The Family Heart Foundation™ Flag, Identify, Network, Deliver—Familial Hypercholesterolemia (FIND-FH™) Program and Collaborative Learning Network (CL

IF 3.6 3区 医学 Q2 PHARMACOLOGY & PHARMACY Journal of clinical lipidology Pub Date : 2024-07-01 DOI:10.1016/j.jacl.2024.04.019
Diane MacDougall MS, George Blike MD
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引用次数: 0

Abstract

Study Funding

Funded in part by AMGEN.

Background/Synopsis

The Family Heart Foundation (FHF) developed a machine learning model (MLM), FIND-FH, to flag undiagnosed individuals at high risk for familial hypercholesterolemia (FH). The model utilizes structured electronic health record (EHR) data. Past implementation of FIND-FH in a large health system identified 2167 high risk patients appropriate for outreach; 153 (7%) were clinically assessed, 46 (30%) diagnosed with FH. FHF was not involved in developing the approach to patient outreach or patient facing materials in this initial deployment.

Objective/Purpose

To characterize interim progress and performance metrics regarding screening, outreach, and diagnosis of patients identified by FIND-FH at 5 health systems participating in FHF led Collaborative Learning Network (CLN).

Methods

The FHF CLN team works with CLN members using implementation and quality science tools including expert interviews, patient journey mapping, current state process mapping and rapid cycle tests of change. Patient facing materials are jointly developed by FHF in conjunction with FH patients and CLN members. Performance metrics include: #Identified as High Risk of FH; #Appropriate for Outreach/Assessment; #Completed Assessment; #New Diagnosis Definite/Probable/Possible FH; #Needing Other CV Risk Reduction Intervention(s).

Results

Currently ∼1.85M EHRs have been screened by FIND-FH, identifying 3,720 at high FH risk. To date, chart reviews completed on 1278 found 628/1278 (49%) unlikely to have FH. The remaining 650/1278 (51%) were deemed appropriate for outreach/assessment. Of 91/650 (14%) patients assessed thus far, 71/91 (78%) were diagnosed as definite/probable/possible FH. Multiple patients not diagnosed with FH, had conditions requiring intervention to lower cardiovascular risk. Patient facing letters and resources were found to be acceptable to individuals diagnosed with FH.

Conclusions

Deployment of FIND-FH through a CLN provides proof-of-concept of the ability of an implementation science framework to improve the diagnosis and care for patients with FH. Preliminary performance metrics are promising, yet difficult to directly compare to prior efforts. Health systems used targeted chart review to avoid outreach and assessment of patients “not likely to have FH.” Patient facing materials developed in conjunction with FH patients and made available to all CLN members prevented duplication of efforts at individual health systems. Insights gained from the CLN are informing the development of more efficient, effective, scalable and sustainable care delivery systems for “FIND”ing individuals living with FH.

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家庭心脏基金会™旗帜、识别、网络、传递--家族性高胆固醇血症(FIND-FH™)计划和协作学习网络(CL
研究经费部分由AMGEN提供。背景/简介家庭心脏基金会(FHF)开发了一种机器学习模型(MLM)--FIND-FH,用于标记未确诊的家族性高胆固醇血症(FH)高风险人群。该模型利用结构化电子健康记录(EHR)数据。过去在一个大型医疗系统中实施 FIND-FH 时,发现了 2167 名适合外展的高风险患者;其中 153 人(7%)接受了临床评估,46 人(30%)确诊为家族性高胆固醇血症。方法FHF CLN 团队与 CLN 成员合作,使用实施和质量科学工具,包括专家访谈、患者旅程映射、现状流程映射和快速变革周期测试。面向患者的材料由 FHF 与 FHF 患者和 CLN 成员共同开发。绩效指标包括#结果目前,FIND-FH 筛选了 185 万份电子病历,确定了 3720 名高风险 FH 患者。迄今为止,已完成对 1278 份病历的审查,发现其中 628 份/1278 份(49%)不可能患有先天性心脏病。其余 650/1278(51%)被认为适合进行外展/评估。在目前已评估的 91/650 例(14%)患者中,有 71/91 例(78%)被诊断为明确/可能/可能患有先天性心脏病。多名未被确诊为先天性心脏病的患者患有需要干预以降低心血管风险的疾病。结论通过 CLN 部署 FIND-FH,证明了实施科学框架改善 FH 患者诊断和护理的能力。初步的绩效指标很有希望,但很难与之前的工作进行直接比较。医疗系统利用有针对性的病历审查来避免对 "不太可能患有先天性心脏病 "的患者进行外展和评估。与 FH 患者共同开发面向患者的材料,并提供给所有 CLN 成员,避免了各个医疗系统的重复工作。从CLN中获得的启示有助于为 "发现 "FH患者开发更高效、有效、可扩展和可持续的医疗服务系统。
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来源期刊
CiteScore
7.00
自引率
6.80%
发文量
209
审稿时长
49 days
期刊介绍: Because the scope of clinical lipidology is broad, the topics addressed by the Journal are equally diverse. Typical articles explore lipidology as it is practiced in the treatment setting, recent developments in pharmacological research, reports of treatment and trials, case studies, the impact of lifestyle modification, and similar academic material of interest to the practitioner. While preference is given to material of immediate practical concern, the science that underpins lipidology is forwarded by expert contributors so that evidence-based approaches to reducing cardiovascular and coronary heart disease can be made immediately available to our readers. Sections of the Journal will address pioneering studies and the clinicians who conduct them, case studies, ethical standards and conduct, professional guidance such as ATP and NCEP, editorial commentary, letters from readers, National Lipid Association (NLA) news and upcoming event information, as well as abstracts from the NLA annual scientific sessions and the scientific forums held by its chapters, when appropriate.
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