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Objective: To investigate the cost-effectiveness of using artificial intelligence (AI) to screen for low ejection fraction (EF) in routine clinical practice using a pragmatic randomized controlled trial (RCT). Patients and Methods: In a post hoc analysis of the electrocardiogram (ECG) AI-guided screening for low ejection fraction trial, we developed a decision analytic model for patients aged 18 years and older without previously diagnosed heart failure and underwent a clinically indicated ECG between August 5, 2019, and March 31, 2020. In the previously published RCT, the intervention arm underwent an AI-guided targeted screening program for low EF with a workflow embedded into routine clinical practice—AI was applied to the ECG to identify patients at high-risk and recommended clinicians to order an ECG and the control arm received usual care without the screening program. We used results from the RCT for rates of low EF diagnosis and a lifetime Markov model to project the long-term outcomes. Quality-adjusted life years (QALYs), costs of intervention and treatment, disease event costs, incremental cost-effectiveness ratio (ICER), and cost for the number needed to screen. Multiple scenario and sensitivity analyses were performed. Results: Compared with usual care, AI-integrated ECG was cost effective, with an incremental cost-effectiveness ratio of $27,858/QALY. The program remained cost effective even with a change in patient age and follow-up time duration, although the specific ICER values varied for these parameters. The program was more cost effective in outpatient settings (ICER $1651/QALY) than in inpatient or emergency room settings. Conclusion: Implementing AI-guided targeted screening for low EF in routine clinical practice was cost effective.

A rigorous evaluation of the implementation of a diabetes quality measure implementation program across community healthcare clinics in Shanghai, China, where both quality measurement and primary care delivery are relatively recent but centrally supported, identified important concerns about the meaningfulness, feasibility, and accuracy of quality measures that are relevant to all quality measurement programs. These include the importance of stakeholder involvement in measure development and implementation, the need to select measures that accurately and reliably reflect care quality, the link between incentives for improved performance and data manipulation, the necessity for scientific credibility and practical feasibility of the measure, and the assurance that measure performance can be impacted by those being evaluated. In addition to elaborating on these aspects of quality measurement, we also discuss the need for quality measures that are balanced across established domains of quality, are not burdensome to participants, and are transparent, parsimonious, nimble, and oriented around continuous evaluation and improvement.

Abstract Introduction: Pragmatic trials aim to speed translation to practice by integrating study procedures in routine care settings. This study evaluated implementation outcomes related to clinician and patient recruitment and participation in a trial of community paramedicine (CP) and presents successes and challenges of maintaining pragmatic study features. Methods: Adults in the pre-hospital setting, emergency department (ED), or hospital being considered for referral to the ED/hospital or continued hospitalization for intermediate-level care were randomized 1:1 to CP care or usual care. Referral and enrollment data were tracked administratively, and patient characteristics were abstracted from the electronic health record (EHR). Enrolled patients completed baseline surveys, and a subset of intervention patients were interviewed. All CPs and a sample of clinicians and administrators were invited to complete a survey and interview. Results: Between January 2022 and February 2023, 240 enrolled patients (42% rural) completed surveys, and 22 completed an interview; 63 staff completed surveys and 20 completed an interview. Ninety-three clinicians in 27 departments made at least one referral. Factors related to referrals included program awareness and understanding the CP practice scope. Most patients were enrolled in the hospital, but characteristics were similar to the primary care population and included older and medically complex patients. Challenges to achieving representativeness included limited EHR infrastructure, constraints related to patient consenting, and clinician concerns about patient randomization disrupting preferred care. Conclusion: Future pragmatic trials in busy clinical settings may benefit from regulatory policies and EHR capabilities that allow for real-world study conduct and representative participation. Trial registration: NCT05232799.

BackgroundPoor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain.MethodsWe used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted “brain age gap” (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis.ResultsWe found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10−4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60–73 (p-value = 2.37 × 10−8).ConclusionPoor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60–73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.

