Editorial Article

The Quality Evolution: The Convergence of Public Health, Population Health, and Precision Medicine

Dr. David Keleti,
Manager, Clinical Outcomes Corporate Medical Management Ameri Health Caritas Family of Companies, 200 Stevens Drive Philadelphia, PA.

Public health — "what we as a society do collectively to assure the conditions in which people can be healthy.”[1]Population health — "an approach [that] focuses on interrelated conditions and factors that influence the health of populations over the life course."[2]”

*Corresponding author:

David Keleti,
dkeleti@gmail.com

Public health: “what we as a society do collectively to assure the conditions in which people can be healthy [1].”.

Population health: “an approach [that] focuses on inter-related conditions and factors that influence the health of populations over the life course, identifies systematic variations in their patterns of occurrence, and applies the resulting knowledge to develop and implement policies and actions to improve the health and well-being of those populations [2].”

The World Health Organization’s 1988 announcement declaring the eradication of smallpox was the epitome of a successful long-term public health campaign of case discov-ery and vaccination against the backdrop of centuries-old public health efforts (e.g., quarantine of infectious popula-tions, vaccination, and wastewater management) [3]. The success of such bold population-targeted initiatives has not entirely been matched in the U.S. healthcare system, whose delivery remains largely ad hoc and fragmented, with major consequences in quality and equitable access.

In 2002, the Institute of Medicine (IOM; now called the National Academy of Medicine) under the charter of the U.S. National Academy of Sciences presciently concluded that:

“To function most effectively, public health professionals… must have a framework for action and an understanding of the ways in which their activities affect the health of individual and populations, and of the multiple determinants affecting health…Improving health outcomes for all components of American society, closing the gaps in access to health care, and assuring equality in quality of care are major challenges for the 21st century [4].”

The broad mandate of this statement is well beyond the scope of traditional government-supported public health sector. Conversely, the specialty of population health concerns itself with accountable care for the entire population, including outliers who have chronically illnesses and are not seeking care, etc. Population health encompasses the definition, measurement, and distribution of health outcomes (e.g., reducing health disparities, developing practice guide-lines); patterns of determinants that affect such outcomes (e.g., medical care, public health intervention, etc.); and associated health policies. A combination of public health-and population health-based strategies are most effective in advancing the quality of care.

This review describes a decade-long evolution toward a quality-based framework for health care delivery, including the adoption of standardized clinical- and process-based quality measures from the vantage point of stakeholders, including patients. providers (physicians), and payers (in-surers). The definition of “quality” means different things to different healthcare stakeholders: for a patient, it can mean easy access to a physician, while for a healthcare insurer, it can mean no emergency department (ED) readmissions, a sentinel event often indicating poorly managed health care [5].

In 1966, Dr. Avedis Donabedian of the University of Michigan first described three essential elements in qual-ity assessment of health care: 1) structure (e.g., facility, equipment, and human resources); 2) process (the sum of all actions that constitute health care); and 3) outcome (e.g., improvements in health status and behaviors) [6]. Over time, the Donabedian model incorporated seven pillars for a quality-based framework in healthcare delivery systems [7], birthing the contemporary quality movement in health care. In 1987, future U.S. Center for Medicare and Medicaid Services administrator Dr. Donald Berwick (2010–2011) and his colleagues launched the National Demonstration Project in Quality Improvement in Health Care, a provider-implemented pilot program aimed at “redesigning health care into a system without errors, waste, delay, and unsus-tainable costs [8].” The program’s overall success spurred the creation of the Institute for Healthcare Improvement (IHI) in 1991, evolving into a leading organization in healthcare quality management worldwide. Following research on multidimensional systems of care in the 1990s, IHI con-vened a series of progressively more ambitious initiatives with the aim of helping millions of people in the United States (the 100,000 Lives Campaign and the 5 Million Lives Campaign) and worldwide (the 100 Million Healthier Lives Campaign) to avoid medical harm and live healthier lives.

