throughout the entire healthcare system, we could make tremendous progress in one absolutely

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C o p y r i g h t 2 0 1 9 . H e a l t h A d m i n i s t r a t i o n P r e s s .

A l l r i g h t s r e s e r v e d . M a y n o t b e r e p r o d u c e d i n a n y f o r m w i t h o u t p e r m i s s i o n f r o m t h e p u b l i s h e r , e x c e p t f a i r u s e s p e r m i t t e d u n d e r U . S . o r a p p l i c a b l e c o p y r i g h t l a w .

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critical intermediate outcome measure—namely, universal coverage. Universal coverage is a critical ingredient to the provision of quality care, as we have seen that, without insurance coverage, clinical outcomes are worse (Institute of Medicine 2001). Although we have made tremendous progress in improv- ing access to healthcare coverage, those of us who believe that every American citizen deserves a “decent minimum” of healthcare coverage know that we still have a long way to go to achieve equity. The United States remains the only first-world industrialized country that lacks universal insurance coverage for its citizens, and prospects for achieving that important goal of quality management seem farther away than ever, at least at the national level.

Quality Measurement and Management Prior to 1965

The main themes of this chapter are an outgrowth of the period before 1965, the year that Congress passed Medicare and Medicaid into law. Over the past half century, our quality measurement tools have become much more refined, but the basic issues remain—we have reasonably good quality measures with inadequate implementation.

Historically, the principal approaches to quality measurement were developed outside the healthcare system, in the manufacturing sector. The giants in the field of industrial quality measurement and management were W. Edwards Deming, Walter Shewhart (Deming’s teacher), and Joseph M. Juran (Deming 1982; Juran 1995). Deming pioneered the use of control charts, which had been developed by Shewhart (Best and Neuhauser 2006). Control charts are arguably the most important tool in the quality measure- ment armamentarium (though they are still not used enough in healthcare). Deming famously said, “Look for the trouble and its explanation and try to remove the cause every time a point goes out of control” (Clarke 2005, 36). Well before the Dartmouth Atlas, which will be described later in this chap- ter, it was Deming who declared that “uncontrolled variation is the enemy of quality” (Kang and Kvam 2012, 19).

Despite Deming’s pioneering work, it was not until the late 1990s that we finally began to reverse many of the perverse incentives of paying more for poor quality outcomes (e.g., hospital complications)—and we are still only at the beginning! As Deming (1982, 11) said, “Defects are not free. Somebody makes them, and gets paid for making them.” Deming clearly stated that a complete shift of financial risk from a payer to a provider means that the payer has abdicated any interest in quality (Schiff and Goldfield 1994).

Readers should keep in mind that effective medical interventions were few and far between in the early twentieth century. General anesthesia with tracheal intubation, the first antibiotics, and the discovery of insulin all occurred

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Chapter 2: History and the Qual i ty Landscape 51

less than 100 years ago. The beginning of healthcare measurement and man- agement can be attributed to the British nurse Florence Nightingale, who used statistics to document an improvement in the mortality rate after her sanitary interventions during the Crimean War in the 1850s. According to many, she paved the way for the first truly modern hospitals (McDonald 2014) with her use of statistics to identify opportunities for improvement (Kopf 1916), her focus on the most important quality measure (mortality), and her changes to managerial practices based on results.

In the early twentieth century, decades before Deming, surgeon Ernest A. Codman (1914, 496) was an early supporter of outcomes research:

We must formulate some method of hospital report showing as nearly as possible

what are the results of the treatment obtained at different institutions. This report

must be made out and published by each hospital in a uniform manner, so that

comparison will be possible. With such a report as a starting point, those interested

can begin to ask questions as to management and efficiency.

Codman’s arguments led to a hospital standardization program by the American College of Surgeons (ACS); however, in a 1918 survey, only 89 of 692 hospi- tals met the basic minimum standards. The results were announced at an ACS committee meeting at the Waldorf Astoria Hotel in New York City in 1919 (Wright 2017), but the leaders of the ACS burned Codman’s papers so that the public would not find out which hospitals had failed his test (ACS 2018b).

Although Codman died a pauper, his research articles and short books remain among the most quoted in the healthcare quality measurement and management literature. Around the same time that Codman’s efforts failed, Abraham Flexner was more successful in reforming America’s medical schools— and hospitals in general—by creating standards for education and licensure of physicians and medical staff (Flexner 1910).

In the 1930s through 1950s, researchers documented the validity of yet another of Deming’s beliefs, the presence of practice pattern variation in such procedures as tonsillectomies (Glover 1938) and hysterectomies (Doyle 1953). Leaders such as Mindel Sheps (1955), Cecil Sheps (Solon, Sheps, and Lee 1960), Leonard Rosenfeld (1957), Sam Shapiro (Berkowitz 1998), and Paul Lembcke (1956) developed new, preliminary approaches to quality mea- surement. Lembcke, for example, described a quality measurement technique he called “medical auditing,” which is now called explicit chart review. In a remarkable article on the impact of medication on health and the importance of medication safety, Henry Beecher (1955) summarized the then scant research literature on the placebo effect. In an era with an unprecedented number of new medications and an increased focus on medication safety, Beecher summarized in a seminal article the 15 studies then extant documenting the placebo effect.

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Around the same time, I. S. Falk, Odin Anderson, Milton Roemer, and Kerr White (White, Williams, and Greenberg 1961) published their research on the organizational and political aspects of quality management, including the connection between quality and the organization and financing of medical care. The first major efforts to provide health insurance coverage in the United States emerged in the 1930s—the same decade that witnessed the publication of findings by Falk and his group of researchers about the best organizational approaches to delivering quality care (Falk, Rorem, and Ring 1932). Based on many years of research, Falk’s team concluded that the group practice model was the best approach for delivering quality medical care; at this time, most physicians were in solo fee-for-service practices. One of the most successful group practice programs is still in existence today: The Kaiser Permanente Medical Group started in the 1930s, became a medical group in the 1940s, and eventually evolved to a prototypical health maintenance organization (HMO) in the 1970s (Cutting and Collen 1992).

By the early 1950s, some aspects of Codman’s dream began to become a reality (Mallon 2014), after the American College of Physicians, the American Hospital Association, the American Medical Association, and the Canadian Medical Association joined the American College of Surgeons to form the Joint Commission on Accreditation of Hospitals (Roberts, Coale, and Redman 1987). However, these were voluntary associations of professional organizations. The government had minimal involvement in quality improvement at the time, as its financial stake was minimal—another example of the Deming dictum of the relationships (or lack thereof) between interest in quality improvement and financial incentives. The Joint Commission and other voluntary associations took a “minimal standards” approach to quality measurement and manage- ment, and thus they did not adopt the quality management approaches that Codman and Deming would have supported.

In the 1960s, Sol Levine and other sociologists connected the political and organizational aspects of healthcare systems to quality measurement and management with their contributions to the rapidly developing field of medi- cal sociology (Freeman, Levine, and Reeder 1963). In 1966, Levine became the founding member of the Department of Behavioral Sciences at Johns Hopkins University, and he came to exemplify the rare researcher who bridged the quality management and measurement fields. With his pioneering work in medical sociology, he paved the way for our understanding of the connections between nonhealthcare factors—such as housing, education, social class, and race—and healthcare outcomes. Levine was one of the first people to focus on such outcomes as quality of life and happiness. In addition to making volumi- nous contributions to our research understanding of these critical issues, he was also a teacher to hundreds around the world. His followers, in turn, have continued his legacy of advancing the science of quality measurement while

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Chapter 2: History and the Qual i ty Landscape 53

connecting the findings to improvement of our healthcare system, particularly for the most vulnerable members of society.

Medicare, Medicaid, and Subsequent Developments

In 1965, the US Congress established the Medicare and Medicaid programs as Title XVIII and Title XIX of the Social Security Act. It was one of the most consequential legislative acts in US history, as it would lead to improved healthcare quality for more than one hundred million low-income Americans— particularly the elderly.

