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Objective: The study objective was to evaluate how the use of a pervasive blood glucose monitoring (BGM) technology relates to glycemic control, report of self-care behavior, and emotional response to BGM of children with type 1 diabetes and their parents.Research Design and Methods: Forty-eight children aged less than 12 years (mean 8.8 years) with type 1 diabetes were randomly assigned to one of two study groups, a control group (conventional care without technology) or an experimental group (conventional care with technology), and followed for 12 months. Families in the experimental group were given the Automated Diabetes Management System (ADMS), which automatically collects blood glucose (BG) values and sends to parent(s) a 21-day BG trending report via e-mail each night. Measures of glycemic control (HbA(1c)) were collected at baseline and at quarterly diabetes clinic visits; BGM effect and diabetes self-care behavior measures were obtained at the baseline, 6-month, and 12-month visits.Results: Children in the experimental group had significantly (P = 0.01) lower HbA(1c) at 12 months (7.44 ± 0.94, -0.35 from baseline) than controls (8.31 ± 1.24, +0.15 from baseline). Improvement in HbA(1c) was more profound in families using the ADMS more frequently. In addition, in these families, parents showed a significant improvement in BGM effect (P = 0.03) and children became more meticulous in diabetes self-care (P = 0.04). Children in both experimental and control groups experienced no change in their emotional response to BGM.Conclusions: Using the ADMS 1-3 times/week may help children with type 1 diabetes improve glycemic control and gain diabetes self-management skills, as well as improve the BGM effect of parents. [ABSTRACT FROM AUTHOR]

Objective: We investigated patient experiences with medication- and test-related cost conversations with healthcare providers to identify their preferences for future informatics tools to facilitate cost-sensitive care decisions., Materials and Methods: We conducted 18 semistructured interviews with diverse patients (ages 24-81) in a Midwestern health system in the United States. We identified themes through 2 rounds of qualitative coding., Results: Patients believed their providers could help reduce medication-related costs but did not see how providers could influence test-related costs. Patients viewed cost conversations about medications as beneficial when providers could adjust medical recommendations or provide resources. However, cost conversations did not always occur when patients felt they were needed. Consequently, patients faced a "cascade of work" to address affordability challenges. To prevent this, collaborative informatics tools could facilitate cost conversations and shared decision-making by providing information about a patient's financial constraints, enabling comparisons of medication/testing options, and addressing transportation logistics to facilitate patient follow-through., Discussion: Like providers, patients want informatics tools that address patient out-of-pocket costs. They want to discuss healthcare costs to reduce the frequency of unaffordable costs and obtain proactive assistance. Informatics interventions could minimize the cascade of patient work through shared decision-making and preventative actions. Such tools might integrate information about efficacy, costs, and side effects to support decisions, present patient decision aids, facilitate coordination among healthcare units, and eventually improve patient outcomes., Conclusion: To prevent a burdensome cascade of work for patients, informatics tools could be designed to support cost conversations and decisions between patients and providers., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: To explore diverse provider perspectives on: strategies for addressing patient medication cost barriers; patient medication cost information gaps; current medication cost-related informatics tools; and design features for future tool development., Materials and Methods: We conducted 38 semistructured interviews with providers (physicians, nurses, pharmacists, social workers, and administrators) in a Midwestern health system in the United States. We used 3 rounds of qualitative coding to identify themes., Results: Providers lacked access to information about: patients' ability to pay for medications; true costs of full medication regimens; and cost impacts of patient insurance changes. Some providers said that while existing cost-related tools were helpful, they contained unclear insurance information and several questioned the information's quality. Cost-related information was not available to everyone who needed it and was not always available when needed. Fragmentation of information across sources made cost-alleviation information difficult to access. Providers desired future tools to compare medication costs more directly; provide quick references on costs to facilitate clinical conversations; streamline medication resource referrals; and provide centrally accessible visual summaries of patient affordability challenges., Discussion: These findings can inform the next generation of informatics tools for minimizing patients' out-of-pocket costs. Future tools should support the work of a wider range of providers and situations and use cases than current tools do. Such tools would have the potential to improve prescribing decisions and better link patients to resources., Conclusion: Results identified opportunities to fill multidisciplinary providers' information gaps and ways in which new tools could better support medication affordability for patients., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Aims: To identify knowledge gaps and preferences for educational material to improve nurse-patient communication and self-care., Design: Using a mixed-methods design, we conducted focus groups and quantitative surveys., Methods: We conducted three focus groups with atrial fibrillation (AF) patients and support persons ( N  = 17 participants; 66 ± 16 years) at critical treatment junctures (recent diagnosis or medication switch). Patients and support persons were also surveyed on patient activation (self-management skills and knowledge), medication adherence, AF knowledge and health literacy. Iterative thematic analysis was performed using focus group transcripts., Results: Although most participants had adequate health literacy, most reported gaps in AF knowledge. Participants lacked disease-related knowledge and were unsure how to manage health behaviours (e.g. diet and exercise). Few felt they received adequate education from their healthcare provider. Results emphasize the need for consistent information from nursing staff, in lay language, via both electronic and printed means., (© 2020 The Authors. Nursing Open published by John Wiley & Sons Ltd.)

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., Competing Interests: Conflict of InterestThe authors declare that they have no conflict of interest., (© Springer Nature Switzerland AG 2018.)

Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Georg Thieme Verlag KG Stuttgart · New York.)

Competing Interests: Conflict of Interest: Michael J. Mirro has research funding from the agency for health care research and quality (AHRQ) and the following financial relationships with industry to disclose: research grants from Biotronik, Inc., Medtronic, Inc., and Janssen Scientific Affairs, LLC; consulting fees/honoraria with McKesson Corporation, iRhythm Technologies, Inc., and Zoll Medical Corporation; and financial partnership with Medical Informatics Engineering, outside the submitted work. Dr. Michael J. Mirro's relationships with academia include serving as trustee of Indiana University and on the Indiana University Health Board. Dr. Tammy Toscos has research funding from the agency for health care research and quality (AHRQ) and the following financial relationships with industry to disclose: research grants from Biotronik, Inc., Medtronic, Inc., and Janssen Scientific Affairs, LLC; and iRhythm Technologies Inc. outside the submitted work. Dr. Romisa Rohani Ghahari is also a co-principal investigator on a research grant from Medtronic, Inc.