Emergency department bounceback characteristics for patients diagnosed with COVID-19.

BACKGROUND: The global healthcare burden of COVID-19 continues to rise. There is currently limited information regarding the disease progression and the need for hospitalizations in patients who present to the Emergency Department (ED) with minimal or no symptoms. OBJECTIVES: This study identifies bounceback rates and timeframes for patients who return to the ED due to COVID-19 after initial discharge on the date of testing. METHODS: Using the NorthShore University Health System's (NSUHS) Enterprise Data Warehouse (EDW), we conducted a retrospective cohort analysis of patients who were tested positive for COVID-19 and were discharged home on the date of testing. A one-month follow-up period was included to ensure the capture of disease progression. RESULTS: Of 1883 positive cases with initially mild symptoms, 14.6% returned to the ED for complaints related to COVID-19. 56.9% of the mildly symptomatic bounceback patients were discharged on the return visit while 39.5% were admitted to the floor and 3.6% to the ICU. Of the 1120 positive cases with no initial symptoms, only four returned to the ED (0.26%) and only one patient was admitted. Median initial testing occurred on day 3 (2-5.6) of illness, and median ED bounceback occurred on day 9 (6.3-12.7). Our statistical model was unable to identify risk factors for ED bouncebacks. CONCLUSION: COVID-19 patients diagnosed with mild symptoms on initial presentation have a 14.6% rate of bounceback due to progression of illness.

A technology-based quality innovation to identify undiagnosed hypertension among active primary care patients.

PURPOSE: The goal of this study was to develop a technology-based strategy to identify patients with undiagnosed hypertension in 23 primary care practices and integrate this innovation into a continuous quality improvement initiative in a large, integrated health system. METHODS: In phase 1, we reviewed electronic health records (EHRs) using algorithms designed to identify patients at risk for undiagnosed hypertension. We then invited each at-risk patient to complete an automated office blood pressure (AOBP) protocol. In phase 2, we instituted a quality improvement process that included regular physician feedback and office-based computer alerts to evaluate at-risk patients not screened in phase 1. Study patients were observed for 24 additional months to determine rates of diagnostic resolution. RESULTS: Of the 1,432 patients targeted for inclusion in the study, 475 completed the AOBP protocol during the 6 months of phase 1. Of the 1,033 at-risk patients who remained active during phase 2, 740 (72%) were classified by the end of the follow-up period: 361 had hypertension diagnosed, 290 had either white-coat hypertension, prehypertension, or elevated blood pressure diagnosed, and 89 had normal blood pressure. By the end of the follow-up period, 293 patients (28%) had not been classified and remained at risk for undiagnosed hypertension. CONCLUSIONS: Our technology-based innovation identified a large number of patients at risk for undiagnosed hypertension and successfully classified the majority, including many with hypertension. This innovation has been implemented as an ongoing quality improvement initiative in our medical group and continues to improve the accuracy of diagnosis of hypertension among primary care patients.

An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system.

An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.

From Testing to Decision-Making: A Data-Driven Analytics COVID-19 Response.

In March 2020, NorthShore University Health System laboratories mobilized to develop and validate polymerase chain reaction based testing for detection of SARS-CoV-2. Using laboratory data, NorthShore University Health System created the Data Coronavirus Analytics Research Team to track activities affected by SARS-CoV-2 across the organization. Operational leaders used data insights and predictions from Data Coronavirus Analytics Research Team to redeploy critical care resources across the hospital system, and real-time data were used daily to make adjustments to staffing and supply decisions. Geographical data were used to triage patients to other hospitals in our system when COVID-19 detected pavilions were at capacity. Additionally, one of the consequences of COVID-19 was the inability for patients to receive elective care leading to extended periods of pain and uncertainty about a disease or treatment. After shutting down elective surgeries beginning in March of 2020, NorthShore University Health System set a recovery goal to achieve 80% of our historical volumes by October 1, 2020. Using the Data Coronavirus Analytics Research Team, our operational and clinical teams were able to achieve 89% of our historical volumes a month ahead of schedule, allowing rapid recovery of surgical volume and financial stability. The Data Coronavirus Analytics Research Team also was used to demonstrate that the accelerated recovery period had no negative impact with regard to iatrogenic COVID-19 infection and did not result in increased deep vein thrombosis, pulmonary embolisms, or cerebrovascular accident. These achievements demonstrate how a coordinated and transparent data-driven effort that was built upon a robust laboratory testing capability was essential to the operational response and recovery from the COVID-19 crisis.

The Value and Institutional Impact of an In-System Laboratory Testing During the COVID-19 Pandemic.