The shift of focus to quality improvement at times stood at odds with the ongoing effort to control health care costs and improving access. In his 1994 book Medicine’s Dilem-mas, Dr. William Kissick of the University of Pennsylvania introduced the concept of “the iron triangle,” highlight-ing trade-offs inherent in all healthcare systems [9]. The vertices of the triangle consist of quality, cost, and access. Consequently, lowering the cost of health care could only occur at the expense of lower quality and access, whereas increasing access could be accomplished at greater cost and lower quality, and so forth.

In 2001, IOM released the Crossing the Qual-ity Chasm report, recommending a redesign of the U.S. healthcare system “to deliver care that is safe, ef-fective, patient -centered, timely, efficient, and equita-ble [10].” Each of these six improvements aims merit further explanation beyond the scope of this review, but the concept of patient -centered care is worthy of specific mention. In 2007, four medical organizations representing 333,000 U.S. primary care physicians released the “Joint Principles of Patient-Centered Medical Home [11].” The characteristics of the patient- centered medi-cal home (PCMH) proposed were: 1) a personal physi-cian to provide continuous and comprehensive patient care; 2) a physician-directed medical practice collectively responsible for ongoing patient care; 3) a whole-person orientation, whereby the physician maintains responsibil-ity for providing all health care needs of the patient; and 4) coordinated and/or integrated care across all elements of the healthcare system and the patient’s community. Over time, the PCMH concept has become one of the most effective frameworks in quality improvement and reducing fragmented health care delivery.

In 2008, IHI proposed an approach to optimizing health system performance with the Triple Aim concept, “a framework for optimizing system performance” by: 1) im-proving the patient experience of care ; 2) improving the health of populations; and 3) reducing the per capita cost of health care [12]. In an implicit challenge to the Iron Triangle concept, IHI recommended that all three triple aim dimensions be addressed simultaneously.

In 2010, the Patient Protection and Affordable Care Act (ACA), the most consequential health care legislation in decades, was signed into law [13]. While obviously impacting access to care, the ACA has also supported the implementation of a variety of Triple Aim-friendly quality innovations, including value-based payment and other cost containment models; novel primary care structures like accountable care organizations (ACOs) and PCMHs; sanctions for avoidable events like ED readmissions; and information technology integration. Improved perfor-mance in specified quality and cost measures is required for hospitals and physicians to receive Medicare payment incentives. ACOs under the Medicare Shared Savings Program and medical practices under the Comprehensive Primary Care Initiative have demonstrated improvements in quality outcomes, better experiences in patient-reported care, and modest cost savings compared to traditional delivery networks.

A population health management approach begins with identifying the target population; assessing their health-related needs and disease risks; and applying risk stratification and predictive modeling to its constituents. Identifying the most impactable high -need populations to reduce health care expenditures is the “holy grail” of quality management: the top 5 percent of patients ranked by healthcare expenses account for 50 percent of total U.S. healthcare expenditures, while the bottom 50 percent account for less than 3 percent of expenditures [14]. Of particular focus in population health interventions has been a group called “superutilizers,” people with complex physical, behavioral, and social needs that are improperly managed within the healthcare system who have high rates of ED visits and inpatient admissions and often have the highest rates of disease comorbidity and/or (re) hospitalization in a population.

Unsurprisingly, chronic disease is a primary predictor of poor outcomes and the high cost of care. One in five of the most frequently treated medical conditions in U.S. hospitals (e.g., coronary heart failure and renal failure) results in a 30 -day readmission [15]. However, chronic disease alone is an insufficient metric to prospectively identify high utilizers [16,17]. Additionally, membership in this group is rather fluid: only 14 percent of subjects still met superutilizer criteria after a two-year study period [16].

