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CC BY-NC-ND 4.0 · Appl Clin Inform 2022; 13(03): 665-676
DOI: 10.1055/s-0042-1750415
Research Article

Higher Electronic Health Record Functionality Is Associated with Lower Operating Costs in Urban—but Not Rural—Hospitals

Claudia A. Rhoades
1   Department of Agricultural Economics, Oklahoma State University, Stillwater, Oklahoma, United States
,
Brian E. Whitacre
1   Department of Agricultural Economics, Oklahoma State University, Stillwater, Oklahoma, United States
,
Alison F. Davis
2   Department of Agricultural Economics, University of Kentucky, Lexington, Kentucky, United States
› Institutsangaben Funding This study was supported by the Federal Office of Rural Health Policy (FORHP), Health Resources and Services Administration (HRSA), U.S. Department of Health and Human Services (HHS) under cooperative agreement # U1ZRH33331–02–01. The information, conclusions, and opinions expressed in this article are those of the authors and no endorsement by FORHP, HRSA, HHS, Oklahoma State University or University of Kentucky is intended or should be inferred.
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Abstract

Objectives The aim of the study is to examine the relationship between electronic health record (EHR) use/functionality and hospital operating costs (divided into five subcategories), and to compare the results across rural and urban facilities.

Methods We match hospital-level data on EHR use/functionality with operating costs and facility characteristics to perform linear regressions with hospital- and time-fixed effects on a panel of 1,596 U.S. hospitals observed annually from 2016 to 2019. Our dependent variables are the logs of the various hospital operating cost categories, and alternative metrics for EHR use/functionality serve as the primary independent variables of interest. Data on EHR use/functionality are retrieved from the American Hospital Association's (AHA) Annual Survey of Hospitals Information Technology (IT) Supplement, and hospital operating cost and characteristic data are retrieved from the American Hospital Directory. We include only hospitals classified as “general medical and surgical,” removing specialty hospitals.

Results Our results suggest, first, that increasing levels of EHR functionality are associated with hospital operating cost reductions. Second, that these significant cost reductions are exclusively seen in urban hospitals, with the associated coefficient suggesting cost savings of 0.14% for each additional EHR function. Third, that urban EHR-related cost reductions are driven by general/ancillary and outpatient costs. Finally, that a wide variety of EHR functions are associated with cost reductions for urban facilities, while no EHR function is associated with significant cost reductions in rural locations.

Conclusion Increasing EHR functionality is associated with significant hospital operating cost reductions in urban locations. These results do not hold across geographies, and policies to promote greater EHR functionality in rural hospitals will likely not lead to short-term cost reductions.

Keywords

electronic health records - hospital costs - rural hospitals - urban hospitals

Protection of Human and Animal Subjects

This research included only secondary analysis of existing hospital-level data that did not include any living individuals or any identifiable private information. As such, no Institutional Review Board (IRB) oversight was required.


Data Accessibility

The data that support the findings of this study are available from the American Hospital Association's (AHA) Information Technology (IT) supplement and the American Hospital Directory (AHD), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.


Author Contributions

C.A.R. aggregated the relevant data, performed the regression analysis, and led the writing of the manuscript. B.E.W. helped with data gathering, led the development of the econometric approach, reviewed the regression results, and contributed to the manuscript writing. A.F.D. contributed to the manuscript writing and provided funding for C.A.R.'s assistantship and the relevant data purchases. All listed authors in this manuscript have approved this final version.


