Service line care delivery model for COVID-19 patient-centric care.

OBJECTIVES: The COVID-19 pandemic has caused hospitals around the world to quickly develop not only strategies to treat patients but also methods to protect health care and frontline workers. STUDY DESIGN: Descriptive study. METHODS: We outlined the steps and processes that we took to respond to the challenges presented by the COVID-19 pandemic while continuing to provide our routine acute care services to our community. RESULTS: These steps and processes included establishing teams focused on maintaining an adequate supply of personal protection equipment, cross-training staff, developing disaster-based triage for the emergency department, creating quality improvement teams geared toward updating care based on the most current literature, developing COVID-19-based units, creating COVID-19-specific teams of providers, maximizing use of our electronic health record system to allocate beds, and providing adequate practitioner coverage by creating a computer-based dashboard that indicated the need for health care practitioners. These processes led to seamless and integrated care for all patients with COVID-19 across our health system and resulted in a reduction in mortality from a high of 20% during the first peak (March and April 2020) to 6% during the plateau period (June-October 2020) to 12% during the second peak (November and December 2020). CONCLUSIONS: The detailed processes put in place will help hospital systems meet the continuing challenges not only of COVID-19 but also beyond COVID-19 when other unique public health crises may present themselves.

Outcomes of hospitalized patients with COVID-19 during the course of the pandemic in a fully integrated health system.

BACKGROUND: Given the rapid spread of COVID-19 and its associated morbidity and mortality, healthcare providers throughout the world have been forced to constantly update and change their care delivery models. OBJECTIVE: To assess the outcomes of COVID-19 hospitalized patients during the course of the pandemic in a well-integrated health system. METHODS: The study used data from the electronic health medical records to assess trends in clinical profile and outcomes of hospitalized adult COVID-19 patients hospitalized in our 5-hospital health system from March 2020-May 2021 (n = 6865). Integration of the health system began in February 2020 and was fully actualized by March 30, 2020. RESULTS: Mortality decreased from 15% during first peak (March-May 2020; the rate includes 19% in March-April and 10% in May 2020) to 6% in summer-fall 2020, increased to 13% during the second peak (November 2020-January 2021), and dropped to 7% during the decline period (February-May 2021) (p<0.01). Resource utilization followed a similar pattern including a decrease in ICU use from 35% (first peak) to 16% (decline period), mechanical ventilation from 16% (first peak, including 45% in March 2020) to 9-11% in subsequent periods (p<0.01). Independent predictors of inpatient mortality across multiple study periods included older age, male sex, higher multi-morbidity scores, morbid obesity, and indicators of severe illness on admission such as oxygen saturation ≤90% and high qSOFA score (all p<0.05). However, admission during the first peak remained independently associated with increased mortality even after adjustment for patient-related factors: odds ratio = 1.8 (1.4-2.4) (p<0.0001). CONCLUSIONS: The creation of a fully integrated health system allowed us to dynamically respond to the everchanging COVID-19 landscape. In this context, despite the increasing patient acuity, our mortality and resource utilization rates have improved during the pandemic.

Independent Predictors of Mortality Among Patients With NAFLD Hospitalized With COVID-19 Infection.

The impact of the coronavirus disease 2019 (COVID-19) pandemic among patients with chronic liver disease is unknown. Given the high prevalence of nonalcoholic fatty liver disease (NAFLD), we determined the predictors of mortality and hospital resource use among patients with NAFLD admitted with COVID-19 by using electronic medical records data for adult patients with COVID-19 hospitalized in a multihospital health system who were discharged between March and December 2020. NAFLD was diagnosed by imaging or liver biopsy without other liver diseases. Charlson's comorbidity index (CCI) and Elixhauser comorbidity index (ECI) scores were calculated. In the study sample, among the 4,835 patients hospitalized for COVID-19, 553 had NAFLD (age: 55 ± 16 years, 51% male, 17% White, 11% Black, 58% Hispanic, 8% Asian, 5% from congregated living, 58% obese, 15% morbid obesity [body mass index ≥ 40], 51% type 2 diabetes, 63% hypertension, mean [SD] baseline CCI of 3.9 [3.2], and baseline ECI of 13.4 [11.3]). On admission, patients with NAFLD had more respiratory symptoms, higher body temperature and heart rate, higher alanine aminotransferase and aspartate aminotransferase than non-NAFLD controls (n = 2,736; P < 0.05). Of the patients with NAFLD infected with COVID-19, 3.9% experienced acute liver injury. The NAFLD group had significantly longer length of stay, intensive care unit use, and mechanical ventilation, with a crude inpatient mortality rate of 11%. In multivariate analysis, independent predictors of inpatient mortality among patients with NAFLD infected with COVID-19 were older age, morbid obesity, ECI score ≥ 11, higher Fibrosis-4 Index (FIB-4) score, and oxygen saturation <90% (all P < 0.05), but not sex, race/ethnicity, or any individual comorbidity (all P > 0.05). Conclusion: Patients with NAFLD infected with COVID-19 tend to be sicker on admission and require more hospital resource use. Independent predictors of mortality included higher FIB-4 and multimorbidity scores, morbid obesity, older age, and hypoxemia on admission.

