Implementing Telestroke in the Inpatient Setting: Identifying Factors for Success.

BACKGROUND: Inpatient telestroke programs have emerged as a solution to provide timely stroke care in underserved areas, but their successful implementation and factors influencing their effectiveness remain underexplored. This study aimed to qualitatively evaluate the perspectives of inpatient clinicians located at spoke hospitals participating in a newly established inpatient telestroke program to identify implementation barriers and facilitators. METHODS: This was a formative evaluation relying on semistructured qualitative interviews with 16 inpatient providers (physicians and nurse practitioners) at 5 spoke sites of a hub-and-spoke inpatient telestroke program. The Integrated-Promoting Action on Research Implementation in Health Services framework guided data analysis, focusing on the innovation, recipients, context, and facilitation aspects of implementation. Interviews were transcribed and coded using thematic analysis. RESULTS: Fifteen themes were identified in the data and mapped to the Integrated-Promoting Action on Research Implementation in Health Services framework. Themes related to the innovation (the telestroke program) included easy access to stroke specialists, the benefits of limiting patient transfers, concerns about duplicating tests, and challenges of timing inpatient telestroke visits and notes to align with discharge workflow. Themes pertaining to recipients (care team members and patients) were communication gaps between teams, concern about the supervision of inpatient telestroke advanced practice providers and challenges with nurse empowerment. With regard to the context (hospital and system factors), providers highlighted familiarity with telehealth technologies as a facilitator to implementing inpatient telestroke, yet highlighted resource limitations in smaller facilities. Facilitation (program implementation) was recognized as crucial for education, standardization, and buy-in. CONCLUSIONS: Understanding barriers and facilitators to implementation is crucial to determining where programmatic changes may need to be made to ensure the success and sustainment of inpatient telestroke services.

Patient Heterogeneity and the J-Curve Relationship Between Time-to-Antibiotics and the Outcomes of Patients Admitted With Bacterial Infection.

OBJECTIVES: Sepsis remains a leading and preventable cause of hospital utilization and mortality in the United States. Despite updated guidelines, the optimal definition of sepsis as well as optimal timing of bundled treatment remain uncertain. Identifying patients with infection who benefit from early treatment is a necessary step for tailored interventions. In this study, we aimed to illustrate clinical predictors of time-to-antibiotics among patients with severe bacterial infection and model the effect of delay on risk-adjusted outcomes across different sepsis definitions. DESIGN: A multicenter retrospective observational study. SETTING: A seven-hospital network including academic tertiary care center. PATIENTS: Eighteen thousand three hundred fifteen patients admitted with severe bacterial illness with or without sepsis by either acute organ dysfunction (AOD) or systemic inflammatory response syndrome positivity. MEASUREMENTS AND MAIN RESULTS: The primary exposure was time to antibiotics. We identified patient predictors of time-to-antibiotics including demographics, chronic diagnoses, vitals, and laboratory results and determined the impact of delay on a composite of inhospital death or length of stay over 10 days. Distribution of time-to-antibiotics was similar across patients with and without sepsis. For all patients, a J-curve relationship between time-to-antibiotics and outcomes was observed, primarily driven by length of stay among patients without AOD. Patient characteristics provided good to excellent prediction of time-to-antibiotics irrespective of the presence of sepsis. Reduced time-to-antibiotics was associated with improved outcomes for all time points beyond 2.5 hours from presentation across sepsis definitions. CONCLUSIONS: Antibiotic timing is a function of patient factors regardless of sepsis criteria. Similarly, we show that early administration of antibiotics is associated with improved outcomes in all patients with severe bacterial illness. Our findings suggest identifying infection is a rate-limiting and actionable step that can improve outcomes in septic and nonseptic patients.

Predictors of Postacute Sequelae of COVID-19 Development and Rehabilitation: A Retrospective Study.

