跨部门利用健康声称数据进行医疗康复质量保证的方法：一项前瞻性纵向和回顾性队列研究的联合研究方案。
A cross-sectoral approach to utilizing health claims data for quality assurance in medical rehabilitation: study protocol of a combined prospective longitudinal and retrospective cohort study.
机构信息
Section of Health Care Research and Rehabilitation Research, Institute of Medical Biometry and Statistics, Faculty of Medicine, Medical Center, University of Freiburg, Freiburg, Germany.
AOK Research Institute (WIdO), Berlin, Germany.
出版信息
BMC Health Serv Res. 2023 Oct 17;23(1):1110. doi: 10.1186/s12913-023-10074-w.
BACKGROUND
Measuring the quality of provided healthcare presents many challenges, especially in the context of medical rehabilitation. Rehabilitation is based on a holistic biopsychosocial model of health that includes a person's long-term functioning; hence, outcome domains are very diverse. In Germany, rehabilitation outcomes are currently assessed via patient and physician surveys. Health insurance claims data has the potential to simplify current quality assurance procedures in Germany, since its comprehensive collection is federally mandated from every healthcare provider. By using a cross-sectoral approach, quality assessments in rehabilitation can be adjusted for the quality provided in previous sectors and individual patient risk factors.
METHODS
SEQUAR combines two studies: In a prospective longitudinal study, 600 orthopedic rehabilitation patients and their physicians are surveyed at 4 and 2 time points, respectively, throughout rehabilitation and a follow-up period of 6 months. The questionnaires include validated instruments used in the current best-practice quality assurance procedures. In a retrospective cohort study, a nationwide claims database with more than 312,000 orthopedic rehabilitation patients will be used to perform exploratory analysis for the identification of quality indicators. The identified SEQUAR claims data quality indicators will be calculated for our prospective study participants and tested for their ability to approximate or replace the currently used, best-practice quality indicators based on primary data.
DISCUSSION
The identified SEQUAR quality indicators will be used to draft a novel, state-of-the-art quality assurance procedure that reduces the administrative burden of current procedures. Further research into the applicability to other indications of rehabilitation is required.
TRIAL REGISTRATION
WHO UTN: U1111-1276-7141; DRKS-ID: DRKS00028747 (Date of Registration in DRKS: 2022/08/10).
背景
衡量所提供的医疗保健质量存在诸多挑战，尤其是在医疗康复领域。康复以健康的整体生物心理社会模式为基础，包括一个人的长期功能；因此，结果领域非常多样化。在德国，康复结果目前通过患者和医生的调查进行评估。健康保险索赔数据有可能简化德国当前的质量保证程序，因为从联邦层面要求每个医疗保健提供者全面收集数据。通过采用跨部门方法，可以根据之前部门提供的质量和患者个体的风险因素来调整康复质量评估。
方法
SEQUAR 结合了两项研究：在一项前瞻性纵向研究中，600 名骨科康复患者及其医生分别在康复期间和 6 个月的随访期间分 4 次和 2 次进行调查。调查问卷包括当前最佳实践质量保证程序中使用的经过验证的工具。在一项回顾性队列研究中，将使用一个拥有超过 312,000 名骨科康复患者的全国性索赔数据库进行探索性分析，以确定质量指标。确定的 SEQUAR 索赔数据质量指标将为我们的前瞻性研究参与者计算，并根据原始数据测试其近似或替代当前使用的最佳实践质量指标的能力。
讨论
确定的 SEQUAR 质量指标将用于起草一种新的、最先进的质量保证程序，以减轻当前程序的行政负担。需要进一步研究其在其他康复适应症中的适用性。
试验注册
WHO UTN：U1111-1276-7141；DRKS-ID：DRKS00028747（DRKS 注册日期：2022/08/10）。
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