Allowing patients direct access to their electronic health record (EHR) notes has been shown to enhance medical understanding and may improve healthcare management and outcome. However, EHR notes contain medical terms, shortened forms, complex disease and medication names, and other domain specific jargon that make them difficult for patients to fathom. In this paper, we present a BioNLP system, NoteAid, that automatically recognizes medical concepts and links these concepts with consumer oriented, simplified definitions from external resources. We conducted a pilot evaluation for linking EHR notes through NoteAid to three external knowledge resources: MedlinePlus, the Unified Medical Language System (UMLS), and Wikipedia. Our results show that Wikipedia significantly improves EHR note readability. Preliminary analyses show that MedlinePlus and the UMLS need to improve both content readability and content coverage for consumer health information. A demonstration version of fully functional NoteAid is available at http://clinicalnotesaid.org.
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Alzheimers Dement
December 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: This study responds to the urgent need for automated and reliable methods to detect cognitive impairments on a large scale. It leverages natural language processing (NLP) techniques to predict dementia and mild cognitive impairment (MCI) using clinical notes from electronic health records (EHR).
Method: Our study used an EHR dataset from Massachusetts General Brigham, which included clinical notes from a 2-year period (2017-2018) covering 12 types of patient encounters.
Am J Health Syst Pharm
December 2024
The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Disclaimer: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
December 2024
Nivel, Netherlands Institute for Health Services Research, Otterstraat 118, Utrecht, 3513 CR, The Netherlands.
Background: At the beginning of the COVID-19 pandemic in 2020, little was known about the spread of COVID-19 in Dutch nursing homes while older people were particularly at risk of severe symptoms. Therefore, attempts were made to develop a nationwide COVID-19 repository based on routinely recorded data in the electronic health records (EHRs) of nursing home residents. This study aims to describe the facilitators and barriers encountered during the development of the repository and the lessons learned regarding the reuse of EHR data for surveillance and research purposes.
View Article and Find Full Text PDFJMIR Aging
December 2024
Department of Medicine, University of California, Davis, Sacramento, CA, United States.
Background: Family and unpaid caregivers play a crucial role in supporting people living with dementia; yet, they are not systematically identified and documented by health systems.
Objective: The aims of the study are to determine the extent to which caregivers are currently identified and documented in the electronic health record (EHR) and to elicit the perspectives of caregivers and clinical staff on how to best identify, engage, and support caregivers of people living with dementia through the EHR.
Methods: People with dementia were identified based on International Classification of Diseases, Tenth Revision (ICD-10) codes or dementia medications in the EHR.
Clin J Pain
December 2024
Department of Public Health and Center for Health Statistics, University of Massachusetts Lowell, Lowell, MA, USA.
Objective: Neurocognitive symptoms (NCS) may be early indicators of opioid-related harm. We aimed to evaluate the incidence and potential attribution of opioid-related NCS among patients on long-term opioid therapy (LTOT) by using natural language processing (NLP) to extract data from the electronic health records (EHR) within the Veterans Health Administration.
Methods: We conducted a retrospective cohort study of patients prescribed LTOT in 2018.
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