OBJECTIVE: To examine the frequency of postacute sequelae of SARS-CoV-2 (PASC) and the factors associated with rehabilitation utilization in a large adult population with PASC. DESIGN: Retrospective study. SETTING: Midwest hospital health system. PARTICIPANTS: 19,792 patients with COVID-19 from March 10, 2020, to January 17, 2021. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: Descriptive analyses were conducted across the entire cohort along with an adult subgroup analysis. A logistic regression was performed to assess factors associated with PASC development and rehabilitation utilization. RESULTS: In an analysis of 19,792 patients, the frequency of PASC was 42.8% in the adult population. Patients with PASC compared with those without had a higher utilization of rehabilitation services (8.6% vs 3.8%, P<.001). Risk factors for rehabilitation utilization in patients with PASC included younger age (odds ratio [OR], 0.99; 95% confidence interval [CI], 0.98-1.00; P=.01). In addition to several comorbidities and demographics factors, risk factors for rehabilitation utilization solely in the inpatient population included male sex (OR, 1.24; 95% CI, 1.02-1.50; P=.03) with patients on angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers 3 months prior to COVID-19 infections having a decreased risk of needing rehabilitation (OR, 0.80; 95% CI, 0.64-0.99; P=.04). CONCLUSIONS: Patients with PASC had higher rehabilitation utilization. We identified several clinical and demographic factors associated with the development of PASC and rehabilitation utilization.

Outpatient metformin use is associated with reduced severity of COVID-19 disease in adults with overweight or obesity.

Observational studies suggest outpatient metformin use is associated with reduced mortality from coronavirus disease-2019 (COVID-19). Metformin is known to decrease interleukin-6 and tumor-necrosis factor-α, which appear to contribute to morbidity in COVID-19. We sought to understand whether outpatient metformin use was associated with reduced odds of severe COVID-19 disease in a large US healthcare data set. Retrospective cohort analysis of electronic health record (EHR) data that was pooled across multiple EHR systems from 12 hospitals and 60 primary care clinics in the Midwest between March 4, 2020 and December 4, 2020. Inclusion criteria: data for body mass index (BMI) > 25 kg/m2 and a positive SARS-CoV-2 polymerase chain reaction test; age ≥ 30 and ≤85 years. Exclusion criteria: patient opt-out of research. Metformin is the exposure of interest, and death, admission, and intensive care unit admission are the outcomes of interest. Metformin was associated with a decrease in mortality from COVID-19, OR 0.32 (0.15, 0.66; p = .002), and in the propensity-matched cohorts, OR 0.38 (0.16, 0.91; p = .030). Metformin was associated with a nonsignificant decrease in hospital admission for COVID-19 in the overall cohort, OR 0.78 (0.58-1.04, p = .087). Among the subgroup with a hemoglobin HbA1c available (n = 1193), the adjusted odds of hospitalization (including adjustment for HbA1c) for metformin users was OR 0.75 (0.53-1.06, p = .105). Outpatient metformin use was associated with lower mortality and a trend towards decreased admission for COVID-19. Given metformin's low cost, established safety, and the mounting evidence of reduced severity of COVID-19 disease, metformin should be prospectively assessed for outpatient treatment of COVID-19.

Racial and Ethnic Disparities in Hospital Admissions from COVID-19: Determining the Impact of Neighborhood Deprivation and Primary Language.

