Care for patients living with chronic conditions using the ICAN Discussion Aid: A mixed methods cluster-randomized trial.

Objectives: To assess the effectiveness of the ICAN Discussion Aid in improving patients' experience of receiving care for their chronic conditions and health professionals' experience of providing their care.

Methods: We conducted a pragmatic, mixed-methods, cluster-randomized trial of the ICAN Discussion Aid at 8 clinics in 4 independent health systems in the US from January 2017 and to August 2018. Sites were randomized 1:1 in pairs.

Decisional Regret Surrounding Dialysis Initiation: A Comparative Analysis.

Rationale & Objective: Dialysis comes with a substantial treatment burden, so patients must select care plans that align with their preferences. We aimed to deepen the understanding of decisional regret with dialysis choices.

Study Design: This study had a mixed-methods explanatory sequential design.

Realtime Diagnosis from Electrocardiogram Artificial Intelligence-Guided Screening for Atrial Fibrillation with Long Follow-Up (REGAL): Rationale and design of a pragmatic, decentralized, randomized controlled trial.

Background: Atrial fibrillation (AF) is associated with increased risks of stroke and dementia. Early diagnosis and treatment could reduce the disease burden, but AF is often undiagnosed. An artificial intelligence (AI) algorithm has been shown to identify patients with previously unrecognized AF; however, monitoring these high-risk patients has been challenging.

Financial Toxicity in the Clinical Encounter: A Paired Survey of Patient and Clinician Perceptions.

Objective: To compare the agreement between patient and clinician perceptions of care-related financial issues.

Patients And Methods: We surveyed patient-clinician dyads immediately after an outpatient medical encounter between September 2019 and May 2021. They were asked to separately rate (1-10) patient's level of difficulty in paying medical bills and the importance of discussing cost issues with that patient during clinical encounters.

Investigating gender-based differential item functioning on the Kansas City Cardiomyopathy Questionnaire (KCCQ) using qualitative content analysis.

Purpose: The purpose of this study is to evaluate potential gender-based differences in interpreting the Kansas City Cardiomyopathy Questionnaire (KCCQ-23) and to explore if there are aspects of health-related quality of life (HRQOL) not captured by the KCCQ-23 that are important to assess in men and/or women with heart failure (HF).

Methods: Patients ≥ 22 years of age with clinician-diagnosed HF and left ventricular ejection fraction ≤ 40% were recruited from two academic medical centers to participate in semi-structured concept elicitation and cognitive debriefing interviews. Enrollment was stratified by patient-identified gender (half women/half men).

Provider Perspectives on Artificial Intelligence-Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study.

Background: The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine.

Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial.

Background: Previous atrial fibrillation screening trials have highlighted the need for more targeted approaches. We did a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted screening approach for identifying previously unrecognised atrial fibrillation.

Methods: For this non-randomised interventional trial, we prospectively recruited patients with stroke risk factors but with no known atrial fibrillation who had an electrocardiogram (ECG) done in routine practice.

Effect of hospital-at-home vs. traditional brick-and-mortar hospital care in acutely ill adults: study protocol for a pragmatic randomized controlled trial.

Background: Delivering acute hospital care to patients at home might reduce costs and improve patient experience. Mayo Clinic's Advanced Care at Home (ACH) program is a novel virtual hybrid model of "Hospital at Home." This pragmatic randomized controlled non-inferiority trial aims to compare two acute care delivery models: ACH vs.

Diabetes Rescue, Engagement and Management (D-REM): rationale and design of a pragmatic clinical trial of a community paramedicine programme to improve diabetes care.

Introduction: Diabetes is one of the most common serious chronic health conditions in the USA. People living with diabetes face multiple barriers to optimal diabetes care, including gaps in access to medical care and self-management education, diabetes distress, and high burden of treatment. Community paramedics (CPs) are uniquely positioned to support multidisciplinary care for patients with diabetes by delivering focused diabetes self-management education and support and bridging the gaps between patients and the clinical and community resources they need to live well with their disease.

Do patients with high versus low treatment and illness burden have different needs? A mixed-methods study of patients living on dialysis.

Background: Approximately 750,000 people in the U.S. live with end-stage kidney disease (ESKD); the majority receive dialysis.

Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial.

Background: Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive.

Objectives: To build infrastructure for digital trials to improve efficiency and generalizability and test it using a study to validate an artificial intelligence algorithm to detect atrial fibrillation (AF).

Design: We will prospectively enroll 1,000 patients who underwent an electrocardiogram for any clinical reason in routine practice, do not have a previous diagnosis of AF or atrial flutter and would be eligible for anticoagulation if AF is detected.