Supplementary Material



Publikationsverlauf

Eingereicht: 27. Januar 2022

Angenommen: 30. April 2022

Artikel online veröffentlicht:
27. Juli 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 PricewaterhouseCoopers. L. L. P. The price of excess: Identifying waste in healthcare spending. Health Research Institute; 2008
    Google Scholar
  • 2 Berwick DM, Hackbarth AD. Eliminating waste in US health care. JAMA 2012; 307 (14) 1513-1516
    CrossrefPubMedGoogle Scholar
  • 3 Topchik M, Gross K, Pinette M. et al. The rural health safety net under pressure: rural hospital vulnerability. Chartis Center for Rural Health; 2020 Accessed February 10, 2021 at: https://www.chartis.com/forum/insight/the-rural-health-safety-net-under-pressure-rural-hospital-vulnerability/
    PubMedGoogle Scholar
  • 4 Kaufman BG, Thomas SR, Randolph RK. et al. The rising rate of rural hospital closures. J Rural Health 2016; 32 (01) 35-43
    CrossrefPubMedGoogle Scholar
  • 5 Thomas SR, Holmes GM, Pink GH. To what extent do community characteristics explain differences in closure among financially distressed rural hospitals?. J Health Care Poor Underserved 2016; 27 (4A): 194-203
    CrossrefPubMedGoogle Scholar
  • 6 Hsia RY, Kellermann AL, Shen YC. Factors associated with closures of emergency departments in the United States. JAMA 2011; 305 (19) 1978-1985
    PubMedGoogle Scholar
  • 7 Wang SJ, Middleton B, Prosser LA. et al. A cost-benefit analysis of electronic medical records in primary care. Am J Med 2003; 114 (05) 397-403
    CrossrefPubMedGoogle Scholar
  • 8 Ford EW, Menachemi N, Phillips MT. Predicting the adoption of electronic health records by physicians: when will health care be paperless?. J Am Med Inform Assoc 2006; 13 (01) 106-112
    CrossrefPubMedGoogle Scholar
  • 9 Payne TH, Bates DW, Berner ES. et al. Healthcare information technology and economics. J Am Med Inform Assoc 2013; 20 (02) 212-217
    CrossrefPubMedGoogle Scholar
  • 10 Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform 2008; 77 (05) 291-304
    CrossrefPubMedGoogle Scholar
  • 11 Girosi F, Meili R, Scoville R. Extrapolating evidence of health information technology savings and costs. RAND Corporation; 2005 Accessed March 21, 2021 at: https://www.rand.org/pubs/monographs/MG410.html
    PubMedGoogle Scholar
  • 12 Borzekowski R. Measuring the cost impact of hospital information systems: 1987-1994. J Health Econ 2009; 28 (05) 938-949
    CrossrefPubMedGoogle Scholar
  • 13 Hillestad R, Bigelow J, Bower A. et al. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff (Millwood) 2005; 24 (05) 1103-1117
    CrossrefPubMedGoogle Scholar
  • 14 Walker J, Pan E, Johnston D. et al. The value of health care information exchange and interoperability: there is a business case to be made for spending money on a fully standardized nationwide system. Health Aff (Millwood) 2005; 24 (01) 5-10
    PubMedGoogle Scholar
  • 15 Sidorov J. It ain't necessarily so: the electronic health record and the unlikely prospect of reducing health care costs. Health Aff (Millwood) 2006; 25 (04) 1079-1085
    CrossrefPubMedGoogle Scholar
  • 16 Furukawa MF, Raghu TS, Shao BB. Electronic medical records, nurse staffing, and nurse-sensitive patient outcomes: evidence from California hospitals, 1998-2007. Health Serv Res 2010; 45 (04) 941-962
    CrossrefPubMedGoogle Scholar
  • 17 Teufel II RJ, Kazley AS, Ebeling MD, Basco Jr WT. Hospital electronic medical record use and cost of inpatient pediatric care. Acad Pediatr 2012; 12 (05) 429-435
    CrossrefPubMedGoogle Scholar
  • 18 Atasoy H, Chen PY, Ganju K. The spillover effects of health IT investments on regional healthcare costs. Manage Sci 2018; 64 (06) 2515-2534