BACKGROUND: Despite past and ongoing efforts to achieve health equity in the USA, racial and ethnic disparities persist and appear to be exacerbated by COVID-19. OBJECTIVE: Evaluate neighborhood-level deprivation and English language proficiency effect on disproportionate outcomes seen in racial and ethnic minorities diagnosed with COVID-19. DESIGN: Retrospective cohort study SETTING: Health records of 12 Midwest hospitals and 60 clinics in Minnesota between March 4, 2020, and August 19, 2020 PATIENTS: Polymerase chain reaction-positive COVID-19 patients EXPOSURES: Area Deprivation Index (ADI) and primary language MAIN MEASURES: The primary outcome was COVID-19 severity, using hospitalization within 45 days of diagnosis as a marker of severity. Logistic and competing-risk regression models assessed the effects of neighborhood-level deprivation (using the ADI) and primary language. Within race, effects of ADI and primary language were measured using logistic regression. RESULTS: A total of 5577 individuals infected with SARS-CoV-2 were included; 866 (n = 15.5%) were hospitalized within 45 days of diagnosis. Hospitalized patients were older (60.9 vs. 40.4 years, p < 0.001) and more likely to be male (n = 425 [49.1%] vs. 2049 [43.5%], p = 0.002). Of those requiring hospitalization, 43.9% (n = 381), 19.9% (n = 172), 18.6% (n = 161), and 11.8% (n = 102) were White, Black, Asian, and Hispanic, respectively. Independent of ADI, minority race/ethnicity was associated with COVID-19 severity: Hispanic patients (OR 3.8, 95% CI 2.72-5.30), Asians (OR 2.39, 95% CI 1.74-3.29), and Blacks (OR 1.50, 95% CI 1.15-1.94). ADI was not associated with hospitalization. Non-English-speaking (OR 1.91, 95% CI 1.51-2.43) significantly increased odds of hospital admission across and within minority groups. CONCLUSIONS: Minority populations have increased odds of severe COVID-19 independent of neighborhood deprivation, a commonly suspected driver of disparate outcomes. Non-English-speaking accounts for differences across and within minority populations. These results support the ongoing need to determine the mechanisms that contribute to disparities during COVID-19 while also highlighting the underappreciated role primary language plays in COVID-19 severity among minority groups.

Super fragmented: a nationally representative cross-sectional study exploring the fragmentation of inpatient care among super-utilizers.

BACKGROUND: Super-utilizers with 4 or more admissions per year frequently receive low-quality care and disproportionately contribute to healthcare costs. Inpatient care fragmentation (admission to multiple different hospitals) in this population has not been well described. OBJECTIVE: To determine the prevalence of super-utilizers who receive fragmented care across different hospitals and to describe associated risks, costs, and health outcomes. RESEARCH DESIGN: We analyzed inpatient data from the Health Care Utilization Project's State Inpatient and Emergency Department database from 6 states from 2013. After identifying hospital super-utilizers, we stratified by the number of different hospitals visited in a 1-year period. We determined how patient demographics, costs, and outcomes varied by degree of fragmentation. We then examined how fragmentation would influence a hospital's ability to identify super-utilizers. SUBJECTS: Adult patients with 4 or more inpatient stays in 1 year. MEASURES: Patient demographics, cost, 1-year hospital reported mortality, and probability that a single hospital could correctly identify a patient as a super-utilizer. RESULTS: Of the 167,515 hospital super-utilizers, 97,404 (58.1%) visited more than 1 hospital in a 1-year period. Fragmentation was more likely among younger, non-white, low-income, under-insured patients, in population-dense areas. Patients with fragmentation were more likely to be admitted for chronic disease management, psychiatric illness, and substance abuse. Inpatient fragmentation was associated with higher yearly costs and lower likelihood of being identified as a super-utilizer. CONCLUSIONS: Inpatient care fragmentation is common among super-utilizers, disproportionately affects vulnerable populations. It is associated with high yearly costs and a decreased probability of correctly identifying super-utilizers.

Evaluation of an Intrahospital Telemedicine Program for Patients Admitted With COVID-19: Mixed Methods Study.

BACKGROUND: The increasing incidence of COVID-19 infection has challenged health care systems to increase capacity while conserving personal protective equipment (PPE) supplies and minimizing nosocomial spread. Telemedicine shows promise to address these challenges but lacks comprehensive evaluation in the inpatient environment. OBJECTIVE: The aim of this study is to evaluate an intrahospital telemedicine program (virtual care), along with its impact on exposure risk and communication. METHODS: We conducted a natural experiment of virtual care on patients admitted for COVID-19. The primary exposure variable was documented use of virtual care. Patient characteristics, PPE use rates, and their association with virtual care use were assessed. In parallel, we conducted surveys with patients and clinicians to capture satisfaction with virtual care along the domains of communication, medical treatment, and exposure risk. RESULTS: Of 137 total patients in our primary analysis, 43 patients used virtual care. In total, there were 82 inpatient days of use and 401 inpatient days without use. Hospital utilization and illness severity were similar in patients who opted in versus opted out. Virtual care was associated with a significant reduction in PPE use and physical exam rate. Surveys of 41 patients and clinicians showed high rates of recommendation for further use, and subjective improvements in communication. However, providers and patients expressed limitations in usability, medical assessment, and empathetic communication. CONCLUSIONS: In this pilot natural experiment, only a subset of patients used inpatient virtual care. When used, virtual care was associated with reductions in PPE use, reductions in exposure risk, and patient and provider satisfaction.

