Perception and Pattern of Use of Electronic Medical Record System by Primary Health Care Physicians in State of Kuwait 2005

2006 ◽  
Vol 36 (2) ◽  
pp. 327-352
Author(s):  
Saadoun Al-Azmi ◽  
Nagwa Shokair ◽  
Manal Hanafi
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Electronic Medical Record System ◽  
Record System ◽  
Primary Health ◽  
Medical Record System ◽  
Primary Health Care Physicians

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. 

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.

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 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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