Community Record of Care for Accountable Communities of Health


Best practices identifying High-Risk, High-Cost patients


The mantra of healthcare in the 21st Century seems to be, “We must better manage the care of high-risk and high-cost patients.” Insights about best practices are emerging from experience outside health maintenance organizations. The American Journal of Managed Care recently had two articles on the topic. The common thread was that data AND human contact are required for success, whether the work is ‘short-listing’ candidates for care management, or coordinating the delivery of care for those short listed. The two articles are discussed in this and a companion post.

The article Clinician Considerations When Selecting High-Risk Patients for Care Management recommends a hybrid approach to identifying high-risk patients. The article points out that at best lists of patients created algorithmically from billing and even clinical data provide a stratified list for providers and care managers to edit.

Based on semi-structured interviews of 20 care teams, the authors found that when providers assess patient risk and aptitude for care management they apply non-clinical patient characteristics such as patient:

  • insight on their health
  • vulnerability and health literacy
  • strengths such as coping skills, home and social environments and financial resources
  • attachment to the health care system

Key among these insights is that providers are best able to analyze patient attachment to health care system. Some patients may not show up or be properly evaluated in billing-based algorithms because they are not seeking necessary care. Or, they are getting proper care, but are better connected with another provider or care team.

The interviews also revealed that provider review can highlight gaps in care offerings, as the providers expressed reluctance to refer patients to care management when they believed necessary resources were not available. This was especially true for patients with substance abuse or psychiatric conditions.

Algorithms can identify patients who may be in need of care management and call out those enrolled in risk contracts. The data generated list can reduce the burden on the provider team as they consider who among their panel are candidates for structured care management programs. The lists can reduce the provider burden, freeing them to focus on matching patients to programs which exist, or need to be created.

MIN-NS helps generate these lists of patients who are being case managed by other community resources. For our subscriber organizations we reviewed the community records of their care to help establish the need for additional palliative care programs. In the palliative care case there was a provider call for the program. MIN-NS data from across the area confirmed and quantified that need.


By Duncan West at 4 Dec 2015, 11:25 AM