Introduction/Objectives: To describe health outcomes of older adults enrolled in the Mayo Clinic Care Transitions (MCCT) program before and during the COVID-19 pandemic compared to unenrolled patients. Methods: We conducted a retrospective cohort study of adults (age >60 years) in the MCCT program compared to a usual care control group from January 1, 2019, to September 20, 2022. The MCCT program involved a home, telephonic, or telemedicine visit by an advanced care provider. Outcomes were 30- and 180-day hospital readmissions, emergency department (ED) visit, and mortality. We performed a subgroup analysis after March 1, 2020 (during the pandemic). We analyzed data with Cox proportional hazards regression models and hazard ratios (HRs) with 95% CIs. Results: Of the 1,012 patients total, 354 were in the MCCT program and 658 were in the usual care group with a mean (SD) age of 81.1 (9.1) years overall. Thirty-day readmission was 16.9% (60 of 354) for MCCT patients and 14.7% (97 of 658) for usual care patients (HR, 1.24; 95% CI, 0.88-1.75). During the pandemic, the 30-day readmission rate was 15.1% (28 of 186) for MCCT patients and 14.9% (68 of 455) for usual care patients (HR, 1.20; 95% CI, 0.75-1.91). There was no difference between groups for 180-day hospitalization, 30- or 180-day ED visit, and 30- or 180-day mortality. Conclusions: Numerous factors involving patients, providers, and health care delivery systems during the pandemic most likely contributed to these findings.

Rationale & Objective: Innovative models are needed to address significant gaps in kidney care follow-up for acute kidney injury (AKI) survivors. Study Design: This quasi-experimental pilot study reports the feasibility of the AKI in Care Transitions (ACT) program, a multidisciplinary approach to AKI survivor care based in the primary care setting. Setting & Participants: The study included consenting adults with stage 3 AKI discharged home without dialysis. Interventions: The ACT intervention included predischarge education from nurses and coordinated postdischarge follow-up with a primary care provider and pharmacist within 14 days. ACT was implemented in phases (Usual Care, Education, ACT). Outcomes: The primary outcome was feasibility. Secondary outcomes included process and clinical outcomes. Results: In total, 46 of 110 eligible adults were enrolled. Education occurred in 18/18 and 14/15 participants in the Education and ACT groups, respectively. 30-day urine protein evaluation occurred in 15%, 28%, and 87% of the Usual Care, Education, and ACT groups, respectively (P

Abstract Background New patient-centered models of care are needed to individualize care and reduce high-cost care, including emergency department (ED) visits and hospitalizations for low- and intermediate-acuity conditions that could be managed outside the hospital setting. Community paramedics (CPs) have advanced training in low- and high-acuity care and are equipped to manage a wide range of health conditions, deliver patient education, and address social determinants of health in the home setting. The objective of this trial is to evaluate the effectiveness and implementation of the Care Anywhere with Community Paramedics (CACP) program with respect to shortening and preventing acute care utilization. Methods This is a pragmatic, hybrid type 1, two-group, parallel-arm, 1:1 randomized clinical trial of CACP versus usual care that includes formative evaluation methods and assessment of implementation outcomes. It is being conducted in two sites in the US Midwest, which include small metropolitan areas and rural areas. Eligible patients are ≥ 18 years old; referred from an outpatient, ED, or hospital setting; clinically appropriate for ambulatory care with CP support; and residing within CP service areas of the referral sites. Aim 1 uses formative data collection with key clinical stakeholders and rapid qualitative analysis to identify potential facilitators/barriers to implementation and refine workflows in the 3-month period before trial enrollment commences (i.e., pre-implementation). Aim 2 uses mixed methods to evaluate CACP effectiveness, compared to usual care, by the number of days spent alive outside of the ED or hospital during the first 30 days following randomization (primary outcome), as well as self-reported quality of life and treatment burden, emergency medical services use, ED visits, hospitalizations, skilled nursing facility utilization, and adverse events (secondary outcomes). Implementation outcomes will be measured using the RE-AIM framework and include an assessment of perceived sustainability and metrics on equity in implementation. Aim 3 uses qualitative methods to understand patient, CP, and health care team perceptions of the intervention and recommendations for further refinement. In an effort to conduct a rigorous evaluation but also speed translation to practice, the planned duration of the trial is 15 months from the study launch to the end of enrollment. Discussion This study will provide robust and timely evidence for the effectiveness of the CACP program, which may pave the way for large-scale implementation. Implementation outcomes will inform any needed refinements and best practices for scale-up and sustainability. Trial registration ClinicalTrials.gov NCT05232799. Registered on 10 February 2022.

