Project Details - Ended
- Grant Number:R01 HS021747
- Funding Mechanism:
- AHRQ Funded Amount:$2,277,681
- Principal Investigator:
- Project Dates:9/30/2012 to 12/31/2018
- Care Setting:
- Medical Condition:
- Type of Care:
- Health Care Theme:
Crohn’s disease is a chronic inflammatory bowel disease that has significant impact on the quality of life of an estimated 500,000 Americans. Treating patients with a combination of immunomodulator and anti-tumor necrosis factor soon after diagnosis but before complications occur leads to better patient outcomes. However, these medications are associated with life-threatening infections and lymphoma. A challenge of treating Crohn’s disease is identifying those patients who will develop severe disease and need medications without over-treating patients who have mild disease. Additionally, patients are often reluctant to begin treatment until the disease becomes more severe with resultant worse outcomes.
This research validated a statistical model that predicts Crohn’s disease severity for an individual patient based on clinical, serologic, and genetic factors. The model was then adapted into a web-based decision aid to help patients weigh the benefits and risks of available treatments for Crohn’s disease. Together, these two tools comprise the Crohn’s Disease Shared Decision-Making Program.
The specific aims of the research were as follows:
- Validate the Crohn’s disease prediction model.
- Optimize the Crohn’s Disease Shared Decision-Making Program.
- Evaluate the Crohn’s Disease Shared Decision-Making Program.
A cluster randomized controlled trial of 202 patients found more patients in the intervention group with the shared decision-making program elected to be treated with the combination treatment. Furthermore, the intervention group had fewer patients who remained untreated, more patients who received the treatment they preferred, less decision conflict, an increased understanding of their disease, and increased trust in their physicians. Researchers believe this model could be adapted for use with other chronic disease states where increased information for patients, together with a personalized risk prediction tool, could facilitate shared decision making.