The immediate antecedents had begun in 1960. Working with Senator Robert Kerr of Oklahoma, Wilbur Mills, the powerful chairman of the House Ways and Means Committee, had attempted to stifle the drive toward Medicare by passing a limited program that provided means-tested health insurance to poor elderly citizens. The program would be administered by state and local governments that had chosen to participate. However, the guidelines for partici- pation were so stringent that only 1 percent of the elderly received benefits. In 1964, Lyndon Baines Johnson won a landslide electoral and legislative victory over Barry Goldwater, delivering a critical electoral message that the country was ripe for Medicare and Medicaid. The Johnson administration revised its Medicare bill to include hospital insurance funded through Social Security taxes, a voluntary program covering physicians’ costs paid for by contributions from beneficiaries and general revenue from the federal government, and an expanded version of the Kerr-Mills Act, administered by the states in a 50-50 match with the federal government. This expanded version would later be called Medicaid.

Along with the legislation came regulatory quality management bodies, beginning with Professional Standard Review Organizations (PSROs) and then, when the PSROs failed, Peer Review Organizations (PROs) in the 1980s. The federal government implemented a number of “screens” such as unscheduled return for surgery and adequacy of discharge planning. This profiling of physi- cians (Goldfield and Boland 1996) and healthcare institutions remains in place through the present federal, state, and private payer value-based purchasing programs that have replaced many of the federal programs of the 1960s.

Quality Measure Development Following the Passage of Medicare and Medicaid Around the time Medicare and Medicaid were enacted, a series of scientific articles had a dramatic impact on healthcare delivery in the United States. Avedis Donabedian (1966) magisterially summarized and expanded upon the quality measurement literature with his voluminous contributions. In a seminal

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article titled “Evaluating the Quality of Medical Care,” he provided a critique of the existing definitions of quality and approaches to the measurement of quality. In particular, he clarified the distinctions between structure, process, and outcomes measures. Donabedian (1966, 197) set forth a research agenda that many have followed ever since: “In addition to conceptual exploration of the meaning of quality, in terms of dimensions of care and the values attached to them, empirical studies are needed of what are the prevailing dimensions and values in relevant population groups.”

Around the same time, an article by Sidney Katz and colleagues reported on the development of the activities of daily living (ADL) measure, one of the first in a veritable avalanche of validated measures that examine functional and mental health status from the perspective of either the patient or the provider (Katz et al. 1963). The ADL and IADL (instrumental activities of daily living) measures are still in use today—a testament to their durability. In the conclusion of their article, Katz and colleagues foreshadowed the enormous importance of the measurement of health status: “The index is proposed as a useful tool in the study of prognosis and the effects of treatment, as a survey instrument, as an objective guide in clinical practice, as a teaching device, and as a means of gaining more knowledge about the aging process” (Katz et al. 1963, 919).

Just a few years after the passage of Medicare and Medicaid, John Wennberg and Alan Gittelsohn (1973) at Dartmouth expanded on a fundamental Deming dictum pertaining to variation in medical practice patterns, noting that these varia- tion patterns exist not just between the East Coast and West Coast of the United States but even within metropolitan areas such as New Haven, Connecticut, and Boston (Wennberg et al. 1989). The researchers not only documented the varia- tions but also suggested ways of dealing with variations from clinical, financial, and organizational points of view. Over decades of subsequent research work, the Dartmouth group of researchers, including Wennberg and Gittelsohn, as well as Paul Batalden (Hayes, Batalden, and Goldmann 2015), Elliott Fisher (Lewis, Fisher, and Colla 2017), Robert Keller (1994), Eugene Nelson (Nelson et al. 2003), Jonathan Skinner (Skinner and Volpp 2017), and John Wasson (2017), among others, pioneered the use of the Dartmouth Atlas, the perspective of the consumer, the Deming philosophy as applied to healthcare, and many other qual- ity measurement and management approaches. Specifically, Fisher developed the accountable care organization (ACO), and Wasson has worked to develop a truly consumer-focused vision of the patient-centered medical home.

Case Mix, Diagnosis-Related Groups, and Risk Adjustment Case mix is defined as the scientific methodology of assigning like patients to the same category. The key is defining the dependent variable, which could be dollars expended, mortality, or complications. The case mix measurement and management tool with the most dramatic impact on the American healthcare

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Chapter 2: History and the Qual i ty Landscape 55

system (and the systems of many other countries throughout the world) emerged in the late 1960s from a group at the Yale School of Management. At Yale, the industrial management professor Bob Fetter and the public healthcare expert John Thompson (a nurse by training) worked with two operations research graduate students—Rich Averill and Ron Mills—to develop diagnosis-related groups (DRGs). Averill focused on operationalizing the DRG concept, and Mills focused on building the analytic infrastructure (Mills et al. 1976; Thompson, Averill, and Fetter 1979; Fetter et al. 1980).

In 1977, after completing the first version of the DRGs, Fetter, Thomp- son, and Averill had a meeting with the senior staff of a major teaching hospital to present their findings on the significant variation (both within and between DRGs) in that hospital’s lengths of stay and costs. They expected that the senior staff would be amazed by the findings and want to take immediate action to decrease the variation and improve the efficiency of care being delivered. However, the findings were simply ignored. Without a financial incentive, the institution had little interest in addressing variation in hospital lengths of stay and cost (Averill, pers. comm.).

After this experience, Fetter, Thompson, and Averill internalized and acted upon the Deming dictum that, without a financial stake in quality improvement, no organization would be interested. This understanding led the three researchers to take advantage of the uncontrolled Medicare costs that had emerged quickly after the passage of the program (Thompson, Averill, and Fetter 1979), and they worked to influence state and federal governments to take action. A successful interim trial of the DRGs in New Jersey in the 1970s led to national implementation of DRGs, also known as the Inpatient Prospec- tive Payment System (IPPS), in 1982, as federal government officials sought to confront Medicare’s dramatic cost overruns (Russell 1989). Ironically, the federal government under the conservative administration of President Ron- ald Reagan implemented the most regulatory government intervention ever undertaken in healthcare.

The IPPS had three objectives, as articulated in Public Law No. 98-21, the Social Security Amendments of 1983: “Restructure the economic incen- tives to establish marketlike forces, to Establish the Federal government as a prudent buyer of services, to Identify the product being purchased on behalf of Medicare beneficiaries.” How were these objectives accomplished? The DRGs restructured hospital payment to be based on a clinically credible unit of payment (the DRG) that linked the clinical and financial aspects of care. Essen- tially, DRGs represent “a clinically meaningful product with a price that was payment in full, thereby providing the strong financial incentive for efficiency” (Schweiker 1982, 34). The program was so successful that no cost increases in the DRG payment were made for several years (Russell and Manning 1989). However, although the IPPS established a fair payment amount for hospital

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care (the DRG price), it did not attempt to adjust the payment amount for the quality of the care provided—a significant omission that is being addressed today. The IPPS was used to pay hospitals, but it has never to date, with the exception of a few demonstration projects, been used for physician payment. Amazingly but possibly not surprisingly, healthcare professional behavior has changed despite this lack of direct financial incentive.

In addition to its financial impact, DRG implementation had four impor- tant effects, all of which have impacted quality measurement and management across all aspects of the healthcare system. First, and most important, researchers and lobbyists alike expressed strong concerns about the potential negative effect of IPPS implementation on hospital quality of care (Coulam and Gaumer 1992). These concerns led to funding for the largest health services research project in US history, the RAND Health Insurance Study (HIS) (Brook et al. 1983). The study’s team of researchers—which included Bob Brook, Shelly Greenfield, Joe Newhouse, and John Ware, to name a few—determined, among many other findings, that quality of hospital care under DRGs had not suffered; in fact, mortality rates went down after DRG implementation (Kahn et al. 1992).

The findings of the RAND HIS led to, as a second impact of DRGs, the development of a whole new generation of quality measures, particularly those focused on outcomes. Such measures included the Short-Form Health Survey (SF-36) and various ways of comparing mortality rates (Ware and Sherbourne 1992; Fink, Yano, and Brook 1989), among many others.

Third, because implementation needed validation of the DRG assign- ment, the federal quality assurance bureaucracy stepped into action (Dans, Weiner, and Otter 1985), and researchers developed additional quality mea- surement tools in response (Gertman and Restuccia 1981).

Finally, the financial success of the IPPS approach led to the development and implementation of prospective payment systems for virtually all aspects of the healthcare system, including ambulatory care (Averill et al. 1993), nursing home services (Fries and Cooney 1985), and rehabilitation care (Granger et al. 2007), along with the application of prospective payment or risk adjustment to managed care organizations (Ellis et al. 1996). The research and development needed to implement these new prospective payment systems, in turn, led to a large number of additional quality measures specific to such areas as rehabilita- tion, home health, and hospital outpatient ambulatory visits (Goldfield, Pine, and Pine 1996). Policymakers and researchers alike used these tools to adjust prospective payment and/or risk adjustment on an ongoing basis.