Diagnostic Discordance, Health Information Exchange, and Inter-Hospital Transfer Outcomes: a Population Study.

BACKGROUND: Studying diagnostic error at the population level requires an understanding of how diagnoses change over time. OBJECTIVE: To use inter-hospital transfers to examine the frequency and impact of changes in diagnosis on patient risk, and whether health information exchange can improve patient safety by enhancing diagnostic accuracy. DESIGN: Diagnosis coding before and after hospital transfer was merged with responses from the American Hospital Association Annual Survey for a cohort of patients transferred between hospitals to identify predictors of mortality. PARTICIPANTS: Patients (180,337) 18 years or older transferred between 473 acute care hospitals from NY, FL, IA, UT, and VT from 2011 to 2013. MAIN MEASURES: We identified discordant Elixhauser comorbidities before and after transfer to determine the frequency and developed a weighted score of diagnostic discordance to predict mortality. This was included in a multivariate model with inpatient mortality as the dependent variable. We investigated whether health information exchange (HIE) functionality adoption as reported by hospitals improved diagnostic discordance and inpatient mortality. KEY RESULTS: Discordance in diagnoses occurred in 85.5% of all patients. Seventy-three percent of patients gained a new diagnosis following transfer while 47% of patients lost a diagnosis. Diagnostic discordance was associated with increased adjusted inpatient mortality (OR 1.11 95% CI 1.10-1.11, p < 0.001) and allowed for improved mortality prediction. Bilateral hospital HIE participation was associated with reduced diagnostic discordance index (3.69 vs. 1.87%, p < 0.001) and decreased inpatient mortality (OR 0.88, 95% CI 0.89-0.99, p < 0.001). CONCLUSIONS: Diagnostic discordance commonly occurred during inter-hospital transfers and was associated with increased inpatient mortality. Health information exchange adoption was associated with decreased discordance and improved patient outcomes.

Insurance Coverage Predicts Mortality in Patients Transferred Between Hospitals: a Cross-Sectional Study.

BACKGROUND: Patients transferred between hospitals are at high risk of adverse events and mortality. The relationship between insurance status, transfer practices, and outcomes has not been definitively characterized. OBJECTIVE: To identify the association between insurance coverage and mortality of patients transferred between hospitals. DESIGN: We conducted a single-institution observational study, and validated results using a national administrative database of inter-hospital transfers. SETTING: Three ICUs at an academic tertiary care center validated by a nationally representative sample of inter-hospital transfers. PATIENTS: The single-institution analysis included 652 consecutive patients transferred from 57 hospitals between 2011 and 2012. The administrative database included 353,018 patients transferred between 437 hospitals. MEASUREMENTS: Adjusted inpatient mortality and 24-h mortality, stratified by insurance status. RESULTS: Of 652 consecutive transfers to three ICUs, we observed that uninsured patients had higher adjusted inpatient mortality (OR 2.67, p = 0.021) when controlling for age, race, gender, Apache-II, and whether the patient was transferred from an ED. Uninsured were more likely to be transferred from ED (OR 2.3, p = 0.026), and earlier in their hospital course (3.9 vs 2.0 days, p = 0.002). Using an administrative dataset, we validated these observations, finding that the uninsured had higher adjusted inpatient mortality (OR 1.24, 95% CI 1.13-1.36, p < 0.001) and higher mortality within 24 h (OR 1.33 95% CI 1.11-1.60, p < 0.002). The increase in mortality was independent of patient demographics, referral patterns, or diagnoses. LIMITATIONS: This is an observational study where transfer appropriateness cannot be directly assessed. CONCLUSIONS: Uninsured patients are more likely to be transferred from an ED and have higher mortality. These data suggest factors that drive inter-hospital transfer of uninsured patients have the potential to exacerbate outcome disparities.

