About the University of California San Francisco Medical Center Pilot Site Project
Project Lead: Katie Raffel
The members of the hospital medicine program developed a triggered 2-provider diagnostic error review in parallel with a provider-level diagnostic error feedback mechanism.
Cases were identified utilizing four triggers-- seven-day all-cause hospital readmissions, autopsy, inpatient mortality, and self-report. These cases were reviewed by two hospital medicine physicians using the SaferDX tool to determine if a diagnostic error had occurred and the impact of said error. Root causes of the error were identified in collaboration with involved providers; this exploration was guided by the DEER Taxonomy tool. Feedback regarding the cases and trends was given to the involved provider as well as the hospital medicine physician group; providers were first contacted by email and then invited to verbal discussion of diagnostic process with focus on potential for systems improvement.
From January-June 2018, there were 4458 discharges from the hospital medicine service with 201 (4.5%) seven-day readmissions; 196 readmissions underwent review. Seventeen (8.7%) were found to contain diagnostic errors representing a breadth of unique diagnoses. Sixteen had a moderate impact on patients including short-term morbidity, increased length of stay, or invasive procedure. The most common categories of root cause included Laboratory/Radiology Tests and Assessment; the most common subcategories were failure/delay in ordering needed test(s), erroneous clinician interpretation of test, and failure/delay to recognize/weigh urgency.
There were several important lessons learned from this work:
1) It is possible to align diagnostic improvement with existing quality/financial institutional priorities such as readmissions
2) A symptom or diagnosis focused approach to diagnostic error exploration (as opposed to more general trigger) may allow for tailored interventions
3) Clinician interpretation of lab/radiology testing may be an area of focus for inpatient diagnosis; solutions could utilize the electronic medical record and artificial intelligence