Adjusting Prospective Payment Case Mix Systems for Severity: A Key to Fair Comparisons of Quality Outcomes Even though the DRGs had been developed, in the Deming tradition, as a management tool to increase hospital efficiency, federal officials implemented

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them largely as a financial cost-control intervention. Concurrent with the federal bureaucracy’s increased efforts to retrospectively monitor hospital quality, many people demanded that policymakers adjust DRG payment to take hospital quality into account. First, however, researchers needed to develop much more detailed severity adjustment of the DRGs and decide whether claims-based data—the type of data readily available and used for payment for all healthcare encounters—were sufficient for making valid assessments of, for example, hospital quality. In the late 1980s and throughout the 1990s, researchers at several organizations—including the Centers for Medicare & Medicaid Services (CMS), which at that time was called the Health Care Financing Administration—developed severity adjustment algorithms for the DRGs (Iezzoni et al. 1993; McGuire 1991). Eventually, the Medicare severity DRGs (MS-DRGs) were implemented in 2007.

Claims-Based Versus Medical Records–Based Quality Metrics and Their Integration into DRG Payment: The Beginning of Value- Based Purchasing Throughout the 1990s, a variety of researchers—such as Lisa Iezzoni (Iezzoni et al. 1993), Shukri Khuri (Best et al. 2002), Ed Hannan (Hannan et al. 2003), Patrick Romano (Romano et al. 2002), and others (Goldfield, Pine, and Pine 1996)—debated the advantages and disadvantages of claims-based metrics versus medical records–based outcomes metrics, such as complication and mortality rates. CMS retreated from the public disclosure of comparative hospital mortal- ity rates in 1989 (Iezzoni 2012), but from a policy point of view, it made an implicit early decision to continue using claims-based measures in its assessment of Medicare quality. However, little could be done in terms of measure develop- ment to incentivize hospitals to improve quality, until policymakers mandated a “flag” on a claims form indicating whether a secondary diagnosis was present on admission. This seemingly innocuous piece of information was a critical first step in identifying whether a condition flagged as present or not present on admission could, for example, be considered a preventable complication.

Many researchers, including Pine and Romano, advocated for the “present-on-admission” flag. They pursued a state-based approach, and Cali- fornia became the first state to effectively implement the flag in the mid-1990s. The National Center for Vital Statistics followed suit, and the notation was implemented on the insurance claims forms in 2006 (Agency for Healthcare Research and Quality 2006). With the flag implemented, researchers in both the public and private sectors rushed to develop the first hospital complication mea- sures (Zhan et al. 2009; Hughes et al. 2006), followed by measures to delineate preventable hospital readmissions (Krumholz et al. 2009; Goldfield et al. 2008).

Although some researchers still question the validity of claims-based metrics, the measures are well established in the policy world. In addition, new items are routinely collected from electronic medical records and then

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linked to claims data, thus increasing the claims-based metrics’ scientific valid- ity (Medicare.gov 2018). At the same time, however, a “parallel universe” has led to separate quality management efforts that rely largely on data abstracted from electronic medical records. Beginning with Hannan and Khuri, research- ers have developed a significant number of quality measures using data typi- cally abstracted from the medical record using a predefined form and trained abstractors. Although some states, notably New York, link the results of some of these medical records–based measures to payment, most use them for inter- nal quality management (New York State Department of Health 2018). Khuri and his team encouraged the American College of Surgeons to embrace this internal quality improvement effort, and it has had a significant impact on surgical complications in thousands of hospitals (ACS 2018a).

Case Example: The Difficulty of Measuring Quality of Mental Health and Substance Abuse Services

Although significant advances have been made in quality measurement for surgery and other aspects of medical care, the development of meaningful quality metrics, especially outcomes measures, for mental health and sub- stance abuse (MHSA) services has faced major challenges. Little progress was made in developing MHSA quality metrics until the aforementioned RAND HIS. During the early 1990s, researchers, including John Ware and Emmett Keeler, developed detailed process measures and initial outcomes measures that would be useful for severe mental health disorders such as schizophrenia and less severe ones such as mild anxiety or depression (RAND Corporation 2018). Ware and other researchers—including G. Richard Smith, Al Tarlov, Sol Levine, Gene Nelson, and Shelly Greenfield—built on this work in the Medical Outcomes Study, the successor study to the RAND HIS (Smith et al. 2002; Tarlov et al. 1989).

Don Steinwachs of Johns Hopkins University and Tony Lehman of the University of Maryland—along with Mady Chalk and Tom McLellan, both of the Treatment Research Institute—were among the many who advanced this research in MHSA services. As part of a new round of federally funded initiatives, the Patient Outcomes Research Teams, Steinwachs and Lehman focused on the outcomes of people with severe mental disabilities—espe- cially those with socioeconomic disparities (Lehman et al. 2004; Kwan, Stratton, and Steinwachs 2017). The researchers addressed the challenges faced by this difficult-to-treat population head-on, pushing the boundaries of quality measurement and determining, for example, that nonhealthcare

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New Quality Management and Measurement Research Institutions Since the Passage of Medicare and Medicaid From a quality management perspective, the US healthcare system has moved, in fits and starts, toward a group practice model of care ever since the 1930s report from the Committee on the Costs of Medical Care. The movement accelerated with the passage of Medicare and Medicaid under President Lyndon B. Johnson and the subsequent push by President Richard Nixon to increase the number of HMOs. From a positive feedback loop perspective, this move toward HMOs led to the development of a number of organizations, such as preferred provider organizations (PPOs) in the 1990s, that policymakers thought could accomplish the same healthcare efficiencies as staff-model HMOs but without the brick-and-mortar expense. More recently, these efforts have culminated in the development of organizations such as ACOs that, in theory, would have an interest in improving population health for the communities they serve (Lewis, Fisher, and Colla 2017). New categories of healthcare professionals, such as case managers and community health workers, have emerged in response to these developments.

Recognizing these organizational and professional trends, foundations such as the Robert Wood Johnson Foundation, the Kaiser Family Foundation, and the John A. Hartford Foundation, together with the federal government, have supported the establishment of institutions that engage in quality measure- ment and management research aligned with an HMO model or a population health perspective. The Kaiser Permanente Center for Health Research is the oldest of these institutions. During the late 1980s, Don Berwick founded the Institute for Healthcare Improvement (IHI), which became the most important institution driving quality measurement research and the effective dissemination of quality management best practices. The Health Research and Educational Trust (HRET) and, over the past two decades, newer institutions

approaches such as subsidized housing and support for employment could lead to significantly improved outcomes (Lehman et al. 2002). Similarly, Chalk and McLellan, among others, emphasized the importance of housing and the positive impact of various types of counseling, including web-based and telephonic counseling, on outcomes (Dugosh et al. 2016).

Researchers have since tied these innovative MHSA quality measures to traditional measures, such as preventable hospital admissions and read- missions, based on the theory that interventions such as employment and web-based counseling can help with the management of expensive medical conditions (Dixon 2000). Despite these advances, much remains to be learned about both quality measurement and management (Goldfield et al. 2016).

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such as the Jefferson College of Population Health have provided further sup- port for innovative approaches to quality measurement and management. In addition, the internet has facilitated interinstitutional collaboration on quality measurement. For instance, the Cochrane Database of Systematic Reviews, easily accessible online, appraises and synthesizes research from diverse sources and has contributed significantly to the evaluation of quality measurement and management interventions.1

Medical Malpractice The results of the Harvard Malpractice Study were reported more than a quarter century ago, but the study remains the gold standard for research on medical malpractice (Brennan et al. 1991). The study found that adverse events occurred in 3.7 percent of hospitalizations and that 27.6 percent of adverse events were caused by negligence. Although 70.5 percent of the adverse events gave rise to disabilities lasting less than six months, 2.6 percent caused permanently disabling injuries, and 13.6 percent led to death. After the Harvard study, the Institute of Medicine’s 2000 report To Err Is Human was the next landmark event in our understanding of medical malpractice and what we can do about it (Kohn, Corrigan, and Donaldson 2000).

