3-step framework helps health care systems identify high-need, high-cost patients

Researchers from the RTI-University of North Carolina (UNC) Evidence-based Practice Center, Research Triangle Park, NC developed a 3-step process to help health care systems identify high-need, high-cost (HNHC) patients. Identifying these patients at early stages can help control hospital and clinic costs. The full review and framework are published in Annals of Internal Medicine.

Finding ways to improve outcomes and reduce spending for patients with complex and costly care needs requires an understanding of their unique needs and characteristics. HNHC patients are those who have multiple chronic conditions or functional limitations, and their care can be further complicated by behavioral health conditions and social risk factors. A challenge for clinicians, health systems, and payers is to distinguish HNHC patients from the larger population of patients with chronic conditions.

Starting from a National Academy of Medicine (NAM) taxonomy, researchers used a "best fit" framework synthesis approach to examine characteristics and criteria to identify HNHC adult patients, defined as those with high use (emergency department, inpatient, or total services) or high cost. Based on their review of 64 studies published in 65 articles, the researchers created a 3-step process for identifying such patients in the health care setting: 1) identify clinical or functional risks; 2) assess for behavior or social risk factors; and 3) identify patterns of health care usage. Patients with multiple comorbidities or chronic clinical conditions, such as heart disease, chronic kidney disease, diabetes, chronic lung disease, cancer, hypertension, and chronic pain were more likely to be HNHC users. Also, having mental illness or substance use disorders or facing poverty, homelessness, or food insecurity increased the risk of being HNHC health care users. Finally, those with a pattern of high health care use in the past were more likely to continue this pattern. According to the researchers, this framework could be a good place to start to help improve quality and efficiency of care in hospitals and clinics.

The authors of an accompanying editorial from the University of Pittsburgh Graduate School of Public Health offer a real-world perspective on how the modified framework contributes to identifying patient needs, what appears to be missing, and possible next steps. They note that more data on patient demographics and characteristics may help to improve the taxonomy, as could incorporating ways to identify mismatched care.