scholarly journals Routinely asking patients about income in primary care: a mixed-methods study

BJGP Open ◽  
2021 ◽  
pp. BJGPO.2021.0090
Author(s):  
Andrew David Pinto ◽  
Erica Shenfeld ◽  
Tatiana Aratangy ◽  
Ri Wang ◽  
Rosane Nisenbaum ◽  
...  
Keyword(s):  
Primary Care ◽  
Mixed Methods ◽  
Electronic Health Records ◽  
Social Determinant ◽  
Mixed Methods Study ◽  
Financial Strain ◽  
Future Research ◽  
Care Organization ◽  
Health Records ◽  
Electronic Health

BackgroundIncome is a key social determinant of health yet it is rare for data on income to be routinely collected and integrated with electronic health records.AimTo examine response bias and evaluate patient perspectives of being asked about income in primary care.Design and settingMixed-methods study in a large, multi-site primary care organization in Toronto, Canada where patients are asked about income in a routinely administered sociodemographic survey.MethodsWe examined data from the electronic health records of patients who answered at least one question on the survey between December 2013 and March 2016 (n=14,247). We compared those who responded to the income question to non-responders. We also conducted structured interviews with 27 patients.Results10,441 (73%) patients responded to both parts of the income question. Female patients, minorities, caregivers of young children and seniors were less likely to respond. From interviews, many patients were comfortable answering the income question, particularly if they understood the connection between income and health, and believed the data would be used to improve care. Several patients found it difficult to estimate their income or felt the options did not reflect fluctuating financial circumstances.ConclusionsMany patients will provide data on income in the context of a survey in primary care, but accurately estimating income can be challenging. Future research should compare self-reported income to perceived financial strain. Data on income linked to health records can help identify health inequities and can help target anti-poverty interventions.

scholarly journals Physician and Pharmacist Medication Decision-Making in the Time of Electronic Health Records: Mixed-Methods Study 

2018 ◽  
Vol 5 (3) ◽  
pp. e24 ◽  
Author(s):  
Kathryn Mercer ◽  
Catherine Burns ◽  
Lisa Guirguis ◽  
Jessie Chin ◽  
Maman Joyce Dogba ◽  
...  
Keyword(s):  
Decision Making ◽  
Mixed Methods ◽  
Electronic Health Records ◽  
Mixed Methods Study ◽  
Health Records ◽  
Electronic Health

Preliminary results from the COPE study using primary-care electronic health records and environmental modelling to examine COPD exacerbations

2018 ◽  
Vol 68 (suppl 1) ◽  
pp. bjgp18X696749 ◽  
Author(s):  
Maimoona Hashmi ◽  
Mark Wright ◽  
Kirin Sultana ◽  
Benjamin Barratt ◽  
Lia Chatzidiakou ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Health Management ◽  
Practice Research ◽  
Chronic Obstructive ◽  
Clinical Practice Research Datalink ◽  
Health Records ◽  
Preliminary Results ◽  
Copd Exacerbations ◽  
Electronic Health

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.

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

Rheumatology ◽  
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.

The relationship between usage intention and adoption of electronic health records at primary care clinics

2013 ◽  
Vol 112 (3) ◽  
pp. 731-737 ◽  
Author(s):  
Usman Iqbal ◽  
Cheng-Hsun Ho ◽  
Yu-Chuan(Jack) Li ◽  
Phung-Anh Nguyen ◽  
Wen-Shan Jian ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Health Records ◽  
Primary Care Clinics ◽  
Electronic Health ◽  
The Relationship

Research and application of artificial intelligence based on electronic health record data from patients with cancer: a systematic review (Preprint)

2021 ◽  
Author(s):  
Xinyu Yang ◽  
Dongmei Mu ◽  
Hao Peng ◽  
Hua Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Ensemble Methods ◽  
Future Research ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Patients With Cancer ◽  
Cancer Management ◽  
Treatment And Prevention ◽  
Electronic Health

BACKGROUND With the accumulation of electronic health records data and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of electronic health records and artificial intelligence in cancer care. OBJECTIVE In this study, we reviewed the literature on the application of AI based on EHR data from patients with cancer, hoping to provide reference for subsequent researchers, and help accelerate the application of EHR data and AI technology in the field of cancer, so as to help patients get more scientific and accurate treatment. METHODS Three databases were systematically searched to retrieve potentially relevant articles published from January 2009 to October 2020. A combination of terms related to "electronic health records", "artificial intelligence" and "cancer" was used to search for these publications. RESULTS Of the 1034 articles considered, 148 met the inclusion criteria. The review has shown that ensemble methods and deep learning were on the rise. It presented the representative literatures on the subfield of cancer diagnosis, treatment and care. In addition, the vast majority of studies in this area were based on private institutional databases, resulting in poor portability of the proposed methodology process. CONCLUSIONS The use of new methods and electronic health records data sharing and fusion were recommended for future research. With the help of specialists, artificial intelligence and the mining of massive electronic medical records could provide great opportunities for improving cancer management.

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