Population Health Management’s Revenue Cycle Impact

May 5, 2017 4:18 pm

As healthcare providers acclimate to new value-based payment models, they face the two-pronged challenge of achieving better outcomes while reining in costs. Providers face increasing pressure to shore up funds to support new quality payment program initiatives and offset potential revenue losses and are looking toward data analytics solutions to identify savings and clinical improvement opportunities with the biggest potential to improve the revenue cycle.

The benefits that can be gleaned from such an approach are exemplified by the experience of Allegiance ACO, an accountable care organization affiliated with Allegiance Health Group in New Jersey and Pennsylvania. Allegiance turned to data integration and analytics to uncover patient care delivery and utilization trends as it worked to build an effective population health management strategy.

The physician-led ACO, which provides services to economically disadvantaged patients in and around Trenton, N.J., used analytics to stratify patient risk, identify opportunities for resource reallocation, and empower data-driven patient care intervention to reduce non-acute emergency department (ED) utilization and inpatient admissions among its patient population.

Patient Risk Stratification: Predicting Threats

Allegiance’s first step in addressing network inefficiencies was to identify those of its patients who were at highest risk of negative health outcomes or episodes. Risk stratification was based on a scoring model that weighed patient expenses against diagnosis, comorbidities, and demographics. Once at-risk patients were identified, the ACO set out to better manage their clinical care. Physicians began using dashboards to track and manage high-risk members. The technology continually analyzed new data to prioritize patients that required focused attention, thereby reducing the cognitive burden on physicians and nurses.

Using populationwide data, predictive analytics enable Allegiance’s providers to understand the potential needs and risks of their patients. Such data provide a means for healthcare organizations to forecast costs and introduce new interventions that are designed to reduce the risk of costly acute care encounters. For example, if laboratory, clinical, electronic health record, and claims data show that a patient with diabetes has HBA1c values greater than 9 percent, blood pressure higher than 130/80, and goes more than three months between physician visits that patient may be eligible for interventions, such as  proactive scheduling of more frequent office visits, greater emphasis on education programs, involvement of pharmacists in medication management and counseling, and others. Assessments on the social determinants of health (e.g., socioeconomic status, physical environment, social support networks) constitute another realm of heath factors influencing patient outcomes. By putting such information into the hands of its care teams, Allegiance enabled its teams to work with patients to identify and remove barriers to primary care engagement.

Resource Utilization: Identifying Costs and Opportunities

Through such financial and operational data analytics, providers can identify care expenditures and patients who fall into a high resource-utilization category. These patient populations provide opportunities for clinicians to implement targeted care intervention programs that reallocate resources and promote more cost-effective preventive care. By identifying gaps in care, providers and care managers can augment workflows to close those gaps and prevent exacerbation of illness.

By analyzing resource utilization in this way, Allegiance detected instances where patients were undergoing duplicate and unnecessary tests in the hospital care setting. This finding prompted the ACO to take steps to improve laboratory service delivery in the office setting, leading to a 9 percent reduction testing costs. The data also made it clear that Allegiance could reduce ED visits by offering early evening office appointments, and on making this change, the ACO also saw a 32 percent concurrent decrease in ambulance services.

Data-Driven Care Coordination: Adapting Care Delivery

The transition from patient data insight to informed care delivery is one of the biggest obstacles in population health management. To effectively execute on outcome improvement initiatives, providers must be able to tether analytics to the point of care. Patient-specific workflow prompts can help physicians and care coordinators boost patient engagement and appointment follow-through.

With a shared view of patient health records and activities, Allegiance care teams were able to streamline transitions of care episodes and settings. The ACO used care coordinators to help patients navigate the health system by addressing challenges in access to care, such as arranging transportation for patients to dialysis appointments. Through such population health management efforts, Allegiance reduced overall ED visits by 8 percent and ED visits leading to hospitalizations by 10 percent in 2016.

To most easily and effectively identify the biggest opportunities for care enhancement, process improvement, and cost savings, providers require ready access to applicable data that can support informed actions. Scalable population health management solutions offer physicians the opportunity to wade into the waters of value-based care, driving effective patient intervention and smarter utilization, one targeted initiative at a time. 

Sanjay Seth, MD, is executive vice president at HealthEC, Piscataway, N.J.


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