Perioperative Nurses’ Perceptions Pre-Implementation of an Electronic Medical Record System

The use of electronic medical record (EMR) systems is transforming health care delivery in hospitals. Perioperative nurses work in a unique high-risk health setting, hence require specific considerations for EMR implementation. This research explored perioperative nurses’ perceptions of facilitators and barriers to the implementation of an EMR in their workplace to make context-specific recommendations about strategies to optimise EMR adoption. Using a qualitative exploratory descriptive design, focus group data were collected from 27 perioperative nurses across three hospital sites. Thematic analyses revealed three themes: 1) The world is going to change; 2) What does it mean for me? and 3) We can do it, but we have some reservations. Mapping coded data to the Theoretical Domains Framework identified prominent facilitators and barriers, and informed recommended implementation strategies for EMR adoption by perioperative nurses.

Older Nurses’ Perceptions of an Electronic Medical Record Implementation

In Australia, almost 40% of nurses are aged 50 years and older. These nurses may be vulnerable to leaving the workforce due to challenges experienced during electronic medical record (EMR) implementations. This research explored older nurses’ perceptions of factors expected to influence their adoption of an EMR, to inform recommendations to support implementation. The objectives were to: 1) measure psychological factors expected to influence older nurses’ adoption of the EMR; and 2) explore older nurses’ perceptions of facilitators and barriers to EMR adoption. An explanatory sequential mixed methods design was used to collect survey and focus group data from older nurses, prior to introducing an EMR system. These nurses were highly engaged with their work; 79.3% reported high wellbeing scores. However, their motivation appeared to be predominantly governed by external rather than internal influences. Themes reflecting barriers to EMR and resistance to adoption emerged in the qualitative data.

Barriers to the Adoption of Electronic Medical Record System in Ethiopia: A Systematic Review (Preprint)

BACKGROUND Electronic Medical Records (EMRs) have been an important tool in improving patient safety, improving the quality of health care, and increasing health efficiency. Various countries have gone through the local application of EMRs to various health care organizations in national implementation and integration of EMRs. Ethiopia lags far behind in this regard, as only a few hospitals have implemented EMR. OBJECTIVE This study aimed to identify barriers to the adoption of EMRs in Ethiopia through systematic literature reviews. METHODS We searched for relevant articles using three search engines (PubMed, Semantic Scholar, and Google Scholar). The search method focuses on peer-reviewed, empirical studies conducted in Ethiopia. The final set that met the inclusion criteria was nine studies. The authors extracted, analysed, and summarized empirical results related to EMR barriers in these studies. RESULTS This systematic review identified the following 19 barriers to EMR adoption: lack of EMR training, lack of access to computers, lack of computer literacy, lack of knowledge of EMR, lack of technical assistance, lack of EMR manual, negative attitude with EMR, limited internet access, lack of management support, electric power interruption, lack of perceived system quality, lack of perceived information quality, user resistance to change, the complexity of the system, performance expectancy, effort expectancy, social influence, lack of IT qualification, and lack of confidence with computer typing ability. CONCLUSIONS The most common barriers identified in the literature are: lack of EMR training, limited computer access, lack of computer literacy, lack of EMR knowledge, lack of technical support, and absence of an EMR manual. These six barriers alone contain 56.8% of the barriers reported in the literature. As this study summarizes the available evidence regarding barriers to the adoption of EMR in Ethiopia, future research will build on the current evidence and will focus on building an appropriate framework for EMR adoption in Ethiopia.

The interaction between high-level electronic medical record adoption and hospitalist staffing levels: A focus on value-based purchasing

Objective This study sought to understand the relationship of hospital performance with high-level electronic medical record (EMR) adoption, hospitalists staffing levels, and their potential interaction. Materials and methods We evaluated 2,699 non-federal, general acute hospitals using 2016 data merged from four data sources. We performed ordinal logistic regression of hospitals’ total performance score (TPS) on their EMR capability and hospitalists staffing level while controlling for other market- and individual-level characteristics. Results Hospitalists staffing level is shown to be positively correlated with TPS. High-level EMR adoption is associated with both short-term and long-term improvement on TPS. Large, urban, non-federal government hospitals, and academic medical centers tend to have lower TPS compared to their respective counterparts. Hospitals belonging to medium- or large-sized healthcare systems have lower TPS. Higher registered nurse (RN) staffing level is associated with higher TPS, while higher percentage of Medicare or Medicaid share of inpatient days is associated with lower TPS. Discussion Although the main effects of hospitalists staffing level and EMR capability are significant, their interaction is not, suggesting that hospitalists and EMR act through separate mechanisms to help hospitals achieve better performance. When hospitals are not able to invest on both simultaneously, given financial constraints, they can still reap the full benefits from each. Conclusion Hospitalists staffing level and EMR capability are both positively correlated with hospitals’ TPS, and they act independently to bolster hospital performance.

A tactical framework for EMR adoption

Hospitals and other health settings across Canada are transitioning from paper or legacy information systems to Electronic Medical Records (EMR) systems to improve patient care and service delivery. The literature speaks to benefits of EMR systems, but also challenges, such as adverse patient events and provider workflow interruptions. Theoretical models have been proposed to help understand the complex interaction between health information technologies and the healthcare environment, but a shortcoming is the transition from conceptual models to actual clinical settings. The health ecosystem is filled with human diversity and organizational culture considerations that cannot be separated from technical implementation strategies. This paper analyzes literature on EMR implementation and adoption to develop a tactical framework for EMR adoption. The framework consists of six categories, each with a set of seed questions to consider when leading technology adoption projects.

Facility and Regional Factors Associated With the New Adoption of Electronic Medical Records in Japan: Nationwide Longitudinal Observational Study (Preprint)

BACKGROUND The rate of adoption of electronic medical record (EMR) systems has increased 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 This study aimed to evaluate the changes in the EMR adoption status over time in hospitals and clinics in Japan and to examine 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 hospitals (n=4410) and clinics (n=67,329) that had no adoption schedule in FY 2011 and for hospitals (n=1068) and clinics (n=3132) 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 A total of 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 was associated with practice size characteristics, such as number of outpatients (from quantile 4 to quantile 1: OR 1.67, 95% CI 1.005-2.84 and OR 2.40, 95% CI 1.80-3.21, respectively), and number of doctors (from quantile 4 to quantile 1: OR 4.20, 95% CI 2.39-7.31 and OR 2.02, 95% CI 1.52-2.64, respectively). 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 (quantile 4 to quantile 1: OR 0.57, 95% CI 0.45-0.72 and OR 0.74, 95% CI 0.58-0.96, respectively) and having a private establisher (quantile 4 to quantile 1: OR 0.27, 95% CI 0.13-0.55 and OR 0.43, 95% CI 0.19-0.91, respectively). 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 (quantile 4 to quantile 1: OR 1.49, 95% CI 1.32-1.69). CONCLUSIONS Different approaches are needed to promote new adoption of EMR systems in hospitals as compared to clinics. It is important to induce decision making in small- and medium-sized hospitals, and regional postdecision technical support is important to avoid cancellation of scheduled EMR adoption in clinics.