SIDM DxQI Seed Grant Program opens 3rd Cohort
A year ago, the Society to Improve Diagnosis in Medicine (SIDM) launched its DxQI Seed Grant Program, a competitive grant process to fund interventions to improve diagnosis. Research has demonstrated that diagnostic errors are the most common, catastrophic, and costly of all causes of preventable medical harm. Given the magnitude of diagnostic error burden, the DxQI Seed Grant Program will select 20 grantees to receive awards of up to $50,000 each to support specific QI work directed towards improving diagnostic safety.
The program is now accepting its third call for proposals and will run until March 25, 2022. The program will also have an online informational webinar on January 14, 2022. Those interested in submitting a proposal can apply here.
The DxQI program focuses on a “bottom-up” approach, where frontline health professionals and patients develop and test plausible solutions that have the potential for scale and spread.
“SIDM’s DxQI Seed Grant Program is designed to stimulate innovation in the field of diagnostic quality, an area where practice improvement activity is lagging,” said Gerry Castro, SIDM Director of Quality Improvement. “Through engaging health professionals and patients in developing and testing promising approaches, the program will lay the groundwork for a multitude of strategies to improve diagnostic quality and safety and unleash the creativity of the healthcare community.”
Participants from the past two cohorts focused on developing interventions to reduce diagnostic errors in three specific disease categories — cancers, vascular events, and infections. Many grantees also focused on improving diagnostic quality outcomes related to health disparities associated with age, race, gender, or other social determinants of health.
Examples of grantees awarded in the first two cohorts include:
- Advocate Aurora Health is initiating an infection screening program for patients in outpatient clinics to diagnose infection and reduce sepsis.
- Atrium Health is evaluating the effectiveness of cognitive machine learning to identify the likelihood that a patient had COPD even if there is no documented medical history of COPD in their health record.
- The Atrium Health Levine Children’s Hospital is improving the accuracy of the referral process for underserved pediatric patients from primary care clinics to a rheumatology specialist in an effort to reduce delayed diagnoses.
- Beth Israel Lahey Health Primary Care is developing a data-driven model to identify primary care patients at high risk of a missed or delayed cancer screening ordered during virtual visits, increasing the equity and quality of the care provided to these patients.
- Brigham and Women’s Hospital is creating new referral and patient navigation networks to provide the appropriate follow-up care for patients with abnormal Pap and HPV test results.
- Children’s Hospital of Philadelphia is improving the way its emergency department providers communicate diagnostic uncertainty within the care team, including patients’ family members, to reduce the risk of diagnostic errors if the patients are admitted to the hospital.