scholarly journals PPX-TEC API APP: A unique approach to sending and receiving health and insurance data

2019 ◽  
Vol 4 (2) ◽  
Keyword(s):  
Medical Record ◽  
Healthcare Systems ◽  
Veterans Affairs ◽  
Patient Record ◽  
Record System ◽  
Medical Record System ◽  
Unique Approach ◽  
Computerized Patient Record ◽  
Insurance Data ◽  
The U.S

In 1971, the U.S. Dept. of Veterans Affairs (VA) became one of the first large healthcare systems to fully implement a computerized patient record system. Shortly thereafter, in 1972, Regenstrief developed the Regenstrief Medical Record System (RMRS), a historically important EMR. The purpose of this early EMR was described in a quote that is still applicable today:

scholarly journals Integrating third-party telehealth records with the general practice electronic medical record system: a solution.

2017 ◽  
Vol 24 (4) ◽  
pp. 317 ◽  
Author(s):  
Mary Paterson ◽  
Alison McAulay ◽  
Brian McKinstry
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Electronic Medical Record System ◽  
Third Party ◽  
Patient Record ◽  
Record System ◽  
System A ◽  
Medical Record System ◽  
General Practices

Background: The implementation of telemonitoring at scale has been less successful than anticipated, often hindered by clinicians’ perceived increase in workload. One important factor has been the lack of integration of patient generated data (PGD) with the electronic medical record (EMR). Clinicians have had problems accessing PGD on telehealth systems especially in patient consultations in primary care.Objective: To design a method to produce a report of PGD that is available to clinicians through their routine EMR system.Method: We modelled a system with a use case approach using Unified Modelling Language to enable us to design a method of producing the required report. Anonymised PGD are downloaded from a third-party telehealth system to National Health Service (NHS) systems and linked to the patient record available in the hospital recording system using the patient NHS ID through an interface accessed by healthcare professionals. The telehealth data are then processed into a report using the patient record. This report summarises the readings in graphical and tabular form with an average calculated and with a recommended follow-up suggested if required. The report is then disseminated to general practitioner practices through routine document distribution pathways.Results: This addition to the telehealth system is viewed positively by clinicians. It has helped to greatly increase the number of general practices using telemonitoring to manage blood pressure in NHS Lothian.

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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