Quality measures that focus on medical malpractice, such as incident reporting, are now routine.2 Researchers and managers have developed strate- gies for disclosing medical errors to the affected parties and providing up-front payment for the cost of the medical error (Kalra and Kopargaonkar 2018). They have also explored adverse events beyond surgical encounters (Mills et al. 2018). Many of the advances in electronic medical records, together with virtually all of the quality measurement tools highlighted in this chapter, can help lessen the occurrence of malpractice and reduce the impact of adverse events that do occur— but only if the quality tools are implemented correctly (Lepelley et al. 2018; McKenzie et al. 2017). Legal strategies relating to malpractice include alternative dispute resolution, evidence of compliance with guidelines, and administrative compensation (Sohn and Bal 2012). Limiting the right to sue and the amount of plaintiff damages that can be awarded has, not surprisingly, had a significant impact on total malpractice awards (Sage, Harding, and Thomas 2016).

The Era of the Consumer Has (Almost) Arrived Twenty-five years ago, we paid scant attention to what is arguably the most important outcome—the perspective of the consumer (i.e., the patient or fam- ily unit). Since that time, we have made dramatic strides in the measurement of that perspective, and concomitantly, the management role of the consumer has increased dramatically. Nonetheless, we have a long way to go.

John Ware and Cathy Sherbourne (1992) specified two types of infor- mation we can obtain from consumers: patient reports (e.g., “Am I able to

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climb a flight of stairs?”) and ratings (e.g., satisfaction with service provided in a hospital). Paul Cleary and others during the 1980s and 1990s established the science of consumer ratings in healthcare, particularly with regard to patient satisfaction with hospital services (Zaslavsky et al. 2001). Policymakers and pay- ers have since implemented patient satisfaction as a cornerstone of value-based purchasing and other incentive programs for all parts of our healthcare system.3

Although patient satisfaction has remained an important metric, other patient-reported outcomes—particularly patient confidence, activation, and empowerment—have been developed and advanced. In preparation for these new measures, a number of researchers explored the relationship between patient satisfaction and the individual’s perception of the extent of control over a chronic disease. For example, research by Larry Linn, from the late 1960s through the late 1980s, documented that “patient satisfaction measures are sensitive to and confounded by patients’ perceived health, view of life and social circumstance” (Linn and Greenfield 1982, 425). During the 1990s, building on the research of Linn and others and together with the burgeoning consumer movement, Judith Hibbard and John Wasson developed patient reports of self- confidence in the management of patients’ healthcare conditions (Greene et al. 2015; Wasson and Coleman 2014). A number of health systems and payers have implemented either the Wasson or Hibbard patient confidence or activa- tion measures.4 These patient-derived outcomes measures, particularly in the management of a chronic illness, represent a critical leg of the three-legged stool mentioned earlier in the chapter: patient activation; financial incentives; and payers’ regular release of transparent, comparative outcomes data, which helps foster collaboration with payers.

Researchers studying factors related to patient confidence have docu- mented the positive impact of shared decision making on, for instance, whether a patient decides to undergo a prostatectomy for prostate cancer (Arterburn et al. 2012). Kate Lorig developed the Stanford Chronic Disease Self-Management Program (Lorig and Holman 2003), which has been used to teach thousands of leaders and has contributed to significant improvement in chronic disease control, with attendant economic impact.5 Nonetheless, even though the value of shared decision making is well known, payers still have not adopted a strong, consistent focus on improving consumer self-confidence.

Importantly, Medicare has begun reimbursing YMCAs throughout the country for implementation of a diabetes program that aims to increase self- confidence in the management of this extremely common disease.6 However, payers and policymakers have barely scratched the surface of how these types of programs can improve patient self-confidence in the management of chronic illness. Instead, most health policymakers, especially at the federal level, continue to focus almost exclusively on the idea that placing a greater financial responsi- bility on the consumer will result in better quality—a questionable proposition

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that is not supported by the research literature (Hwang, Garrett, and Miller 2017). This tendency is largely the result of the increasingly politicized nature of the quality measurement and management debate.

Quality Management, Evidence-Based Medicine, and the Impact of Politics It is commonly known among healthcare professionals that too many back surgeries are performed in cases of lumbar disc problems and that too many magnetic resonance imaging (MRI) scans are done in the early stages of diseases that often should simply be left alone (Furlan et al. 2015). In the late 1980s, the then Agency for Health Care Policy and Research (AHCPR) addressed these concerns with the release of evidence-based guidelines about the appro- priate use of MRIs and the outcomes of back surgeries. However, the North American Spine Society—possibly motivated by financial incentives—disagreed with the findings (Chassin and Loeb 2011) and engaged in lobbying efforts against quality measurement and management. The society was nearly success- ful in eliminating funding for AHCPR. The agency survived, but it was told to change its focus and get out of the guideline business. Hence, it became the Agency for Healthcare Research and Quality (AHRQ) (Gray, Gusmano, and Collins 2003).

There is no point in having quality measures and an outstanding Deming-inspired quality management system if the evidence supporting medical interventions is not robust. Healthcare professionals and consumers alike must have a commitment to evidence-based medicine, which can be defined as the “conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients” (Sackett et al. 1996, 71). As stated by the National Academy of Medicine, “Evidence is the cornerstone of a high-performing health care system” (Patashnik, Gerber, and Dowling 2017, 6). The impact of technology on our healthcare system is everywhere; it is both facilitating the expansion of medical knowledge (Singer 2017) and becoming inextricably embedded into regular medical practice (Goldfield 2017).

Unsurprisingly, given our society’s political polarization and the ever- increasing role of technology in medical practice, the politics of quality mea- surement and management have become more toxic, well beyond the afore- mentioned example of back surgery. Thus, we need to conclude this historical overview of quality measures and quality management with an explicit acknowl- edgment of the importance of politics in this area.

As highlighted in Patashnik, Gerber, and Dowling’s (2017) book Unhealthy Politics, politicians of both major political parties, as recently as 2008, tried to argue in favor of the pursuit of evidence for both quality measurement and management. Some degree of bipartisan commitment to evidence can be seen, for instance, in the fact that both political parties acknowledge that any nuclear

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Chapter 2: History and the Qual i ty Landscape 63

power plant must adhere to scientifically accepted safety standards mandated by the federal government. Along the same lines, the baseball executive Billy Beane and politicians Newt Gingrich and John Kerry (2008) wrote an article in the New York Times focusing on the common-sense need for greater attention to the evidence base for medicine. Reflecting what now seems like a bygone era of bipartisanship, Gingrich, a Republican, and Kerry, a Democrat, together wrote:

Remarkably, a doctor today can get more data on the starting third baseman on his

fantasy baseball team than on the effectiveness of life-and-death medical proce-

dures . . . . Nearly 100,000 Americans are killed every year by preventable medical

errors. We can do better if doctors have better access to concise, evidence-based

medical information . . . . To deliver better health care, we should learn from the suc-

cessful teams that have adopted baseball’s new evidence-based methods.

However, as the authors of Unhealthy Politics point out, politicians tend to lose political clout when they fight with medical societies, device manufac- turers, and hospital associations that lobby against evidence-based methods and in favor of ineffective procedures (Patashnik, Gerber, and Dowling 2017). For example, the American Medical Association, the North American Spine Society, various device manufacturers (e.g., AvaMed), and the Pharmaceutical Manufacturers Association, among many other groups, have had a long tradi- tion of resisting efforts to research and promote the scientific basis for medical practice. As a consequence of these efforts, agencies critical to the develop- ment of valid quality measures and the best methods of quality management have lost significant funding—and, in some cases, been defunded completely (Patashnik, Gerber, and Dowling 2017).

New Outcomes Metrics and the Future of Quality Measurement and Management

The healthcare landscape has been transformed by numerous landmark events since the start of the twentieth century, and groundbreaking advances continue to take shape. Insulin and antibiotics were not in use 100 years ago, and dialysis and coronary artery bypass grafts were just beginning 50 years ago. Similarly, the SF-36 measure for patient-derived health ratings, measures of consumer empowerment, ways to identify potentially preventable readmissions, and the entire field of case mix and risk adjustments did not exist 50 years ago; many of these things did not exist even 25 years ago. The leaders mentioned in this chapter, along with many others, have made significant contributions to advance the field of quality measurement and management (Averill, Hughes, and Goldfield 2011; Texas Health and Human Services 2018).

