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. traditional brick-and-mortar hospital care in acutely ill patients. METHODS: We aim to enroll 360 acutely ill adult patients (≥18 years) who are admitted to three hospitals in Arizona, Florida, and Wisconsin, two of which are academic medical centers and one is a community-based practice. The eligibility criteria will follow what is used in routine practice determined by local clinical teams, including clinical stability, social stability, health insurance plans, and zip codes. Patients will be randomized 1:1 to ACH or traditional inpatient care, stratified by site. The primary outcome is a composite outcome of all-cause mortality and 30-day readmission. Secondary outcomes include individual outcomes in the composite endpoint, fall with injury, medication errors, emergency room visit, transfer to intensive care unit (ICU), cost, the number of days alive out of hospital, and patient-reported quality of life. A mixed-methods study will be conducted with patients, clinicians, and other staff to investigate their experience. DISCUSSION: The pragmatic trial will examine a novel virtual hybrid model for delivering high-acuity medical care at home. The findings will inform patient selection and future large-scale implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05212077. Registered on 27 January 2022.

Hospital quality reporting in the pandemic era: to what extent did hospitals' COVID-19 census burdens impact 30-day mortality among non-COVID Medicare beneficiaries?

OBJECTIVES: Highly visible hospital quality reporting stakeholders in the USA such as the US News & World Report (USNWR) and the Centers for Medicare & Medicaid Services (CMS) play an important health systems role via their transparent public reporting of hospital outcomes and performance. However, during the pandemic, many such quality measurement stakeholders and pay-for-performance programmes in the USA and Europe have eschewed the traditional risk adjustment paradigm, instead choosing to pre-emptively exclude months or years of pandemic era performance data due largely to hospitals' perceived COVID-19 burdens. These data exclusions may lead patients to draw misleading conclusions about where to seek care, while also masking genuine improvements or deteriorations in hospital quality that may have occurred during the pandemic. Here, we assessed to what extent hospitals' COVID-19 burdens (proportion of hospitalised patients with COVID-19) were associated with their non-COVID 30-day mortality rates from March through November 2020 to inform whether inclusion of pandemic-era data may still be appropriate. DESIGN: This was a retrospective cohort study using the 100% CMS Inpatient Standard Analytic File and Master Beneficiary Summary File to include all US Medicare inpatient encounters with admission dates from 1 April 2020 through 30 November 2020, excluding COVID-19 encounters. Using linear regression, we modelled the association between hospitals' COVID-19 proportions and observed/expected (O/E) ratios, testing whether the relationship was non-linear. We calculated alternative hospital O/E ratios after selective pandemic data exclusions mirroring the USNWR data exclusion methodology. SETTING AND PARTICIPANTS: We analysed 4 182 226 consecutive Medicare inpatient encounters from across 2601 US hospitals. RESULTS: The association between hospital COVID-19 proportion and non-COVID O/E 30-day mortality was statistically significant (p<0.0001), but weakly correlated (r2=0.06). The median (IQR) pairwise relative difference in hospital O/E ratios comparing the alternative analysis with the original analysis was +3.7% (-2.5%, +6.7%), with 1908/2571 (74.2%) of hospitals having relative differences within ±10%. CONCLUSIONS: For non-COVID patient outcomes such as mortality, evidence-based inclusion of pandemic-era data is methodologically plausible and must be explored rather than exclusion of months or years of relevant patient outcomes data.

A Multisite Assessment of Inpatient Safety Event Rates During the Coronavirus Disease 2019 Pandemic.

To date, there has been a notable lack of peer-reviewed or publicly available data documenting rates of hospital quality outcomes and patient safety events during the coronavirus disease 2019 pandemic era. The dearth of evidence is perhaps related to the US health care system triaging resources toward patient care and away from reporting and research and also reflects that data used in publicly reported hospital quality rankings and ratings typically lag 2-5 years. At our institution, a learning health system assessment is underway to evaluate how patient safety was affected by the pandemic. Here we share and discuss early findings, noting the limitations of self-reported safety event reporting, and suggest the need for further widespread investigations at other US hospitals. During the 2-year study period from January 1, 2020, through December 31, 2021 across 3 large US academic medical centers at our institution, we documented an overall rate of 25.8 safety events per 1000 inpatient days. The rate of events meeting "harm" criteria was 12.4 per 1000 inpatient days, the rate of nonharm events was 11.1 per 1000 inpatient days, and the fall rate was 2.3 per 1000 inpatient days. This descriptive exploratory analysis suggests that patient safety event rates at our institution did not increase over the course of the pandemic. However, increasing health care worker absences were nonlinearly and strongly associated with patient safety event rates, which raises questions regarding the mechanisms by which patient safety event rates may be affected by staff absences during pandemic peaks.