Evaluation of a Clinical Decision Support Tool to Guide Adoption of the American Heart Association Telemetry Monitoring Practice Standards.

BACKGROUND: The objectives of this study were to (1) evaluate telemetry use pre- and postimplementation of clinical decision support tools to support American Heart Association practice standards for telemetry monitoring and (2) understand the factors that may contribute to variation of telemetry monitoring in practice. METHODS AND RESULTS: First, we captured overall variability in telemetry use pre- and postimplementation of the clinical decision support intervention. We then conducted semistructured interviews with telemetry-ordering providers to identify key barriers and facilitators to adoption. During the study period, 399 physicians met criteria for inclusion and were divided into excessive and nonexcessive orderers. Distribution of telemetry use was bimodal. Among nonexcessive users, 24.4% of patient days were with telemetry compared with 51.6% among excessive users. On average, both excessive (6.1% reduction) and nonexcessive users (2.8% reduction) decreased telemetry use postimplementation, and these reductions were sustained over a 16-month period. Sixteen interviews were conducted. Physicians believed that the tool was successful because it caused them to more closely consider if telemetry was indicated for each patient. Physicians also voiced frustration with interruptions to their workflow, and some noted that they commonly use telemetry outside of practice standards to monitor patients who were acutely but not critically ill. CONCLUSIONS: Embedding telemetry practice standards into the electronic health record in the form of clinical decision support is effective at reducing excess telemetry use. Although the intervention was well received, there are persistent barriers, such as preexisting views on telemetry and existing workflow habits, that may inhibit higher adoption of standards.

Building to learn: Information technology innovations to enable rapid pragmatic evaluation in a learning health system.

Background: Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability. Objectives: We established a pragmatic clinical trial unit ("RapidEval") to support the development of an LHS. To further advance the field of LHS, we sought to further characterize the role of health information technology (HIT), including innovative solutions and challenges that occur, to improve LHS project delivery. Methods: During the period from December 2021 to February 2023, eight projects were selected out of 51 applications to the RapidEval program, of which five were implemented, one is currently in pilot testing, and two are in planning. We evaluated pre-study planning, implementation, analysis, and study closure approaches across all RapidEval initiatives to summarize approaches across studies and identify key innovations and learnings by gathering data from study investigators, quality staff, and IT staff, as well as RapidEval staff and leadership. Implementation Results: Implementation approaches spanned a range of HIT capabilities including interruptive alerts, clinical decision support integrated into order systems, patient navigators, embedded micro-education, targeted outpatient hand-off documentation, and patient communication. Study approaches include pre-post with time-concordant controls (1), randomized stepped-wedge (1), cluster randomized across providers (1) and location (3), and simple patient level randomization (2). Conclusions: Study selection, design, deployment, data collection, and analysis required close collaboration between data analysts, informaticists, and the RapidEval team.

Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial.

BACKGROUND: Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted. OBJECTIVE: Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting. The primary goal of this project is to evaluate the effect of the 3 versions of the Opioid Management for You (OPY) tool on measures of opioid use relative to the standard of care by leveraging a pragmatic randomized controlled trial (RCT). METHODS: A team of researchers from the Center for Learning Health System Sciences (CLHSS) at the University of Minnesota partnered with M Health Fairview to design, build, and test the 3 versions of the OPY tool: social influence, precommitment, and testimonial version. The tool is being built using the Epic Care Companion (Epic Inc) platform and interacts with the patient through their existing MyChart (Epic Systems Corporation) personal health record account, and Epic patient portal, accessed through a phone app or the MyChart website. We have demonstrated feasibility with pilot data of the social influence version of the OPY app by targeting our pilot to a specific cohort of patients undergoing upper-extremity procedures. This study will use a group sequential RCT design to test the impact of this important health system initiative. Patients who meet OPY inclusion criteria will be stratified into low, intermediate, and high risk of opiate use based on their type of surgery. RESULTS: This study is being funded and supported by the CLHSS Rapid Prospective Evaluation and Digital Technology Innovation Programs, and M Health Fairview. Support and coordination provided by CLHSS include the structure of engagement, survey development, data collection, statistical analysis, and dissemination. The project was initially started in August 2022. The pilot was launched in February 2023 and is still running, with the data last counted in August 2023. The actual RCT is planned to start by early 2024. CONCLUSIONS: Through this RCT, we will test our hypothesis that patient opioid use and diverted prescription opioid availability can both be improved by information delivery applied through a behavioral economics lens via sending nudges directly to the opioid users through their personal health record. TRIAL REGISTRATION: ClinicalTrials.gov NCT06124079; https://clinicaltrials.gov/study/NCT06124079. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52882.

