scholarly journals Nurse Motivation, Engagement and Well-Being before an Electronic Medical Record System Implementation: A Mixed Methods Study

Author(s):  
Rebecca M. Jedwab ◽  
Alison M. Hutchinson ◽  
Elizabeth Manias ◽  
Rafael A. Calvo ◽  
Naomi Dobroff ◽  
...  

Implementation of an electronic medical record (EMR) is a significant workplace event for nurses in hospitals. Understanding nurses’ key concerns can inform EMR implementation and ongoing optimisation strategies to increase the likelihood of nurses remaining in the nursing workforce. This concurrent mixed-methods study included surveys from 540 nurses (response rate 15.5%), and interviews with 63 nurses to examine their perceptions of using a new EMR prior to implementation at a single healthcare organisation. Survey findings revealed 32.2% (n = 174) of nurses reported low well-being scores and 28.7% (n = 155) were experiencing burnout symptoms. In contrast, 40.3% (n = 216) of nurses reported high work satisfaction, 62.3% (n = 334) had high intentions of staying in their role, and 34.3% (n = 185) were engaged in their work. Nearly half (n = 250, 46.3%) reported intrinsic motivation towards EMR use. Thematic analysis of focus group interviews revealed two themes, each with three subthemes: (1) Us and Them, detailed the juxtaposition between nurses’ professional role and anticipated changes imposed on them and their work with the EMR implementation; and (2) Stuck in the middle, revealed nurses’ expectations and anticipations about how the EMR may affect the quality of nurse-patient relationships. In conclusion, anticipation of the EMR implementation emerged as a stressor for nursing staff, with some groups of nurses particularly vulnerable to negative consequences to their well-being.

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 749
Author(s):  
Gumpili Sai Prashanthi ◽  
Nareen Molugu ◽  
Priyanka Kammari ◽  
Ranganath Vadapalli ◽  
Anthony Vipin Das

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.


JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Andrew J King ◽  
Luca Calzoni ◽  
Mohammadamin Tajgardoon ◽  
Gregory F Cooper ◽  
Gilles Clermont ◽  
...  

Abstract With the extensive deployment of electronic medical record (EMR) systems, EMR usability remains a significant source of frustration to clinicians. There is a significant research need for software that emulates EMR systems and enables investigators to conduct laboratory-based human–computer interaction studies. We developed an open-source software package that implements the display functions of an EMR system. The user interface emphasizes the temporal display of vital signs, medication administrations, and laboratory test results. It is well suited to support research about clinician information-seeking behaviors and adaptive user interfaces in terms of measures that include task accuracy, time to completion, and cognitive load. The Simple EMR System is freely available to the research community and is on GitHub.


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