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Pajama Time: The Association of EHR Documentation Time with Family Medicine Resident Outcomes [Education and training]

Annals of Family Medicine

Context: Multiple studies have identified that working on the electronic health record (EHR) after clinic hours ("pajama time") is a source of burnout and decreasing professional satisfaction. Study Design and Analysis: Survey of US family medicine (FM) residents. Population Studied: PGY2 and above US FM residents.

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Artificial Intelligence in Health Care

Integrated Care News by CFHA

Reclaiming Time and Attention Primary care professionals may log more than eleven hours a day, over half of it in the electronic health record (Menchaca, 2025). Annals of Family Medicine , 23(1), 5–6. Evidence‑informed care was literally at his fingertips. References Menchaca, J. For AI in Primary Care, Start With the Problem.

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Electronic Health Record Use and Patient-Centredness [Education and training]

Annals of Family Medicine

Context: Large-scale electronic health record (EHR) programs have reported a number of issues to their implementation in primary care including physician patient-centredness and clinical performance.

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Implementation of a novel linkage of primary care electronic medical record data with hospital data in South Eastern Ontario [Big data]

Annals of Family Medicine

Objectives: To link primary care electronic medical record (EMR) data with community and hospital data and to test the utility of the merged dataset through a targeted quality improvement (QI) intervention among high-risk patients with chronic obstructive pulmonary disease (COPD).

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Test-Retest Reliability of Electronic Handgrip Dynamometry and Accelerometry Measured Muscle Function in Older Adults [Musculoskeletal and rheumatology]

Annals of Family Medicine

Utilizing new electronic handgrip dynamometer and accelerometer technologies may allow for additional muscle function attributes to be feasibly measured, which in turn, may help elevate prognostic value and specificity for intervention referral. for maximal strength, 0.99 for asymmetry, 0.65 for submaximal control, 0.92

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Machine learning models for atrial fibrillation detection in primary care using electronic health records: systematic review [Cardiovascular disease]

Annals of Family Medicine

Machine learning (ML) models offer potential for improved detection of AFib from electronic health records (EHR). Context: Atrial fibrillation (AFib) significantly impacts patient morbidity and mortality, despite existing screening practices.

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Transitional Care Management care team impact on no-show rates to hospital discharge appointments [Patient education/adherence]

Annals of Family Medicine

Setting: Wellstar MCG Health is a 520-bed tertiary hospital with a Family Medicine inpatient service and an attached Family Medicine resident continuity clinic. Population Studied: Inpatients were referred if they were being discharged from the Family Medicine service to home.