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Browsing by Author "Diiulio, Julie"
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Item Cognitive requirements for primary care providers during the referral process: Information needed from and interactions with an electronic health record system(Elsevier, 2019-09) Savoy, April; Militello, Laura; Diiulio, Julie; Midboe, Amanda M.; Weiner, Michael; Abbaszadegan, Hamed; Herout, Jennifer; Computer and Information Science, School of ScienceObjectives This study sought to identify and describe the cognitive requirements and associated information needs of referring primary care providers (PCPs) during the referral process as well as characterize referring PCPs’ experiences with current health information technology. Materials and methods We interviewed 62 referring PCPs. Our four-member analysis team used hierarchical task analysis to construct a goal-directed hierarchy. We utilized extensions of the task analysis to describe PCPs’ common experiences with health information technologies throughout the referral process. Results The resultant goal hierarchy includes one main goal (Referral for Additional Care), two sub-goals (Assess Patient’s Condition and Manage Referrals), and four major tasks with respective decisions (What consultation is warranted; What information should I provide; What additional action is needed; and How to integrate specialists’ findings). Approximately 22 information needs were commonly identified and PCPs described their use of various sources - other PCPs, electronic health records, chat software, and paper- to satisfy those information needs. Conclusion Cognitive demand for referring PCPs is high throughout the referral process. They have to search, identify, compose, track, and integrate information across multiple screens, systems, and people. Existing interfaces do not adequately support the communication, information exchange, or care coordination related to the referral process. Results from this study provide an important foundation for developing patient-centered displays that support PCPs’ decision-making process and reduce cognitive challenges.Item Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care(Thieme, 2022) Allen, Katie S.; Danielson, Elizabeth C.; Downs, Sarah M.; Mazurenko, Olena; Diiulio, Julie; Salloum, Ramzi G.; Mamlin, Burke W.; Harle, Christopher A.; Health Policy and Management, School of Public HealthObjectives: The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design. Methods: We conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts. Results: We identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality. Conclusion: Tools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences.Item Study protocol for a type III hybrid effectiveness-implementation trial to evaluate scaling interoperable clinical decision support for patient-centered chronic pain management in primary care(Springer Nature, 2022-07-15) Salloum, Ramzi G.; Bilello, Lori; Bian, Jiang; Diiulio, Julie; Gonzalez Paz, Laura; Gurka, Matthew J.; Gutierrez, Maria; Hurley, Robert W.; Jones, Ross E.; Martinez‑Wittinghan, Francisco; Marcial, Laura; Masri, Ghania; McDonnell, Cara; Militello, Laura G.; Modave, François; Nguyen, Khoa; Rhodes, Bryn; Siler, Kendra; Willis, David; Harle, Christopher A.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBackground: The US continues to face public health crises related to both chronic pain and opioid overdoses. Thirty percent of Americans suffer from chronic noncancer pain at an estimated yearly cost of over $600 billion. Most patients with chronic pain turn to primary care clinicians who must choose from myriad treatment options based on relative risks and benefits, patient history, available resources, symptoms, and goals. Recently, with attention to opioid-related risks, prescribing has declined. However, clinical experts have countered with concerns that some patients for whom opioid-related benefits outweigh risks may be inappropriately discontinued from opioids. Unfortunately, primary care clinicians lack usable tools to help them partner with their patients in choosing pain treatment options that best balance risks and benefits in the context of patient history, resources, symptoms, and goals. Thus, primary care clinicians and patients would benefit from patient-centered clinical decision support (CDS) for this shared decision-making process. Methods: The objective of this 3-year project is to study the adaptation and implementation of an existing interoperable CDS tool for pain treatment shared decision making, with tailored implementation support, in new clinical settings in the OneFlorida Clinical Research Consortium. Our central hypothesis is that tailored implementation support will increase CDS adoption and shared decision making. We further hypothesize that increases in shared decision making will lead to improved patient outcomes, specifically pain and physical function. The CDS implementation will be guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. The evaluation will be organized by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. We will adapt and tailor PainManager, an open source interoperable CDS tool, for implementation in primary care clinics affiliated with the OneFlorida Clinical Research Consortium. We will evaluate the effect of tailored implementation support on PainManager's adoption for pain treatment shared decision making. This evaluation will establish the feasibility and obtain preliminary data in preparation for a multi-site pragmatic trial targeting the effectiveness of PainManager and tailored implementation support on shared decision making and patient-reported pain and physical function. Discussion: This research will generate evidence on strategies for implementing interoperable CDS in new clinical settings across different types of electronic health records (EHRs). The study will also inform tailored implementation strategies to be further tested in a subsequent hybrid effectiveness-implementation trial. Together, these efforts will lead to important new technology and evidence that patients, clinicians, and health systems can use to improve care for millions of Americans who suffer from pain and other chronic conditions.Item Understanding how primary care clinicians make sense of chronic pain(Springer, 2018-11) Militello, Laura G.; Anders, Shilo; Downs, Sarah M.; Diiulio, Julie; Danielson, Elizabeth C.; Hurley, Robert W.; Harle, Christopher A.; Health Policy and Management, School of Public HealthChronic pain leads to reduced quality of life for patients, and strains health systems worldwide. In the US and some other countries, the complexities of caring for chronic pain are exacerbated by individual and public health risks associated with commonly used opioid analgesics. To help understand and improve pain care, this article uses the data frame theory of sensemaking to explore how primary care clinicians in the US manage their patients with chronic noncancer pain. We conducted Critical Decision Method interviews with ten primary care clinicians about 30 individual patients with chronic pain. In these interviews, we identified several patients, social/environmental, and clinician factors that influence the frames clinicians use to assess their patients and determine a pain management plan. Findings suggest significant ambiguity and uncertainty in clinical pain management decision making. Therefore, interventions to improve pain care might focus on supporting sensemaking in the context of clinical evidence rather than attempting to provide clinicians with decontextualized and/or algorithm-based decision rules. Interventions might focus on delivering convenient and easily interpreted patient and social/environmental information in the context of clinical practice guidelines.