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Development and Evaluation of EHR-enabled Population Health Outreach Strategies to Improve Diabetes Screening in a Safety-net Health System: a Pragmatic Randomized Controlled Trial

Development and Evaluation of EHR-enabled Population Health Outreach Strategies to Improve Diabetes Screening in a Safety-net Health System: a Pragmatic Randomized Controlled Trial
制定和评估基于电子病历的人口健康推广策略,以改善安全网卫生系统中的糖尿病筛查:一项务实的随机对照试验
批准号:
10364512
负责人:
Michael Edward Bowen
金额:
$81.11万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-03-01 至 2026-11-30

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中文摘要
翻译
项目总结/摘要 尽管有公认的国家筛查指南,2型糖尿病(T2 D)筛查仍不理想。 在美国,730万T2 D成人和74.5万糖尿病前期(PDM)成人仍未确诊。尽管 机会性筛查在临床实践中,近三分之一的初级保健患者未被诊断 糖尿病(PDM + T2 D)。为了缩小筛查差距,需要新的战略。我们采用基于证据的 从癌症筛查到将T2 D筛查概念化为多步骤过程的方法(风险评估, 筛选邀请、测试排序和测试完成),需要患者、提供者和 卫生系统接口。我们之前开发了帕克兰精神障碍检测程序(PDDP)- 基于EHR的多组分人群T2 D健康筛查干预,可自动进行风险评估, 批量订购筛查测试,并通过筛查邀请促进批量患者外展。PDDP关闭 筛查过程中的多个缺口,并补充临床实践中的机会性筛查。在我们 PDDP试点研究,一个单一的,通用的“逾期筛选”邀请有41%的响应率,而通常为13% 独自照顾。在完成筛选的患者中,37%患有PDM,5%患有T2 D,这代表病例“漏诊”, 机会性筛查虽然PDDP帮助缩小了总体筛查差距,并发现了 未诊断的精神障碍,对通用邀请的应答率在种族/民族亚组中相似 (西班牙裔42%; NH黑人41%; NH白人39%)和已知PDM与未知血糖状态的患者 (38% vs. 41%)。解决少数种族/族裔和患有糖尿病的人群中已知的筛查和结局差异 PDM,需要公平(不平等)筛选。在这一建议中,我们寻求改善PDDP的响应, 种族/族裔少数群体和已知患有PDM的人,以实现更公平的筛选。为了实现这一点, 我们将开发靶向(按人种/种族)、定制(按已知PDM与未知血糖状态)(TT) 筛选邀请(目标1),以增加高风险亚组的参与。然后我们将进行一个三臂 分簇随机对照试验(目标2),以评价PDDP提供的TT筛查外展+导航的疗效, 无应答者与PDDP提供的通用邀请,以提高高风险患者的筛选完成率 并评估TT PDDP和通用PDDP与常规PDDP相比提高筛查完成率的有效性 护理机会性筛查最后,我们将进行成本效益分析(目标3), 成本和每例筛选患者的成本以及三个研究组中发现的病例。总之,这些发现 将提供临床和成本效益高的方法,以缩小高风险人群的筛查差距, 患者由于PDDP是高度自动化和可扩展的,使用一个共同的EHR,我们的研究结果可以 在大多数卫生系统中实际执行。我们的发现将对临床和 寻求缩小T2 D筛查差距和减少筛查差异的卫生系统, 人群健康T2 D筛查策略,以补充常规护理中的机会性筛查。
英文摘要
PROJECT SUMMARY/ABSTRACT Type 2 diabetes (T2D) screening remains suboptimal in spite of well-recognized, national screening guidelines. In the US, 7.3 million adults with T2D and 74.5 with prediabetes (PDM) remain undiagnosed. In spite of opportunistic screening in clinical practice, nearly one-third of primary care patients have undiagnosed dysglycemia (PDM + T2D). To close screening gaps, new strategies are needed. We adapt evidence-based approaches from cancer screening to conceptualize T2D screening as a multi-step process (risk assessment, screening invitation, test ordering, and test completion) requiring coordination across patient, provider, and health system interfaces. We previously developed the Parkland Dysglycemia Detection Program (PDDP) – an EHR-based, multicomponent population health T2D screening intervention that automates risk assessment, bulk orders screening tests, and facilitates bulk patient outreach via screening invitations. The PDDP closes multiple gaps in the screening process and supplements opportunistic screening in clinical practice. In our PDDP pilot study, a single, generic ‘overdue for screening’ invitation had a 41% response rate vs. 13% in usual care alone. Of those completing screening, 37% had PDM and 5% had T2D, representing cases ‘missed’ by opportunistic screening alone. Although the PDDP helped close overall screening gaps and detected cases of undiagnosed dysglycemia, response rates to generic invitations were similar across racial/ethnic subgroups (Hispanics 42%; NH Blacks 41%; NH whites 39%) and those with known PDM vs. unknown glycemic status (38% vs. 41%). To address known screening and outcome disparities in racial/ethnic minorities and those with PDM, equitable (not equal) screening is needed. In this proposal, we seek to improve the PDDP response in racial/ethnic minorities and those with known PDM to achieve more equitable screening. To accomplish this, we will develop Targeted (by race/ethnicity), Tailored (by known PDM vs. unknown glycemic state) (TT) screening invitations (Aim 1) to increase engagement of high risk subgroups. We will then conduct a 3-arm split-cluster RCT (Aim 2) to evaluate the efficacy of PDDP-delivered TT screening outreach + navigation of non-responders vs. PDDP-delivered generic invitations to improve screening completion in high risk patients and evaluate the effectiveness of the TT PDDP and Generic PDDP to improve screening completion vs. usual care, opportunistic screening. Lastly, we will conduct cost-effectiveness analyses (Aim 3) to compare direct costs and the cost per patient screened and case found across the three study arms. Together, these findings will provide actionable evidence on clinical and cost-effective ways to close screening gaps in high-risk patients. Because the PDDP is highly automated and scalable using a common EHR, our findings can be practically implemented in most health systems. Our findings will have important implications for clinics and health systems seeking to close T2D screening gaps and decrease screening disparities through scalable, population-health T2D screening strategies to supplement opportunistic screening in usual care.
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Predicting Diabetes Risk Using Glucose Data
  • 批准号:
    9313248
  • 项目类别:
  • 资助金额:
    $17.54万
  • 财政年份:
    2014
  • 负责人:
    Michael Edward Bowen
  • 依托单位:
Predicting Diabetes Risk Using Glucose Data
  • 批准号:
    9091500
  • 项目类别:
  • 资助金额:
    $17.1万
  • 财政年份:
    2014
  • 负责人:
    Michael Edward Bowen
  • 依托单位:
海外基金