Consequently, analysts apply predictive modeling algorithms in “medical intelligence” to mine and analyze a range of patient data—medical and pharmacy claims, laboratory results, comorbid conditions, vital signs, and family history—for predicting future utilization patterns. However, making accurate predictions to improve the quality of care requires the input of additional metrics. A myriad of risks (behavioral and physical health status, utilization, and functional status) multiplied by impactabil-ity (readiness and capacity, social determinants, physical environment, and access) combine to tell a comprehensive story of patient health, providing actionable information for dramatically improving outcomes (Figure 1 ). For the moment, the effective incorporation of all such variables in predictive modeling remains elusive, although mod-est successes have been reported with currently available algorithms [18].

Clyto Access

Figure 1. : Reproduced with the kind permission of Sima Blank, M.S.

Identifying superutilizers is also performed by “hotspot-ting,” a process borrowed from urban community polic-ing during the 1990s. The use of hotspots-locations in a geographical area that account for its highest health care expenditures-supports the view that “your zip code may be more important to your health than your genetic code [19].” Dr. Jeffrey Brenner developed a superutilizer map of Camden, New Jersey, based on the city’s hospital ad-missions. He discovered that, in a 6 ½-year period, 900 people living in two apartment buildings in underserved North Camden accounted for more than 4,000 hospital visits at a cost of about $200 million. Furthermore, only 1 percent of the 100,000 people using Camden’s medical facilities accounted for 30 percent of the facilities health care costs [18].

The outcomes phases of population health management strategies employ generalized and/or targeted interventions; measure outcomes and report results. Complex issues associated with high utilizers (e.g., high chronic disease burden, health care utilization and costs) are primarily a function of inadequate health care and social service coordination. Successful interventions often supplement primary care services with care management and com-munity health navigator support, along with specialized services like pharmacy, as dictated by the local health ecosystems. Physicians lead a team of registered nurse practitioners, community health navigators, care manag-ers, social workers, and/or other specialists in performing telephonic or in-home care management care based on patient need [20]. Once acute care needs have been met, the patient may require ongoing complex chronic care management or episodic care management and coordination as needed. Providing effective treatments are combined with assisting high-need patients navigating the healthcare system and local community resources (e.g., food pantry, church, etc.), creating an individualized “communome” (to coin a term from genomics) to provide multifaceted and comprehensive wraparound services around the requirements of high-need populations in their own communities.

Just how effective can this health care strategy be? From baseline to post-intervention, the Camden Coalition witnessed a 47 percent reduction in hospitalization (from 62 to 37 hospital visits per month) and a 56 percent reduction in expenditures (from about $1.2 million to $500,000 per month) [18].

While clinical guidelines to treat bone fractures es-sentially show little patient-to-patient variance, treatment of chronic diseases like cancer require greater versatility. The traditional “one-size-fits -all” approach to treating the “average” patient delivers low-quality care and poor population-level outcomes. The 2003 completion of the Human Genome Project (HGP) finally provided transla-tional researchers with access to the entire human genetic blueprint along with highly sensitive molecular tools to screen for disease-causing mutations and test the effective-ness of individualized treatments. Precision medicine has emerged as an approach to disease management that con-siders “individual variability in environment, lifestyle, and genes for each person [21].” Cancer has been a particular focus of attention by precision medicine because it remains refractory to the “blunt force” approach of traditional treatment strategies [22]. The hope of precision medicine in cancer is to replace traditional chemotherapies that cause indiscriminate tissue damage and side effects with directed therapies that specifically repair cancer-causing genetic defects without harming normal cells.

However, genetic predisposition to disease accounts for only 30 percent of early deaths [23]. Since the genetic approach is insufficient, “a myriad of other components— molecular, developmental, physiological, social, and environmental—also must be monitored, aligned, and integrated in order to arrive at a meaningfully precise and actionable understanding of disease mechanisms and of an individual’s state of health and disease [24].” Moreover, the progression of disease in two patients with the same initial tumor-causing mutation may be dramatically different as new mutations develop to evade varied chemotherapies and host responses, so periodic resequencing is necessary. Nevertheless, numerous examples exist of cancer patients responding well to treatments predicted by precisionmedicine-based techniques like genetic testing [25]. As the armamentarium of available treatments accelerates, clinicians will have greater opportunities to tailor effective treatment plans to individual patients. Wearable and implantable devices will provide continuous real-time data monitoring of a person’s vitals, whereupon all data can be integrated into the patient’s electronic medical record.