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In their joint New York Times article, Beane, Gingrich, and Kerry (2008) wrote that “the best way to start improving quality and lowering costs is to study the stats.” However, we must also be cautious not to fetishize quality measurement. The idea that “if you can’t measure it, you can’t manage it” is a costly myth. Indeed, running a company on visible figures alone was one of Deming’s seven deadly diseases of management (Deming 1982; Deming Institute 2018). The best way to avoid the fetishizing of statistics is for health policy leaders to loudly proclaim that quality outcomes measurement must be inextricably linked to quality management or organizational continuous quality improvement.

Several decades ago, early pioneers such as Donabedian at the Uni- versity of Michigan and Shapiro at Johns Hopkins University established the underpinnings and key definitions of the quality measurement field, and later researchers have carried their work forward. Today’s quality outcomes measures are increasingly scientifically valid, and alongside case mix and risk adjustment, they will get even better. Significant improvement will occur in three ways:

1. In the coming years, information abstracted from claims data will be linked easily to information from electronic medical records across the entire continuum of healthcare (McCullough et al. 2011).

2. Further advances in technology will provide us with additional information about not only individual consumers and their families (taking into account genetic predisposition to certain diseases) but also the communities in which these individuals live. Much if not all of this information will be available to consumers on a real-time basis.

3. Information will be available to predict, with increasing reliability, the potential occurrence of adverse events and risks that can be mitigated with action. Prediction science is already here for both inpatient and outpatient services, but it will get even better (Chang et al. 2011).

In the coming years, consumers, both individually and in organized groups and associations, will be able to use this increasingly precise informa- tion to generate report cards and evaluations of quality that reflect their own, not the government’s or trade associations’, priorities.7 Even so, we need to recognize that quality measurement is and always will be, at least in part, an art. We are dealing with human beings as opposed to industrial assembly lines, and new challenges and complications are certain to emerge. At the start of 2018, for instance, the New York Times reported that senior administrators at some Veterans Affairs hospitals had been avoiding complicated, high-risk patients in an effort to raise the hospitals’ quality ratings (Philipps 2018).

The quality landscape will be further shaped by three evolving trends. First, increased organizational consolidation of providers (e.g., hospital mergers,

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Chapter 2: History and the Qual i ty Landscape 65

hospital acquisition of physician practices) with greater assumption of financial risk will bring a heightened focus to population health. Second, the degree to which quality measurement is politicized will determine the impact that the increasingly precise measures can have. Third, the number of Americans without health insurance coverage will influence the degree to which quality management and measurement have overall relevance to society.

Conclusion

We have addressed some of the drawbacks associated with today’s political climate, in which leaders are breaking many long-held norms of decorum and behavior. However, this climate also offers a meaningful benefit: Research- ers and institutional leaders in healthcare are increasingly acknowledging the need to be engaged in politics, with a small p, as it relates to issues of quality management and measurement. This engagement may take the form of, for example, lobbying for federal research funding.

One wild card is the potential role of consumers, their families, and the communities in which they live. Can empowered consumers (especially those newly covered under the Affordable Care Act (ACA), whether individually or in organized groups, fully exercise their big-P political power to encourage elected officials to improve quality in the healthcare system and begin to sta- bilize costs? Such activity might include, for example, fighting for or against the repeal of the ACA with direct support for political candidates. A histori- cal analogy from a different field involves Senator Ted Kennedy and a rising movement of consumers who had become discontented with airline prices in the 1970s. Seeking to burnish his credentials with this group, Kennedy fought government regulations and was victorious over the airline industry (Patash- nik, Gerber, and Dowling 2017). The deregulated airline industry is now very competitive (albeit with some challenges in quality of service).

Newly empowered consumers—along with groups of healthcare profes- sionals and possibly a few health systems committed to population health—hold the key to a reformed healthcare system that is truly based on relentless quality management, using measures described in this chapter. As Lorig (2017, 188) stated in a slightly different context, “Patients can be true partners in care. More than forty years ago, Dr. Tom Ferguson said, ‘doctors (health profession- als) would get off their pedestals when patients got off their knees.’ All of us, patients and health professionals, need to do a bit more to adjust our posture. It is not we against them. We are all in this together.” Valid and reliable quality measurement, intertwined with effective quality management, will be the glue that binds us together and points the way toward a continuously improving healthcare system in the United States.

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Notes

1. For more information about the founding of the Institute for Healthcare Improvement, see www.ihi.org/about/pages/history.aspx. For more information of the Cochrane Database of Systematic Reviews, see www. cochranelibrary.com/about/about-cochrane-reviews.

2. For an example, see the New York State Department of Health resource at www.health.ny.gov/professionals/doctors/conduct/.

3. See www.cms.gov/Outreach-and-Education/Medicare-Learning- Network-MLN/MLNProducts/downloads/Hospital_VBPurchasing_ Fact_Sheet_ICN907664.pdf for information about the CMS Hospital Value-Based Purchasing Program.

4. See https://suffolkcare.org/pubfiles/NYDOH_PAMGuidelines_ 09.16.2015.pdf for information on the Patient Activation Measure used by the New York State Department of Health.

5. For more information on the Stanford program, see www.selfmanagement resource.com.

6. See www.ymca.net/diabetes-prevention/ for more information on the YMCA diabetes-prevention program.

7. The Healthcare Bluebook website at www.healthcarebluebook.com serves as one example of an initiative to support transparency and consumer empowerment.

Study Questions

1. What is the difference between patient reports and ratings of health and healthcare?

2. What tools and techniques for addressing malpractice have been implemented in the years since the Harvard Malpractice Study? Which do you agree with? What more needs to be done?

3. What do you see as the future in quality measurement? What is the role of technology? What other factors will be important?

4. What are the roles of competition and antitrust laws in the future of quality measurement? What other factors will be important?

5. How does the consumer fit in with the manifold changes taking place in our healthcare system?

6. In what ways should healthcare professionals become involved in today’s polarized political environment?

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Chapter 2: History and the Qual i ty Landscape 67

References

Advisory Board. 2017. “CMS: US Health Care Spending to Reach Nearly 20% of GDP by 2025.” Published February 16. www.advisory.com/daily-briefing/2017/02/16/ spending-growth.

Agency for Healthcare Research and Quality (AHRQ). 2006. The Case for the Present-on- Admission (POA) Indicator. Healthcare Cost and Utilization Project Methods Series. Published June. www.hcup-us.ahrq.gov/reports/methods/2006_1.pdf.

American College of Surgeons (ACS). 2018a. “ACS National Surgical Quality Improve- ment Program.” Accessed September 10. www.facs.org/quality-programs/ acs-nsqip.

———. 2018b. “1918: Most Hospitals Fail to Meet Minimum Standards of College Hospital Standardization Program.” Accessed September 24. http://timeline. facs.org/1913.html.

Arterburn, D., R. Wellman, E. Westbrook, C. Rutter, T. Ross, D. McCulloch, M. Handley, and C. Jung. 2012. “Introducing Decision Aids at Group Health Was Linked to Sharply Lower Hip and Knee Surgery Rates and Costs.” Health Affairs (Millwood) 31 (9): 2094–104.

Averill, R. F., N. I. Goldfield, M. E. Wynn, T. E. McGuire, R. L. Mullin, L. W. Gregg, and J. A. Bender. 1993. “Design of a Prospective Payment Patient Classification System for Ambulatory Care.” Health Care Financing Review 15 (1): 71–100.

Averill, R. F., J. S. Hughes, and N. I. Goldfield. 2011. “Paying for Outcomes, Not Performance: Lessons from the Medicare Inpatient Prospective Payment Sys- tem.” Joint Commission Journal on Quality and Patient Safety 37 (4): 184–92.

Beane, B., N. Gingrich, and J. Kerry. 2008. “How to Take American Health Care from Worst to First.” New York Times. Published October 24. www.nytimes. com/2008/10/24/opinion/24beane.html.

Beecher, H. K. 1955. “The Powerful Placebo.” Journal of the American Medical Association 159 (17): 1602–6.

Berkowitz, E. 1998. “History of Health Services Research Project: Interview with Sam Shapiro.” US National Library of Medicine. Published March 6. www.nlm.nih. gov/hmd/nichsr/shapiro.html.