Abstract Background As providers look to scale high-acuity care in the patient home setting, hospital-at-home is becoming more prevalent. The traditional model of hospital-at-home usually relies on care delivery by in-home providers, caring for patients in urban communities through academic medical centers. Our objective is to describe the process and outcomes of Mayo Clinic’s Advanced Care at Home (ACH) program, a hybrid virtual and in-person hospital-at-home model combining a single, virtual provider-staffed command center with a vendor-mediated in-person medical supply chain to simultaneously deliver care to patients living near an urban hospital-at-home command center and patients living in a rural region in a different US state and time zone. Methods A descriptive, retrospective medical records review of all patients admitted to ACH between July 6, 2020, and December 31, 2021. Patients were admitted to ACH from an urban academic medical center in Florida and a rural community hospital in Wisconsin. We collected patient volumes, age, sex, race, ethnicity, insurance type, primary hospital diagnosis, 30-day mortality rate, in-program mortality, 30-day readmission rate, rate of return to hospital during acute phase, All Patient Refined-Diagnosis Related Groups (APR-DRG) Severity of Illness (SOI), and length of stay (LOS) in both the inpatient-equivalent acute phase and post-acute equivalent restorative phase. Results Six hundred and eighty-six patients were admitted to the ACH program, 408 in Florida and 278 in Wisconsin. The most common diagnosis seen were infectious pneumonia (27.0%), septicemia / bacteremia (11.5%), congestive heart failure exacerbation (11.5%), and skin and soft tissue infections (6.3%). Median LOS in the acute phase was 3 days (IQR 2–5) and median stay in the restorative phase was 22 days (IQR 11–26). In-program mortality rate was 0% and 30-day mortality was 0.6%. The mean APR-DRG SOI was 2.9 (SD 0.79) and the 30-day readmission rate was 9.7%. Conclusions The ACH hospital-at-home model was able to provide both high-acuity inpatient-level care and post-acute care to patients in their homes through a single command center to patients in urban and rural settings in two different geographical locations with favorable outcomes of low mortality and hospital readmissions.

Abstract Background The COVID-19 pandemic resulted in unprecedented increases in mortality in the U.S. and worldwide. To better understand the impact of the COVID-19 pandemic on mortality in the state of Minnesota, U.S.A., we characterize the changes in the causes of death during 2020 (COVID-19 period), compared to 2018–2019 (baseline period), assessing for differences across ages, races, ethnicities, sexes, and geographic characteristics. Methods Longitudinal population-based study using Minnesota death certificate data, 2018–2020. Using Poisson regression models adjusted for age and sex, we calculated all-cause and cause-specific (by underlying causes of death) mortality rates per 100,000 Minnesotans, the demographics of the deceased, and years of life lost (YLL) using the Chiang’s life table method in 2020 relative to 2018–2019. Results We identified 89,910 deaths in 2018–2019 and 52,030 deaths in 2020. The mean daily mortality rate increased from 123.1 (SD 11.7) in 2018–2019 to 144.2 (SD 22.1) in 2020. COVID-19 comprised 9.9% of deaths in 2020. Other categories of causes of death with significant increases in 2020 compared to 2018–2019 included assault by firearms (RR 1.68, 95% CI 1.34–2.11), accidental poisonings (RR 1.49, 95% CI 1.37–1.61), malnutrition (RR 1.48, 95% CI 1.17–1.87), alcoholic liver disease (RR, 95% CI 1.14–1.40), and cirrhosis and other chronic liver diseases (RR 1.28, 95% CI 1.09–1.50). Mortality rates due to COVID-19 and non-COVID-19 causes were higher among racial and ethnic minority groups, older adults, and non-rural residents. Conclusions The COVID-19 pandemic was associated with a 17% increase in the death rate in Minnesota relative to 2018–2019, driven by both COVID-19 and non-COVID-19 causes. As the COVID-19 pandemic enters its third year, it is imperative to examine and address the factors contributing to excess mortality in the short-term and monitor for additional morbidity and mortality in the years to come.

