scholarly journals ELECTRONIC HEALTH RECORD (EHR) ADOPTION IN SOUTH AFRICAN HEALTHCARE CENTRES: A CASE OF NW PROVINCE

2019 ◽  
Author(s):  
Thatoyaone Modise ◽  
Nehemiah Mavetera ◽  
Mmaki Jantjies
Keyword(s):  
Electronic Health Record ◽  
South African ◽  
Health Record ◽  
Ehr Adoption ◽  
Electronic Health

scholarly journals Drivers of expectations: Why are Norwegian general practitioners skeptical of a prospective electronic health record?

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822098729
Author(s):  
Morten Hertzum ◽  
Gunnar Ellingsen ◽  
Line Melby
Keyword(s):  
Electronic Health Record ◽  
General Practitioners ◽  
Current System ◽  
Implementation Process ◽  
Health Record ◽  
Early Implementation ◽  
Ehr Adoption ◽  
Electronic Health

While expectations are well-known drivers of electronic health record (EHR) adoption, the drivers of expectations are more elusive. On the basis of interviews with general practitioners (GPs), we investigate how the early implementation process drives their expectations of an EHR that is being implemented in Norway. The GPs’ expectations of the prospective EHR are driven by (a) satisfying experiences with their current system, (b) the transfer of others’ experiences with the prospective EHR, (c) a sense of alignment, or lack thereof, with those in charge of the implementation process, (d) uncertainty about the inclusion of GP needs, and (e) competing technological futures. To manage expectations, starting early is important. Mismanaged expectations produce a need for convincing people to reverse their expectations. This appears to be the situation in Norway, where the GPs are currently skeptical of the prospective EHR.

scholarly journals Measuring Electronic Health Record Use in Primary Care: A Scoping Review

2018 ◽  
Vol 09 (01) ◽  
pp. 015-033 ◽  
Author(s):  
Michael Huang ◽  
Candace Gibson ◽  
Amanda Terry
Keyword(s):  
Primary Care ◽  
Electronic Health Record ◽  
Scoping Review ◽  
Web Sites ◽  
Health Record ◽  
Current State ◽  
Level Of Use ◽  
Ehr Adoption ◽  
Electronic Health ◽  
Citation Databases

Background Simple measures of electronic health record (EHR) adoption may be inadequate to evaluate EHR use; and positive outcomes associated with EHRs may be better gauged when varying degrees of EHR use are taken into account. In this article, we aim to assess the current state of the literature regarding measuring EHR use. Objective This article conducts a scoping review of the literature to identify and classify measures of primary care EHR use with a focus on the Canadian context. Methods We conducted a scoping review. Multiple citation databases were searched, as well as gray literature from relevant Web sites. Resulting abstracts were screened for inclusion. Included full texts were reviewed by two authors. Data from the articles were extracted; we synthesized the findings. Subsequently, we reviewed these results with seven EHR stakeholders in Canada. Results Thirty-seven articles were included. Eighteen measured EHR function use individually, while 19 incorporated an overall level of use. Eight frameworks for characterizing overall EHR use were identified. Conclusion There is a need to create standardized frameworks for assessing EHR use.

Measuring the Impact of Electronic Health Record Adoption on Charge Capture

2016 ◽  
Vol 74 (5) ◽  
pp. 582-594 ◽  
Author(s):  
Nicholas Edwardson ◽  
Bita A. Kash ◽  
Ramkumar Janakiraman
Keyword(s):  
Primary Care ◽  
Electronic Health Record ◽  
Fixed Effects ◽  
Pediatric Primary Care ◽  
Health Record ◽  
Care Network ◽  
Collection Data ◽  
Ehr Adoption ◽  
Electronic Health ◽  
The Impact

We examine the impact of electronic health record (EHR) adoption on charge capture—the ability of providers to properly ensure that billable services are accurately recorded and reported for payment. Drawing on billing and practice management data from a large, integrated pediatric primary care network that was previously a paper-based organization, monthly encounter, charge, and collection data were collected from 2008 through 2013. Two-level fixed effects models were built to test the impact of EHR adoption on charge capture. The introduction of the EHR to the pediatric primary care network was independently associated with an $11.09 increase in average per patient charges, an $11.49 increase in average per patient collections, and an improvement in physicians’ charge-to-collection ratios. Despite high initial outlays and operating costs related to EHR adoption, these results suggest organizations may recoup many of these costs over the long term.

scholarly journals A system architecture for ensuring interoperability in a South African national electronic health record system

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Tamir Tsegaye ◽  
Stephen Flowerday
Keyword(s):  
Electronic Health Record ◽  
South African ◽  
Patient Information ◽  
System Architecture ◽  
Healthcare Delivery ◽  
Electronic Record ◽  
Health Record ◽  
Record System ◽  
System Architectures ◽  
Electronic Health

ountries such as South Africa have attempted to leverage eHealth by digitising patients' medical records with the aim of improving the delivery of healthcare. This involves the use of an electronic health record (EHR) which is a longitudinal electronic record of a patient's information. The EHR includes all the patient's encounters that have been made with different health facilities. In the national context, the EHR is also known as a national EHR, which enables the sharing of patient information between points of care. However, a lack of interoperability exists between many South African health information systems making communication between these disparate systems impossible. As a result, the sharing of patient information is inhibited and the benefit of improving healthcare delivery cannot be realised. This paper proposes a system architecture for addressing interoperability challenges and indicates how interoperability can be ensured in a national EHR system. The proposed system architecture is differentiated from other national EHR system architectures found in the literature in order to emphasise its novelty. Secondary data obtained from a systematic literature review was analysed using content analysis, resulting in 9482 tags which informed the development of the proposed system architecture.

