JMIR Publications

ABSTRACT

Background:

The rate of adoption of electronic medical record (EMR) systems has risen internationally, and new EMR adoption is currently a major topic in Japan. However, no study has performed a detailed analysis of longitudinal data to evaluate the changes in the EMR adoption status over time.

Objective:

To evaluate the changes over time in the EMR adoption status in hospitals versus clinics in Japan, and to evaluate the facility and regional factors associated with these changes.

Methods:

Secondary longitudinal data were created by matching data in fiscal year (FY) 2011 and FY 2014 using reference numbers. EMR adoption status was defined as “EMR adoption”, “specified adoption schedule”, or “no adoption schedule”. Data were obtained for: (1) hospitals (n=4,410) and clinics (n=67,329) that had no adoption schedule in FY 2011; (2) hospitals (n=1,068) and clinics (n=3,132) with a specified adoption schedule in FY 2011. The EMR adoption statuses of medical institutions in FY 2014 were also examined. A multinomial logistic model was used to investigate the associations between EMR adoption status in FY 2014 and facility and regional factors in FY 2011. Considering the regional variations of these models, multilevel analyses with second levels were conducted. These models were constructed separately for hospitals and clinics, resulting in four multinomial logistic models. The odds ratio (OR) and 95% Bayesian credible interval (CI) were estimated for each variable.

Results:

6.9% of hospitals and 14.82% of clinics with no EMR adoption schedules in FY 2011 had adopted EMR by FY 2014, while 10.49% of hospitals and 33.65% of clinics with specified adoption schedules in FY 2011 had cancelled the scheduled adoption by FY 2014. For hospitals with no adoption schedules in FY 2011, EMR adoption/scheduled adoption were associated with practice size characteristics, such as number of outpatients (OR(95% CI) of quantile 4 to quantile 1, 1.67(1.005–2.84)/2.40(1.80–3.21)), and number of doctors (OR(95% CI) of quantile 4 to quantile 1, 4.20(2.39–7.31)/2.02(1.52–2.64)). For clinics with specified EMR adoption schedules in FY 2011, the factors negatively associated with EMR adoption/cancellation of scheduled EMR adoption were the presence of beds (OR(95% CI), 0.57(0.45–0.72)/0.74(0.58–0.96)) and having a private establisher (OR(95% CI), 0.27(0.13–0.55)/0.43(0.19–0.91)). No regional factors were significantly associated with the EMR adoption status of hospitals with no EMR adoption schedules; population density was positively associated with EMR adoption in clinics with no EMR adoption schedule (OR(95% CI) of quantile 4 to quantile 1, 1.49(1.32–1.69)).

Conclusions:

Different approaches are needed to promote new adoption of EMR systems in hospitals versus clinics. It is important to induce decision-making in small- and medium-sized hospitals, while regional post-decision technical support is important to avoid the cancellation of scheduled EMR adoption in clinics.