Determining patient eligibility for a physical activity referral scheme through EHR data extraction.

IF 3.8 3区 医学 Q2 MEDICAL INFORMATICS
James Rosemeyer, Jennifer L Trilk, Meenu Jindal, John M Brooks, Lia K McNulty, Mark Stoutenberg
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引用次数: 0

Abstract

Background: Physical Activity Referral Schemes (PARS) are an effective treatment option for promoting physical activity and positively impacting patient care. Retrospective evaluation of PARS implementation requires identifying the eligible patient population that was reached. However, during a clinic visit, health care providers (HCPs) may decide that a physical activity referral is not appropriate for various reasons, such as acute illness or recent surgical history. Including patient visits with these health conditions in assessing a PARS may lead to an overestimation of patients eligible for referral.

Objective: To develop a process that more accurately determines patient eligibility for a physical activity referral when retrospectively extracting patient visit data from the electronic health record (EHR).

Methods: Inclusion criteria were developed to identify patient visits potentially eligible for a physical activity referral based on five chronic conditions. These conditions were highlighted during the standardized training that staff received when implementing the PARS. Development of exclusion criteria incorporated exercise contraindications from published literature, input from practicing HCPs, and refinement by a multidisciplinary healthcare team. Inclusion/exclusion criteria were pooled and mapped to International Classification of Diseases, 10th edition, codes and applied to EHR data.

Results: A total of 334 referrals (numerator) were identified from the pool of eligible patient visits meeting the inclusion criteria. In calculating the denominator for our reach estimate, 479,536 patient visits were initially extracted from the EHR. Applying the inclusion criteria, 58% of these visits were PARS-eligible (n = 277,515). The eligible visits further decreased by 23% (n = 63,203) with the application of the exclusion criteria, leaving a total of 214,312 PARS-eligible visits (denominator), a 55% reduction from the initial number of total patient visits.

Conclusion: Through this multi-step process, we developed a novel approach for retrospectively identifying patient visits eligible for a physical activity referral that can be applied to extracted EHR data for subsequent evaluation. This process can be used by other healthcare systems and researchers in the assessment of PARS. Ongoing refinement of the exclusion criteria is needed to best reflect the eligible population and provide the most accurate estimate of the overall PARS reach.

Clinical trial registration: Not applicable.

Abstract Image

Abstract Image

通过电子病历数据提取确定患者是否适合体育活动转诊方案。
背景:体力活动转诊计划(PARS)是促进体力活动和积极影响患者护理的有效治疗选择。对PARS实施情况的回顾性评价需要确定已达到的符合条件的患者群体。然而,在诊所访问期间,卫生保健提供者(HCPs)可能会因各种原因(如急性疾病或最近的手术史)决定不适合进行体育活动转诊。在评估PARS时包括有这些健康状况的患者就诊可能会导致对有资格转诊的患者的高估。目的:开发一种从电子健康记录(EHR)中回顾性提取患者就诊数据时更准确地确定患者是否有资格进行体育活动转诊的流程。方法:制定纳入标准,以确定基于五种慢性疾病的患者可能有资格进行体育活动转诊。工作人员在执行par时接受的标准化培训强调了这些条件。排除标准的制定纳入了来自已发表文献的运动禁忌症、来自执业医护人员的意见以及多学科医疗团队的改进。将纳入/排除标准汇总并映射到《国际疾病分类》第10版代码,并应用于电子病历数据。结果:从符合纳入标准的患者访视池中共确定了334名转诊患者(分子)。在计算我们的覆盖范围估计的分母时,最初从电子病历中提取了479,536例患者就诊。应用纳入标准,58%的患者符合pars条件(n = 277,515)。采用排除标准后,符合条件的就诊次数进一步减少了23% (n = 63,203),总共有214,312例pars符合条件的就诊次数(分母),比最初的患者总就诊次数减少了55%。结论:通过这个多步骤的过程,我们开发了一种新的方法,用于回顾性识别符合体育活动转诊条件的患者就诊,该方法可用于提取EHR数据以进行后续评估。其他医疗保健系统和研究人员可以在评估PARS时使用该过程。需要不断改进排除标准,以最好地反映符合条件的人群,并对PARS的总体覆盖范围提供最准确的估计。临床试验注册:不适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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