How Are ACOs Using Segmentation to Manage High-Need, High-Cost Patients?

A recent report by The Commonwealth Fund found no consistent set of subgroups for high-need, high-cost (HNHC) patients managed by accountable care organizations (ACOs), but it did suggest methods by which segmenting the sickest and most costly patients could help drive improved care outcomes.

Many ACOs have used predictive modeling and risk stratification to sort their entire population into broad risk levels. But with 5% of patients in the United States accounting for 50% of healthcare spending, even further division of patients into smaller subgroups has been suggested as a way to create targeted interventions and use limited resources, the report noted.

In this report, researchers interviewed 10 national experts and 34 respondents from 18 ACOs. They found that when primary care providers (PCPs) were involved in refining segmentation approaches, the clinical relevance of the results improved. In addition, more providers were willing to use different approaches.

For instance, many ACOs ask the PCP or other clinical staff to review the segmentation results, allowing staff to add or remove patients, instead of relying solely on an algorithm.

Segmenting helped mature ACOs understand which skill sets and staff were needed to deliver enhanced care management.

Most ACOs use both quantitative information, such as claims data, and qualitative information, including clinician assessments, to risk stratify their population, the report said. Sixteen of the 18 also used limited clinical data elements from their electronic health records (EHRs) in their stratification process.

Of the 13 ACOs reporting HNHC population subgroups, 7 defined their subgroups by incorporating clinical evaluation and risk assessment data that have been gathered in person from patients. Only 4 of these ACOs used data on patients’ social and behavioral needs.

Capturing social determinants of health in the EHR proved challenging for ACOs, the report said. There is also a lack of connection and coordination between the data used by community agencies and clinical providers.

When subgroups were identified, they included the frail elderly; those with advanced illness (palliative, hospice, and end-of-life care); those with comorbid medical conditions, such as diabetes or chronic obstructive pulmonary disease; those with comorbid medical and mental health conditions; and those with end-stage renal disease.

Survey respondents advised against using single disease—focused segments because of the possibility of missing the underlying cause of a patient’s problems or failing to address comorbidity.

The report said that, similar to other research, “algorithms based solely on claims data do not capture sufficient information on clinical, behavioral health, or social needs.”