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BackgroundPatient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults who have multiple chronic conditions. Following identification of patient priorities, this information is documented in the patient’s electronic health record (EHR). ObjectiveOur goal is to develop and validate a natural language processing (NLP) model that reliably documents when clinicians identify patient priorities (ie, values, outcome goals, and care preferences) within the EHR as a measure of PPC adoption. MethodsThis is a retrospective analysis of unstructured National Veteran Health Administration EHR free-text notes using an NLP model. The data were sourced from 778 patient notes of 658 patients from encounters with 144 social workers in the primary care setting. Each patient’s free-text clinical note was reviewed by 2 independent reviewers for the presence of PPC language such as priorities, values, and goals. We developed an NLP model that utilized statistical machine learning approaches. The performance of the NLP model in training and validation with 10-fold cross-validation is reported via accuracy, recall, and precision in comparison to the chart review. ResultsOf 778 notes, 589 (75.7%) were identified as containing PPC language (kappa=0.82, P

We partnered with veteran-serving nonprofits in order to identify distressed rural veterans and provide them with a mental health workshop in community-based settings. Community organizations helped recruit veterans and provided space for 1-day (5-h) Acceptance and Commitment Therapy (ACT) group workshops conducted in rural locations. Qualitative interviews were conducted at 1- and 3-months post-intervention to assess acceptability. Quantitative measures were conducted at baseline, 1- and 3-months post-intervention to measure effectiveness. We successfully engaged community partners throughout every stage of the research and delivered workshops to thirty-one veterans in rural community-based locations. Veterans appreciated the structure, content, and environment of the workshops; most implemented ACT skills into their daily lives and some initiated new treatment following workshop participation. Quantitative measures showed improvements in functioning (Cohen's d ranging from .27 to .40), reintegration (Cohen's d = .45), meaning and purpose (Cohen's d = .40), and reductions in distress (Cohen's d ranging from .28 to .40) 3-months following workshop participation. Collaborating with rural veteran-serving nonprofit organizations holds promise for engaging hard-to-reach distressed veterans in mental health care., Author(s): Lilian Dindo [sup.1] [sup.2] [sup.3] , McKenzie K. Roddy [sup.4] , Derrecka Boykin [sup.5] [sup.4] , Ken Woods [sup.2] [sup.5] , Merlyn Rodrigues [sup.2] [sup.3] , Tracey L. Smith [...]

The concept of psychopathy refers to a pattern of chronic antisocial behavior and personality features, such as emotional detachment, lovelessness, and guiltlessness, attributable in part to a temperament deficit. Historically, that deficit has been seen as a low reactivity to fear, but recent research has documented a second temperament deficit in adults that involves poor emotional and behavioral control. Both pathways are found in the child psychopathology literature, pointing to multifactorial developmental pathways from childhood to adult psychopathy. [ABSTRACT FROM AUTHOR]

Background: Patient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults who have multiple chronic conditions. Following identification of patient priorities, this information is documented in the patient's electronic health record (EHR). Objective: Our goal is to develop and validate a natural language processing (NLP) model that reliably documents when clinicians identify patient priorities (ie, values, outcome goals, and care preferences) within the EHR as a measure of PPC adoption. Methods: This is a retrospective analysis of unstructured National Veteran Health Administration EHR free-text notes using an NLP model. The data were sourced from 778 patient notes of 658 patients from encounters with 144 social workers in the primary care setting. Each patient's free-text clinical note was reviewed by 2 independent reviewers for the presence of PPC language such as priorities, values, and goals. We developed an NLP model that utilized statistical machine learning approaches. The performance of the NLP model in training and validation with 10-fold cross-validation is reported via accuracy, recall, and precision in comparison to the chart review. Results: Of 778 notes, 589 (75.7%) were identified as containing PPC language (kappa=0.82, P <.001). The NLP model in the training stage had an accuracy of 0.98 (95% CI 0.98-0.99), a recall of 0.98 (95% CI 0.98-0.99), and precision of 0.98 (95% CI 0.97-1.00). The NLP model in the validation stage had an accuracy of 0.92 (95% CI 0.90-0.94), recall of 0.84 (95% CI 0.79-0.89), and precision of 0.84 (95% CI 0.77-0.91). In contrast, an approach using simple search terms for PPC only had a precision of 0.757. Conclusions: An automated NLP model can reliably measure with high precision, recall, and accuracy when clinicians document patient priorities as a key step in the adoption of PPC. JMIR Med Inform 2021;9(2):e18756 doi:10.2196/18756 [ABSTRACT FROM AUTHOR]