A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients.

Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases. A manual case review to obtain this estimate indicated our sample incidence of PASC (13%) was representative of the estimated population proportion (95% CI: 19±6.22%). However, the high number of cases classified as indeterminate demonstrates the challenges in classifying PASC even among experienced clinicians. Lastly, this study developed a dashboard to display views of aggregated PASC symptoms and measured its utility using the System Usability Scale. Overall comments related to the dashboard's potential were positive. This pipeline is crucial for monitoring post-COVID-19 patients with potential for use in clinical settings.

Predictors of discharge disposition and mortality following hospitalization with SARS-CoV-2 infection.

IMPORTANCE: The SARS-CoV-2 pandemic has overwhelmed hospital capacity, prioritizing the need to understand factors associated with type of discharge disposition. OBJECTIVE: Characterization of disposition associated factors following SARS-CoV-2. DESIGN: Retrospective study of SARS-CoV-2 positive patients from March 7th, 2020, to May 4th, 2022, requiring hospitalization. SETTING: Midwest academic health-system. PARTICIPANTS: Patients above the age 18 years admitted with PCR + SARS-CoV-2. INTERVENTION: None. MAIN OUTCOMES: Discharge to home versus PAC (inpatient rehabilitation facility (IRF), skilled-nursing facility (SNF), long-term acute care (LTACH)), or died/hospice while hospitalized (DH). RESULTS: We identified 62,279 SARS-CoV-2 PCR+ patients; 6,248 required hospitalizations, of whom 4611(73.8%) were discharged home, 985 (15.8%) to PAC and 652 (10.4%) died in hospital (DH). Patients discharged to PAC had a higher median age (75.7 years, IQR: 65.6-85.1) compared to those discharged home (57.0 years, IQR: 38.2-69.9), and had longer mean length of stay (LOS) 14.7 days, SD: 14.0) compared to discharge home (5.8 days, SD: 5.9). Older age (RRR:1.04, 95% CI:1.041-1.055), and higher Elixhauser comorbidity index [EI] (RRR:1.19, 95% CI:1.168-1.218) were associated with higher rate of discharge to PAC versus home. Older age (RRR:1.069, 95% CI:1.060-1.077) and higher EI (RRR:1.09, 95% CI:1.071-1.126) were associated with more frequent DH versus home. Blacks, Asians, and Hispanics were less likely to be discharged to PAC (RRR, 0.64 CI 0.47-0.88), (RRR 0.48 CI 0.34-0.67) and (RRR 0.586 CI 0.352-0.975). Having alpha variant was associated with less frequent PAC discharge versus home (RRR 0.589 CI 0.444-780). The relative risks for DH were lower with a higher platelet count 0.998 (CI 0.99-0.99) and albumin levels 0.342 (CI 0.26-0.45), and higher with increased CRP (RRR 1.006 CI 1.004-1.007) and D-Dimer (RRR 1.070 CI 1.039-1.101). Increased albumin had lower risk to PAC discharge (RRR 0.630 CI 0.497-0.798. An increase in D-Dimer (RRR1.033 CI 1.002-1.064) and CRP (RRR1.002 CI1.001-1.004) was associated with higher risk of PAC discharge. A breakthrough (BT) infection was associated with lower likelihood of DH and PAC. CONCLUSION: Older age, higher EI, CRP and D-Dimer are associated with PAC and DH discharges following hospitalization with COVID-19 infection. BT infection reduces the likelihood of being discharged to PAC and DH.

Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults.

Importance: The Deterioration Index (DTI), used by hospitals for predicting patient deterioration, has not been extensively validated externally, raising concerns about performance and equitable predictions. Objective: To locally validate DTI performance and assess its potential for bias in predicting patient clinical deterioration. Design, Setting, and Participants: This retrospective prognostic study included 13 737 patients admitted to 8 heterogenous Midwestern US hospitals varying in size and type, including academic, community, urban, and rural hospitals. Patients were 18 years or older and admitted between January 1 and May 31, 2021. Exposure: DTI predictions made every 15 minutes. Main Outcomes and Measures: Deterioration, defined as the occurrence of any of the following while hospitalized: mechanical ventilation, intensive care unit transfer, or death. Performance of the DTI was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Bias measures were calculated across demographic subgroups. Results: A total of 5 143 513 DTI predictions were made for 13 737 patients across 14 834 hospitalizations. Among 13 918 encounters, the mean (SD) age of patients was 60.3 (19.2) years; 7636 (54.9%) were female, 11 345 (81.5%) were White, and 12 392 (89.0%) were of other ethnicity than Hispanic or Latino. The prevalence of deterioration was 10.3% (n = 1436). The DTI produced AUROCs of 0.759 (95% CI, 0.756-0.762) at the observation level and 0.685 (95% CI, 0.671-0.700) at the encounter level. Corresponding AUPRCs were 0.039 (95% CI, 0.037-0.040) at the observation level and 0.248 (95% CI, 0.227-0.273) at the encounter level. Bias measures varied across demographic subgroups and were 14.0% worse for patients identifying as American Indian or Alaska Native and 19.0% worse for those who chose not to disclose their ethnicity. Conclusions and Relevance: In this prognostic study, the DTI had modest ability to predict patient deterioration, with varying degrees of performance at the observation and encounter levels and across different demographic groups. Disparate performance across subgroups suggests the need for more transparency in model training data and reinforces the need to locally validate externally developed prediction models.

Robust Methods for Quantifying the Effect of a Continuous Exposure From Observational Data.

A cornerstone of clinical medicine is intervening on a continuous exposure, such as titrating the dosage of a pharmaceutical or controlling a laboratory result. In clinical trials, continuous exposures are dichotomized into narrow ranges, excluding large portions of the realistic treatment scenarios. The existing computational methods for estimating the effect of continuous exposure rely on a set of strict assumptions. We introduce new methods that are more robust towards violations of these assumptions. Our methods are based on the key observation that changes of exposure in the clinical setting are often achieved gradually, so effect estimates must be "locally" robust in narrower exposure ranges. We compared our methods with several existing methods on three simulated studies with increasing complexity. We also applied the methods to data from 14 k sepsis patients at M Health Fairview to estimate the effect of antibiotic administration latency on prolonged hospital stay. The proposed methods achieve good performance in all simulation studies. When the assumptions were violated, the proposed methods had estimation errors of one half to one fifth of the state-of-the-art methods. Applying our methods to the sepsis cohort resulted in effect estimates consistent with clinical knowledge.

Prevalence of Medical Contraindications to Nirmatrelvir/Ritonavir in a Cohort of Hospitalized and Nonhospitalized Patients With COVID-19.

This analysis describes the prevalence of contraindications to nirmatrelvir/ritonavir among 66 007 patients with coronavirus disease 2019 in a large health care system. A possible contradiction was present in 9830 patients (14.8%), with the prevalence of contraindications increasing with higher acuity of illness.

Comparison of Outcomes and Process of Care for Patients Treated at Hospitals Dedicated for COVID-19 Care vs Other Hospitals.

