Browsing by Author "Toscos, Tammy R."

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    Erratum to: Providing Patients with Implantable Cardiac Device Data through a Personal Health Record: A Qualitative Study
    (Thieme Medical Publishers, 2017-10) Daley, Carly N.; Chen, Elizabeth M.; Roebuck, Amelia E.; Ghahari, Romisa Rohani; Sami, Areej F.; Skaggs, Cayla G.; Carpenter, Maria D.; Mirro, Michael J.; Toscos, Tammy R.; BioHealth Informatics, School of Informatics and Computing
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    Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers
    (Springer, 2018-09-04) Kunjan, Kislaya; Wu, Huanmei; Toscos, Tammy R.; Doebbeling, Bradley N.; BioHealth Informatics, School of Informatics and Computing
    Patient-centered appointment access is of critical importance at community health centers (CHCs) and its optimal implementation entails the use of advanced data analytics. This study seeks to optimize patient-centered appointment scheduling through data mining of Electronic Health Record/Practice Management (EHR/PM) systems. Data was collected from different EHR/PM systems in use at three CHCs across the state of Indiana and integrated into a multidimensional data warehouse. Data mining was performed using decision tree modeling, logistic regression, and visual analytics combined with n-gram modeling to derive critical influential factors that guide implementation of patient-centered open-access scheduling. The analysis showed that appointment adherence was significantly correlated with the time dimension of scheduling, with lead time for an appointment being the most significant predictor. Other variables in the time dimension such as time of the day and season were important predictors as were variables tied to patient demographic and clinical characteristics. Operationalizing the findings for selection of open-access hours led to a 16% drop in missed appointment rates at the interventional health center. The study uncovered the variability in factors affecting patient appointment adherence and associated open-access interventions in different health care settings. It also shed light on the reasons for same-day appointment through n-gram-based text mining. Optimizing open-access scheduling methods require ongoing monitoring and mining of large-scale appointment data to uncover significant appointment variables that impact schedule utilization. The study also highlights the need for greater "in-CHC" data analytic capabilities to re-design care delivery processes for improving access and efficiency.
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    Naturalistic Decision Making in Everyday Self-care Among Older Adults With Heart Failure
    (Wolters Kluwer, 2020-12-23) Daley, Carly N.; Cornet, Victor P.; Toscos, Tammy R.; Bolchini, Davide P.; Mirro, Michael J.; Holden, Richard J.; Regenstrief Institute, School of Medicine
    BACKGROUND: Every day, older adults living with heart failure make decisions regarding their health that may ultimately affect their disease trajectory. Experts describe these decisions as instances of naturalistic decision making influenced by the surrounding social and physical environment and involving shifting goals, high stakes, and the involvement of others. OBJECTIVE: This study applied a naturalistic decision-making approach to better understand everyday decision making by older adults with heart failure. METHODS: We present a cross-sectional qualitative field research study using a naturalistic decision-making conceptual model and critical incident technique to study health-related decision making. The study recruited 24 older adults with heart failure and 14 of their accompanying support persons from an ambulatory cardiology center. Critical incident interviews were performed and qualitatively analyzed to understand in depth how individuals made everyday health-related decisions. RESULTS: White, male (66.7%), older adults' decision making accorded with a preliminary conceptual model of naturalistic decision making occurring in phases of monitoring, interpreting, and acting, both independently and in sequence, for various decisions. Analyses also uncovered that there are barriers and strategies affecting the performance of these phases, other actors can play important roles, and health decisions are made in the context of personal priorities, values, and emotions. CONCLUSIONS: Study findings lead to an expanded conceptual model of naturalistic decision making by older adults with heart failure. In turn, the model bears implications for future research and the design of interventions grounded in the realities of everyday decision making.
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    Providing Patients with Implantable Cardiac Device Data through a Personal Health Record: A Qualitative Study
    (Thieme, 2017-10) Daley, Carly N.; Chen, Elizabeth M.; Roebuck, Amelia E.; Ghahari, Romisa Rohani; Sami, Areej F.; Skaggs, Cayla G.; Carpenter, Maria D.; Mirro, Michael J.; Toscos, Tammy R.; BioHealth Informatics, School of Informatics and Computing
    Erratum to: Providing Patients with Implantable Cardiac Device Data through a Personal Health Record: A Qualitative Study. [Appl Clin Inform. 2017]
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