Applying Positive Deviance Methods to Harness Optimal Practices for Effective Pain Management in Community Living Centers

应用正偏差方法来利用最佳实践,在社区生活中心进行有效的疼痛管理

基本信息

项目摘要

Background: Unrelieved pain is highly prevalent and devastating for Veterans in VA’s 134 Community Living Centers (CLCs). Imminent removal of pain as one of the CLC quality measures offers an opportunity, per VA’s Office of Geriatrics and Extended Care (GEC), to develop new, risk-adjusted measures that more accurately characterize CLC pain management. These measures can identify CLCs successful at pain management while minimizing biased underestimates for CLCs with the sickest residents. Then, by diving deeply into structures and processes of high performers, we can learn how to intervene. My background in gerontology, quantitative methods, and implementation science partially prepares me for this work. But I need additional training in risk adjustment, qualitative research, cutting-edge analytic methods, and intervention study designs for the study and my health services research career to succeed. Specific Aims: The proposed CDA simultaneously fills the considerable gaps in my background and provides VA with rigorous, actionable research on which to ground future quality improvement efforts. A social- ecological model frames the work. GEC commits to serving as an invested partner. I have 3 aims, which I will achieve with my mentors and training. 1. Evaluate how risk adjustment changes judgements of CLC pain management performance. 2. Use mixed methods to perform in-depth studies of CLCs with high outlying performance. 3. Adapt an existing, evidence-based intervention comprising lessons learned from “positive deviants.” Methods: Aim 1: Using VA administrative data of CLC residents, I will (1) calculate unadjusted pain measures, (2) apply risk adjustment, (3) assess the measures’ reliability and validity, and (4) identify high and low outlying performance on pain management. Aim 2: I will use quantitative (survey) and qualitative data from staff and residents at 5 top-performing CLCs, contrasted with qualitative data from 5 low-performing CLCs, to develop hypotheses of contextual factors and pain management practices unique to positive deviants. I will test causal relationships using configurational comparative analytic methods. Aim 3: I will adapt an existing nursing home pain management intervention for use in VA CLCs, using empirical evidence from Aim 2 about necessary conditions for optimal pain management. A modified e-Delphi panel of CLC stakeholders and pain management experts will provide feedback on the intervention package’s design. I will use a developmental formative evaluation of qualitative data from staff at 1 low-performing CLC to assess the intervention’s feasibility and acceptability, in preparation for rigorous testing in future work. Expected Results and Next Steps: I will provide GEC with interim deliverables to enable assessment of CLC pain management quality, guide CLC policy, and support clinical practice in CLCs struggling with pain management. Knowledge from this CDA will lead me to develop studies to refine risk adjustment methods for quality measurement in other critical areas and to rigorously evaluate, using a hybrid type II design, clinical effectiveness and implementation of the intervention. Significance & Relevance to Veterans’ Health: Coming at a critical juncture in my VA research career, this timely study responds to the VA priority of Greater Choice for Veterans, ORD’s priority to increase substantial real-world impact of VA research, and HSR&D’s Long-term Care and Opioid/Pain priority domains. Although pain is highly prevalent and debilitating for the 42,000 vulnerable Veterans CLCs serve, almost nothing is systematically known about CLCs’ pain management quality. And current pain measures are about to disappear. This study seizes this opportunity, developing nuanced, VA-specific approaches that are custom- made to reflect the accurate state of CLC pain management and help improve VA long-term care.
背景:在弗吉尼亚州的134个社区生活中,未缓解的疼痛对退伍军人来说是非常普遍和毁灭性的 中心(CLC)。作为CLC质量措施之一,立即消除疼痛提供了一个机会, 老年病和扩展护理办公室(GEC),以制定新的,风险调整的措施,更准确地 描述CLC疼痛管理。这些措施可以确定CLC成功的疼痛管理, 最大限度地减少对患病最严重的居民的CLC的偏倚低估。然后,通过深入研究建筑物 和高绩效者的过程中,我们可以学习如何干预。我的老年学背景,定量 方法和实施科学为我的这项工作做了部分准备。但我需要额外的风险训练 调整,定性研究,尖端的分析方法,和干预研究设计的研究 和我的健康服务研究事业取得成功 具体目标:拟议的综合发展分析同时填补了我的背景中的相当大的空白,并提供了 VA与严格的,可操作的研究,以地面未来的质量改进工作。一种社会- 生态模型框架的工作。GEC承诺作为投资合作伙伴。我有三个目标, 我的导师和训练。 1.评估风险调整如何改变对CLC疼痛管理表现的判断。 2.使用混合方法对具有高离群性能的CLC进行深入研究。 3.调整现有的、基于证据的干预措施,包括从“积极的偏差”中吸取的教训。 方法:目的1:使用CLC居民的VA管理数据,我将(1)计算未校正的疼痛测量值, (2)应用风险调整,(3)评估测量的信度和效度,(4)识别高和低离群值 疼痛管理的表现。目标2:我将使用工作人员提供的定量(调查)和定性数据, 5个表现最好的CLC的居民,与5个表现较低的CLC的定性数据相比, 情境因素和疼痛管理实践的假设独特的积极偏差。我会测试因果关系 关系使用配置比较分析方法。目标3:我将改造现有的养老院 疼痛管理干预用于VA CLC,使用来自目标2的经验证据, 最佳疼痛管理的条件。CLC利益相关者和痛苦的修改后的e-Delphi小组 管理专家将对干预方案的设计提供反馈意见。我会用一个发展的 对来自1个低绩效CLC工作人员的定性数据进行形成性评价,以评估干预措施的 可行性和可接受性,为今后工作中的严格测试做准备。 预期结果和后续步骤:我将向GEC提供中期可交付成果,以评估CLC 疼痛管理质量,指导CLC政策,并支持与疼痛作斗争的CLC的临床实践 管理本CDA的知识将引导我开展研究,以完善风险调整方法, 在其他关键领域的质量测量,并严格评估,使用混合II型设计, 干预的有效性和实施。 意义和退伍军人健康的相关性:在我的VA研究生涯的关键时刻到来,这 及时的研究响应了退伍军人的更大选择VA优先级,ORD的优先级,以增加实质性 VA研究的现实影响,以及HSR&D的长期护理和阿片类药物/疼痛优先领域。虽然 疼痛是非常普遍和衰弱的42,000脆弱的退伍军人CLC服务,几乎没有什么是 系统地了解CLC的疼痛管理质量。目前的疼痛测量方法 消失本研究抓住了这一机会,开发了细致入微的、针对VA的方法,这些方法是定制的, 以反映CLC疼痛管理的准确状态,并帮助改善VA长期护理。

