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.
Introduction: This article describes the implementation of a novel technology tool to capture demographic distribution and clinical presentation of patients in rural vision centers using the eyeSmart electronic medical record (EMR) app in a three-tier eyecare network in India. Methods: A two-year retrospective review of all patients who presented to the rural vision centers of LV Prasad Eye Institute (LVPEI) eyecare network was performed from September 2016 to August 2018 using an in-house developed eyeSmart EMR app. Results: A total of 501 771 patients were captured on the eyeSmart EMR app across the LVPEI network. The ratio of males (n=273 985, 54.60%) and females (n=227 786, 45.40%) presenting to the rural vision centers was 1.2:1. The most prevalent ocular disorder was refractive errors (n=273 720, 44.32%). Conclusions: This study details the demographic distribution and prevalence of ocular disorders in a large cohort of Indian patients and demonstrates the potential for real-time analytics through the use of EMR systems. This provides rigorous evidence for the community and eyecare providers that the use of a web application/tablet app (eyeSmart) aids in providing better eyecare services in rural areas. It helps in creating government policies and improves the treatment strategies for various preventable ocular disorders in India.
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.
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.
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