scholarly journals The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials

Author(s):  
Mohammad B. Ateya ◽  
Brendan C. Delaney ◽  
Stuart M. Speedie
2020 ◽  
Vol 17 (4) ◽  
pp. 370-376
Author(s):  
Benjamin A Goldstein

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.


Addiction ◽  
2012 ◽  
Vol 108 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Udi E. Ghitza ◽  
Robert E. Gore-Langton ◽  
Robert Lindblad ◽  
David Shide ◽  
Geetha Subramaniam ◽  
...  

2012 ◽  
Vol 19 (2) ◽  
pp. 117-126 ◽  
Author(s):  
M Diane Lougheed ◽  
Janice Minard ◽  
Shari Dworkin ◽  
Mary-Ann Juurlink ◽  
Walley J Temple ◽  
...  

In a novel knowledge translation initiative, the Government of Ontario’s Asthma Plan of Action funded the development of an Asthma Care Map to enable adherence with the Canadian Asthma Consensus Guidelines developed under the auspices of the Canadian Thoracic Society (CTS). Following its successful evaluation within the Primary Care Asthma Pilot Project, respiratory clinicians from the Asthma Research Unit, Queen’s University (Kingston, Ontario) are leading an initiative to incorporate standardized Asthma Care Map data elements into electronic health records in primary care in Ontario. Acknowledging that the issue of data standards affects all respiratory conditions, and all provinces and territories, the Government of Ontario approached the CTS Respiratory Guidelines Committee. At its meeting in September 2010, the CTS Respiratory Guidelines Committee agreed that developing and standardizing respiratory data elements for electronic health records are strategically important. In follow-up to that commitment, representatives from the CTS, the Lung Association, the Government of Ontario, the National Lung Health Framework and Canada Health Infoway came together to form a planning committee. The planning committee proposed a phased approach to inform stakeholders about the issue, and engage them in the development, implementation and evaluation of a standardized dataset. An environmental scan was completed in July 2011, which identified data definitions and standards currently available for clinical variables that are likely to be included in electronic medical records in primary care for diagnosis, management and patient education related to asthma and COPD. The scan, sponsored by the Government of Ontario, includes compliance with clinical nomenclatures such as SNOMED-CT®and LOINC®. To help launch and create momentum for this initiative, a national forum was convened on October 2 and 3, 2011, in Toronto, Ontario. The forum was designed to bring together key stakeholders across the spectrum of respiratory care, including clinicians, researchers, health informaticists and administrators to explore and recommend a potential scope, approach and governance structure for this important project. The Pan-Canadian REspiratory STandards INitiative for Electronic Health Records (PRESTINE) goal is to recommend respiratory data elements and standards for use in electronic medical records across Canada that meet the needs of providers, administrators, researchers and policy makers to facilitate evidence-based clinical care, monitoring, surveillance, benchmarking and policy development. The focus initially is expected to include asthma, chronic obstructive pulmonary disease and pulmonary function standards elements that are applicable to many respiratory conditions. The present article summarizes the process and findings of the forum deliberations.


2018 ◽  
Vol 68 (suppl 1) ◽  
pp. bjgp18X696749 ◽  
Author(s):  
Maimoona Hashmi ◽  
Mark Wright ◽  
Kirin Sultana ◽  
Benjamin Barratt ◽  
Lia Chatzidiakou ◽  
...  

BackgroundChronic Obstructive Airway Disease (COPD) is marked by often severely debilitating exacerbations. Efficient patient-centric research approaches are needed to better inform health management primary-care.AimThe ‘COPE study’ aims to develop a method of predicting COPD exacerbations utilising personal air quality sensors, environmental exposure modelling and electronic health records through the recruitment of patients from consenting GPs contributing to the Clinical Practice Research Datalink (CPRD).MethodThe study made use of Electronic Healthcare Records (EHR) from CPRD, an anonymised GP records database to screen and locate patients within GP practices in Central London. Personal air monitors were used to capture data on individual activities and environmental exposures. Output from the monitors were then linked with the EHR data to obtain information on COPD management, severity, comorbidities and exacerbations. Symptom changes not equating to full exacerbations were captured on diary cards. Linear regression was used to investigate the relationship between subject peak flow, symptoms, exacerbation events and exposure data.ResultsPreliminary results on the first 80 patients who have completed the study indicate variable susceptibility to environmental stressors in COPD patients. Some individuals appear highly susceptible to environmental stress and others appear to have unrelated triggers.ConclusionRecruiting patients through EHR for a study is feasible and allows easy collection of data for long term follow up. Portable environmental sensors could now be used to develop personalised models to predict risk of COPD exacerbations in susceptible individuals. Identification of direct links between participant health and activities would allow improved health management thus cost savings.


Rheumatology ◽  
2021 ◽  
Author(s):  
Dahai Yu ◽  
George Peat ◽  
Kelvin P Jordan ◽  
James Bailey ◽  
Daniel Prieto-Alhambra ◽  
...  

Abstract Objectives Better indicators from affordable, sustainable data sources are needed to monitor population burden of musculoskeletal conditions. We propose five indicators of musculoskeletal health and assessed if routinely available primary care electronic health records (EHR) can estimate population levels in musculoskeletal consulters. Methods We collected validated patient-reported measures of pain experience, function and health status through a local survey of adults (≥35 years) presenting to English general practices over 12 months for low back pain, shoulder pain, osteoarthritis and other regional musculoskeletal disorders. Using EHR data we derived and validated models for estimating population levels of five self-reported indicators: prevalence of high impact chronic pain, overall musculoskeletal health (based on Musculoskeletal Health Questionnaire), quality of life (based on EuroQoL health utility measure), and prevalence of moderate-to-severe low back pain and moderate-to-severe shoulder pain. We applied models to a national EHR database (Clinical Practice Research Datalink) to obtain national estimates of each indicator for three successive years. Results The optimal models included recorded demographics, deprivation, consultation frequency, analgesic and antidepressant prescriptions, and multimorbidity. Applying models to national EHR, we estimated that 31.9% of adults (≥35 years) presenting with non-inflammatory musculoskeletal disorders in England in 2016/17 experienced high impact chronic pain. Estimated population health levels were worse in women, older aged and those in the most deprived neighbourhoods, and changed little over 3 years. Conclusion National and subnational estimates for a range of subjective indicators of non-inflammatory musculoskeletal health conditions can be obtained using information from routine electronic health records.


2013 ◽  
Vol 112 (3) ◽  
pp. 731-737 ◽  
Author(s):  
Usman Iqbal ◽  
Cheng-Hsun Ho ◽  
Yu-Chuan(Jack) Li ◽  
Phung-Anh Nguyen ◽  
Wen-Shan Jian ◽  
...  

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.


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