Advanced Care Planning Content Encoding with Natural Language Processing.

While advanced care planning (ACP) is an essential practice for ensuring patient-centered care, its adoption remains poor and the completeness of its documentation variable. Natural language processing (NLP) approaches hold promise for supporting ACP, including its use for decision support to improve ACP gaps at the point of care. ACP themes were annotated on palliative care notes across four annotators (Fleiss kappa = 0.753) and supervised models trained (Huggingface models bert-base-uncased and Bio_ClinicalBERT) using 5-fold cross validation (F1=0.8, precision=0.75, recall=0.86, any theme). When applied across the full note corpus of 12,711 notes, we observed variability in documentation of ACP information. Our findings demonstrate the promise of NLP approaches for informatics-based approaches for ACP and patient-centered care.

Development and Usability Evaluation of an Opioid Management App.

We describe the development and usability evaluation of a novel patient engagement tool (OPY) in its early stage from perspectives of both experts and end-users. The tool is aimed at engaging patients in positive behaviors surrounding the use, weaning, and disposal of opioid medications in the post-surgical setting. The messaging and design of the application were created through a behavioral economics lens. Expert-based heuristic analysis and user testing were conducted and demonstrated that while patients found the tool to be easy to use and subjectively somewhat useful, additional work to enhance the user interface and features is needed in close partnership with developers and stakeholders.

Crowdsourcing Electronic Health Record Improvements at Scale across an Integrated Health Care Delivery System.

BACKGROUND AND OBJECTIVE: Despite widespread adoption of electronic health records (EHRs), these systems have significant room for improved efficiency and efficacy. While the idea of crowdsourcing EHR improvement ideas has been reported, little is known about how this might work across an integrated health care delivery system in practice. METHODS: Our program solicited EHR improvement submissions during two timeframes across 10 hospitals and 60 clinics in an upper-Midwest integrated health care delivery system. Submissions were primarily collected via an EHR help feature. RESULTS: A total of 262 and 294 submissions were received in 2019 and 2022, with a majority initiated from physicians (73.5 and 46.9%, 2019 and 2022) specializing in family medicine (52.0 and 59.3%). In 2022, the program reached a larger variety of personnel than 2019, with 53.0% of submissions from advanced practice providers, nurses, administrative staff, and other roles (p < 0.0001). Many ideas (36.4 and 50.0% in 2019 and 2022) reflected a lack of user understanding of EHR features and were addressed through training/education. Significant (27.1 and 25.9%) or simple (24.0 and 14.7%) EHR optimizations were required to address most remaining suggestions, with a number part of planned EHR improvement projects already (16.3 and 17.6%). CONCLUSION: Our experience using a crowdsourcing approach for EHR improvement ideas provided clinicians and staff the opportunity to address frustrations with the EHR and offered concrete feedback and solutions. While previous studies have suggested EHR technology improvements as paramount, we observed large numbers of users having a misunderstanding of EHR features, highlighting the need for improved EHR user competency and training.