Most General Practitioners (GPs) in Norway use Electronic Health Record (EHR) systems to support their daily work processes. These systems were developed with basis in local needs. Electronic collaboration between the different actors has developed over time. Larger national projects like the ePrescription and the Core EHR are examples of projects that interact with the GPs EHR systems. The requirements from these projects need to be addressed by the vendors of the EHR systems. At the same time the GPs see a need for further development of their EHR systems to make them more suited as tools to support the daily work processes. This paper addresses the how GPs can influence on the design and development of their EHR systems in a situation with a preexisting installed base of systems and increasing requirements from many actors.
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J Gen Intern Med
January 2025
Icahn School of Medicine at Mount Sinai, Institute for Health Equity Research, New York, USA.
Background: Over 60 million patients in the USA have limited English proficiency (LEP) and experience barriers in care. Still, there exists no standardized method of monitoring the utilization of language interpreting services (LIS).
Objective: To introduce a methodological approach to systematically monitor utilization of LIS for LEP patients.
Gerontologist
January 2025
Center for Healthcare Delivery Sciences, Department of Medicine and Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Background And Objectives: Care partners are critical for making treatment decisions in persons living with dementia. However, identifying them is challenging, hindering the broader use of interventions, such as those using digital technologies. We aimed to (i) assess the feasibility of identifying and contacting care partners using electronic health record (EHR) systems, and (ii) elicit their perspectives on electronic interventions for deprescribing.
View Article and Find Full Text PDFHealth Serv Res
January 2025
College of Nursing, Marquette University, Milwaukee, Wisconsin, USA.
Objective: To assess how patient and caregiver factors influence caregiver readiness for hospital discharge in palliative care patients.
Study Setting And Design: This transitional care study uses cross-sectional data from a randomized controlled trial conducted from 2018 to 2023 testing an intervention for caregivers of hospitalized adult patients with a serious or life-limiting illness who received a palliative care consult prior to transitioning out of the hospital.
Data Sources And Analytical Sample: Caregiver readiness was measured with the Family Readiness for Hospital Discharge Scale (n = 231).
Health Serv Res
January 2025
Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA.
Objective: To assess the utility and challenges of using natural language processing (NLP) in electronic health records (EHRs) to ascertain health-related social needs (HRSNs) among older adults.
Study Setting And Design: We extracted HRSN information using the NLP system Clinical Text Analysis and Knowledge Extraction System (cTAKES), combined with Concept Unique Identifiers and Systematized Nomenclature for Medicine codes. We validated cTAKES performance, via manual chart review, on two HRSNs: food insecurity, which was included in the healthcare system's HRSN screening tool, and housing insecurity, which was not.
BMC Med Res Methodol
January 2025
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030-R, Boston, MA, 02120, USA.
Background: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting their utility in research. This study aims to assess whether an "out of the box" implementation of open-source large language models (LLMs) without any fine-tuning can accurately extract social determinants of health (SDoH) data from free-text clinical notes.
Methods: We conducted a cross-sectional study using EHR data from the Mass General Brigham (MGB) system, analyzing free-text notes for SDoH information.
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