    CrossrefPubMedGoogle Scholar
  • 19 Amarasingham R, Plantinga L, Diener-West M, Gaskin DJ, Powe NR. Clinical information technologies and inpatient outcomes: a multiple hospital study. Arch Intern Med 2009; 169 (02) 108-114
    CrossrefPubMedGoogle Scholar
  • 20 Himmelstein DU, Wright A, Woolhandler S. Hospital computing and the costs and quality of care: a national study. Am J Med 2010; 123 (01) 40-46
    CrossrefPubMedGoogle Scholar
  • 21 Agha L. The effects of health information technology on the costs and quality of medical care. J Health Econ 2014; 34: 19-30
    CrossrefPubMedGoogle Scholar
  • 22 Dranove D, Forman C, Goldfarb A. et al. The trillion dollar conundrum: complementarities and health information technology. Am Econ J Econ Policy 2014; 6: 239-270
    CrossrefPubMedGoogle Scholar
  • 23 Jones SS, Rudin RS, Perry T, Shekelle PG. Health information technology: an updated systematic review with a focus on meaningful use. Ann Intern Med 2014; 160 (01) 48-54
    CrossrefPubMedGoogle Scholar
  • 24 Highfill T. Do hospitals with electronic health records have lower costs? A systematic review and meta-analysis. Int J Healthc Manag 2019; 13 (01) 65-71
    CrossrefPubMedGoogle Scholar
  • 25 Craven CK, Sievert MC, Hicks LL, Alexander GL, Hearne LB, Holmes JH. CAH to CAH: EHR implementation advice to critical access hospitals from peer experts and other key informants. Appl Clin Inform 2014; 5 (01) 92-117
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 26 Houser SH, Au D, Weech-Maldonado R. The impact of geography on hospital electronic health records implementation in Alabama: implications for meaningful use. Appl Clin Inform 2011; 2 (03) 270-283
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 27 Puccinelli-Ortega N, Cromo M, Foley KL. et al. Facilitators and barriers to implementing a digital informed decision making tool in primary care: a qualitative study. Appl Clin Inform 2022; 13 (01) 1-9
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 28 Cromwell J, Mitchell JB, Calore KA, Iezzoni L. Sources of hospital cost variation by urban-rural location. Med Care 1987; 25 (09) 801-829
    CrossrefPubMedGoogle Scholar
  • 29 Coyne JS, Richards MT, Short R, Shultz K, Singh SG. Hospital cost and efficiency: do hospital size and ownership type really matter?. J Healthc Manag 2009; 54 (03) 163-174
    PubMedGoogle Scholar
  • 30 Assis-Hassid S, Grosz BJ, Zimlichman E, Rozenblum R, Bates DW. Assessing EHR use during hospital morning rounds: a multi-faceted study. PLoS One 2019; 14 (02) e0212816
    CrossrefPubMedGoogle Scholar
  • 31 Schopf TR, Nedrebø B, Hufthammer KO, Daphu IK, Lærum H. How well is the electronic health record supporting the clinical tasks of hospital physicians? A survey of physicians at three Norwegian hospitals. BMC Health Serv Res 2019; 19 (01) 934
    CrossrefPubMedGoogle Scholar
  • 32 Silow-Carroll S, Edwards JN, Rodin D. Using electronic health records to improve quality and efficiency: the experiences of leading hospitals. Issue Brief (Commonw Fund) 2012; 17 (01) 1-40
    PubMedGoogle Scholar
  • 33 Charles D, Gabriel M, Furukawa MF. Adoption of electronic health record systems among US non-federal acute care hospitals: 2008–2012. ONC Data Brief 2013; 9: 1-9
    PubMedGoogle Scholar
  • 34 Parasrampuria S, Henry J. Hospitals' use of electronic health records data, 2015–2017. ONC Data Brief; 2019
    Google Scholar
  • 35 Adler-Milstein J, DesRoches CM, Furukawa MF. et al. More than half of US hospitals have at least a basic EHR, but stage 2 criteria remain challenging for most. Health Aff (Millwood) 2014; 33 (09) 1664-1671
