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

• Published in: Journal of medical Internet research 2018-08, Vol.20 (8), p.e10458-e10458
• Main Authors: Kharrazi, Hadi, Gonzalez, Claudia P, Lowe, Kevin B, Huerta, Timothy R, Ford, Eric W
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Archives & records; Computational linguistics; Computerized medical records; Diffusion models; Drug administration; Electronic health records; Electronic Health Records - trends; Electronic records; First year; Forecasting; Forecasts and trends; Health care industry; Health care reform; Health records; Hospitals; Hospitals - trends; Humans; Information technology; Innovations; Laboratories; Language processing; Maturation; Medical care; Medical records; Medical technology; Multimedia; Natural language interfaces; Original Paper; Pharmacy; Policy making; Prediction models; Quality management; Quality of care; Quality of Health Care - standards; Retrospective Studies; Software; Technology adoption; United States
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/10458

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. This study forecasts the maturation of electronic health record functionality adoption among US hospitals through 2035. 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. 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. We forecasted the adoption of electron