scholarly journals Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: A Retrospective Analysis and Predictive Model (Preprint)

2018 ◽  
Author(s):  
Hadi Kharrazi ◽  
Claudia P Gonzalez ◽  
Kevin B Lowe ◽  
Timothy R Huerta ◽  
Eric W Ford
Keyword(s):  
Electronic Health Record ◽  
Technology Diffusion ◽  
Health Care Quality ◽  
Care Delivery ◽  
Care Quality ◽  
Direct Care ◽  
Health Record ◽  
Bass Model ◽  
Ehr Adoption ◽  
Electronic Health

BACKGROUND The Meaningful Use (MU) program has promoted electronic health record (EHR) adoption among US hospitals. Studies have shown that EHR 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 EHRs (beyond MU) nor forecasted EHR maturation over an extended period in a holistic fashion. Additional research is needed to prospectively assess US hospitals’ EHR technology adoption and advancement patterns. OBJECTIVE This study forecasts the maturation of EHR 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 EHR 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 EHR 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 EHR 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 EHR 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 EHRs as defined by EMRAM’s Stage 7.

scholarly journals Understanding key factors affecting on hospital electronic health record (EHR) adoption

2020 ◽  
Vol 9 (8) ◽  
pp. 4348
Author(s):  
Hamed Tabesh ◽  
Zahra Ebnehoseini ◽  
Mahmood Tara ◽  
FatemehHami Dindar ◽  
Sepideh Hasibian
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Key Factors ◽  
Factors Affecting ◽  
Ehr Adoption ◽  
Electronic Health

scholarly journals Two-year longitudinal assessment of physicians’ perceptions after replacement of a longstanding homegrown electronic health record: does a J-curve of satisfaction really exist?

2016 ◽  
Vol 24 (e1) ◽  
pp. e157-e165 ◽  
Author(s):  
David A Hanauer ◽  
Greta L Branford ◽  
Grant Greenberg ◽  
Sharon Kileny ◽  
Mick P Couper ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Physician Satisfaction ◽  
Health Record ◽  
Longitudinal Assessment ◽  
Safety Communication ◽  
J Curve ◽  
Exam Room ◽  
Ehr Adoption ◽  
Electronic Health ◽  
Prospective Longitudinal

This report describes a 2-year prospective, longitudinal survey of attending physicians in 3 clinical areas (family medicine, general pediatrics, internal medicine) who experienced a transition from a homegrown electronic health record (EHR) to a vendor EHR. Participants were already highly familiar with using EHRs. Data were collected 1 month before and 3, 6, 13, and 25 months post implementation. Our primary goal was to determine if perceptions followed a J-curve pattern in which they initially dropped but eventually surpassed baseline measures. A J-curve was not found for any measures, including workflow, safety, communication, and satisfaction. Only the reminders and alerts measure dropped and then returned to baseline (U-curve); a few remained flatlined. Most dropped and remained below baseline (L-curve). The only measure that remained above baseline was documenting in the exam room with the patient. This study adds to the literature about current controversies surrounding EHR adoption and physician satisfaction.

scholarly journals Adherence to recommended electronic health record safety practices across eight health care organizations

2018 ◽  
Vol 25 (7) ◽  
pp. 913-918 ◽  
Author(s):  
Dean F Sittig ◽  
Mandana Salimi ◽  
Ranjit Aiyagari ◽  
Colin Banas ◽  
Brian Clay ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Care Organizations ◽  
National Policy ◽  
Health Record ◽  
Health It ◽  
Clinical Communication ◽  
Safety Assurance ◽  
Ehr Adoption ◽  
Electronic Health ◽  
System Interfaces

Abstract Objective The Safety Assurance Factors for EHR Resilience (SAFER) guides were released in 2014 to help health systems conduct proactive risk assessment of electronic health record (EHR)- safety related policies, processes, procedures, and configurations. The extent to which SAFER recommendations are followed is unknown. Methods We conducted risk assessments of 8 organizations of varying size, complexity, EHR, and EHR adoption maturity. Each organization self-assessed adherence to all 140 unique SAFER recommendations contained within 9 guides (range 10–29 recommendations per guide). In each guide, recommendations were organized into 3 broad domains: “safe health IT” (total 45 recommendations); “using health IT safely” (total 80 recommendations); and “monitoring health IT” (total 15 recommendations). Results The 8 sites fully implemented 25 of 140 (18%) SAFER recommendations. Mean number of “fully implemented” recommendations per guide ranged from 94% (System Interfaces—18 recommendations) to 63% (Clinical Communication—12 recommendations). Adherence was higher for “safe health IT” domain (82.1%) vs “using health IT safely” (72.5%) and “monitoring health IT” (67.3%). Conclusions Despite availability of recommendations on how to improve use of EHRs, most recommendations were not fully implemented. New national policy initiatives are needed to stimulate implementation of these best practices.

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