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电子健康记录如何与矫形外科医生的生产力和计费相关联?

How are Electronic Health Records Associated with Provider Productivity and Billing in Orthopaedic Surgery?

机构信息

N. Dandu, Drexel University College of Medicine, Philadelphia, PA, USA B. Zmistowski, T. Chapman, Rothman Institute, Sidney Kimmel College of Medicine at Thomas Jefferson University, Philadelphia, PA, USA A. F. Chen, Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA M. Howley, Lebow College of Business at Drexel University, Philadelphia, PA, USA.

出版信息

Clin Orthop Relat Res. 2019 Nov;477(11):2443-2451. doi: 10.1097/CORR.0000000000000896.

Abstract

BACKGROUND

Electronic health records (EHRs) have become ubiquitous in orthopaedics. Although they offer certain benefits, they have been cited as a factor that can contribute to provider burnout. Little is known about the degree to which EHR adoption is associated with provider and practice characteristics or outpatient and surgical volume.

QUESTIONS/PURPOSES: (1) What was the rate of EHR adoption in orthopaedics and how are physician and practice characteristics associated with adoption? (2) How is EHR adoption related to outpatient productivity? (3) How is EHR adoption associated with surgical volume?

METHODS

We conducted this retrospective analysis by linking three publicly available Medicare databases, which we chose for their reliability in reporting because they are provided by a government-funded entity. We included providers in the 2016 Physician Compare dataset who reported a primary specialty of orthopaedic surgery. The EHR adoption status for these providers between 2011 and 2016 was determined using the Meaningful Use Eligible Professional public use files, which we chose to standardize both adoption and usage of EHRs. Provider characteristics, from the Physician Compare dataset, were compared between non-adopters, early adopters (who adopted EHR in 2011 and 2012), and late adopters (2016) using a multivariate logistic analysis, due to the binary nature of the dependent variable (adoption). To measure productivity and billing, we used the 2012 and 2016 Medicare Utilization and Payment datasets. To measure productivity before and after EHR adoption, we compared the number of services for select Current Procedural Terminology codes between 2012 and 2016 for providers who first adopted EHR in 2013, and performed the same comparison for non-adopters for the same years. Paired t-tests were used where volume in 2012 and 2016 were being compared, and multivariate analysis was performed.

RESULTS

By 2016, 10,904 of 21,484 orthopaedic providers (51%) had adopted EHRs, with an increase from 8% to 46% during the incentive phase (2011 to 2014) and an increase from 44% to 51% during the penalty phase (2015 to 2016). After analyzing factors associated with adoption, it was most notable that for every additional year since graduation, the odds of adopting EHR later increased by 4.14 (95% confidence interval 4.00 to 4.33; p < 0.001). After adoption, providers who adopted EHRs increased the mean number of Medicare outpatient visits per year from 439 to 470 (mean difference, increase of 31 procedures [95% CI 24 to 39]; p < 0.001), and providers who did not use EHRs decreased from 378 to 368 visits per year (median difference, decrease of 10 procedures [95% CI 8.0 to 12.0]; p < 0.001). EHR was not associated with billing for Level 4-5 visits, after adjusting for practice size and pre-adoption volumes (p = 0.32; R = 0.51). EHR adoption was not associated with surgical volume for 10 of 11 common orthopaedic procedures. However, two additional TKA procedures annually could be attributed to EHR adoption, when compared with non-adopters (p = 0.03; R = 0.65). After adoption, orthopaedic surgeons increased their annual TKA volume from 42 to 48 (mean difference, increase of 6 [95% CI 4.0 to 7.0]; p < 0.001), while non-adopting orthopaedic surgeons increased their annual surgical volume for TKA from 28 to 30 (median difference, increase of 2 [95% CI 2.0 to 4.0]; p < 0.001).

CONCLUSIONS

In orthopaedics, the Health Information Technology for Economic and Clinical Health (HITECH) Act resulted in approximately half of self-reported orthopaedic surgeons adopting EHR from 2011 to 2016. Considering the high cost of most EHRs and the substantial investment in adoption incentives, this adoption rate may not be sufficient to fully realize the objectives of the HITECH Act. Diffusion of technology is a vast field of study within social theory. Prominent sociologist Everett M. Rogers details its complexity in Diffusion of Innovations. Diffusion of technology is impacted by factors such as the possibility to sample the innovation without commitment, opinion leadership, and observability of results in a peer network, to name a few. Incorporating these principles, where appropriate, into a more focused action plan may facilitate technological diffusion for future innovations. Lastly, EHR adoption was not associated with higher-level billing or surgical volume. This might suggest that EHRs have not had a meaningful clinical benefit, but this needs to be further investigated by relating these trends to patient outcomes or other quality measures.

