Search

Background: In response to the COVID-19 pandemic and as part of the statewide healthcare coalition response the Minnesota Critical Care Working Group (CCWG), composed of Interprofessional leaders from the state's nine largest health systems was established and entrusted to plan and coordinate critical care support for Minnesota from March 2020 through July1, 2021., Research Question: Can a statewide Critical Care Working Group develop contingency and crisis level surge strategies and indicators in response to the COVID-19 pandemic while evolving into a highly collaborative team?, Study Design and Methods: CCWG members (Intensivists, ethicists, nurses, MDH and MHA leaders) met by audio video conferencing as often as daily assessing COVID and non-COVID hospitalization data, developed surge indicators reflecting contingency versus crisis conditions, and planned responses collaboratively. A foundation of collaboration and teamwork developed which facilitated an effective statewide response., Results: Pandemic surge healthcare system strategies included use of surge intensive care unit (ICU) beds, adapted staffing models, restriction of non-emergency procedures, augmentation of tele-ICU care, ability to recognize increasing staff shortages, use of pediatric ICU beds for younger adults, and use of non-invasive ventilation (NIV) in non-ICU settings. CCWG supported development of the Minnesota Medical Operations Coordination Center, instrumental in load-balancing and mitigating crisis conditions. Minnesota surge strategies are compared to published pre- and pandemic experiences regarding staff; space; supplies, medications/equipment; and system strategies. Adopted severe surge best practices included use of adapted staffing models and NIV in non-ICU settings. CCWG effectively developed shared strategies and facilitated ICU load-balancing, which supported a regionally consistent standard of care., Interpretation: CCWG developed statewide critical care surge strategies assisting healthcare organization response to COVID-19 surges, providing a platform for clinical and operational activities. Collaboration, trust, and teamwork between CCWG leaders and healthcare organizations was essential to success and serves as a model for future events., (Copyright © 2024. Published by Elsevier Inc.)

Background: COVID-19 led to unprecedented inpatient capacity challenges, particularly in ICUs, which spurred development of statewide or regional placement centers for coordinating transfer (load-balancing) of adult patients needing intensive care to hospitals with remaining capacity., Research Question: Do Medical Operations Coordination Centers (MOCC) augment patient placement during times of severe capacity challenges?, Study Design and Methods: The Minnesota MOCC was established with a focus on transfer of adult ICU and medical-surgical patients; trauma, cardiac, stroke, burn, and extracorporeal membrane oxygenation cases were excluded. The center operated within one health care system's bed management center, using a dedicated 24/7 telephone number. Major health care systems statewide and two tertiary centers in a neighboring state participated, sharing information on system status, challenges, and strategies. Patient volumes and transfer data were tracked; client satisfaction was evaluated through an anonymous survey., Results: From August 1, 2020, through March 31, 2022, a total of 5,307 requests were made, 2,008 beds identified, 1,316 requests canceled, and 1,981 requests were unable to be fulfilled. A total of 1,715 patients had COVID-19 (32.3%), and 2,473 were negative or low risk for COVID-19 (46.6%). COVID-19 status was unknown in 1,119 (21.1%). Overall, 760 were patients on ventilators (49.1% COVID-19 positive). The Minnesota Critical Care Coordination Center placed most patients during the fall 2020 surge with the Minnesota Governor's stay-at-home order during the peak. However, during the fall 2021 surge, only 30% of ICU patients and 39% of medical-surgical patients were placed. Indicators characterizing severe surge include the number of Critical Care Coordination Center requests, decreasing placements, longer placement times, and time series analysis showing significant request-acceptance differences., Interpretation: Implementation of a large-scale Minnesota MOCC program was effective at placing patients during the first COVID-19 pandemic fall 2020 surge and was well regarded by hospitals and health systems. However, under worsening duress of limited resources during the fall 2021 surge, placement of ICU and medical-surgical patients was greatly decreased., Competing Interests: Financial/Nonfinancial Disclosures None declared., (Copyright © 2023 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Medical schools initially removed students from clinical rotations at the outset of COVID-19 for safety reasons when students were eager to help and health systems needed personnel. In response, we rapidly implemented an innovative 2-week rotation for medical students to participate in health systems operations and care through remote efforts including triage and resource allocation. The curriculum also contained online self-paced educational modules covering topics including ethics, crisis standards of care, and modeling. As the health system needs shifted, so too did learners' work. One hundred and twenty-five 3rd and 4th-year students completed the experience over 10 months. Learner satisfaction, confidence, and knowledge assessed through pre- and post-rotation surveys showed statistically significant and educationally meaningful improvement. A near uniform change greater than 1 point (on a 5-point scale) was demonstrated upon rotation completion. Blending health systems and educational structures to meet the needs of both creates unique opportunities to educate students in new ways.

