Background

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.

Objective

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.

Study Design

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.

Results

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.

Conclusion

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.

中文翻译：

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开发一种算法来评估妇科护理中未测量的症状严重程度

 背景

医疗保健差异研究往往因对人群健康状况差异的不完整解释而受到限制。在美国，子宫切除术因种族和地域的不同而存在显着差异，但如果不考虑推动子宫切除术决策的妇科症状严重程度的差异，就很难理解是什么因素导致了这些差异。

 客观的

本研究旨在展示一种使用电子健康记录衍生数据创建综合症状严重程度指数的方法，以更全面地捕获影响子宫切除术决策的相关标记。

 研究设计

这是一项回顾性队列研究，研究对象为来自北卡罗来纳州 10 家医院和 >100 门诊诊所的 1993 名女性，这些女性在 2014 年 4 月 4 日至 2017 年 12 月 31 日期间接受了子宫切除术。电子健康记录数据，包括账单、药房、实验室数据和自由文本注释，用于识别子宫切除术的 3 种常见适应症的标志：大量症状（子宫增大造成的压力）、阴道出血和盆腔疼痛。为了开发加权症状指数，我们根据每个标志物与客观测量的关系，结合临床专业知识，最终确定了评分算法，目标是综合症状严重程度指数具有足够的变化，可用于比较不同的患者组和患者。允许区分体积大、出血或疼痛等严重症状。

 结果

3个指数的症状严重程度评分范围各不相同，包括综合总体评分（0-14）、阴道出血评分（0-44）和疼痛评分（0-30）。对于那些分别具有阴道出血、大量症状或盆腔疼痛诊断代码的人来说，每个复合症状严重程度指数的平均值更大。然而，每个指数都显示出整个子宫切除病例组的差异，并确定了具有和不具有目标诊断代码的患者的严重程度不同的症状。

 结论

利用多源数据创建综合症状严重程度指数，为评估子宫切除术的常见妇科指征提供了更大的区分力。这些方法可以提高对长期不平等情况下医疗保健使用的理解，并应用于不同人群，以解释以前无法解释的种族、地理和其他社会指标之间的差异。