The association of acute COVID-19 infection with Patient Safety Indicator-12 events in a multisite healthcare system.

BACKGROUND: Patient Safety Indicator (PSI)-12, a hospital quality measure designed by Agency for Healthcare Research and Quality (AHRQ) to capture potentially preventable adverse events, captures perioperative venous thromboembolism (VTE). It is unclear how COVID-19 has affected PSI-12 performance. OBJECTIVE: We sought to compare the cumulative incidence of PSI-12 in patients with and without acute COVID-19 infection. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective cohort study including PSI-12-eligible events at three Mayo Clinic medical centers (4/1/2020-10/5/2021). EXPOSURE, MAIN OUTCOMES, AND MEASURES: We compared the unadjusted rate and adjusted risk ratio (aRR) for PSI-12 events among patients with and without COVID-19 infection using Fisher's exact χ2  test and the AHRQ risk-adjustment software, respectively. We summarized the clinical outcomes of COVID-19 patients with a PSI-12 event. RESULTS: Our cohort included 50,400 consecutive hospitalizations. Rates of PSI-12 events were significantly higher among patients with acute COVID-19 infection (8/257 [3.11%; 95% confidence interval {CI}, 1.35%-6.04%]) compared to patients without COVID-19 (210/50,143 [0.42%; 95% CI, 0.36%-0.48%]) with a PSI-12 event during the encounter (p < .001). The risk-adjusted rate of PSI-12 was significantly higher in patients with acute COVID-19 infection (1.50% vs. 0.38%; aRR, 3.90; 95% CI, 2.12-7.17; p < .001). All COVID-19 patients with PSI-12 events had severe disease and 4 died. The most common procedure was tracheostomy (75%); the mean (SD) days from surgical procedure to VTE were 0.12 (7.32) days. CONCLUSION: Patients with acute COVID-19 infection are at higher risk for PSI-12. The present definition of PSI-12 does not account for COVID-19. This may impact hospitals' quality performance if COVID-19 infection is not accounted for by exclusion or risk adjustment.

Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook.

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.

Deployment of an Interdisciplinary Predictive Analytics Task Force to Inform Hospital Operational Decision-Making During the COVID-19 Pandemic.

In March 2020, our institution developed an interdisciplinary predictive analytics task force to provide coronavirus disease 2019 (COVID-19) hospital census forecasting to help clinical leaders understand the potential impacts on hospital operations. As the situation unfolded into a pandemic, our task force provided predictive insights through a structured set of visualizations and key messages that have helped the practice to anticipate and react to changing operational needs and opportunities. The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted for effective implementation at other institutions to provide evidence-based messaging for operational decision-making. For hospitals without such a structure, immediate consideration may be warranted in light of the devastating COVID-19 third-wave which has arrived for winter 2020-2021.

When to Operate, Hesitate and Reintegrate: Society of Gynecologic Oncology Surgical Considerations during the COVID-19 Pandemic.

The COVID-19 pandemic has challenged our ability to provide timely surgical care for our patients. In response, the U.S. Surgeon General, the American College of Srugeons, and other surgical professional societies recommended postponing elective surgical procedures and proceeding cautiously with cancer procedures that may require significant hospital resources and expose vulnerable patients to the virus. These challenges have particularly distressing for women with a gynecologic cancer diagnosis and their providers. Currently, circumstances vary greatly by region and by hospital, depending on COVID-19 prevalence, case mix, hospital type, and available resources. Therefore, COVID-19-related modifications to surgical practice guidelines must be individualized. Special consideration is necessary to evaluate the appropriateness of procedural interventions, recognizing the significant resources and personnel they require. Additionally, the pandemic may occur in waves, with patient demand for surgery ebbing and flowing accordingly. Hospitals, cancer centers and providers must prepare themselves to meet this demand. The purpose of this white paper is to highlight all phases of gynecologic cancer surgical care during the COVID-19 pandemic and to illustrate when it is best to operate, to hestitate, and reintegrate surgery. Triage and prioritization of surgical cases, preoperative COVID-19 testing, peri-operative safety principles, and preparations for the post-COVID-19 peak and surgical reintegration are reviewed.