The growing use of artificial intelligence (AI) in the health and human service planning is getting increasing scrutiny. The big concern is that AI as currently used reinforces systemic biases—such as race, gender, disability, and sexual orientation (see Preventing Bias In Algorithms To Detect Suicide Risk, Algorithmic Bias in Health Care Exacerbates Social Inequities—How to Prevent It, and The Double Pandemic: How COVID-19 Is Bringing to Light Health Inequities That Have Long Been a Problem in America).
In fact, many consumer advocates fear that the increasing use of data-driven decision making tools flies in the face of of person-centered planning. Those advocates think that AI tools limit consumer choices (see A Reality Check On Artificial Intelligence: Are Healthcare Claims Overblown? and AI Use In Medicine Is Raising Worry Over Risks For Patients).
But that doesn’t have to be the case. Managers of health care provider organizations need to increase their use of data—for efficiency, to improve consumer outcomes, and to reduce unnecessary resource use. The key is a consumer-centric framework in using data to manage care. That was the theme of a recent presentation, Eliminating 80/20: Lowering Costs While Improving Outcomes & Equitable Care With Success-Focused Artificial Intelligence (SF-AI), by Kate Cordell, Ph.D., co-founder of Opeeka, at The 2021 OPEN MINDS Technology & Analytics Institute. Her point is that “big data” can be used to identify all the possible options for consumers—along with their risks. And this will contribute to more consumer-driven, person-centered decision making.
Consumer-centric data-driven decisions should start with the approach of ‘story mapping’ as a tool for collaborative care planning. Utilizing real-time data on outcomes, clinical professionals can see how the consumer’s health status changed over time. According to Dr. Cordell, “success is measured by looking at the person’s condition when they first came in with the highest level of need, versus where they are today. Then determining what drove that to happen.”
The second step is creating a ‘success map’ for the consumer. The success map should incorporate a consumer’s strengths, resilience factors, and goals—such as education, work, cultural beliefs, social determinants—which influence the types of treatment and therapies offered. Dr. Cordell believes “this approach is critical for getting consumers out of that ‘20%’ high need, high-cost category and into the ‘80%’.” Through story maps, specialty provider organizations can create a very personalized and person-centered picture of the whole-person.
The final step—and the value of AI to consumers—presenting the consumer with options and engaging in a collaborative case planning process focused on the options. For example, for consumers with frequent emergency room visits, Dr. Cordell illustrated the process. “Through story mapping the driving factors identified could relate to age, days to the initial evaluation, medication adherence, or lower blood pressure. Age cannot be controlled but we can influence the other three factors. Now we have something actionable to go on.” In this case, clinical professionals could work with consumers on options for medication reminders, stress reduction activities, or nutrition programs.
This consumer-centric framework is not antithetical to the learning power of AI. Using success-focused AI, specialty provider organizations should analyze consumer characteristics, treatment plans, and outcomes—and continually learn from it. Dr. Cordell closed with the observation that not only does this improve health care delivery, “consumers need to feel less isolated and more satisfied with their care so they will access it sooner, and satisfaction in care is also a driver.”