scholarly journals Nurses’ perceptions about and confidence in using an electronic medical record system

2017 ◽  
Vol 27 (2) ◽  
pp. 110-117 ◽  
Author(s):  
Ahmad H Abu Raddaha
Keyword(s):  
Health Care ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Survey Design ◽  
Electronic Medical Record System ◽  
University Hospital ◽  
Cross Sectional Survey ◽  
Record System ◽  
Cross Sectional ◽  
Nurses Perceptions

Introduction: Nurses are among the largest potential users of electronic medical record (EMR) systems in health care settings. Yet little is known about their perceptions and confidence toward using such systems. This study explored nurses’ perceptions toward and confidence in using the EMR system. Predictors for confidence status in using the system among nurses were postulated. Methods: A cross-sectional survey design was used. A sample of 169 nurses were recruited from a general governmental university hospital in Muscat, Oman. Results: Most of study participants did not have prior experience with EMR systems elsewhere. About half (52.1%) perceived that they were confident in using the system. A logistic regression model showed nurses who (a) had six or more years of experience in using the system, (b) perceived that their suggestions regarding improving the system were taken into consideration by the system managing team, (c) perceived that the changes introduced in the system were important to their work, and (d) perceived that the information retrieved through the system was updated, to be more likely confident in using the system. Discussion: When customizing the EMR system, the informatics team that manages the system is invited to more consider suggestions for improvement that are raised by nurses. More training on the system is suggested to increase confidence among nurses who had little experience in using the system. In order to enhance the preparation of future nurses with contemporary technology-driven health care practices, nursing schools officials are encouraged to include general computer information technology training into nursing curricula.

scholarly journals Automated Categorization of Systemic Disease and Duration From Electronic Medical Record System Data Using Finite-State Machine Modeling: Prospective Validation Study

10.2196/24490 ◽  
2020 ◽  
Vol 4 (12) ◽  
pp. e24490
Author(s):  
Gumpili Sai Prashanthi ◽  
Ayush Deva ◽  
Ranganath Vadapalli ◽  
Anthony Vipin Das
Keyword(s):  
Health Care ◽  
Positive Predictive Value ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Finite State Machine ◽  
Electronic Medical Record System ◽  
State Machine ◽  
Record System ◽  
Medical Record System ◽  
Finite State

Background One of the major challenges in the health care sector is that approximately 80% of generated data remains unstructured and unused. Since it is difficult to handle unstructured data from electronic medical record systems, it tends to be neglected for analyses in most hospitals and medical centers. Therefore, there is a need to analyze unstructured big data in health care systems so that we can optimally utilize and unearth all unexploited information from it. Objective In this study, we aimed to extract a list of diseases and associated keywords along with the corresponding time durations from an indigenously developed electronic medical record system and describe the possibility of analytics from the acquired datasets. Methods We propose a novel, finite-state machine to sequentially detect and cluster disease names from patients’ medical history. We defined 3 states in the finite-state machine and transition matrix, which depend on the identified keyword. In addition, we also defined a state-change action matrix, which is essentially an action associated with each transition. The dataset used in this study was obtained from an indigenously developed electronic medical record system called eyeSmart that was implemented across a large, multitier ophthalmology network in India. The dataset included patients’ past medical history and contained records of 10,000 distinct patients. Results We extracted disease names and associated keywords by using the finite-state machine with an accuracy of 95%, sensitivity of 94.9%, and positive predictive value of 100%. For the extraction of the duration of disease, the machine’s accuracy was 93%, sensitivity was 92.9%, and the positive predictive value was 100%. Conclusions We demonstrated that the finite-state machine we developed in this study can be used to accurately identify disease names, associated keywords, and time durations from a large cohort of patient records obtained using an electronic medical record system.

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.

scholarly journals Use of more than one electronic medical record system within a single health care organization

2012 ◽  
Vol 03 (04) ◽  
pp. 462-474 ◽  
Author(s):  
J. Fellner ◽  
C. Dugowson ◽  
D. Liebovitz ◽  
G. Fletcher ◽  
T. Payne
Keyword(s):  
Health Care ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Health Care Organization ◽  
Electronic Medical Record System ◽  
Healthcare Organization ◽  
Care Organization ◽  
Data Types ◽  
Record System ◽  
Medical Record System