The technologies developed over the course of the HGP inspired the development of several in the precision medicine industries like high-throughput next-generation sequencing technology, thereby increasing efficiency and and reducing the activity profiling costs of individual patient tumors. Genetic testing is another component of precision medicine that can provide stakeholders with critical information for predicting the risk of disease development, progression, and response to treatment. Active management of complex genetic tests can cut rates of mistesting and associated expenditures; Stanford University Medical Center’s genetic testing utilization service cut mistesting rates by half and saved $250,000 in annual costs [26]. Despite these and other successes in cancer patients, concerns remain regarding ordering, interpretation, and patient counseling, so the majority of genetic testing occurs in a laboratory setting. In 2015, the personal genomics company 23andMe received U.S. Food and Drug Administration approval to market a full line of direct-to-consumer (DTC) carrier tests for several autosomal recessive conditions and other companies like deCODE, Pathway Genomics, and DNA4Life are compet-ing in the market. In April 2017, 23andME also received approval to market the Personal Genome Services Genetic Health Risk (GHR) tests, the first DTC genetic tests that provide information on a person’s risk of developing a disease [27]. The tests work by testing a saliva-isolated DNA sample against more than 500,000 genetic variants, the presence or absence of which is associated with an increased risk of developing late -onset Alzheimer’s dis-ease and nine other diseases. The test is proposed to help individuals “make appropriate lifestyle choices and prove useful in discussions with healthcare providers.”

On the payer side, coverage decisions of genetic tests have been challenging due to difficulties in evidence-based decision making (including lack of proven clinical utility); lack of effective sources for informing decisions; and limited professional guidelines or regulatory oversight, especially for laboratory-based testing. Payers are often unsure of the practical value of such tests or which gene panels they should cover for a given patient. Nevertheless, commercial payers have made inroads into providing data platforms to connect clinical care with trans-lational genomics research. Geisinger Health’s MyCode® Community Health Initiative is a “system- wide bio-repository of blood, serum and DNA samples for broad research use, including genome analysis [28].” Data ob-tained from analyzing these samples, voluntarily provided by 150,000 members, is linked to the participants’ digital health records, diagnosing medical conditions earlier and helping to guide evidence-based treatment decisions. For example, a MyCode® participant with undiagnosed cancer having a particular mutated variant may be identified before symptoms manifest themselves for surgical and therapeutic intervention.

The quality evolution in population health has dra-matically changed the process in which health care is delivered and the outcomes measured, both systemically and operationally, in the United States. For example, the shift from volume-based to value-based payment systems for health care delivery have spurred the development of more efficient models of healthcare delivery, like ACOs, PCMHs, and the like.

There is considerable debate regarding how, or even whether, advances in precision medicine will improve population health [29]. While trends in precision medicine appear to greatly diverge from population health, surprising conver-gences also appear to be developing over time. Numerous examples exist where precision medicine has achieved superior individual health outcomes, but challenges remain in developing a comprehensive precision medicine -based approach for population-based care, including the dearth of biomarkers linking a specific mutation to disease risk or response to targeted therapy. The discovery of additional molecular drivers of particular diseases will improve patient’s disease detection, predictive analytics, and drug discovery. Additionally, as molecular profiling becomes more routine, patient populations are categorized less by their disease state and more by their specific disease susceptibility, disease etiology and prognosis, and treat-ments responses. How the population health profiles and precision medicine-based molecular profiles will converge in future diagnosis, preventive care, and treatment options is an open question.

The views and opinions expressed in the article are those of the author and do not necessarily reflect the views and opinions of AmeriHealth Caritas.

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Published: 10 May 2017

Copyright:

Copyright: © 2017 David Keleti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.