scholarly journals Installing and Implementing a Computer-based Patient Record System in Sub-Saharan Africa: The Mosoriot Medical Record System

2003 ◽  
Vol 10 (4) ◽  
pp. 295-303 ◽  
Author(s):  
J. K. Rotich
Keyword(s):  
Medical Record ◽  
Sub Saharan Africa ◽  
Patient Record ◽  
Record System ◽  
Medical Record System ◽  
Computer Based ◽  
Sub Saharan

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.

Cancer registration using a computer-based medical record system

1987 ◽  
Vol 12 (4) ◽  
pp. 307-307
Author(s):  
Yoshiaki Nose ◽  
Kouhei Akazawa ◽  
Yoshiaki Watanabe ◽  
Masao Yokota ◽  
Motoomi Nakamura ◽  
...  
Keyword(s):  
Medical Record ◽  
Cancer Registration ◽  
Record System ◽  
Medical Record System ◽  
Computer Based

A Computer Based Medical Record System

1973 ◽  
Vol 12 (03) ◽  
pp. 137-142 ◽  
Author(s):  
J. D. Buckley ◽  
V. X. Gledhill ◽  
J. D. Mathews ◽  
I. R. Mackay
Keyword(s):  
Medical Record ◽  
High Speed ◽  
Clinical History ◽  
Data File ◽  
Statistical Research ◽  
Record System ◽  
General Medical Ward ◽  
Remote Terminal ◽  
Medical Record System ◽  
Computer Based

This paper reports the design and implementation of a computer based medical record system. Clinical data from a 27-bed general medical ward are captured at the point of generation and entered into a computer through a remote terminal. The data stored at present for each patient-admission include the identification, diagnoses, clinical history, findings on physical examination and a problem oriented case synopsis. Commands are available to enter, validate, reformat, and finally store the data permanently on computer files. Other commands locate all or part of specified records, decode and reformat the data for output, and print the output at the remote terminal or on a high speed line printer. Commands are also available for data file maintenance and for statistical research on the stored records. The system has been in operation for two years and over 1,000 records are currently computer-stored.

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.

scholarly journals Understanding cause of stillbirth: a prospective observational multi-country study from sub-Saharan Africa

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Mamuda Aminu ◽  
Sarah Bar-Zeev ◽  
Sarah White ◽  
Matthews Mathai ◽  
Nynke van den Broek
Keyword(s):  
Sierra Leone ◽  
Cause Of Death ◽  
Expert Panel ◽  
Healthcare Providers ◽  
Sub Saharan Africa ◽  
Primary Data ◽  
Middle Income ◽  
Computer Based ◽  
Sub Saharan ◽  
Low And Middle Income

Abstract Background Every year, an estimated 2.6 million stillbirths occur worldwide, with up to 98% occurring in low- and middle-income countries (LMIC). There is a paucity of primary data on cause of stillbirth from LMIC, and particularly from sub-Saharan Africa to inform effective interventions. This study aimed to identify the cause of stillbirths in low- and middle-income settings and compare methods of assessment. Methods This was a prospective, observational study in 12 hospitals in Kenya, Malawi, Sierra Leone and Zimbabwe. Stillbirths (28 weeks or more) were reviewed to assign the cause of death by healthcare providers, an expert panel and by using computer-based algorithms. Agreement between the three methods was compared using Kappa (κ) analysis. Cause of stillbirth and level of agreement between the methods used to assign cause of death. Results One thousand five hundred sixty-three stillbirths were studied. The stillbirth rate (per 1000 births) was 20.3 in Malawi, 34.7 in Zimbabwe, 38.8 in Kenya and 118.1 in Sierra Leone. Half (50.7%) of all stillbirths occurred during the intrapartum period. Cause of death (range) overall varied by method of assessment and included: asphyxia (18.5–37.4%), placental disorders (8.4–15.1%), maternal hypertensive disorders (5.1–13.6%), infections (4.3–9.0%), cord problems (3.3–6.5%), and ruptured uterus due to obstructed labour (2.6–6.1%). Cause of stillbirth was unknown in 17.9–26.0% of cases. Moderate agreement was observed for cause of stillbirth as assigned by the expert panel and by hospital-based healthcare providers who conducted perinatal death review (κ = 0.69; p < 0.0005). There was only minimal agreement between expert panel review or healthcare provider review and computer-based algorithms (κ = 0.34; 0.31 respectively p < 0.0005). Conclusions For the majority of stillbirths, an underlying likely cause of death could be determined despite limited diagnostic capacity. In these settings, more diagnostic information is, however, needed to establish a more specific cause of death for the majority of stillbirths. Existing computer-based algorithms used to assign cause of death require revision.

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