Neutralizing Monoclonal Antibody Treatment Reduces Hospitalization for Mild and Moderate Coronavirus Disease 2019 (COVID-19): A Real-World Experience.

BACKGROUND: Neutralizing monoclonal antibody (NmAb) treatments have received Emergency Use Authorization to treat patients with mild or moderate COVID-19 infection. To date, no real- world data on the efficacy of NmAbs have been reported from clinical practice. We assessed the impact of NmAb treatment given in the outpatient clinical practice setting on hospital utilization. METHODS: Electronic medical records were used to identify adult COVID-19 patients who received NmAbs (bamlanivimab [BAM] or casirivimab and imdevimab [REGN-COV2]) and historic COVID-19 controls. Post-index hospitalization rates were compared. RESULTS: 707 confirmed COVID-19 patients received NmAbs and 1709 historic COVID-19 controls were included; 553 (78%) received BAM, 154 (22%) received REGN-COV2. Patients receiving NmAb infusion had significantly lower hospitalization rates (5.8% vs 11.4%, P < .0001), shorter length of stay if hospitalized (mean, 5.2 vs 7.4 days; P = .02), and fewer ED visits within 30 days post-index (8.1% vs 12.3%, P = .003) than controls. Hospitalization-free survival was significantly longer in NmAb patients compared with controls (P < .0001). There was a trend towards a lower hospitalization rate among patients who received NmAbs within 2-4 days after symptom onset. In multivariate analysis, having received an NmAb transfusion was independently associated with a lower risk of hospitalization after adjustment for age, sex, race, BMI, and referral source (adjusted HR [95% CI], .54 [0.38-0.79]; P = .0012). Overall mortality was not different between the 2 groups. CONCLUSIONS: NmAb treatment reduced hospital utilization, especially when received within a few days of symptom onset. Further study is needed to validate these findings.

Development of CancerLinQ, a Health Information Learning Platform From Multiple Electronic Health Record Systems to Support Improved Quality of Care.

PURPOSE: ASCO, through its wholly owned subsidiary, CancerLinQ LLC, developed CancerLinQ, a learning health system for oncology. A learning health system is important for oncology patients because less than 5% of patients with cancer enroll in clinical trials, leaving evidence gaps for patient populations not enrolled in trials. In addition, clinical trial populations often differ from the overall cancer population with respect to age, race, performance status, and other clinical parameters. MATERIALS AND METHODS: Working with subscribing practices, CancerLinQ accepts data from electronic health records and transforms the local representation of a patient's care into a standardized representation on the basis of the Quality Data Model from the National Quality Forum. CancerLinQ provides this information back to the subscribing practice through a series of tools that support quality improvement. CancerLinQ also creates de-identified data sets for secondary research use. RESULTS: As of March 2020, CancerLinQ includes data from 63 organizations across the United States that use nine different electronic health records. The database includes 1,426,015 patients with a primary cancer diagnosis, of which 238,680 have had additional information abstracted from unstructured content. CONCLUSION: As CancerLinQ continues to onboard subscribing practices, the breadth of potential applications for a learning health care system widen. Future practice-facing tools could include real-world data visualization, recommendations for treatment of patients with actionable genetic variations, and identification of patients who may be eligible for clinical trials. Feeding these insights back into oncology practice ensures that we learn how to treat patients with cancer not just on the basis of the selective experience of the 5% that enroll in clinical trials, but from the real-world experience of the entire spectrum of patients with cancer in the United States.

Building a Rapid Learning Health Care System for Oncology: Why CancerLinQ Collects Identifiable Health Information to Achieve Its Vision.

The ever-increasing volume of scientific discoveries, clinical knowledge, novel diagnostic tools, and treatment options juxtaposed with rising costs in health care challenge physicians to identify, prioritize, and use new information rapidly to deliver efficient and high-quality care to a growing and aging patient population. CancerLinQ, a rapid learning health care system in oncology, is an initiative of the American Society of Clinical Oncology and its Institute for Quality that addresses these challenges by collecting information from the electronic health records of large numbers of patients with cancer. CancerLinQ is first and foremost a quality measurement and reporting system through which oncologists can harness the depth and power of their patients' clinical records and other data to assess, monitor, and improve the care they deliver. However, in light of privacy and security concerns with regard to collection, use, and disclosure of patient information, this article addresses the need to collect protected health information as defined under the Health Insurance Portability and Accountability Act of 1996 to drive rapid learning through CancerLinQ.