Over 27 million Americans have Type 2 Diabetes Mellitus (T2DM), accounting for more than 7.7 million hospital admissions and $245 billion in healthcare costs each year. Hospitalized T2DM patients are at increased risk of readmission; in 2010, 20.3% of patients admitted for DM with complications were readmitted within 30 days, making diabetes one of the top conditions for rehospitalizations. In many instances, the social determinants of health?such as housing and food instability, benefits denials, and lack of income?are responsible for preventable hospitalizations, as they undermine the patient?s ability to self-manage diabetes and prevent complications. Often these unmet social needs are informally identified in the course of clinical care. However, there is no streamlined process to comprehensively identify, prioritize, and address the most important health-related social needs of our most vulnerable patients. Our previous research has increased our understanding of the multitude of social determinants related to readmission risk and poor outcomes. It has underscored the importance of developing methods to efficiently assess and prioritize social determinants and to develop profiles of the individuals at the highest risk of readmission. We propose to develop a measurement system, Re-Engineered Discharge for Diabetes-Computer Adaptive Testing (REDD-CAT) to efficiently capture and create a personalized profile of health-related social needs for patients with diabetes to reduce avoidable hospitalization and emergency department visits.
Our aims are: 1) to develop and validate the REDD-CAT; 2) to utilize the newly developed REDD-CAT measures, existing measures from PROMIS, medical record data, and retrospective claims data to generate personalized risk assessment profiles; and 3) to conduct a pilot feasibility trial to assess the acceptability of implementing the REDD-CAT in a clinical context. Achievement of these three aims will set the stage for a prospective, randomized trial of the REDD-CAT implementation in a hospital setting to assess its impact on 30-day readmissions. As the name of our proposed assessment tool implies, the longer-term goal of our research is to create a process for the Re-Engineered Discharge for Diabetes and to embed the REDD-CAT in this process as the primary data source. Ultimately, we anticipate that the methods we use to research and develop the REDD-CAT will find wide application across a number of different settings and chronic diseases.
Over 27 million Americans are diagnosed with Type 2 Diabetes Mellitus (T2DM), and their health outcomes, including hospitalization, emergency department use, and hospital readmission, are largely driven by social determinants; moreover, diabetes complications are largely attributable to unmet health-related social needs. We propose to develop and validate the Re-Engineered Discharge for Diabetes-Computer Adaptive Testing (REDD-CAT) system to efficiently capture and prioritize these needs; create a personalized profile of readmission risks; and conduct a pilot feasibility trial of the REDD-CAT to inform the design of a future, fully- powered randomized controlled trial. REDD-CAT will allow clinical staff to preemptively link patients with community-based social services tailored to meet their unique needs in order to reduce avoidable hospitalization and emergency department visits.
