在农村糖尿病自我管理教育和支持计划中采用耗时最少的方法进行定期、持续的成果评估:通过事后回顾性研究进行验证。

Xin Zhang, Tiaha E McGettigan
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引用次数: 0

摘要

目的:本研究的目的是介绍并验证一种最低时间的方法,用于定期、持续评估农村糖尿病自我管理教育和支持(DSMES)项目的实践和个人水平的结果。方法:该方法涉及在电子健康记录系统中开发的报告,以捕获项目中患者的初始糖化血红蛋白数据以及报告运行时他们的最新糖化血红蛋白数据。为了验证该方法持续评估结果的能力,在2023年10月、11月和12月连续3个月进行了3项回顾性前后研究。研究对象是2型或1型糖尿病患者,他们在这几个月内完成了该项目的首次就诊。将患者队列的A1C变化及其统计学意义作为实践水平的结果进行分析,并通过绘制和分析患者数据来监测个体水平的结果。结果:报告准确地捕获了数据,使分析的耗时降到最低。该方法允许基于A1C变化的方案有效性的持续评估和监测个体患者的进展。在2023年10月和12月(但11月没有)以及所有3个月的综合数据中,观察到平均A1C有统计学意义的降低。数据绘图有助于确定可能从随访中受益的个体受试者。结论:该方法可行且准确,可用于持续的疗效评估,为临床医生提供及时反馈,促进实践变革以改善患者预后。它也很灵活,适用于其他DSMES项目。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Minimally Time-Consuming Method for Regular, Ongoing Outcome Assessments in a Rural Diabetes Self-Management Education and Support Program: Validation via Retrospective Pre-Post Studies.

PurposeThe purpose of this study was to introduce and validate a minimally time-consuming method for regular, ongoing assessments of practice- and individual-level outcomes in a rural diabetes self-management education and support (DSMES) program.MethodsThe method involves a report developed within an electronic health record system to capture the initial A1C data of patients in the program and their most recent A1C data at the time the report is run. To validate the method's ability to continuously assess outcomes, 3 retrospective pre-post studies were conducted over 3 consecutive months: October, November, and December 2023. The subjects were individuals with type 2 or type 1 diabetes who completed their initial visits in the program during these months. A1C changes in patient cohorts and their statistical significance were analyzed as practice-level outcomes, and individual-level outcomes were monitored by plotting and analyzing patient data.ResultsThe report accurately captured data, enabling minimally time-consuming analyses. The method allowed both continuous assessment of program effectiveness based on A1C changes and monitoring of individual patient progress. Statistically significant reductions in average A1C were observed for subjects seen in October and December 2023 (but not in November) and across the combined data from all 3 months. Data plotting helped identify individual subjects who may benefit from follow-up.ConclusionsThe method is feasible and accurate for ongoing outcome assessments, providing timely feedback to clinicians and promoting practice changes to improve patient outcomes. It is also flexible and adaptable to other DSMES programs.

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