Barriers Specialists Face in Population Health Management

This has precipitated a significant shift in what many healthcare stakeholders considered specialists’ traditional responsibilities. In the early days of capitation and ACO models, specialists were often treated as the “spokes” of patient care, radiating out from primary care “hubs” or gatekeepers. Now, specialists are called on not only to optimize care for diseases they treat but to assume accountability for the total cost of care for defined treatment episodes across all co-morbidities and care settings.

Leading-edge specialty practices, recognizing that they are assuming the role of risk-bearers, are actively transforming their practices to adjust to this new reality. They are embracing new approaches and tools that were once the primary domain of payers – under the umbrella of population health management. They seek to optimize the holistic quality, cost, and experience of care delivery in an identified patient cohort.

Effective population health management is a tall order for any provider group, requiring unprecedented levels of patient insight drawn from across disparate, siloed systems; the harmonization of this big data into a single, holistic patient view; application of complex models for risk stratification; and the deployment of advanced, predictive analytics to identify quality and cost outliers ahead of time. Once these pieces are in place, specialty groups must also have ready a variety of interventions to minimize risk, from evidence-based care plans to triage protocols, patient navigation, and team-based care management.

How does this work in practice? Consider the example of the approximately 3,000 oncologists in the Oncology Care Model (OCM), an advanced alternative payment program introduced by the Center for Medicare & Medicaid Innovation (CMMI) in 2016. Participating oncologists collect a $160 per beneficiary per month fee during qualifying 6-month episodes of chemotherapy administration in exchange for transforming their practices to provide more coordinated, quality care at the same or lower cost. Those practices that exceed defined cost-efficiency targets can also partake in a share of savings. Along the way, practices must report out on a host of measures to validate their progress.

As practices enter their second year of the OCM, their struggles to implement population health management — and strategies for overcoming obstacles —provide a guide for other specialty practices as well as healthcare stakeholders generally. Among their many experiences, five stand out:

• Knocking down data silos: In the days of fee-for-service healthcare, the EHR was the primary source of patient insight for oncology practices. With value-based care, the EHR is just one data source among many that are essential to assembling a complete picture of holistic patient well-being – claims data, labs, pharmacy data, ADT feeds, genomic data, and many more components hold invaluable information about cost and quality.

While many OCM practices began by conducting point analyses of historical claims data provided by CMS or ran reports drawn from their EHR, some took a page from the payer playbook and integrated diverse big data sources, including clinical, financial, lab, Rx, hospital, sociodemographic and genomic sources using Health Information Exchange-like technology. They hooked up disparate source systems that could draw out and harmonize data into a holistic, patient-specific view.

• Ensuring a reliable data foundation: Even when source systems were identified, and data harmonized, practices learned they were still missing critical inputs. They often found their EHR data was incomplete, for example. This was either because information had been entered incorrectly or inconsistently in structured fields or was buried in notes as unstructured data – if it had been captured at all. Without it, completeness and accuracy of reporting was in question.

As a result, several OCM practices decided they needed to curate their data. This meant manually reviewing and reconstructing critical data sources to ensure consistency and quality. A typical approach was to enlist skilled individuals or teams with clinical backgrounds to conduct manual chart abstraction from EHRs prior to data submissions.

• Generating actionable insights: In the early days of the OCM program, many practices lacked the analytics skills and tools to conduct more than retrospective analyses of patient panels or program performance. For example, they did their best to estimate qualifying episodes for which they could bill CMS according to its guidelines. A year later, CMS reconciled the practice’s attempts at episode attribution with its logic – and clawed back 25-30% of all funds it had already paid out, shocking practices.

Recognizing that retrospective reporting would result in unwanted performance (and therefore financial) surprises, leading OCM practices tapped new harmonized data sources with a series of analytics dashboards. These dashboards often focused on three areas: program performance, cost metrics and quality measures. While initial analytics aimed to provide a near real-time view of status, as OCM practices evolved their purview expanded to incorporate predictive models that prescribed interventions for high-risk cohorts before costly events occurred.

• Intervening to create impact: OCM practices recognized that to drive meaningful savings, they had to focus scarce resources on addressable high costs and on those patients likely to be at greatest risk. To accomplish that, they:
• Risk-stratified populations to predict high-cost claimants, those likely to be hospitalized, have adverse events, utilize ERs/SNFs, be candidates for hospice, etc. and then engage them early with care management services.
• Managed unnecessary use of ER, hospital, and SNF. When patients did get admitted to these settings, they carefully case managed transitions home.
• Selected drug regimens that provided greatest efficiency and efficacy, at the lowest total cost of care.
• Improved appropriate use of palliative care and hospice for advanced disease, when the patient would see little further benefit from additional chemotherapy.
• Operationalizing ongoing performance improvements: Leading OCM practices realized that advances in data management and analysis would not have the desired effect on value-based care performance, without investments in new skills, capabilities, and workflows to monitor progress in real-time and respond as needed. In the case of the OCM, several of these had been explicitly prescribed by CMS – from 24/7 availability of a clinician with chart access to evidence-based care planning. Above and beyond that, the practices established new governance mechanisms among clinical and financial leaders to surface findings and agree on coordinated responses.

Amid the many debates around the future of US healthcare, the need for a value-based approach has remained remarkably consistent and resilient across stakeholders. As providers and payers shift to new ways of measuring and compensating for the cost and quality of patient care, population health management has become a primary mechanism for making the transition sustainably. However, it’s not an easy transition and requires new approaches to capturing, harmonizing, and acting on big data. Fortunately, the experiences of specialists in general – and oncologists participating in the Oncology Care Model program specifically – provide early lessons that peers can emulate as they progress along their own journeys.