Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Aug 7;20(8):e10458.
doi: 10.2196/10458.

Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: Retrospective Analysis and Predictive Model

Affiliations
Free PMC article

Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: Retrospective Analysis and Predictive Model

Hadi Kharrazi et al. J Med Internet Res. .
Free PMC article

Abstract

Background: The Meaningful Use (MU) program has promoted electronic health record adoption among US hospitals. Studies have shown that electronic health record adoption has been slower than desired in certain types of hospitals; but generally, the overall adoption rate has increased among hospitals. However, these studies have neither evaluated the adoption of advanced functionalities of electronic health records (beyond MU) nor forecasted electronic health record maturation over an extended period in a holistic fashion. Additional research is needed to prospectively assess US hospitals' electronic health record technology adoption and advancement patterns.

Objective: This study forecasts the maturation of electronic health record functionality adoption among US hospitals through 2035.

Methods: The Healthcare Information and Management Systems Society (HIMSS) Analytics' Electronic Medical Record Adoption Model (EMRAM) dataset was used to track historic uptakes of various electronic health record functionalities considered critical to improving health care quality and efficiency in hospitals. The Bass model was used to predict the technological diffusion rates for repeated electronic health record adoptions where upgrades undergo rapid technological improvements. The forecast used EMRAM data from 2006 to 2014 to estimate adoption levels to the year 2035.

Results: In 2014, over 5400 hospitals completed HIMSS' annual EMRAM survey (86%+ of total US hospitals). In 2006, the majority of the US hospitals were in EMRAM Stages 0, 1, and 2. By 2014, most hospitals had achieved Stages 3, 4, and 5. The overall technology diffusion model (ie, the Bass model) reached an adjusted R-squared of .91. The final forecast depicted differing trends for each of the EMRAM stages. In 2006, the first year of observation, peaks of Stages 0 and 1 were shown as electronic health record adoption predates HIMSS' EMRAM. By 2007, Stage 2 reached its peak. Stage 3 reached its full height by 2011, while Stage 4 peaked by 2014. The first three stages created a graph that exhibits the expected "S-curve" for technology diffusion, with inflection point being the peak diffusion rate. This forecast indicates that Stage 5 should peak by 2019 and Stage 6 by 2026. Although this forecast extends to the year 2035, no peak was readily observed for Stage 7. Overall, most hospitals will achieve Stages 5, 6, or 7 of EMRAM by 2020; however, a considerable number of hospitals will not achieve Stage 7 by 2035.

Conclusions: We forecasted the adoption of electronic health record capabilities from a paper-based environment (Stage 0) to an environment where only electronic information is used to document and direct care delivery (Stage 7). According to our forecasts, the majority of hospitals will not reach Stage 7 until 2035, absent major policy changes or leaps in technological capabilities. These results indicate that US hospitals are decades away from fully implementing sophisticated decision support applications and interoperability functionalities in electronic health records as defined by EMRAM's Stage 7.

Keywords: Bass diffusion model; HIMSS EMRAM; United States; electronic health records; hospitals.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Historical Electronic Medical Record Adoption Model stages among US hospitals from 2006 to 2014.
Figure 2
Figure 2
Electronic health record functionality-level adoption among US hospitals using the Electronic Medical Record Adoption Model maturation stages (2014-2035 years are forecasted using the Bass model; vertical-axis represents the number of hospitals).
Figure 3
Figure 3
Cumulative electronic health record functionality-level adoption among US hospitals using the Electronic Medical Record Adoption Model maturation stages (2014-2035 years are forecasted using the Bass model; vertical-axis represents the cumulative number of hospitals).

Similar articles

See all similar articles

Cited by 13 articles

See all "Cited by" articles

References

    1. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006 May 16;144(10):742–752. - PubMed
    1. Thune T, Mina A. Hospitals as innovators in the health-care system: A literature review and research agenda. Research Policy. 2016 Oct;45(8):1545–1557. doi: 10.1016/j.respol.2016.03.010. - DOI
    1. Henry J, Pylypchuk Y, Searcy T, Patel V. The Office of the National Coordinator for Health Information Technology. 2016. May, [2018-03-20]. Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008-2015 https://dashboard.healthit.gov/evaluations/data-briefs/non-federal-acute-care-hospital-ehr-adoption-2008-2015.php .
    1. Geels FW, Schot J. Typology of sociotechnical transition pathways. Research Policy. 2007 Apr;36(3):399–417. doi: 10.1016/j.respol.2007.01.003. - DOI
    1. Blind K, Petersen SS, Riillo CA. The impact of standards and regulation on innovation in uncertain markets. Research Policy. 2017 Feb;46(1):249–264. doi: 10.1016/j.respol.2016.11.003. - DOI

Publication types

Feedback