Best, M., and D. Neuhauser. 2006. “Walter A. Shewhart, 1924, and the Hawthorne Factory.” Quality and Safety in Health Care 15 (2): 142–43.

Best, W. R., S. F. Khuri, M. Phelan, K. Hur, W. G. Henderson, J. G. Demakis, and J. Daley. 2002. “Identifying Patient Preoperative Risk Factors and Postoperative Adverse Events in Administrative Databases: Results from the Department of Veterans Affairs National Surgical Quality Improvement Program.” Journal of the American College of Surgeons 194 (3): 257–66.

Brennan, T. A., L. L. Leape, N. M. Laird, L. Hebert, A. R. Localio, A. G. Lawthers, J. P. Newhouse, P. C. Weiler, and H. H. Hiatt. 1991. “Incidence of Adverse

00_Nash (2382).indb 67 2/22/19 10:56 AM

EBSCOhost - printed on 9/18/2023 10:37 PM via NATIONAL UNIVERSITY. All use subject to https://www.ebsco.com/terms-of-use

The Healthcare Qual i ty Book68

Events and Negligence in Hospitalized Patients—Results of the Harvard Medical Practice Study I.” New England Journal of Medicine 324: 370–76.

Brook, R. H., J. E. Ware Jr., W. H. Rogers, E. B. Keeler, A. R. Davies, C. A. Donald, G. A. Goldberg, K. N. Lohr, P. C. Masthay, and J. P. Newhouse. 1983. “Does Free Care Improve Adults’ Health? Results from a Randomized Controlled Trial.” New England Journal of Medicine 309 (23): 1426–34.

Chang, Y.-J., M.-L. Yeh, Y.-C. Li, C.-Y. Hsu, C.-C. Lin, M.-S. Hsu, and W.-T. Chiu. 2011. “Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters.” PLOS One 6 (8): e23137.

Chassin, M., and J. Loeb. 2011. “The Ongoing Quality Improvement Journey: Next Stop, High Reliability.” Health Affairs 30 (4): 559–68.

Clarke, C. 2005. Automotive Production Systems and Standardization: From Ford to the Case of Mercedes-Benz. New York: Springer.

Codman, E. A. 1914. “The Product of a Hospital.” Surgery, Gynecology and Obstetrics 18: 491–96.

Coulam, R. F., and G. L. Gaumer. 1992. “Medicare’s Prospective Payment System: A Critical Appraisal.” Health Care Financing Review 1991 (Suppl.): 45–77.

Cutting, C. C., and M. F. Collen. 1992. “A Historical Review of the Kaiser Permanente Medical Care Program.” Journal of the Society for Health Systems 3 (4): 25–30.

Dans, P. E., J. P. Weiner, and S. E. Otter. 1985. “Peer Review Organizations: Promises and Potential Pitfalls.” New England Journal of Medicine 313: 1131–37.

Deming, W. E. 1982. Out of the Crisis. Boston: MIT Press. Deming Institute. 2018. “Seven Deadly Diseases of Management.” Accessed September

11. https://deming.org/explore/seven-deadly-diseases. Dixon, L. 2000. “Assertive Community Treatment: Twenty-Five Years of Gold.”

Psychiatric Services 51 (6): 759–65. Donabedian, A. 1966. “Evaluating the Quality of Medical Care.” Milbank Memorial

Fund Quarterly 44 (3): 166–206. Doyle, J. C. 1953. “Unnecessary Hysterectomies: Study of 6,248 Operations in Thirty-

Five Hospitals During 1948.” Journal of the American Medical Association 151 (5): 360–65.

Dugosh, K., A. Abraham, B. Seymour, K. McLoyd, M. Chalk, and D. Festinger. 2016. “A Systematic Review on the Use of Psychosocial Interventions in Conjunction with Medications for the Treatment of Opioid Addiction.” Journal of Addiction Medicine 10 (2): 91–101.

Ellis, R. P., G. C. Pope, L. Iezzoni, J. Z. Ayanian, D. W. Bates, H. Burstin, and A. S. Ash. 1996. “Diagnosis-Based Risk Adjustment for Medicare Capitation Pay- ments.” Health Care Financing Review 17 (3): 101–28.

Falk, I. S., C. R. Rorem, and M. D. Ring. 1932. The Costs of Medical Care: A Summary of Investigations on the Economic Aspects of the Prevention and Care of Illness. Chicago: University of Chicago Press.

00_Nash (2382).indb 68 2/22/19 10:56 AM

EBSCOhost - printed on 9/18/2023 10:37 PM via NATIONAL UNIVERSITY. All use subject to https://www.ebsco.com/terms-of-use

Chapter 2: History and the Qual i ty Landscape 69

Fetter, R. B., Y. Shin, J. L. Freeman, R. F. Averill, and J. D. Thompson. 1980. “Case Mix Definition by Diagnosis-Related Groups.” Medical Care 18 (2 Suppl.): iii, 1–53.

Fink, A., E. M. Yano, and R. H. Brook. 1989. “The Condition of the Literature on Differences in Hospital Mortality.” Medical Care 27 (4): 315–36.

Flexner, A. 1910. Medical Education in the United States and Canada: A Report to the Carnegie Foundation for the Advancement of Teaching. New York: Mer- rymount Press.

Freeman, H. E., S. Levine, and L. G. Reeder. 1963. Handbook of Medical Sociology. Princeton, NJ: Prentice Hall.

Fries, B. E., and L. M. Cooney Jr. 1985. “Resource Utilization Groups: A Patient Classification System for Long Term Care.” Medical Care 23 (2): 110–22.

Furlan, A. D., A. Malmivaara, R. Chou, C. G. Maher, R. A. Deyo, M. Schoene, G. Bronfort, and M. W. van Tulder. 2015. “2015 Updated Method Guideline for Systematic Reviews in the Cochrane Back and Neck Group.” Spine 40 (21): 1660–73.

Gertman, P. M., and J. D. Restuccia. 1981. “The Appropriateness Evaluation Protocol: A Technique for Assessing Unnecessary Days of Hospital Care.” Medical Care 19 (8): 855–71.

Glover, J. A. 1938. “The Incidence of Tonsillectomy in School Children.” Proceedings of the Royal Society of Medicine 31 (10): 1219–36.

Goldfield, N. 2017. “Dramatic Changes in Health Care Professions in the Past 40 Years.” Journal of Ambulatory Care Management 40 (3): 169–75.

Goldfield, N., and P. Boland. 1996. Physician Profiling and Risk Adjustment. Gaith- ersburg, MD: Aspen.

Goldfield, N., R. Fuller, J. Vertrees, and E. McCullough. 2016. “How Encouraging Provider Collaboration and Financial Incentives Can Improve Outcomes for Per- sons with Severe Psychiatric Disorders.” Psychiatric Services 67 (12): 1368–69.

Goldfield, N. I., E. C. McCullough, J. S. Hughes, A. M. Tang, B. Eastman, L. K. Rawlins, and R. F. Averill. 2008. “Identifying Potentially Preventable Readmis- sions.” Health Care Financing Review 30 (1): 75–91.

Goldfield, N., M. Pine, and J. Pine. 1996. Measuring and Managing Health Care Quality: Procedures, Techniques, and Protocols. Gaithersburg, MD: Aspen.

Granger, C. V., A. Deutsch, C. Russell, T. Black, and K. J. Ottenbacher. 2007. “Modi- fications of the FIM Instrument Under the Inpatient Rehabilitation Facility Prospective Payment System.” American Journal of Physical Medicine & Reha- bilitation 86 (11): 883–92.

Gray, B. H., M. K. Gusmano, and S. R. Collins. 2003. “AHCPR and the Changing Politics of Health Services Research.” Health Affairs (Millwood) Suppl. W3: 283–307.

00_Nash (2382).indb 69 2/22/19 10:56 AM

EBSCOhost - printed on 9/18/2023 10:37 PM via NATIONAL UNIVERSITY. All use subject to https://www.ebsco.com/terms-of-use

The Healthcare Qual i ty Book70

Greene, J., J. H. Hibbard, R. Sacks, V. Overton, and C. D. Parrotta. 2015. “When Patient Activation Levels Change, Health Outcomes and Costs Change, Too.” Health Affairs (Millwood) 34 (3): 431–37.

