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 Cohort profile: protocol and baseline survey for the Shanghai Suburban Adult Cohort and Biobank (SSACB) study

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e035430 ◽  
Author(s):  
Qi Zhao ◽  
Bo Chen ◽  
Ruiping Wang ◽  
Meiying Zhu ◽  
Yueqin Shao ◽  
...  
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Electronic Medical Record System ◽  
Baseline Data ◽  
Local Health ◽  
Record System ◽  
Face To Face ◽  
System A ◽  
Registry System ◽  
Medical Record System

PurposeThe Shanghai Suburban Adult Cohort and Biobank (SSACB) was established to identify environmental, lifestyle and genetic risk factors for non-communicable chronic diseases (NCDs) in adults (20–74 years old) living in a suburban area of Shanghai with rapid urbanisation.ParticipantsTwo of eight suburban district were purposely selected according to participant willingness, health service facilities, population, geographic region and electronic medical record system. From these suburban districts, four communities were selected based on economic level and population size. At stage three, one-third of the committees/villages were randomly selected from each community. All residents aged 20–74 years old were invited as study participants.Findings to dateThe baseline data on demographics, lifestyle and physical health-related factors were collected using a face-to-face questionnaire interview. All participants completed physical examinations and had blood and urine tests. Blood and urine samples from these tests were stored in a biobank. From 6 April 2016 through 31 October 2017, we conducted face-to-face interviews and clinical examinations in 44 887 participants: 35 727 from Songjiang District and 9160 from Jiading District. The average age of participants was 56.4±11.2 years in Songjiang and 56.6±10.5 years in Jiading. The prevalence of hypertension, diabetes and dyslipidaemia was 34.0%, 8.2% and 11.1%, respectively.Future plansIn-person surveys will be conducted every 5 years. For annual tracking, baseline data was linked to the local health information system, which was composed of an electronic medical record system, a chronic disease management system, a cancer registry system, an infectious disease report system and a death registry system. The data of the SSACB cohort is located in the School of Public Health, Fudan University. International and domestic collaborative research projects are encouraged and inherent in the project.

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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