Rationale & Objective: Widespread delivery of high-quality care for acute kidney injury (AKI) survivors after hospital discharge requires a multidisciplinary team. We aimed to compare management approaches between nephrologists and primary care providers (PCPs) and explored strategies to optimize collaboration. Study Design: Explanatory sequential mixed-methods study using a case-based survey followed by semi-structured interviews. Setting & Participants: Nephrologists and PCPs providing AKI survivor care at 3 Mayo Clinic sites and the Mayo Clinic Health System were included. Outcomes: Survey questions and interviews elucidated participants’ recommendations for post-AKI care. Analytical Approach: Descriptive statistics were used to summarize survey responses. Qualitative data analysis used deductive and inductive strategies. A connecting and merging approach was used for mixed-methods data integration. Results: 148 of 774 (19%) providers submitted survey responses (24/72 nephrologists and 105/705 PCPs). Nephrologists and PCPs recommended laboratory monitoring and follow-up with a PCP shortly after hospital discharge. Both indicated that the need for nephrology referral, and its timing should be dictated by clinical and non-clinical patient-specific factors. There were opportunities for improvement in medication and comorbid condition management in both groups. Incorporation of multidisciplinary specialists (eg, pharmacists) was recommended to expand knowledge, optimize patient-centered care, and alleviate provider workload. Limitations: Survey findings may have been affected by non-response bias and the unique challenges facing clinicians and health systems during the COVID-19 pandemic. Participants were from a single health system, and their views or experiences may differ from those in other health systems or serving different populations. Conclusions: A multidisciplinary team-based model of post-AKI care may facilitate implementation of a patient-centered care plan, improve adherence to best practices, and reduce clinician and patient burden. Individualizing care for AKI survivors based on clinical and non-clinical patient-specific factors is needed to optimize outcomes for patients and health systems.

Abstract Background Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with high morbidity and limited treatment options. Type 2 diabetes mellitus (T2DM) is a common comorbid illness among patients with IPF and is often treated with metformin, the first-line agent in the management of T2DM. There is growing evidence demonstrating metformin’s anti-fibrotic properties; however, there is little real-world clinical data regarding its potential effectiveness in IPF. This study aims to evaluate the clinical benefit of metformin in patients with IPF and T2DM. Methods This nationwide cohort study used de-identified administrative claims data from OptumLabs® Data Warehouse to identify 3599 adults with IPF and concomitant T2DM between January 1, 2014 and June 30, 2019. Two cohorts were created: a cohort treated with metformin (n = 1377) and a cohort not treated with metformin (n = 2222). A final 1:1 propensity score-matched cohort compared 1100 patients with IPF and T2DM receiving metformin to those with both diagnoses but not receiving metformin; matching accounted for age, sex, race/ethnicity, residence region, year, medications, oxygen use, smoking status, healthcare use, and comorbidities. Outcomes were all-cause mortality (primary) and hospitalizations (secondary). Results Among 2200 patients with IPF and T2DM included in this matched analysis, metformin therapy was associated with a reduction in all-cause mortality (hazard ratio [HR], 0.46; 95% confidence interval [CI], 0.36–0.58; p

Pragmatic trials aim to generate timely evidence while ensuring feasibility, minimizing practice burden, and maintaining real-world conditions. We conducted rapid-cycle qualitative research in the preimplementation period of a trial evaluating a community paramedic program to shorten and prevent hospitalizations. Between December 2021 and March 2022, interviews (n = 30) and presentations/discussions (n = 17) were conducted with clinical and administrative stakeholders. Two investigators analyzed interview and presentation data to identify potential trial challenges, and team reflections were used to develop responsive strategies. Solutions were implemented prior to the commencement of trial enrollment and were aimed at bolstering feasibility and building ongoing practice feedback loops.