Hannan, E. L., C. Wu, T. J. Ryan, E. Bennett, A. T. Culliford, J. P. Gold, A. Hart- man, O. W. Isom, R. H. Jones, B. McNeil, E. A. Rose, and V. A. Subramanian. 2003. “Do Hospitals and Surgeons with Higher Coronary Artery Bypass Graft Surgery Volumes Still Have Lower Risk-Adjusted Mortality Rates?” Circulation 108 (7): 795–801.

Hayes, C. W., P. B. Batalden, and D. Goldmann. 2015. “A ‘Work Smarter, Not Harder’ Approach to Improving Healthcare Quality.” BMJ Quality & Safety 24 (2): 100–102.

Hughes, J. S., R. F. Averill, N. I. Goldfield, J. C. Gay, J. Muldoon, E. McCullough, and J. Xiang. 2006. “Identifying Potentially Preventable Complications Using a Present on Admission Indicator.” Health Care Financing Review 27 (3): 63–82.

Hwang, A., D. Garrett, and M. Miller. 2017. “Competing Visions for Consumer Engagement in the Dawn of the Trump Administration.” Journal of Ambula- tory Care Management 40 (4): 259–64.

Iezzoni, L. I. (ed.). 2012. Risk Adjustment for Measuring Healthcare Outcomes, 4th ed. Chicago: Health Administration Press.

Iezzoni, L. I., E. K. Hotchkin, A. S. Ash, M. Shwartz, and Y. Mackiernan. 1993. “MedisGroups Data Bases. The Impact of Data Collection Guidelines on Pre- dicting In-Hospital Mortality.” Medical Care 31 (3): 277–83.

Institute of Medicine. 2001. Coverage Matters: Insurance and Health Care. Washington, DC: National Academies Press.

Juran, J. M. 1995. A History of Managing for Quality: The Evolution, Trends, and Future Directions of Managing for Quality. Milwaukee, WI: Quality Press.

Kahn, K. L., D. Draper, E. B. Keeler, W. H. Rogers, L. V. Rubenstein, J. Kosecoff, M. J. Sherwood, E. J. Reinisch, M. F. Carney, C. J. Kamberg, S. S. Bentow, K. B. Wells, H. Allen, D. Reboussin, C. P. Roth, C. Chew, and R. H. Brook. 1992. The Effects of the DRG-Based Prospective Payment System on Quality of Care for Hospitalized Medicare Patients. RAND Corporation. Accessed September 10, 2018. www.rand.org/pubs/reports/R3931.html.

Kalra, J. J., and A. Kopargaonkar. 2018. “Quality Care and Patient Safety: Strategies to Disclose Medical Errors.” In Advances in Human Factors and Ergonomics in Healthcare and Medical Devices, edited by V. Duffy and N. Lightner, 159–67. Cham, Switzerland: Springer International.

Kang, C. W., and P. H. Kvam. 2012. Basic Statistical Tools for Improving Quality. Hoboken, NJ: Wiley & Sons.

Katz, S., A. B. Ford, R. W. Moskowitz, B. A. Jackson, and M. W. Jaffe. 1963. “Studies of Illness in the Aged. The Index of ADL: A Standardized Measure of Biologi- cal and Psychosocial Function.” Journal of the American Medical Association 185 (12): 914–19.

00_Nash (2382).indb 70 2/22/19 10:56 AM

EBSCOhost - printed on 9/18/2023 10:37 PM via NATIONAL UNIVERSITY. All use subject to https://www.ebsco.com/terms-of-use

Chapter 2: History and the Qual i ty Landscape 71

Keller, R. B. 1994. “Outcomes Dissemination. The Maine Study Group Model.” National Institutes of Health grant. Accessed September 10, 2018. http:// grantome.com/grant/NIH/R18-HS006813-04.

Kohn, L. T., J. M. Corrigan, and M. S. Donaldson (eds.). 2000. To Err Is Human: Building a Safer Health System. Washington, DC: National Academies Press.

Kopf, E. W. 1916. “Florence Nightingale as Statistician.” Publications of the American Statistical Association 15 (116): 388–404.

Krumholz, H. M., A. R. Merrill, E. M. Schone, G. C. Schreiner, J. Chen, E. H. Brad- ley, Y. Wang, Y. Wang, Z. Lin, B. M. Straube, M. T. Rapp, S. L. Normand, and E. E. Drye. 2009. “Patterns of Hospital Performance in Acute Myocardial Infarction and Heart Failure 30-Day Mortality and Readmission.” Circulation: Cardiovascular Quality and Outcomes 2 (5): 407–13.

Kwan, L. Y., K. Stratton, and D. M. Steinwachs (eds.). 2017. Accounting for Social Risk Factors in Medicare Payment. Washington, DC: National Academies Press.

Lehman, A. F., R. Goldberg, L. B. Dixon, S. McNary, L. Postrado, A. Hackman, and K. McDonnell. 2002. “Improving Employment Outcomes for Persons with Severe Mental Illnesses.” Archives of General Psychiatry 59 (2): 165–72.

Lehman, A. F., J. Kreyenbuhl, R. W. Buchanan, F. B. Dickerson, L. B. Dixon, R. Gold- berg, L. D. Green-Paden, W. N. Tenhula, D. Boerescu, C. Tek, N. Sandson, and D. M. Steinwachs. 2004. “The Schizophrenia Patient Outcomes Research Team (PORT): Updated Treatment Recommendations 2003.” Schizophrenia Bulletin 30 (2): 193–217.

Lembcke, P. A. 1956. “Medical Audit by Scientific Methods: Illustrated by Major Female Pelvic Surgery.” Journal of the American Medical Association 162 (7): 646–55.

Lepelley, M., C. Genty, A. Lecoanet, B. Allenet, P. Bedouch, M. R. Mallaret, P. Gillois, and J. L. Bosson. 2018. “Electronic Medication Regimen Complexity Index at Admission and Complications During Hospitalization in Medical Wards: A Tool to Improve Quality of Care?” International Journal for Quality in Health Care 30 (1): 32–38.

Lewis, V. A., E. S. Fisher, and C. H. Colla. 2017. “Explaining Sluggish Savings Under Accountable Care.” New England Journal of Medicine 377 (19): 1809–11.

Linn, L. S., and S. Greenfield. 1982. “Patient Suffering and Patient Satisfaction Among the Chronically Ill.” Medical Care 20 (4): 425–31.

Lorig, K. 2017. “Commentary on ‘Evidence-Based Self-Management Programs for Seniors and Other with Chronic Diseases’: Patient Experience—Patient Health— Return on Investment.” Journal of Ambulatory Care Management 40 (3): 185–88.

Lorig, K. R., and H. Holman. 2003. “Self-Management Education: History, Defini- tion, Outcomes, and Mechanisms.” Annals of Behavioral Medicine 26 (1): 1–7.

Mallon, B. 2014. “Amory Codman: The End Result of a Dream to Revolutionize Medi- cine.” Boston Shoulder Institute. Accessed September 10, 2018. http://boston shoulderinstitute.com/wp-content/uploads/2014/07/Codman-Society-Bio.pdf.

00_Nash (2382).indb 71 2/22/19 10:56 AM

EBSCOhost - printed on 9/18/2023 10:37 PM via NATIONAL UNIVERSITY. All use subject to https://www.ebsco.com/terms-of-use

The Healthcare Qual i ty Book72

McCullough, E., C. Sullivan, P. Banning, N. Goldfield, and J. Hughes. 2011. “Chal- lenges and Benefits of Adding Laboratory Data to a Mortality Risk Adjustment Method.” Quality Management in Health Care 20 (4): 253–62.

McDonald, L. 2014. “Florence Nightingale and Her Crimean War Statistics: Les- sons for Hospital Safety, Public Administration and Nursing.” Lecture for the Gresham College / British Society of the History of Mathemat- ics Conference, October 2014. www.gresham.ac.uk/lectures-and-events/ florence-nightingale-and-her-crimean-war-statistics-lessons-for-hospital-safety-.

McGuire, T. E. 1991. “An Evaluation of Diagnosis-Related Group Severity and Com- plexity Refinement.” Health Care Financing Review 12 (4): 49–60.

McKenzie, E., M. L. Potestio, J. M. Boyd, D. J. Niven, R. Brundin-Mather, S. M. Bagshaw, and H. T. Stelfox. 2017. “Reconciling Patient and Provider Priori- ties for Improving the Care of Critically Ill Patients: A Consensus Method and Qualitative Analysis of Decision Making.” Health Expectations 20 (6): 1367–74.

