Data, patient feedback can ease challenge of measuring quality of programs for the seriously ill

Community-based programs for patients with serious illnesses are becoming more common, but there are few strategies to measure the quality of such programs. One way to make data collection easier: Piggyback on established programs.

Many of the quality measures that are used for palliative and end-of-life care can be broadened to encompass more people with serious conditions, according to an article published in Health Affairs. But that would require additional data collection on those patients to make it work.

“Promoting care that is competent, person- and family-centered, coordinated and compassionate for people with serious, life-threatening illnesses is a pressing policy challenge, and investments in quality measurement are urgently needed to ensure accountability for providing such care,” write Joan M. Teno, M.D., a gerontology professor at the University of Washington; Rebecca Anhang Price, Ph.D., a senior policy researcher at RAND Corporation; and Lena K. Makaroun, M.D., a geriatric fellow at UW.

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Much of the valuable data to craft quality measures is available in electronic health records or claims reports, according to the article, providing information on large populations for a low cost.

However, when it comes to conversations about advance directives or other tough discussions with patients, this data only shows that the interactions occurred, not how well they went. So the available data must be supplemented with patient surveys to gauge the quality of communication.

There are a number of challenges in this effort as well, according to the article. Using utilization as a guide doesn’t gel with value-based incentives and can lead providers to limit access to care. Patient goals are also rarely constant; what they or their caregivers request can be different depending on when discussions occur and how providers broach the subject of advanced care planning.

Future research should pool data without masking variations between groups in order to uncover trends over time, the authors said.