Division of Decision, Risk and Operations, Columbia Business School, New York, NY.
Department of Health Care Policy, Harvard Medical School, Boston, MA.
Med Care. 2018 May;56(5):448-454. doi: 10.1097/MLR.0000000000000891.
We sought to build on the template-matching methodology by incorporating longitudinal comorbidities and acute physiology to audit hospital quality.
Patients admitted for sepsis and pneumonia, congestive heart failure, hip fracture, and cancer between January 2010 and November 2011 at 18 Kaiser Permanente Northern California hospitals.
We generated a representative template of 250 patients in 4 diagnosis groups. We then matched between 1 and 5 patients at each hospital to this template using varying levels of patient information.
Data were collected retrospectively from inpatient and outpatient electronic records.
Matching on both present-on-admission comorbidity history and physiological data significantly reduced the variation across hospitals in patient severity of illness levels compared with matching on administrative data only. After adjustment for longitudinal comorbidity and acute physiology, hospital rankings on 30-day mortality and estimates of length of stay were statistically different from rankings based on administrative data.
Template matching-based approaches to hospital quality assessment can be enhanced using more granular electronic medical record data.
我们旨在通过纳入纵向合并症和急性生理学数据来改进模板匹配方法，以审核医院质量。
2010 年 1 月至 2011 年 11 月间，加利福尼亚州北部 18 家 Kaiser Permanente 医院收治的脓毒症和肺炎、充血性心力衰竭、髋部骨折和癌症患者。
我们生成了 4 个诊断组中 250 名患者的代表性模板。然后，我们使用不同数量的患者信息，在每个医院与该模板进行 1 到 5 名患者的匹配。
数据从住院和门诊电子病历中回顾性收集。
与仅基于行政数据匹配相比，同时匹配入院时的合并症病史和生理数据可显著降低医院间患者严重程度的差异。在调整了纵向合并症和急性生理学数据后，医院在 30 天死亡率和住院时间估计值方面的排名与基于行政数据的排名存在统计学差异。
通过使用更详细的电子病历数据，可以改进基于模板匹配的医院质量评估方法。
