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.

Relationship Between Use of Electronic Health Record Features and Health Care Quality

Medical Care ◽  
2010 ◽  
Vol 48 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Eric G. Poon ◽  
Adam Wright ◽  
Steven R. Simon ◽  
Chelsea A. Jenter ◽  
Rainu Kaushal ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Health Care Quality ◽  
Care Quality ◽  
Health Record ◽  
Electronic Health

Benchmarking: Extracting Clinical Measures and Practice Performance From the Electronic Health Record

2009 ◽  
Vol 2 (1) ◽  
pp. 23-26
Author(s):  
Julie A. Lindenberg
Keyword(s):  
Health Care ◽  
Chronic Disease ◽  
Disease Management ◽  
Electronic Health Record ◽  
Health Care Quality ◽  
Care Quality ◽  
Health Record ◽  
Practice Performance ◽  
Electronic Health ◽  
Near Term

Adoption of electronic health record (EHR) systems can lead to health care savings, reduction in medical errors, and improvement in health. The intersection of health information technology (HIT) and health care quality is a result of the following two developments: the quality improvement movement and the maturation of HIT. The potential safety benefits of EHR systems focus largely on alerts, reminders, and other components of ambulatory computerized provider order entry. EHR systems are integral throughout the disease management process. Using HIT for near-term chronic disease management programs has the benefits of identifying people with a potential or active chronic disease, targeting services based on their level of risk, and monitoring their risk. The purposes of benchmarking and survey reports are to provide a quantifiable measure of performance and to quantify gaps between your practice and best practices.

The Adoption of the Electronic Health Record by Physicians

2019 ◽  
Vol 58 (02/03) ◽  
pp. 063-070 ◽  
Author(s):  
Saja A. Al-Rayes ◽  
Arwa Alumran ◽  
Weam AlFayez
Keyword(s):  
Social Influence ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Health Care Quality ◽  
Resistance To Change ◽  
Systems Design ◽  
Care Quality ◽  
Perceived Usefulness ◽  
Health Record ◽  
Electronic Health

Abstract Background Health information technology, especially the electronic health record (EHR) systems, improves health care quality and patient safety. Objectives This study's objectives are as follows: first, to explore the adoption of EHR systems among physicians in Saudi Arabia (with King Fahd Military Medical Complex as the location of the pilot study), and second, to identify the factors that influence these physicians' adoption of such systems. Methods This cross-sectional quantitative study is based on a paper survey that was administered to a sample of 213 physicians. The theoretical model is a version of the Technology Acceptance Model (TAM) that features the following additional variables: resistance to change, training, and social influence. Results The sample includes 133 (62%) physicians who used EHRs and 80 (38%) who did not. The main findings show that users and nonusers of the EHR system differ significantly for several factors such as perceived usefulness, perceived ease of use, social influence, and resistance to change. In addition, age, work experience, and medical specialty are significantly associated with physicians' use of the EHR system. Conclusion To increase EHR systems' adoption rate, the following elements should be improved: the systems' design, the social environments, and the physicians' awareness of the systems' benefits. This is the first study to produce a valid and reliable instrument for measuring the factors that influences physicians' use of the EHR system at a Saudi hospital in the Eastern Province. Further studies are needed to measure how these factors influence physicians' use of EHRs in other settings.

scholarly journals What Can Apologies in the Electronic Health Record Tell Us About Health Care Quality, Processes, and Safety?

2018 ◽  
Vol 16 (3) ◽  
pp. e187-e193
Author(s):  
John C. Matulis ◽  
Frederick North
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Health Care Quality ◽  
Care Quality ◽  
Health Record ◽  
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.

scholarly journals The Impact of Disruption of the Care Delivery System by Commercial Laboratory Testing in a Children's Health Care System

2018 ◽  
Vol 143 (1) ◽  
pp. 115-121 ◽  
Author(s):  
Beverly B. Rogers ◽  
James L. Adams ◽  
Alexis B. Carter ◽  
Francine Uwindatwa ◽  
Cynthia B. Brawley ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Median Time ◽  
Laboratory Testing ◽  
Care Delivery ◽  
Reference Laboratory ◽  
Health Record ◽  
Multiple Sources ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
The Impact

Context.— Disruption of outpatient laboratory services by routing the samples to commercial reference laboratories may seem like a cost-saving measure by the payers, but results in hidden costs in quality and resources to support this paradigm. Objective.— To identify differences when outpatient tests are performed at the Children's Healthcare of Atlanta (Children's) Hospital lab compared to a commercial reference lab, and the financial costs to support the reference laboratory testing. Design.— Outpatient testing was sent to 3 different laboratories specified by the payer. Orders were placed in the Children's electronic health record, blood samples were drawn by the Children's phlebotomists, samples were sent to the testing laboratory, and results appeared in the electronic health record. Data comparing the time to result, cancelled samples, and cost to sustain the system of ordering and reporting were drawn from multiple sources, both electronic and manual. Results.— The median time from phlebotomy to result was 0.7 hours for testing at the Children's lab and 20.72 hours for the commercial lab. The median time from result posting to caregiver acknowledgment was 5.4 hours for the Children's lab and 18 hours for the commercial lab. The commercial lab cancelled 2.7% of the tests; the Children's lab cancelled 0.8%. The financial cost to support online ordering and reporting for testing performed at commercial labs was approximately $640,000 per year. Conclusions.— Tangible monetary costs, plus intangible costs related to delayed results, occur when the laboratory testing system is disrupted.

Sign in / Sign up

Export Citation Format

Share Document