项目成果

期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Integrating CFIR-ERIC and e-Delphi Methods to Increase Telegeriatrics Uptake.
整合 CFIR-ERIC 和 e-Delphi 方法以提高远程老年医学的采用率。
  • DOI:
    10.1093/geront/gnac107
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kernan,LauraM;Dryden,EileenM;Nearing,Kathryn;Kennedy,MeaghanA;Hung,Will;Moo,Lauren;Pimentel,CamillaB
  • 通讯作者:
    Pimentel,CamillaB
Perceived benefits of geriatric specialty telemedicine among rural patients and caregivers.
  • DOI:
    10.1111/1475-6773.14055
  • 发表时间:
    2023-02
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Dryden, Eileen M.;Kennedy, Meaghan A.;Conti, Jennifer;Boudreau, Jacqueline H.;Anwar, Chitra P.;Nearing, Kathryn;Pimentel, Camilla B.;Hung, William W.;Moo, Lauren R.
  • 通讯作者:
    Moo, Lauren R.
Response to Letter to the Editor: Account for Identity in URM Mentorship.
对致编辑的信的回复:URM 指导中的身份说明。
  • DOI:
    10.1007/s11606-021-07186-6
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    5.7
  • 作者:
    Quach,EmmaD;Pimentel,CamillaB;Hartmann,ChristineW
  • 通讯作者:
    Hartmann,ChristineW
The Development and Use of a New Visual Tool (REVISIT) to Support Participant Recall: Web-Based Interview Study Among Older Adults.
开发和使用新的视觉工具(REVISIT)来支持参与者回忆:基于网络的老年人访谈研究。
  • DOI:
    10.2196/52096
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    2.2
  • 作者:
    Dryden,EileenM;Anwar,Chitra;Conti,Jennifer;Boudreau,JacquelineH;Kennedy,MeaghanA;Hung,WilliamW;Nearing,KathrynA;Pimentel,CamillaB;Moo,Lauren
  • 通讯作者:
    Moo,Lauren
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Camilla Benedicto Pimentel其他文献

Camilla Benedicto Pimentel的其他文献

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{{ truncateString('Camilla Benedicto Pimentel', 18)}}的其他基金

Applying Positive Deviance Methods to Harness Optimal Practices for Effective Pain Management in Community Living Centers
应用正偏差方法来利用最佳实践,在社区生活中心进行有效的疼痛管理
  • 批准号:
    10443564
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:
Applying Positive Deviance Methods to Harness Optimal Practices for Effective Pain Management in Community Living Centers
应用正偏差方法来利用最佳实践,在社区生活中心进行有效的疼痛管理
  • 批准号:
    10178377
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:
Healthcare providers and public reporting of Community Living Center (CLC) quality: Investigating responses and opportunities for intervention through the PROACTIVE mixed-methods study
医疗保健提供者和社区生活中心 (CLC) 质量的公开报告:通过前瞻性混合方法研究调查干预措施的反应和机会
  • 批准号:
    10186470
  • 财政年份:
    2019
  • 资助金额:
    --
  • 项目类别:

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