Characterizing Primary Care for Patients With Major Depressive Disorder Using Electronic Health Records of a US-Based Healthcare Provider

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.

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Estimating the population health burden of musculoskeletal conditions using primary care electronic health records

Rheumatology ◽

10.1093/rheumatology/keab109 ◽

2021 ◽

Author(s):

Dahai Yu ◽

George Peat ◽

Kelvin P Jordan ◽

James Bailey ◽

Daniel Prieto-Alhambra ◽

...

Keyword(s):

Primary Care ◽

Chronic Pain ◽

Low Back Pain ◽

Back Pain ◽

Electronic Health Records ◽

High Impact ◽

Low Back ◽

Musculoskeletal Health ◽

Health Records ◽

Electronic Health

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.

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