Population Health Management Information Technology—The View from the Trenches

One Foot on the Boat and the Other on the Dock

The 2008 Institute for Healthcare Improvement's Triple Aim ¹ defined a road map for transformation of the American health care system by calling for improvement in the health of the population, enhancing the patient care experience, and reducing the per capita cost of care, thus providing the scaffolding for the 2011 Affordable Care Act, which set in motion unprecedented changes in the American health care system. Massive financial investments were undertaken to introduce electronic medical records (EMRs)—seen as foundational to creating a more coordinated, integrated health care system in which the newly available data would support measuring and improving the quality of care. Payment reform, outlined in the bipartisan Medicare and CHIP Reauthorization Act of 2015, ² which likely will outlive the Affordable Care Act, required a shift from a fee-for-service “volume-based” payment model to a quality and outcome driven “value-based” system. Last year, the Centers for Medicare & Medicaid Services announced a target of tying 85% of all fee-for-service Medicare payments to quality or value by 2016 and 90% by 2018. ³

Innovative practice models such as the patient-centered medical home have flourished as a framework for practices to operationalize these mandated changes. Population health evolved naturally and rapidly as the academic and evidence-based discipline supporting the intensive practice transformation under way across the entire health care system. By 2016, most delivery systems were participating in some form of value-based payment program, such as mandated pay-for-performance programs, gain-sharing arrangements with commercial payers, accountable care organizations (ACOs), and bundled payment programs. Unfortunately, the rate and impact of these changes has varied significantly across the country. A recent study ⁴ found that 95% of all physician payments in 2013 were still purely fee for service based. Many systems are currently operating in a payment reality that metaphorically has them having one foot on the boat and the other on the dock in that they are unable to wean from fee-for-service dependence to focus sufficient attention and the necessary resources on the small but growing part of their care delivery that is now value based. This very dichotomy also is having a deleterious effect on the progress and maturation of population health management information technology (IT). Currently, population health management software companies are ambivalently providing tools that support customers' success in a fee-for-service world while innovating in anticipation of the shift to value-based reimbursement or, even more challengingly, in real time as this shift occurs.

The measurement of practice pattern variation (PPV) is very illustrative of this dichotomy. It is estimated that up to 34% of health care expenditure in the United States is wasteful, redundant, and not helpful to patients, and thus deemed “low-value” care. ⁵ Overtreatment is thought to be a significant contributor to this waste. The measurement of PPV is an extremely effective methodology for identifying and exploring overtreatment and overutilization. ⁶ Software solutions are now available that apply automated sophisticated algorithms to analyze practice patterns across countless clinical activities, from prescription-writing patterns to variations among physicians in their approach to managing chronic disease, performing joint replacements, or ordering expensive imaging tests. Despite this, few organizations have deployed these capabilities or undertaken a systematic approach to tackling the issue of unwarranted variation.

There are 2 major reasons for this reluctance. The first is at the heart of the dichotomy between the fee-for-service and value-based payment methodologies. Simply put, in the fee-for-service world, reducing variation that manifests as overutilization results in a direct reduction in a delivery system's revenue, creating extremely misaligned incentives to tackle issues of overuse and misuse. The second reason is the lack of widespread access to adjudicated health insurance claims. Across the country, there are a growing number of delivery organizations involved either in Medicare ACOs or in alternative payment arrangements with commercial payers who have access to adjudicated claims data for at least some of their attributed patients. Unfortunately, most health care delivery systems continue to operate with no access to adjudicated claims, thus depriving them of the ability to tackle the question of PPV in meaningful ways. As alternative payment methodologies evolve, it is imperative that there are state mandates for health insurance companies to fully and transparently share adjudicated claims data with the delivery systems responsible for the cost and quality of the care rendered. In this regard, the leading population health management IT tools are patiently waiting for the health care system to get off the dock and join them on the boat.

Impacting the Quality of Care Has Become a Little Like Teaching Botany in a Forest Fire

Engaging in any alternative payment initiative or participating in mandated quality reporting programs has exposed clinicians and health delivery systems to a cacophony of quality measure requirements. We have observed that an average-sized multispecialty group is engaged in programs that require reporting on upwards of 100 “quality” measures on varying schedules during any given year. Clinicians, health systems, EMRs, and population health management platforms have been distracted from “teaching botany” to continuously fight the “quality measure forest fire.” The cost to physician practices has been staggering. Time and cost estimates for meeting these measurement requirements are 15.1 hours per physician per week and $15.4 billion per year across the health care delivery system. ⁷ In his “Era 3 for Medicine and Health Care” ⁸ Berwick labels this measurement excessive, useless, and irresponsible. He calls for reductions in the volume and cost of this measurement by 75% in 6 years. When this is achieved, there will be many cheering, including exhausted clinicians and “forest-fire fighters” in the health IT industry.

Oddly, if there is any silver lining to the quality measurement debacle it is that it fully exposed the serious flaws in the quality and consistency of the data available. It showed that there continues to be an absence of sufficient meaningful interoperability among EMRs and other HIT systems and provided the impetus for the development of innovative data exchange protocols such as FHIR, or Fast Health Interoperability Resources, which is a proposed interoperability standard developed by the health care IT standards body known as Health Level Seven. ⁹ FHIR departs from the previous interoperability standards in that it is designed to make it simpler and more efficient to exchange and receive specific discrete data elements rather than complete documents.

With these challenges also came the realization that, because data exchange is necessary on a large scale to support quality care and population health management, it is essential to create a national patient identifier. In the absence of such an identifier there is increasing inefficiency and risk in the requisite matching and combining of disparate data sets.

The quality measurement debacle further demonstrated that clinicians rebelling against EMR technology that did not work well for them came up with idiosyncratic, poor documentation workflows that often bypassed creating the data points necessary for measurement. Finally, it proved again that EMRs were designed as records of reference for individual patients and thus, understandably, lack the data structure or ethos to support the newly required population attribution algorithms or views. At the core of EMR platforms are the patient and the care of illness, whereas at the core of population health IT is the consumer or member of a community/population, with an emphasis on wellness, risk mitigation, prevention, cost, and outcomes. Furthermore EMRs lack the native ability to integrate seamlessly with multiple disparate data sources, such as adjudicated claims data and social determinants of health.

Why then deem these stark revelations as the silver lining? The reason for this is that these revelations have provided the necessary impetus for the development of state-of-the-art population health management IT platforms able to support the holistic mission of population health: understanding the distribution of health outcomes within a population, the factors that influence this distribution, and the policies and interventions that affect these factors. ¹⁰

Robust population health IT platforms address the needs of multiple stakeholders–health care providers, communities, payers/health plans, ACOs, employers, and members of the community. Utilizing this technology to lower the burden of morbidity and cost benefits these stakeholders, all of whom share the financial risk for the required health care services.

The forest fire is extinguished and the botany lesson truly begins when innovative providers utilize population health management platforms that integrate data from multiple sources; support primary, secondary, and tertiary prevention; integrate accurate risk assessment and predictive analytic tools; support team-based care management workflows that are not physician-centric; and always include engagement of community members, patients, and family caregivers. This is the health care environment in which true value is created—high-quality care at reduced costs with engaged patients and physicians.