Medicare.gov. 2018. “Depression Screenings.” Accessed September 10. www.medicare. gov/coverage/depression-screenings.html.

Mills, P. D., B. V. Watts, B. Shiner, and R. R. Hemphill. 2018. “Adverse Events Occur- ring on Mental Health Units.” General Hospital Psychiatry 50: 63–68.

Mills, R., R. B. Fetter, D. C. Riedel, and R. Averill. 1976. “AUTOGRP: An Interac- tive Computer System for the Analysis of Health Care Data.” Medical Care 14 (7): 603–15.

Nelson, E. C., P. B. Batalden, K. Homa, M. M. Godfrey, C. Campbell, L. A. Headrick, T. P. Huber, J. J. Mohr, and J. H. Wasson. 2003. “Microsystems in Health Care: Part 2. Creating a Rich Information Environment.” Joint Commission Journal on Quality and Safety 29 (1): 5–15.

New York State Department of Health. 2018. “Cardiovascular Disease Data and Statistics.” Accessed September 10. www.health.ny.gov/statistics/diseases/ cardiovascular/.

Patashnik, E. M., A. S. Gerber, and C. M. Dowling. 2017. Unhealthy Politics: The Battle over Evidence-Based Medicine. Princeton, NJ: Princeton University Press.

Philipps, D. 2018. “At Veterans Hospital in Oregon, a Push for Better Ratings Puts Patients at Risk, Doctors Say.” New York Times. Published January 1. www. nytimes.com/2018/01/01/us/at-veterans-hospital-in-oregon-a-push-for- better-ratings-puts-patients-at-risk-doctors-say.html.

RAND Corporation. 2018. “Mental Health Inventory Survey.” Accessed September 12. www.rand.org/health/surveys_tools/mos/mental-health.html.

Roberts, J. S., J. G. Coale, and R. R. Redman. 1987. “A History of the Joint Com- mission on Accreditation of Hospitals.” JAMA 258 (7): 936–40.

Romano, P. S., B. K. Chan, M. E. Schembri, and J. A. Rainwater. 2002. “Can Admin- istrative Data Be Used to Compare Postoperative Complication Rates Across Hospitals?” Medical Care 40 (10): 856–67.

00_Nash (2382).indb 72 2/22/19 10:56 AM

EBSCOhost - printed on 9/18/2023 10:37 PM via NATIONAL UNIVERSITY. All use subject to https://www.ebsco.com/terms-of-use

Chapter 2: History and the Qual i ty Landscape 73

Rosenfeld, L. 1957. “Quality of Medical Care in Hospitals.” American Journal of Public Health 47: 405–14.

Russell, L. B. 1989. Medicare’s New Hospital Payment System: Is It Working? Washing- ton, DC: Brookings Institution Press.

Russell, L. B., and C. L. Manning. 1989. “The Effect of Prospective Payment on Medicare Expenditures.” New England Journal of Medicine 320: 439–44.

Sackett, D. L., W. M. C. Rosenberg, J. A. M. Gray, R. B. Haynes, and W. S. Richard- son. 1996. “Evidence-Based Medicine: What It Is and What It Isn’t.” British Journal of Medicine 312: 71–72.

Sage, W. M., M. C. Harding, and E. J. Thomas. 2016. “Resolving Malpractice Claims After Tort Reform: Experience in a Self-Insured Texas Public Academic Health System.” Health Services Research 51 (Suppl. 3): 2615–33.

Schiff, G. D., and N. I. Goldfield. 1994. “Deming Meets Braverman: Toward a Progres- sive Analysis of the Continuous Quality Improvement Paradigm.” International Journal of Health Services 24 (4): 655–73.

Schweiker, R. S. 1982. “Report to Congress: Hospital Prospective Payment for Medi- care.” US Department of Health and Human Services. Published December. https://archive.org/details/reporttocongress00schw.

Sheps, M. C. 1955. “Approaches to the Quality of Medical Care.” Public Health Reports 70 (9): 877–86.

Singer, N. 2017. “How Big Tech Is Going After Your Health Care.” New York Times. Published December 26. www.nytimes.com/2017/12/26/technology/big- tech-health-care.html.

Skinner, J. S., and K. G. Volpp. 2017. “Replacing the Affordable Care Act: Lessons from Behavioral Economics.” JAMA 317 (19): 1951–52.

Smith, G. R., T. L. Kramer, J. A. Hollenberg, C. L. Mosley, R. L. Ross, and A. Burnam. 2002. “Validity of the Depression-Arkansas (D-ARK) Scale: A Tool for Measur- ing Major Depressive Disorder.” Mental Health Services Research 4 (3): 167–73.

Sohn, D. H., and B. S. Bal. 2012. “Medical Malpractice Reform: The Role of Alter- native Dispute Resolution.” Clinical Orthopaedics and Related Research 470 (5): 1370–78.

Solon, J. A., C. G. Sheps, and S. S. Lee. 1960. “Delineating Patterns of Medical Care.” American Journal of Public Health 50 (8): 1105–13.

Tarlov, A. R., J. E. Ware Jr., S. Greenfield, E. C. Nelson, E. Perrin, and M. Zubkoff. 1989. “The Medical Outcomes Study: An Application of Methods for Monitor- ing the Results of Medical Care.” JAMA 262 (7): 925–30.

Texas Health and Human Services. 2018. “Pay-for-Quality (P4Q) Program.” Accessed October 12. https://hhs.texas.gov/about-hhs/process-improvement/ medicaid-chip-quality-efficiency-improvement/pay-quality-p4q-program.

Thompson, J. D., R. F. Averill, and R. B. Fetter. 1979. “Planning, Budgeting, and Controlling—One Look at the Future: Case-Mix Cost Accounting.” Health Services Research 14 (2): 111–25.

00_Nash (2382).indb 73 2/22/19 10:56 AM

EBSCOhost - printed on 9/18/2023 10:37 PM via NATIONAL UNIVERSITY. All use subject to https://www.ebsco.com/terms-of-use

The Healthcare Qual i ty Book74

Ware, J. E. Jr., and C. D. Sherbourne. 1992. “The MOS 36-Item Short-Form Health Survey (SF-36): I. Conceptual Framework and Item Selection.” Medical Care 30 (6): 473–83.

Wasson, J. 2017. “A Troubled Asset Relief Program for the Patient-Centered Medical Home.” Journal of Ambulatory Care Management 40 (2): 89–100.

Wasson, J., and E. A. Coleman. 2014. “Health Confidence: An Essential Measure for Patient Engagement and Better Practice.” Family Practice Management 21 (5): 8–12.

Wennberg, J. E., J. L. Freeman, R. M. Shelton, and T. A. Bubolz. 1989. “Hospital Use and Mortality Among Medicare Beneficiaries in Boston and New Haven.” New England Journal of Medicine 321 (17): 1168–73.

Wennberg, J., and A. Gittelsohn. 1973. “Small Area Variations in Health Care Deliv- ery.” Science 182 (4117): 1102–8.

White, K. L., T. F. Williams, and B. G. Greenberg. 1961. “The Ecology of Medical Care.” New England Journal of Medicine 265: 885–92.

Wright, J. R. Jr. 2017. “The American College of Surgeons, Minimum Standards for Hospitals, and the Provision of High-Quality Laboratory Services.” Archives of Pathology & Laboratory Medicine 141 (5): 704–17.

Zaslavsky, A. M., L. B. Zaborski, L. Ding, J. A. Shaul, M. J. Cioffi, and P. D. Cleary. 2001. “Adjusting Performance Measures to Ensure Equitable Plan Compari- sons.” Health Care Financing Review 22 (3): 109–126.

Zhan, C., A. Elixhauser, C. L. Richards Jr., Y. Wang, W. B. Baine, M. Pineau, N. Verzier, R. Kliman, and D. Hunt. 2009. “Identification of Hospital-Acquired Catheter-Associated Urinary Tract Infections from Medicare Claims: Sensitivity and Positive Predictive Value.” Medical Care 47 (3): 364–69.

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EBSCOhost - printed on 9/18/2023 10:37 PM via NATIONAL UNIVERSITY. All use subject to https://www.ebsco.com/terms-of-use