SummaryHealthcare organizations vary in the number of electronic medical record (EMR) systems they use. Some use a single EMR for nearly all care they provide, while others use EMRs from more than one vendor. These strategies create a mixture of advantages, risks and costs. Based on our experience in two organizations over a decade, we analyzed use of more than one EMR within our two health care organizations to identify advantages, risks and costs that use of more than one EMR presents. We identified the data and functionality types that pose the greatest challenge to patient safety and efficiency. We present a model to classify patterns of use of more than one EMR within a single healthcare organization, and identified the most important 28 data types and 4 areas of functionality that in our experience present special challenges and safety risks with use of more than one EMR within a single healthcare organization. The use of more than one EMR in a single organization may be the chosen approach for many reasons, but in our organizations the limitations of this approach have also become clear. Those who use and support EMRs realize that to safely and efficiently use more than one EMR, a considerable amount of IT work is necessary. Thorough understanding of the challenges in using more than one EMR is an important prerequisite to minimizing the risks of using more than one EMR to care for patients in a single healthcare organization. Citation: Payne T, Fellner J, Dugowson C, Liebovitz D, Fletcher G. Use of more than one electronic medical record system within a single health care organization. Appl Clin Inf 2012; 3: 462–474http://dx.doi.org/10.4338/ACI-2012-10-RA-0040

scholarly journals Automated Categorization of Systemic Disease and Duration From Electronic Medical Record System Data Using Finite-State Machine Modeling: Prospective Validation Study (Preprint)

2020 ◽  
Author(s):  
Gumpili Sai Prashanthi ◽  
Ayush Deva ◽  
Ranganath Vadapalli ◽  
Anthony Vipin Das
Keyword(s):  
Health Care ◽  
Positive Predictive Value ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Finite State Machine ◽  
Electronic Medical Record System ◽  
State Machine ◽  
Record System ◽  
Medical Record System ◽  
Finite State

BACKGROUND One of the major challenges in the health care sector is that approximately 80% of generated data remains unstructured and unused. Since it is difficult to handle unstructured data from electronic medical record systems, it tends to be neglected for analyses in most hospitals and medical centers. Therefore, there is a need to analyze unstructured big data in health care systems so that we can optimally utilize and unearth all unexploited information from it. OBJECTIVE In this study, we aimed to extract a list of diseases and associated keywords along with the corresponding time durations from an indigenously developed electronic medical record system and describe the possibility of analytics from the acquired datasets. METHODS We propose a novel, finite-state machine to sequentially detect and cluster disease names from patients’ medical history. We defined 3 states in the finite-state machine and transition matrix, which depend on the identified keyword. In addition, we also defined a state-change action matrix, which is essentially an action associated with each transition. The dataset used in this study was obtained from an indigenously developed electronic medical record system called eyeSmart that was implemented across a large, multitier ophthalmology network in India. The dataset included patients’ past medical history and contained records of 10,000 distinct patients. RESULTS We extracted disease names and associated keywords by using the finite-state machine with an accuracy of 95%, sensitivity of 94.9%, and positive predictive value of 100%. For the extraction of the duration of disease, the machine’s accuracy was 93%, sensitivity was 92.9%, and the positive predictive value was 100%. CONCLUSIONS We demonstrated that the finite-state machine we developed in this study can be used to accurately identify disease names, associated keywords, and time durations from a large cohort of patient records obtained using an electronic medical record system.

scholarly journals Exploring patients satisfaction after the implementation of an electronic medical record system at Al-Wakrah primary health center, Qatar, 2016

2017 ◽  
Vol 4 (10) ◽  
pp. 3511 ◽  
Author(s):  
Ayman F. Al-Dahshan ◽  
Noura Al-Kubaisi ◽  
Mohamed Abdel Halim Chehab ◽  
Nour Al-Hanafi
Keyword(s):  
Health Care ◽  
Patient Satisfaction ◽  
Primary Health Care ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Electronic Medical Record System ◽  
Record System ◽  
Health Care Center ◽  
Primary Health ◽  
Medical Record System

Background: The healthcare industry has focused much attention on patient satisfaction with the quality of healthcare services. However, there remains a lack of research on patient satisfaction towards the implementation of an electronic medical record system at a primary healthcare setting. This study aimed at assessing the level of patient satisfaction regarding primary health care services after the implementation of an electronic medical record (EMR) system. Methods: A descriptive cross-sectional study was conducted at the Al-Wakrah health care center, with a random/convenient sample of 52 patients attending the center. Furthermore, the investigators interviewed the participants, in the waiting area, regarding their satisfaction with the primary health care services provided following the EMR system implementation. A structured interview-based questionnaire for measuring patient satisfaction was employed. Results: The vast majority of participants indicated that the overall service at the health center greatly improved after EMR implementation. Furthermore, most interviewees were totally satisfied with the overall workflow at the health care center such as the time spent at the registration desk (76.9%), before seeing a physician (65.4%), while the physician used the computer (76.9%), physical examination (69.3%), laboratory testing (73.1%), and collecting the medication (65.4%). Regarding health education and informativeness, the participants found that labeling medication bottles was quite informative. However, less than two-thirds (61.5%) of the patients were satisfied with the health education delivered by physicians. Conclusions: The results revealed that although overall patient satisfaction was relatively high, certain aspects of the health care service remained to be a source of dissatisfaction. Thus, this study demonstrated patient acceptance and support for the electronic medical record system at the primary health care setting. 

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