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., Competing Interests: The authors disclose no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: After the publication of a 2014 consensus statement regarding mass critical care during public health emergencies, much has been learned about surge responses and the care of overwhelming numbers of patients during the COVID-19 pandemic. Gaps in prior pandemic planning were identified and require modification in the midst of severe ongoing surges throughout the world., Research Question: A subcommittee from The Task Force for Mass Critical Care (TFMCC) investigated the most recent COVID-19 publications coupled with TFMCC members anecdotal experience in order to formulate operational strategies to optimize contingency level care, and prevent crisis care circumstances associated with increased mortality., Study Design and Methods: TFMCC adopted a modified version of established rapid guideline methodologies from the World Health Organization and the Guidelines International Network-McMaster Guideline Development Checklist. With a consensus development process incorporating expert opinion to define important questions and extract evidence, the TFMCC developed relevant pandemic surge suggestions in a structured manner, incorporating peer-reviewed literature, "gray" evidence from lay media sources, and anecdotal experiential evidence., Results: Ten suggestions were identified regarding staffing, load-balancing, communication, and technology. Staffing models are suggested with resilience strategies to support critical care staff. ICU surge strategies and strain indicators are suggested to enhance ICU prioritization tactics to maintain contingency level care and to avoid crisis triage, with early transfer strategies to further load-balance care. We suggest that intensivists and hospitalists be engaged with the incident command structure to ensure two-way communication, situational awareness, and the use of technology to support critical care delivery and families of patients in ICUs., Interpretation: A subcommittee from the TFMCC offers interim evidence-informed operational strategies to assist hospitals and communities to plan for and respond to surge capacity demands resulting from COVID-19., (Copyright © 2021 American College of Chest Physicians. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist.

Objective: Ensuring an efficient response to COVID-19 requires a degree of inter-system coordination and capacity management coupled with an accurate assessment of hospital utilization including length of stay (LOS). We aimed to establish optimal practices in inter-system data sharing and LOS modeling to support patient care and regional hospital operations., Materials and Methods: We completed a retrospective observational study of patients admitted with COVID-19 followed by 12-week prospective validation, involving 36 hospitals covering the upper Midwest. We developed a method for sharing de-identified patient data across systems for analysis. From this, we compared 3 approaches, generalized linear model (GLM) and random forest (RF), and aggregated system level averages to identify features associated with LOS. We compared model performance by area under the ROC curve (AUROC)., Results: A total of 2068 patients were included and used for model derivation and 597 patients for validation. LOS overall had a median of 5.0 days and mean of 8.2 days. Consistent predictors of LOS included age, critical illness, oxygen requirement, weight loss, and nursing home admission. In the validation cohort, the RF model (AUROC 0.890) and GLM model (AUROC 0.864) achieved good to excellent prediction of LOS, but only marginally better than system averages in practice., Conclusion: Regional sharing of patient data allowed for effective prediction of LOS across systems; however, this only provided marginal improvement over hospital averages at the aggregate level. A federated approach of sharing aggregated system capacity and average LOS will likely allow for effective capacity management at the regional level., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Current models of quality improvement and patient safety (QIPS) education are not fully integrated with clinical care delivery, representing a major impediment toward achieving widespread QIPS competency among health professions learners and practitioners. The Royal College of Physicians and Surgeons of Canada organized a 2-day consensus conference in Niagara Falls, Ontario, Canada, called Building the Bridge to Quality, in September 2016. Its goal was to convene an international group of educational and health system leaders, educators, frontline clinicians, learners, and patients to engage in a consensus-building process and generate a list of actionable strategies that individuals and organizations can use to better integrate QIPS education with clinical care.Four strategic directions emerged: prioritize the integration of QIPS education and clinical care, build structures and implement processes to integrate QIPS education and clinical care, build capacity for QIPS education at multiple levels, and align educational and patient outcomes to improve quality and patient safety. Individuals and organizations can refer to the specific tactics associated with the 4 strategic directions to create a road map of targeted actions most relevant to their organizational starting point.To achieve widespread change, collaborative efforts and alignment of intrinsic and extrinsic motivators are needed on an international scale to shift the culture of educational and clinical environments and build bridges that connect training programs and clinical environments, align educational and health system priorities, and improve both learning and care, with the ultimate goal of achieving improved outcomes and experiences for patients, their families, and communities.

