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  1. Article ; Online: Nutri: A Behavioral Science-Based Clinical Decision Support for Chronic Disease Management.

    Burgermaster, Marissa / Rosenthal, Madalyn / Tierney, William M / Altillo, Brandon S / Nordquist, Eric / Enriquez, Christina / Andrews, Steven / Klatt, Caroline / Daniels, Grant

    AMIA ... Annual Symposium proceedings. AMIA Symposium

    2023  Volume 2022, Page(s) 299–308

    Abstract: Clinical decision support systems (CDSS) for the ongoing decision making required to support health behavior change for chronic disease management should incorporate behavioral science (e.g., a collaborative goal setting workflow) with more common CDSS ... ...

    Abstract Clinical decision support systems (CDSS) for the ongoing decision making required to support health behavior change for chronic disease management should incorporate behavioral science (e.g., a collaborative goal setting workflow) with more common CDSS components (i.e., an evidence-based knowledge base that processes patient data). Given known challenges with CDSS usability and adoption, engaging clinician end-users in designing new CDSS is vital. Therefore, we tested Nutri, a CDSS for collaborative diet goal setting, with 10 clinicians in a simulated primary care appointment with a patient actor. Simulation recordings, usability surveys, and debriefing interviews provided a multi-method view of clinicians' perceptions of Nutri's value and usability. 100% of participating clinicians achieved Nutri's main objective: selecting a high impact diet goal during a collaborative goal setting discussion with the patient; participants found Nutri usable, potentially timesaving, and increased their diet counseling self-efficacy. Insights will improve Nutri's usability and clinical workflow integration.
    MeSH term(s) Humans ; Decision Support Systems, Clinical ; Chronic Disease ; Surveys and Questionnaires ; Health Education ; Disease Management
    Language English
    Publishing date 2023-04-29
    Publishing country United States
    Document type Journal Article
    ISSN 1942-597X
    ISSN (online) 1942-597X
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: P93 Precision Behavioral Nutrition: Development of the NutriPCP Inference Engine for Data-driven Diet Goals in Primary Care

    Rosenthal, Madalyn / Larson, Dagny / Henning, Jacqueline / Nayak, Eesha / Dala, Gracia / Martinez, Krystal / Altillo, Brandon A. / Burgermaster, Marissa

    Journal of nutrition education and behavior. 2021 July, v. 53, no. 7

    2021  

    Abstract: To develop a computational system that uses dietary recall data to prioritize behavioral goals to facilitate efficient, personalized collaborative goal-setting in primary care.The Chronic Care Model posits that synergy between the healthcare system and ... ...

    Abstract To develop a computational system that uses dietary recall data to prioritize behavioral goals to facilitate efficient, personalized collaborative goal-setting in primary care.The Chronic Care Model posits that synergy between the healthcare system and patient self-management will improve chronic disease outcomes. Thus, improving how diet is addressed in primary care could augment the benefit of dietary self-management. Collaborative goal-setting with primary care providers (PCPs) can facilitate patient behavior change. However, PCPs lack time and training to set effective diet goals with patients. NutriPCP aims to address this gap by presenting PCPs with a set of evidence-based goals prioritized using patient data.PCPsNutriPCP uses ASA24 diet recall data to compute patient status for each of 9 previously developed, MyPlate-based goals (eg, “Make half my grains whole”). NutriPCP's inference engine consists of Python rule statements that synthesize a patient's data and compare it to evidence-based targets for nutrient consumption personalized for patient characteristics (eg, kcal intake/sex/age). PCPs are then presented with a list of the patient's status for each goal prioritized by degree of improvement needed.We tested our inference engine with test data (n = 12), and our team of nutrition, technology, and clinical experts validated the output. We used NHANES data to establish reasonable population-wide estimates.Testing revealed challenges for goal prioritization because datasets reflected consumption far from evidence-based targets. Therefore, we created standardized ranges to improve variability for relative ranking across goals. For goals with upper and lower limits (eg, “Reduce portion size”) we added warnings for inadequate intake.We demonstrated that computational rules can automatically process recall data into prioritized behavioral goals. To our knowledge, this is the first system that personalizes MyPlate recommendations based on an individual's data. This has implications for nutrition education in primary care. Future research will examine implementation feasibility for PCPs and patients.
    Keywords MyPlate ; National Health and Nutrition Examination Survey ; Python ; behavior change ; chronic diseases ; data collection ; diet recall ; health services ; models ; nutrition education ; patients ; portion size ; prioritization
    Language English
    Dates of publication 2021-07
    Size p. S67.
    Publishing place Elsevier Inc.
    Document type Article
    ZDB-ID 2080501-9
    ISSN 1708-8259 ; 1499-4046
    ISSN (online) 1708-8259
    ISSN 1499-4046
    DOI 10.1016/j.jneb.2021.04.152
    Database NAL-Catalogue (AGRICOLA)

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  3. Article ; Online: User-Centered Design of a Clinical Tool for Shared Decision-making About Diet in Primary Care.

    Tierney, William M / Henning, Jacqueline M / Altillo, Brandon S / Rosenthal, Madalyn / Nordquist, Eric / Copelin, Ken / Li, Jiaxin / Enriquez, Christina / Lange, Jordan / Larson, Dagny / Burgermaster, Marissa

    Journal of general internal medicine

    2022  Volume 38, Issue 3, Page(s) 715–726

    Abstract: Background: Health information technology is a leading cause of clinician burnout and career dissatisfaction, often because it is poorly designed by nonclinicians who have limited knowledge of clinicians' information needs and health care workflow.: ... ...

    Abstract Background: Health information technology is a leading cause of clinician burnout and career dissatisfaction, often because it is poorly designed by nonclinicians who have limited knowledge of clinicians' information needs and health care workflow.
    Objective: Describe how we engaged primary care clinicians and their patients in an iterative design process for a software application to enhance clinician-patient diet discussions.
    Design: Descriptive study of the steps followed when involving clinicians and their at-risk patients in the design of the content, layout, and flow of an application for collaborative dietary goal setting. This began with individual clinician and patient interviews to detail the desired informational content of the screens displayed followed by iterative reviews of intermediate and final versions of the program and its outputs.
    Participants: Primary care clinicians practicing in an urban federally qualified health center and two academic primary care clinics, and their patients who were overweight or obese with diet-sensitive conditions.
    Main measures: Descriptions of the content, format, and flow of information from pre-visit dietary history to the display of evidence-based, guideline-driven suggested goals to final display of dietary goals selected, with information on how the patient might reach them and patients' confidence in achieving them.
    Key results: Through three iterations of design and review, there was substantial evolution of the program's content, format, and flow of information. This involved "tuning" of the information desired: from too little, to too much, to the right amount displayed that both clinicians and patients believed would facilitate shared dietary goal setting.
    Conclusions: Clinicians' well-founded criticisms of the design of health information technology can be mitigated by involving them and their patients in the design of such tools that clinicians may find useful, and use, in their everyday medical practice.
    MeSH term(s) Humans ; User-Centered Design ; Decision Making, Shared ; Primary Health Care ; Diet
    Language English
    Publishing date 2022-09-20
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, P.H.S.
    ZDB-ID 639008-0
    ISSN 1525-1497 ; 0884-8734
    ISSN (online) 1525-1497
    ISSN 0884-8734
    DOI 10.1007/s11606-022-07804-x
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    In stock of ZB MED Cologne/Königswinter

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