The Impact of Market Factors on Meaningful Use of Electronic Health Records Among Primary Care Providers: Evidence From Florida Using Resource Dependence Theory and Information Uncertainty Perspective.
The Impact of Market Factors on Meaningful Use of Electronic Health Records Among Primary Care Providers: Evidence From Florida Using Resource Dependence Theory and Information Uncertainty Perspective.
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市场因素对初级保健提供者有意义地使用电子健康记录的影响：来自佛罗里达州的证据，使用资源依赖理论和信息不确定性的角度。
DOI：
10.1097/mlr.0000000000001980
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发表时间：
2024
期刊：
影响因子：
3
通讯作者：
Alexandre,Kessie
中科院分区：
文献类型：
--
作者：
Alexandre,PierreK;Monestime,JudithP;Alexandre,Kessie
背景：利用 2009 年《经济和临床健康健康信息技术法案》中的联邦资金，医疗保险和医疗补助服务中心资助了全国范围内的 2011-2021 年医疗补助电子健康记录 (EHR) 激励计划。 目标：通过采用、改进或改进初级保健提供者 (PCP) 加入佛罗里达州 EHR 激励计划后，确定与 EHR 的有意义使用 (MU) 相关的市场因素升级 (AIU) 电子病历技术。研究设计：使用 8464 名医疗补助提供者的 2011-2018 年计划记录进行回顾性队列研究。主要结果：第一年激励后的 MU 成就。自变量：使用资源依赖理论和信息不确定性视角生成关键自变量，包括县的农村性、教育程度、贫困、健康维护组织渗透率和人均 PCP 数量。分析方法：将所有县率折算为高、中、低三分位数对应的 3 个二分法。计算描述性统计和双变量统计。使用广义分层线性模型是因为 MU 数据在县级（第 2 级）进行聚类并在实践级别（第 1 级）进行测量。结果：总体而言，41.9% 的佛罗里达州医疗补助提供者在获得第一年激励后实现了 MU。农村地区与 MU 呈正相关（P<0.001）。当我们比较贫困率（P = 0.002）、健康维护组织普及率（P = 0.02）和人均 PCP 数量（P = 0.01）的“高”三分位数和“低”三分位数时，发现 MU 成就存在显着差异。这些关系是负面的。结论：政策制定者和医疗保健管理者不应忽视市场因素在 EHR 采用中的贡献。
Background:Using federal funds from the 2009 Health Information Technology for Economic and Clinical Health Act, the Centers for Medicare and Medicaid Services funded the 2011–2021 Medicaid electronic health record (EHR) incentive programs throughout the country.Objective:Identify the market factors associated with Meaningful Use (MU) of EHRs after primary care providers (PCPs) enrolled in the Florida—EHR incentives program through Adopting, Improving, or Upgrading (AIU) an EHR technology.Research Design:Retrospective cohort study using 2011–2018 program records for 8464 Medicaid providers.Main Outcome:MU achievement after first-year incentives.Independent Variables:The resource dependence theory and the information uncertainty perspective were used to generate key-independent variables, including the county’s rurality, educational attainment, poverty, health maintenance organization penetration, and number of PCPs per capita.Analytical Approach:All the county rates were converted into 3 dichotomous measures corresponding to high, medium, and low terciles. Descriptive and bivariate statistics were calculated. A generalized hierarchical linear model was used because MU data were clustered at the county level (level 2) and measured at the practice level (level 1).Results:Overall, 41.9% of Florida Medicaid providers achieved MU after receiving first-year incentives. Rurality was positively associated with MU (P< 0.001). Significant differences in MU achievements were obtained when we compared the “high” terciles with the “low” terciles for poverty rates (P= 0.002), health maintenance organization penetration rates (P= 0.02), and number of PCPs per capita (P= 0.01). These relationships were negative.Conclusions:Policy makers and health care managers should not ignore the contribution of market factors in EHR adoption.