LEVEL OF EVIDENCE

Level III, therapeutic study.

摘要

背景

电子健康记录 (EHR) 在骨科中已广泛应用。尽管它们具有某些优势,但据报道,它们是导致提供者倦怠的一个因素。对于 EHR 采用与提供者和实践特征或门诊和手术量之间的关系,我们知之甚少。

问题/目的:(1) 骨科中 EHR 的采用率是多少,医师和实践特征与采用情况有何关联?(2) EHR 采用与门诊生产力有何关系?(3) EHR 采用与手术量有何关联?

方法

我们通过链接三个公开可用的 Medicare 数据库进行了这项回顾性分析,我们选择这些数据库是因为它们在报告方面的可靠性,因为它们是由政府资助的实体提供的。我们纳入了 2016 年 Physician Compare 数据集中报告主要专业为矫形外科的提供者。通过使用有意义的使用合格专业人员公共使用文件确定这些提供者在 2011 年至 2016 年期间的 EHR 采用情况,我们选择了标准化 EHR 的采用和使用。由于因变量(采用)的二元性质,使用多元逻辑分析比较了非采用者、早期采用者(2011 年和 2012 年采用 EHR)和晚期采用者(2016 年)之间来自 Physician Compare 数据集的提供者特征。为了衡量生产力和计费,我们使用了 2012 年和 2016 年 Medicare 利用率和支付数据集。为了比较采用 EHR 前后的生产力,我们比较了 2013 年首次采用 EHR 的提供者在 2012 年和 2016 年之间选择的特定当前程序术语 (CPT) 代码的服务数量,并且对于同一年份的非采用者进行了相同的比较。如果比较 2012 年和 2016 年的数量,则使用配对 t 检验,并且进行了多变量分析。

结果

到 2016 年,21484 名骨科提供者中的 10904 名(51%)采用了 EHR,激励阶段(2011 年至 2014 年)采用率从 8%增加到 46%,惩罚阶段(2015 年至 2016 年)采用率从 44%增加到 51%。在分析与采用相关的因素后,值得注意的是,自毕业以来,每年增加一年,采用 EHR 的几率就会增加 4.14(95%置信区间 4.00 至 4.33;p <0.001)。采用 EHR 后,采用 EHR 的提供者每年的 Medicare 门诊就诊次数从 439 次增加到 470 次(平均差异,增加 31 次就诊[95%CI 24 至 39];p <0.001),而未使用 EHR 的提供者每年就诊次数从 378 次减少到 368 次(中位数差异,减少 10 次就诊[95%CI 8.0 至 12.0];p <0.001)。在调整了实践规模和采用前的就诊量后,EHR 与第 4-5 级就诊的计费无关(p = 0.32;R = 0.51)。EHR 与 11 种常见骨科手术中的 10 种手术量无关。然而,与非采用者相比,每年可归因于 EHR 采用的额外 2 次 TKA 手术(p = 0.03;R = 0.65)。采用 EHR 后,骨科医生每年的 TKA 手术量从 42 次增加到 48 次(平均差异,增加 6 次[95%CI 4.0 至 7.0];p <0.001),而非采用 EHR 的骨科医生每年的 TKA 手术量从 28 次增加到 30 次(中位数差异,增加 2 次[95%CI 2.0 至 4.0];p <0.001)。

结论

在骨科中,健康信息技术促进经济和临床健康法案(HITECH)导致自 2011 年至 2016 年,约有一半的自我报告矫形外科医生采用了 EHR。考虑到大多数 EHR 的高昂成本和采用激励措施的大量投资,这种采用率可能不足以充分实现 HITECH 法案的目标。技术扩散是社会理论中一个广泛的研究领域。杰出的社会学家 Everett M. Rogers 详细介绍了其复杂性在创新扩散中。技术扩散受到许多因素的影响,例如无需承诺即可试用创新、意见领袖以及同行网络中结果的可观察性,仅举几例。在更有针对性的行动计划中纳入这些原则,可能有助于未来创新的技术扩散。最后,EHR 采用与更高水平的计费或手术量无关。这可能表明 EHR 对临床没有明显的益处,但这需要通过将这些趋势与患者结果或其他质量指标联系起来进一步研究。

证据水平

III 级,治疗性研究。

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