Development of an algorithm to assess unmeasured symptom severity in gynecologic care

Healthcare disparities research is often limited by incomplete accounting for differences in health status by populations. In the United States, hysterectomy shows marked variation by race and geography, but it is difficult to understand what factors cause these variations without accounting for differences in the severity of gynecologic symptoms that drive the decision-making for hysterectomy.

This study aimed to demonstrate a method for using electronic health record-derived data to create composite symptom severity indices to more fully capture relevant markers that influence the decision for hysterectomy.

This was a retrospective cohort study of 1993 women who underwent hysterectomy between April 4, 2014, and December 31, 2017, from 10 hospitals and >100 outpatient clinics in North Carolina. Electronic health record data, including billing, pharmacy, laboratory data, and free-text notes, were used to identify markers of 3 common indications for hysterectomy: bulk symptoms (pressure from uterine enlargement), vaginal bleeding, and pelvic pain. To develop weighted symptom indices, we finalized a scoring algorithm based on the relationship of each marker to an objective measure, in combination with clinical expertise, with the goal of composite symptom severity indices that had sufficient variation to be useful in comparing different patient groups and allow discrimination among severe symptoms of bulk, bleeding, or pain.

The ranges of symptom severity scores varied across the 3 indices, including composite bulk score (0-14), vaginal bleeding score (0-44), and pain score (0-30). The mean values of each composite symptom severity index were greater for those who had diagnostic codes for vaginal bleeding, bulk symptoms, or pelvic pain, respectively. However, each index demonstrated a variation across the entire group of hysterectomy cases and identified symptoms that ranged in severity among those with and without the target diagnostic codes.

Leveraging multisource data to create composite symptom severity indices provided greater discriminatory power to assess common gynecologic indications for hysterectomy. These methods can improve the understanding in healthcare use in the setting of long-standing inequities and be applied across populations to account for previously unexplained variations across race, geography, and other social indicators.