In the world of care management, evaluating patients, giving a diagnosis, and providing clear instructions for treatment and evaluating patients is the job of a team of health professionals working with a patient. However, there are multiple factors that play a role in a patient’s treatment - and ultimate outcome - outside of relatively infrequent and short visits to a doctor’s office. This is where the essential role of a care manager comes into play.
The outcomes of healthcare interactions that require patient engagement are as varied as patients’ behavioral preferences. Three patients who are prescribed the same medication treatment from their doctor may have totally different outcomes due to their level of engagement in their health. The first patient may diligently take medication, but choose to ignore the lifestyle and diet changes recommended by their healthcare team. On the other hand, the second patient may completely change their diet, but may be less than reliable when it comes to maintaining a regular exercise regimen or remembering to pick up their prescriptions. The third patient placed under the same treatment plan may have managed to adhere to both medication and lifestyle changes, but may fail to show up to routine appointments meant to monitor their progress. Care managers help to close these gaps between what is expected from the patient and what they actually do by helping patients address their unique barriers to adherence.
When left unaddressed, barriers to treatment adherence limit the effectiveness of otherwise efficacious treatments, and lead to a tremendous healthcare burden. In this study, we’ve explored how AI can improve the productivity of care managers and remove inefficiencies, and ultimately increase the volume and effectiveness at which they can help their patients to live in a way that increases their chances of a healthy outcome.
Care planning often relies upon population-based evidence. However, individuals respond differently to interventions. The end goal of this research is simple: empower care managers to drive better engagement by helping them focus their attention on patients who are more likely to respond well to different interventions. The team set out to apply machine learning to real world data from care management records to personalize care planning strategies to the individual. Individual-level care planning strategies learned from practice by the Behavioral Response Inference Framework (BRIeF) framework achieved 87.24 percent accuracy, outperforming population-level strategies and delivering more accurate intervention recommendations for goal attainment. These results may lead to increased engagement, which is a goal unto itself as it can increase a patient's sense of control and agency, and is generally a necessary precursor to improved outcomes.
This collaboration between IBM Research and IBM Watson Health was awarded a Distinguished Paper Award at the 2018 American Medical Informatics Association Annual Symposium. Research such as this is crucial to lay a foundation for a healthier and aging society. As our population becomes older, the most effective way to alleviate our healthcare burden will be to move the needle on chronic disease from treatment to prevention. Ensuring that critical players of this effort, such as care managers, are supported will be crucial to reinforcing the prevention arm of our healthcare system.