Patient Work and Treatment Burden in Type 2 Diabetes: A Mixed-Methods Study.

Objective: To use quantitative and qualitative methods to characterize the work patients with type 2 diabetes mellitus (T2DM) enact and explore the interactions between illness, treatment, and life.

Patients And Methods: In this mixed-methods, descriptive study, adult patients with T2DM seen at the outpatient diabetes clinic at Mayo Clinic in Rochester, Minnesota, from February 1, 2016, through March 31, 2017, were invited to participate. The study had 3 phases.

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure.

Increasing risk-concordant cardiovascular care in diverse health systems: a mixed methods pragmatic stepped wedge cluster randomized implementation trial of shared decision making (SDM4IP).

Background: The primary prevention of cardiovascular (CV) events is often less intense in persons at higher CV risk and vice versa. Clinical practice guidelines recommend that clinicians and patients use shared decision making (SDM) to arrive at an effective and feasible prevention plan that is congruent with each person's CV risk and informed preferences. However, SDM does not routinely happen in practice.

Minimally disruptive medicine (MDM) in clinical practice: a qualitative case study of the human immunodeficiency virus (HIV) clinic care model.

Background: Recent evidence suggests the need to reframe healthcare delivery for patients with chronic conditions, with emphasis on minimizing healthcare footprint/workload on patients, caregivers, clinicians and health systems through the proposed Minimally Disruptive Medicine (MDM) care model named. HIV care models have evolved to further focus on understanding barriers and facilitators to care delivery while improving patient-centered outcomes (e.g.

Determinants of Self-reported Health Outcomes in Adrenal Insufficiency: A Multisite Survey Study.

Context: Current evidence on determinants of adverse health outcomes in patients with adrenal insufficiency (AI) is scarce, especially in regards to AI subtypes.

Objective: To determine predictors of adverse outcomes in different subtypes of AI.

Design And Setting: Cross-sectional survey study at 2 tertiary centers.

Normalization of a conversation tool to promote shared decision making about anticoagulation in patients with atrial fibrillation within a practical randomized trial of its effectiveness: a cross-sectional study.

Background: Shared decision making (SDM) implementation remains challenging. The factors that promote or hinder implementation of SDM tools for use during the consultation, including contextual factors such as clinician burnout and organizational support, remain unclear. We explored these factors in the context of a practical multicenter randomized trial evaluating the effectiveness of an SDM conversation tool for patients with atrial fibrillation considering anticoagulation therapy.

Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE).

The article details the materials that will be used in a clinical trial - ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial [1]. It includes a clinician-facing action recommendation report that will translate an artificial intelligence algorithm to routine practice and an alert when a positive screening result is found. This report was developed using a user-centered approach via an iterative process with input from multiple physician groups.

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

Background: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment.

Objectives: To prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices.

Changing conversations in primary care for patients living with chronic conditions: pilot and feasibility study of the ICAN Discussion Aid.

Purpose: To pilot test the impact of the ICAN Discussion Aid on clinical encounters.

Methods: A pre-post study involving 11 clinicians and 100 patients was conducted at two primary care clinics within a single health system in the Midwest. The study examined clinicians' perceptions about ICAN feasibility, patients' and clinicians' perceptions about encounter success, videographic differences in encounter topics, and medication adherence 6 months after an ICAN encounter.

Reflecting on shared decision making: A reflection-quantification study.

Background: Reflecting ("stop-and-think") before rating may help patients consider the quality of shared decision making (SDM) and mitigate ceiling/halo effects that limit the performance of self-reported SDM measures.

Methods: We asked a diverse patient sample from the United States to reflect on their care before completing the 3-item CollaboRATE SDM measure. Study 1 focused on rephrasing CollaboRATE items to promote reflection before each item.

Patient capacity for self-care in the medical record of patients with chronic conditions: a mixed-methods retrospective study.

Background: Patients with chronic conditions must mobilize capacity to access and use healthcare and enact self-care. In order for clinicians to create feasible treatment plans with patients, they must appreciate the limits and possibilities of patient capacity. This study seeks to characterize the amount, nature, and comprehensiveness of the information about patient capacity documented in the medical record.

Shared decision-making following disclosure of coronary heart disease genetic risk: results from a randomized clinical trial.

Unlabelled: Whether disclosure of genetic risk for coronary heart disease (CHD) influences shared decision-making (SDM) regarding use of statins to reduce CHD risk is unknown. We randomized 207 patients, age 45-65 years, at intermediate CHD risk, and not on statins, to receive the 10-year risk of CHD based on conventional risk factors alone (n=103) or in combination with a genetic risk score (n=104). A genetic counselor disclosed this information followed by a physician visit for SDM regarding statin therapy.