    CrossrefPubMedGoogle Scholar
  • 36 Walker DM, Diana ML. Hospital adoption of health information technology to support public health infrastructure. J Public Health Manag Pract 2016; 22 (02) 175-181
    CrossrefPubMedGoogle Scholar
  • 37 Adler-Milstein J, Holmgren AJ, Kralovec P, Worzala C, Searcy T, Patel V. Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide. J Am Med Inform Assoc 2017; 24 (06) 1142-1148
    CrossrefPubMedGoogle Scholar
  • 38 Brewster AL, Lee S, Curry LA, Bradley EH. Association between community social capital and hospital readmission rates. Popul Health Manag 2019; 22 (01) 40-47
    CrossrefPubMedGoogle Scholar
  • 39 Sharma R, Zachrison KS, Viswanathan A. et al. Trends in telestroke care delivery: a 15-year experience of an academic hub and its network of spokes. Circ Cardiovasc Qual Outcomes 2020; 13 (03) e005903
    CrossrefPubMedGoogle Scholar
  • 40 Health Resources and Services Administration. FORHP Eligible ZIP Codes. Federal Office of Rural Health Policy Data Files. 2020 Accessed January 15, 2021 at: https://www.hrsa.gov/rural-health/about-us/definition/datafiles.html
    PubMedGoogle Scholar
  • 41 American Hospital Directory. Department Definitions and Methodology: Cost Center Statistics. 2021 Accessed July 14, 2021 at: https://www.ahd.com/definitions/dept_rcc.html
    PubMedGoogle Scholar
  • 42 American Hospital Directory. Department Definitions and Methodology: General Service Costs. 2021 Accessed July 14, 2021 at: https://www.ahd.com/definitions/dept_gen.html
    PubMedGoogle Scholar
  • 43 American Hospital Directory. Definitions and Methodology: Hospital Statistics. 2021 Accessed July 14, 2021 at: https://www.ahd.com/definitions/statistics.html
    PubMedGoogle Scholar
  • 44 Watts CA, Klastorin TD. The impact of case mix on hospital cost: a comparative analysis. Inquiry 1980; 17 (04) 357-367
    PubMedGoogle Scholar
  • 45 Hay JW. Hospital cost drivers: an evaluation of 1998-2001 state-level data. Am J Manag Care 2003; 9 (Spec No 1, SP1): SP13-SP24
    PubMedGoogle Scholar
  • 46 Wu VY, Shen YC, Yun MS, Melnick G. Decomposition of the drivers of the U.S. hospital spending growth, 2001-2009. BMC Health Serv Res 2014; 14 (01) 230
    CrossrefPubMedGoogle Scholar
  • 47 Jann B. NLCHECK: Stata Module to Check Linearity Assumption after Model Estimation. Statistical Software Components S456968.. Boston College of Economics; 2008
    Google Scholar
  • 48 American Hospital Association. Rural Report: Challenges Facing Rural communities and the Roadmap to Ensure Local Access to High-quality, Affordable Care. 2019 Accessed May 25, 2021 at: https://www.aha.org/system/files/2019-02/rural-report-2019.pdf
    PubMedGoogle Scholar
  • 49 Everson J, Rubin JC, Friedman CP. Reconsidering hospital EHR adoption at the dawn of HITECH: implications of the reported 9% adoption of a “basic” EHR. J Am Med Inform Assoc 2020; 27 (08) 1198-1205
    CrossrefPubMedGoogle Scholar
  • 50 Centers for Medicare and Medicaid Services. Promoting Interoperability Programs. 2021 Accessed January 20, 2021 at: https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms
    PubMedGoogle Scholar
  • 51 Greenberg AJ, Haney D, Blake KD, Moser RP, Hesse BW. Differences in access to and use of electronic personal health information between rural and urban residents in the United States. J Rural Health 2018; 34 (Suppl. 01) s30-s38
    CrossrefPubMedGoogle Scholar
  • 52 Economic Research Service (ERS). Rural Urban Continuum Codes. 2022 Accessed March 16, 2022 at: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx
    PubMedGoogle Scholar