Importance: Early in the SARS-CoV-2 pandemic, the M Health Fairview Hospital System established dedicated hospitals for establishing cohorts and caring for patients with COVID-19, yet the association between treatment at COVID-19-dedicated hospitals and mortality and complications is not known. Objective: To analyze the mortality rate and complications associated with treatment at the COVID-19-dedicated hospitals. Design, Setting, and Participants: This retrospective cohort study evaluated data prospectively collected from March 1, 2020, through June 30, 2021, from 11 hospitals in Minnesota, including 2 hospitals created solely to care for patients with COVID-19. Data obtained included demographic characteristics, treatments, and outcomes of interest for all patients with a confirmed COVID-19 infection admitted to this hospital system during the study period. Exposures: Patients were grouped based on whether they received treatment from 1 of the 2 COVID-19-dedicated hospitals compared with the remainder of the hospitals within the hospital system. Main Outcomes and Measures: Multivariate analyses, including risk-adjusted logistic regression and propensity score matching, were performed to evaluate the primary outcome of in-hospital mortality and secondary outcomes, including complications and use of COVID-specific therapeutics. Results: There were 5504 patients with COVID-19 admitted during the study period (median age, 62.5 [IQR, 45.0-75.6] years; 2854 women [51.9%]). Of these, 2077 patients (37.7%) (median age, 63.4 [IQR, 50.7-76.1] years; 1080 men [52.0%]) were treated at 1 of the 2 COVID-19-dedicated hospitals compared with 3427 (62.3%; median age, 62.0 [40.0-75.1] years; 1857 women (54.2%) treated at other hospitals. The mortality rate was 11.6% (n = 241) at the dedicated hospitals compared with 8.0% (n = 274) at the other hospitals (P < .001). However, risk-adjusted in-hospital mortality was significantly lower for patients in the COVID-19-dedicated hospitals in both the unmatched group (n = 2077; odds ratio [OR], 0.75; 95% CI, 0.59-0.95) and the propensity score-matched group (n = 1317; OR, 0.78; 95% CI, 0.58-0.99). The rate of overall complications in the propensity score-matched group was significantly lower (OR, 0.81; 95% CI, 0.66-0.99) and the use of COVID-19-specific therapeutics including deep vein thrombosis prophylaxis (83.9% vs 56.9%; P < .001), high-dose corticosteroids (56.1% vs 22.2%; P < .001), remdesivir (61.5% vs 44.5%; P < .001), and tocilizumab (7.9% vs 2.0; P < .001) was significantly higher. Conclusions and Relevance: In this cohort study, COVID-19-dedicated hospitals had multiple benefits, including providing high-volume repetitive treatment and isolating patients with the infection. This experience suggests improved in-hospital mortality for patients treated at dedicated hospitals owing to improved processes of care and supports the use of establishing cohorts for future pandemics.

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19.

OBJECTIVE: To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). METHODS: We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. RESULTS: The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). CONCLUSION: A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

Responding to COVID-19 Through Interhospital Resource Coordination: A Mixed-Methods Evaluation.

OBJECTIVES: The COVID-19 pandemic stressed hospital operations, requiring rapid innovations to address rise in demand and specialized COVID-19 services while maintaining access to hospital-based care and facilitating expertise. We aimed to describe a novel hospital system approach to managing the COVID-19 pandemic, including multihospital coordination capability and transfer of COVID-19 patients to a single, dedicated hospital. METHODS: We included patients who tested positive for SARS-CoV-2 by polymerase chain reaction admitted to a 12-hospital network including a dedicated COVID-19 hospital. Our primary outcome was adherence to local guidelines, including admission risk stratification, anticoagulation, and dexamethasone treatment assessed by differences-in-differences analysis after guideline dissemination. We evaluated outcomes and health care worker satisfaction. Finally, we assessed barriers to safe transfer including transfer across different electronic health record systems. RESULTS: During the study, the system admitted a total of 1209 patients. Of these, 56.3% underwent transfer, supported by a physician-led System Operations Center. Patients who were transferred were older (P = 0.001) and had similar risk-adjusted mortality rates. Guideline adherence after dissemination was higher among patients who underwent transfer: admission risk stratification (P < 0.001), anticoagulation (P < 0.001), and dexamethasone administration (P = 0.003). Transfer across electronic health record systems was a perceived barrier to safety and reduced quality. Providers positively viewed our transfer approach. CONCLUSIONS: With standardized communication, interhospital transfers can be a safe and effective method of cohorting COVID-19 patients, are well received by health care providers, and have the potential to improve care quality.