The Evolution of Disease State Management: Historical Milestones and Future Directions

Twenty-five years ago, the Journal of Managed Care Pharmacy introduced its readers to disease state management, which attempted to break the siloed culture of the U.S. health care system. Disease state management has been transformed, in part, to population health management. This shift was marked by 3 main inflection points: the rise of the web-enabled smartphone, the Patient Protection and Affordable Care Act (ACA), and the adoption of artificial intelligence (AI). The introduction of smartphones filled the communication gap through improved patient engagement and accessible mobile applications, giving patients access to their clinical data. In addition, through the ACA, bundled payment models moved away from a volume-based to a value-based payment approach and attempted to incorporate population health concerns, such as the social determinants of health. The advancement of AI will allow the health care system to collect comprehensive health data and to predict the population at higher risk. Despite these advancements, some challenges from 25 years ago remain, yet rapid technology advancements may expedite the next wave of change.

T wenty-five years ago, disease state m a n a g e m e n t sought to improve patient outcomes by maximizing the interrelationship between physicians, payers, pharmaceutical companies, pharmacists, and patients to manage chronic conditions.On the 25th anniversary of the Journal of Managed Care & Specialty Pharmacy, in which Hadsall and Sargent (1995) first introduced the concept of disease state management to readers, 1 we reflect on the evolution of disease state management.We also address 3 key inflection points-the rise of the web-enabled smartphone, the Patient Protection and Affordable Care Act (ACA), and artificial intelligence (AI)-that have collectively transitioned disease state management to population health management.

C O N T E M P O R A R Y R E F L E C T I O N
The Evolution of Disease State Management: Historical Milestones and Future Directions

■■ Historical Evolution Disease State and Care Management Models
Before 1995, practice guidelines were introduced to promote uniformity of care. 2 However, the guidelines largely failed to earn support from physicians, who regarded them as "cookbook" medicine that eliminated the role of clinical judgement.
In addition, without appropriate financial incentives, guidelines could have meant decreasing the volume of services that were perceived as needed.Despite the initial resistance of physicians toward practice guidelines, 3 disease state management, which heavily relied on them, emerged in 1995.
5][6] Although shortterm savings were observed, long-term projections of such programs remained unclear.The early stage of this system favored relatively low-risk patients, while individuals with complex and costly health conditions were assigned to specialized companies.Although unintended, these specialized interventions added to the fragmentation of care. 6The initial challenges of disease state management were reluctance from physicians, patient retention, and the pharmaceutical companies as early adopters (which was perceived as a marketing strategy). 4In retrospect, disease state management might have succeeded in managing patients with relatively expensive, but manageable chronic conditions.However, the lack of transparency between physicians and managed care organizations hindered this process. 7n an attempt to unify health care delivery, payers introduced care management programs for complex patients in the outpatient setting. 8,9Care management has been shown to increase communication between physicians and patients, to use evidence-based care, and to enhance adherence. 9][10] Some observers believed that the success of care management as compared with disease state management was a result of its ability to use technology to identify patients. 11

■■ From Disease Management to Population Health Management
Population health is defined as "the distribution of health outcomes within a population, the determinants that influence distribution, and the policies and interventions that affect the determinants." 12Compared with disease state management, population health management attempted to look beyond the rudimentary clinical picture to capture health outcomes at the population level.This comprehensive approach was shaped by the following inflection points: the web-enabled smartphone, the ACA, and AI.

The Rise of the Web-Enabled Smartphone
The lack of patient engagement was one of the early pitfalls of disease state management.The smartphone was an easy and inexpensive solution to reach patients as most Americans began to own them.By 2010, there were 5,805 health-related mobile applications, with 3% of them pertaining to chronic diseases. 13Concurrently, there was an increase in the use of electronic health record (EHR) systems, supported by the Health Information Technology for Economic and Clinical Health Act of 2009. 14A national survey from the Centers for Disease Control and Prevention showed that the use of any EHR system increased from 18.2% in 2001 to 50.7% in 2010. 15uch expansion allowed physicians to record clinical information electronically and patients to access their own health data remotely.Although access to smartphone data based on socioeconomic and health status are potential barriers, the smartphone is a promising step toward a unified health care system. 16,17he smartphone has revolutionized disease state management by creating a secure and open stream of shared information at a relatively low cost for patients and physicians.At the same time, advanced EHR systems, which included comprehensive health data, helped to change disease state management to population health management.

The Affordable Care Act
In 2010, the passage of the ACA boosted population health management through the expansion of health care coverage, improvement of quality of care, prevention and health promotion, and community-based programs. 18Specifically, it focused on improving the quality and efficiency of care through the Centers for Medicare & Medicaid Services innovations models. 19The bundled payment system was created to move away from traditional fee for service and instead to test whether a system focused on episodes of care could decrease health care expenditures.This directly addressed one of the impediments to disease state management success: lack of a financial incentive structure.However, as in disease state management, defining an episode of care for a chronic condition remains a challenge.To address this limitation, bundled payment models are beginning to take a wider view by incorporating population health concerns such as comorbidities and social determinants of health.

Artificial Intelligence
Despite the advancement of the current EHR analytical capabilities, most still lack the power to extract clinical information.Yet, as health care evolves toward a comprehensive data-driven environment, AI can maximize our ability to collect information from unstructured narrative texts, such as clinical notes.Natural language processing turns the texts to machinereadable data, which then can be analyzed to supplement and enrich the medical records. 20With comprehensive health data and the predictive power of AI, risk prediction can further improve current population health management.Risk predictive algorithms used at the population level can support decision making for chronic disease prevention. 21The capability of AI to provide high-resolution clinical data and sharing such data in a collaborative clinical practice environment has the potential to advance efficient and high-quality care.However, leadership from policymakers and engagement of all health care stakeholders is needed for widespread adoption of AI. 22

■■ Conclusions
Disease state management transformed into population health management around 3 main inflection points: the introduction of the web-enabled smartphone, the ACA, and the adoption of AI.In the last 25 years, this movement progressed into a comprehensive and data-driven approach to care for patients with complex and chronic diseases.Regrettably, some of the health care issues identified 25 years ago persist today, such as system fragmentation and the lack of data transparency.Despite the advances seen in the health care system, the transition from disease state management to population health is not yet complete.Yet, rapidly changing technology suggests that changes to improve patient outcomes may occur faster than in the past.