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

Clin Orthop Relat Res

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 Sc

Published: November 2019


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6903845PMC
http://dx.doi.org/10.1097/CORR.0000000000000896DOI Listing

Publication Analysis

Top Keywords

ehr adoption
36
adoption
16
adoption associated
16
ehr
14
2012 2016
12
difference increase
12
surgical volume
12
orthopaedic surgeons
12
0
9
electronic health
8

Similar Publications

Using precision analytics approaches with population health data helps identify localized patterns of social determinants and comorbidities, supporting the design of tailored interventions. The University of Kentucky College of Public Health (UKCPH) and UK King's Daughters (UKKD) have partnered to create a Precision Public Health Alliance (PPHA) applying precision analytics to UKKD electronic health records (EHR) as well as secondary datasets to map social, demographic, and clinical comorbidity factors onto colorectal cancer (CRC) screening data in UKKD's rural service area (the northeastern Kentucky counties of Boyd, Carter, Greenup, and Lawrence and southeast Ohio county of Lawrence). In addition to UKKD and UKCPH clinicians and researchers, PPHA includes a community-based Action Team of local social services, behavioral health, and public health agencies and Cooperative Extension agents responsible for translating findings into quality improvement priorities.

View Article and Find Full Text PDF

Challenges and facilitators of electronic health record implementation: a scoping review.

Int J Med Inform

September 2025

Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran.

Background And Objective: The rapid advancement of technology has made eHealth a vital part of modern healthcare. Electronic Health Records (EHRs), as core tools of eHealth, enhance care quality, enable access to medical data, and improve coordination among healthcare providers. Implementing EHRs successfully requires understanding the challenges and facilitators involved to inform effective policymaking and management.

View Article and Find Full Text PDF

Medication reconciliation was adopted as a National Patient Safety Goal by the Joint Commission in 2005 and is now standard practice across care settings. More recently, the concept of medication optimization has gained attention, recognizing that safe medication use requires more than reconciliation alone. Home healthcare (HHC) is one setting with a critical need for medication optimization.

View Article and Find Full Text PDF

The Impact of Team-Based Ordering Workflows on Ambulatory Physician EHR Time, Order Volume, and Visit Volume.

Health Serv Res

September 2025

Division of Clinical Informatics and Digital Transformation, Director, Center for Clinical Informatics and Improvement Research, University of California - San Francisco, San Francisco, CA, San Francisco, California, USA.

Objective: To analyze national rates of team-based ordering and evaluate changes in key outcomes following adoption.

Study Setting And Design: We conducted an observational pre-post intervention-comparison study of 249,463 ambulatory physicians across 401 organizations using the Epic EHR. Our intervention was the adoption of team-based ordering, measured as the proportion of orders involving team support.

View Article and Find Full Text PDF

Background: The US healthcare system is currently facing significant challenges in quality, affordability, and labor shortages. Artificial intelligence (AI) promises to transform healthcare delivery by making it safer, more effective, less wasteful, and more patient-centered. With more than $30 billion invested in healthcare AI companies in the past three years, the proliferation of AI solutions is expected to bring much-needed relief to the strained healthcare industry.

View Article and Find Full Text PDF