Background: Acute kidney injury (AKI) survivors are at heightened risk for poor short- and long-term health outcomes. Even among those who recover after an AKI episode, the risk for chronic kidney disease is 4- to 6-fold higher than in patients without AKI, underscoring the importance of identifying methods to improve AKI survivorship. Objective: The purpose of this report was to describe the development and feasibility of a novel multidisciplinary approach to caring for AKI survivors at care transitions (ACT). Design: Observational process improvement initiative. Setting: Single academic medical center in the United States. Patients: The studied population was adults with stage 3 AKI not discharging on dialysis who were established with a primary care provider (PCP) at our institution. Methods: An electronic health record tool was developed prior to implementation to identify AKI survivors. The ACT program encompassed engaging patients in the hospital, delivering education by nephrology-trained nurses before discharge, completing recommended laboratory testing after discharge, and conducting structured kidney-focused follow-up with a pharmacist and a PCP within 7 to 14 days after discharge. Patients could be referred for nephrology evaluation at the discretion of the PCP. Results: Preliminary data demonstrated that most AKI survivors of interest could be identified, educated, and followed up with this model. This strategy appeared feasible, scalable, and maximized the unique expertise of each member of the multidisciplinary team. Limitations: Small sample size, future assessment of process, clinical, and patient-reported outcomes needed. Conclusions: The multidisciplinary ACT workflow supported by clinical decision support was feasible and addressed gaps in existing care transition models. Team-based care delivery in primary care appears to be a mechanism to extend the capacity for kidney health monitoring for AKI survivors.

Abstract Introduction: Research career development awards (CDAs) facilitate development of clinician-scientists. This study compared the academic achievements of individuals in a structured institutional “pre-K” CDA program, the Mayo Clinic Kern Scholars program, with individuals who applied for but were not admitted to the Kern program (“Kern applicants”), and awardees of other unstructured internal CDAs. Methods: This was a longitudinal cohort study of clinicians engaged in research at Mayo Clinic between 2010 and 2019. The primary outcome was time to the 15th new peer-reviewed publication after the program start, adjusted for baseline number of publications. Secondarily, we described successful awarding of federal funding by the NIH or VA. Results: The median (IQR) number of baseline publications was highest among Kern Scholars compared to Kern Applicants or other CDA awardees [16 (12, 29) vs 5 (1, 11) and 8 (5, 16); P < 0.001]. After adjustment for baseline publications, the time to 15th new publication was significantly shorter for Kern Scholars than for the two comparator groups (P

Background: Multisector collaboratives are increasingly popular strategies for improving population health. To be comprehensive, collaboratives must coordinate the activities of many organizations across a geographic region. Many policy-relevant models encourage creation and use of centralized hub organizations to do this work, yet there is little guidance on how to evaluate implementation of such hubs and track their network reach. We sought to demonstrate how social network analysis (SNA) could be used for this purpose.Methods: Through formative research, we defined and conceptualized key characteristics of a bridging hub network and identified a set of candidate measures—(1) network membership, (2) network interaction, (3) role and reach of the bridging hub, and (4) network collaboration—to evaluate its implementation within a pre-determined geographic region of Southeast Minnesota, USA. We then developed and administered a survey to assess outcomes as part of a SNA. We commented on the feasibility and usefulness of the methods.Results: The initial surveyed network consisted of 50 healthcare organizational sites and 50 community organizations representing sectors of public health, education, research, health promotion, social services, and long-term care and supports. Fifty-three of these organizations responded to the survey. The network's level of collaboration was “Cooperation” (level 2 of 5) and reported levels of collaboration varied by organization. Thirty-eight additional, unsurveyed organizations were identified as collaborators by respondents, pushing the theoretical network denominator up to 138 organizations. These additional organizations included grocery stores, ambulance services, and smaller, independent healthcare and community-based services focused on meeting the needs of underserved populations. The bridging hub organization had the highest betweenness centrality and was in good position to bridge healthcare and the community, although its organizational reach was estimated at only 51%. The SNA methods were feasible and useful for identifying opportunities and guiding implementation.Conclusions: Bridging hub organizations are not likely to link—or even be aware of—all relevant organizations in a geographic region at initial implementation. SNA may be a useful method for evaluating the value and reach of a bridging hub organization and guiding ongoing implementation efforts.Trial registration: http://ClinicalTrials.gov; #NCT03046498

Objective We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States.Design We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports.Interventions Issuance of stay-at-home orders.Primary and secondary outcome measures Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility.Results Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%–6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties.Conclusion Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.