Background: Integrating teaching and hands-on experience in quality improvement (QI) may increase the learning and the impact of resident QI work., Objective: We sought to determine the clinical and educational impact of an integrated QI curriculum., Methods: This clustered, randomized trial with early and late intervention groups used mixed methods evaluation. For almost 2 years, internal medicine residents from Dartmouth-Hitchcock Medical Center on the inpatient teams at the White River Junction VA participated in the QI curriculum. QI project effectiveness was assessed using statistical process control. Learning outcomes were assessed with the Quality Improvement Knowledge Application Tool-Revised (QIKAT-R) and through self-efficacy, interprofessional care attitudes, and satisfaction of learners. Free text responses by residents and a focus group of nurses who worked with the residents provided information about the acceptability of the intervention., Results: The QI projects improved many clinical processes and outcomes, but not all led to improvements. Educational outcome response rates were 65% (68 of 105) at baseline, 50% (18 of 36) for the early intervention group at midpoint, 67% (24 of 36) for the control group at midpoint, and 53% (42 of 80) for the late intervention group. Composite QIKAT-R scores (range, 0-27) increased from 13.3 at baseline to 15.3 at end point ( P  < .01), as did the self-efficacy composite score ( P  < .05). Satisfaction with the curriculum was rated highly by all participants., Conclusions: Learning and participating in hands-on QI can be integrated into the usual inpatient work of resident physicians., Competing Interests: The authors declare they have no competing interests.

Problem: Current models of health care quality improvement do not explicitly describe the role of health professions education. The authors propose the Exemplary Care and Learning Site (ECLS) model as an approach to achieving continual improvement in care and learning in the clinical setting., Approach: From 2008-2012, an iterative, interactive process was used to develop the ECLS model and its core elements--patients and families informing process changes; trainees engaging both in care and the improvement of care; leaders knowing, valuing, and practicing improvement; data transforming into useful information; and health professionals competently engaging both in care improvement and teaching about care improvement. In 2012-2013, a three-part feasibility test of the model, including a site self-assessment, an independent review of each site's ratings, and implementation case stories, was conducted at six clinical teaching sites (in the United States and Sweden)., Outcomes: Site leaders reported the ECLS model provided a systematic approach toward improving patient (and population) outcomes, system performance, and professional development. Most sites found it challenging to incorporate the patients and families element. The trainee element was strong at four sites. The leadership and data elements were self-assessed as the most fully developed. The health professionals element exhibited the greatest variability across sites., Next Steps: The next test of the model should be prospective, linked to clinical and educational outcomes, to evaluate whether it helps care delivery teams, educators, and patients and families take action to achieve better patient (and population) outcomes, system performance, and professional development.