Facilitators and Barriers to the Adoption of an Electronic Medical Record System by Intensive Care Nurses

2021 ◽  
Author(s):  
Somtochukwu Amaka Osajiuba ◽  
Rebecca Jedwab ◽  
Rafael Calvo ◽  
Naomi Dobroff ◽  
Nicholas Glozier ◽  
...  
Keyword(s):  
Intensive Care ◽  
Medical Record ◽  
Electronic Medical Record ◽  
New Technology ◽  
Electronic Medical Record System ◽  
Unintended Consequences ◽  
Record System ◽  
Medical Record System ◽  
Facilitators And Barriers ◽  
Nurses Perceptions

Introducing new technology, such as an electronic medical record (EMR) into an Intensive Care Unit (ICU), can contribute to nurses’ stress and negative consequences for patient safety. The aim of this study was to explore ICU nurses’ perceptions of factors expected to influence their adoption of an EMR in their workplace. The objectives were to: 1) measure psychological factors expected to influence ICU nurses’ adoption of EMR, and 2) explore perceptions of facilitators and barriers to the implementation of an EMR in their workplace. Using an explanatory sequential mixed method approach, data were collected using surveys and focus groups. ICU nurses reported high scores for motivation, work engagement and wellbeing. Focus group analyses revealed two themes: Hope the EMR will bring a new world and Fear of unintended consequences. Recommendations relate to strategies for education and training, environmental restructuring and enablement. Overall, ICU nurses were optimistic about EMR implementation.

Direct Text Entry in Electronic Progress Notes

2003 ◽  
Vol 42 (01) ◽  
pp. 61-67 ◽  
Author(s):  
J.F. Hurdle ◽  
M.A. Felgar ◽  
J.M. Hoffman ◽  
B. Roth ◽  
J.R. Nebeker ◽  
...  
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
New Technology ◽  
Electronic Medical Record System ◽  
Veterans Administration ◽  
Record System ◽  
Keyboard Input ◽  
Progress Notes ◽  
Medical Record System ◽  
Physical Information

Summary Objectives: It is not uncommon that the introduction of a new technology fixes old problems while introducing new ones. The Veterans Administration recently implemented a comprehensive electronic medical record system (CPRS) to support provider order entry. Progress notes are entered directly by clinicians, primarily through keyboard input. Due to concerns that there may be significant, invisible disruptions to information flow, this study was conducted to formally examine the incidence and characteristics of input errors in the electronic patient record. Methods: Sixty patient charts were randomly selected from all 2,301 inpatient admissions during a 5-month period. A panel of clinicians with informatics backgrounds developed the review criteria. After establishing inter-rater reliability, two raters independently reviewed 1,891 notes for copying, copying errors, inconsistent text, inappropriate object insertion and signature issues. Results: Overall, 60% of patients reviewed had one or more input-related errors averaging 7.8 errors per patient. About 20% of notes showed evidence of copying, with an average of 1.01 error per copied note. Copying another clinician’s note and making changes had the highest risk of error. Templating resulted in large amounts of blank spaces. Overall, MDs make more errors than other clinicians even after controlling for the number of notes. Conclusions: Moving towards a more progressive model for the electronic medical record, where actions are recorded only once, history and physical information is encoded for use later, and note generation is organized around problems, would greatly minimize the potential for error.

scholarly journals Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 749
Author(s):  
Gumpili Sai Prashanthi ◽  
Nareen Molugu ◽  
Priyanka Kammari ◽  
Ranganath Vadapalli ◽  
Anthony Vipin Das
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Time Series Data ◽  
Resource Planning ◽  
Healthcare Services ◽  
Electronic Medical Record System ◽  
Series Data ◽  
Record System ◽  
Outpatient Visits ◽  
Medical Record System

India is home to 1.3 billion people. The geography and the magnitude of the population present unique challenges in the delivery of healthcare services. The implementation of electronic health records and tools for conducting predictive modeling enables opportunities to explore time series data like patient inflow to the hospital. This study aims to analyze expected outpatient visits to the tertiary eyecare network in India using datasets from a domestically developed electronic medical record system (eyeSmart™) implemented across a large multitier ophthalmology network in India. Demographic information of 3,384,157 patient visits was obtained from eyeSmart EMR from August 2010 to December 2017 across the L.V. Prasad Eye Institute network. Age, gender, date of visit and time status of the patients were selected for analysis. The datapoints for each parameter from the patient visits were modeled using the seasonal autoregressive integrated moving average (SARIMA) modeling. SARIMA (0,0,1)(0,1,7)7 provided the best fit for predicting total outpatient visits. This study describes the prediction method of forecasting outpatient visits to a large eyecare network in India. The results of our model hold the potential to be used to support the decisions of resource planning in the delivery of eyecare services to patients.

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