Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic 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...
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| Format: | Article |
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MDPI AG
2021-06-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/9/6/749 |
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| author | Gumpili Sai Prashanthi Nareen Molugu Priyanka Kammari Ranganath Vadapalli Anthony Vipin Das |
| author_facet | Gumpili Sai Prashanthi Nareen Molugu Priyanka Kammari Ranganath Vadapalli Anthony Vipin Das |
| author_sort | Gumpili Sai Prashanthi |
| collection | DOAJ |
| description | 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)<sub>7</sub> 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. |
| first_indexed | 2024-03-10T10:18:15Z |
| format | Article |
| id | doaj.art-250fe535c9f04f5cbce7fa1cf8c0f0d0 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T10:18:15Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-250fe535c9f04f5cbce7fa1cf8c0f0d02023-11-22T00:37:54ZengMDPI AGHealthcare2227-90322021-06-019674910.3390/healthcare9060749Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record SystemGumpili Sai Prashanthi0Nareen Molugu1Priyanka Kammari2Ranganath Vadapalli3Anthony Vipin Das4Department of EyeSmart EMR & AEye, L. V. Prasad Eye Institute, Hyderabad 500034, IndiaLVPEI Center for Innovation, L.V. Prasad Eye Institute, Banjara Hills, Hyderabad 500034, IndiaDepartment of EyeSmart EMR & AEye, L. V. Prasad Eye Institute, Hyderabad 500034, IndiaDepartment of EyeSmart EMR & AEye, L. V. Prasad Eye Institute, Hyderabad 500034, IndiaDepartment of EyeSmart EMR & AEye, L. V. Prasad Eye Institute, Hyderabad 500034, IndiaIndia 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)<sub>7</sub> 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.https://www.mdpi.com/2227-9032/9/6/749forecastingelectronic health recordshealth resourcespatient flowSARIMA |
| spellingShingle | Gumpili Sai Prashanthi Nareen Molugu Priyanka Kammari Ranganath Vadapalli Anthony Vipin Das Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System Healthcare forecasting electronic health records health resources patient flow SARIMA |
| title | Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System |
| title_full | Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System |
| title_fullStr | Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System |
| title_full_unstemmed | Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System |
| title_short | Forecast of Outpatient Visits to a Tertiary Eyecare Network in India Using the EyeSmart Electronic Medical Record System |
| title_sort | forecast of outpatient visits to a tertiary eyecare network in india using the eyesmart electronic medical record system |
| topic | forecasting electronic health records health resources patient flow SARIMA |
| url | https://www.mdpi.com/2227-9032/9/6/749 |
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