In-system clinical laboratories have proven themselves to be a fundamentally important resource to their institutions during the COVID-19 pandemic of the past year. The ability to provide SARS-CoV-2 molecular testing to our hospital system allowed us to offer the best possible care to our patients, and to support neighboring hospitals and nursing homes. In-house testing led to significant revenue enhancement to the laboratory and institution, and attracted new patients to the system. Timely testing of inpatients allowed the majority who did not have COVID-19 infection to be removed from respiratory and contact isolation, conserving valuable personal protective equipment and staff resources at a time that both were in short supply. As 2020 evolved and our institution restarted delivery of routine care, the availability of in-system laboratory testing to deliver both accurate and timely results was absolutely critical. In this article, we attempt to demonstrate the value and impact of an in-system laboratory during the COVID-19 pandemic. A strong in-house laboratory service was absolutely critical to institutional operational and financial success during 2020, and will ensure resiliency in the future as well.

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models.

BACKGROUND: Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. OBJECTIVE: We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. MATERIALS & METHODS: We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. RESULTS: Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85. DISCUSSION: We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.

Evaluating Primary Care Physician Performance in Diabetes Glucose Control.

This study demonstrates that it is possible to identify primary care physicians (PCPs) who perform better or worse than expected in managing diabetes. Study subjects were 14 033 adult diabetics and their 133 PCPs. Logistic regression was used to predict the odds that a patient would have uncontrolled diabetes (defined as HbA1c ≥8%) based on patient-level characteristics alone. A second model predicted diabetes control from physician-level identity and characteristics alone. A third model combined the patient- and physician-level models using hierarchical logistic regression. Physician performance is calculated from the difference between the expected and observed proportions of patients with uncontrolled diabetes. After adjusting for important patient characteristics, PCPs were identified who performed better or worse than expected in managing diabetes. This strategy can be used to characterize physician performance in other chronic conditions. This approach may lead to new insights regarding effective and ineffective treatment strategies.

Outpatient advance care planning for patients with metastatic cancer: a pilot quality improvement initiative.

BACKGROUND: Despite American Society of Clinical Oncology (ASCO) and National Comprehensive Cancer Network (NCCN) guidelines recommending that oncologists discuss advance care planning (ACP) with patients with stage IV cancer early in treatment, in standard practice ACP remains a late step of a terminal illness. ACP preserves comfort and dignity at the end of life, ensuring patients receive the care that they desire. METHODS AND MATERIALS: A feasibility study in patients with stage IV cancer was developed to test whether incorporating ACP immediately after a stage IV cancer diagnosis is feasible. Inclusion criteria were consecutive new gastrointestinal and thoracic oncology patients treated by one of two oncologists. The project included creation of new workflow; development of an ACP patient education guidebook; training seminars for oncology staff; and enhancements to the electronic health record (EHR) to improve ACP documentation. RESULTS: The oncologists recorded 33 of 48 (69%) advance directive notes (ADNs) and 22 of 48 (46%) code status orders (CSOs) in the EHR of patients newly diagnosed with stage IV cancer by following ACP protocol during the 6-month trial period. Twenty-one of 33 ADNs were entered within 7 days of first consultation. The median time to ADN placement was 1 day after consultation. Twenty-two of 33 patients with ADNs had CSOs placed, of which 16 were do-not-resuscitate (DNR) and 6 were full code. One year prior to the feasibility study, only 1 of 75 deceased patients of the two oncologists had outpatient ADNs and CSOs. CONCLUSIONS: Outpatient ACP is feasible early in the care of patients with stage IV cancer through systematic improvement in workflow and motivated providers. Education and infrastructure were pivotal to routine development of advance care plans.

Incorporating social network effects into cost-effectiveness analysis: a methodological contribution with application to obesity prevention.

OBJECTIVES: To develop a methodology for integrating social networks into traditional cost-effectiveness analysis (CEA) studies. This will facilitate the economic evaluation of treatment policies in settings where health outcomes are subject to social influence. DESIGN: This is a simulation study based on a Markov model. The lifetime health histories of a cohort are simulated, and health outcomes compared, under alternative treatment policies. Transition probabilities depend on the health of others with whom there are shared social ties. SETTING: The methodology developed is shown to be applicable in any healthcare setting where social ties affect health outcomes. The example of obesity prevention is used for illustration under the assumption that weight changes are subject to social influence. MAIN OUTCOME MEASURES: Incremental cost-effectiveness ratio (ICER). RESULTS: When social influence increases, treatment policies become more cost effective (have lower ICERs). The policy of only treating individuals who span multiple networks can be more cost effective than the policy of treating everyone. This occurs when the network is more fragmented. CONCLUSIONS: (1) When network effects are accounted for, they result in very different values of incremental cost-effectiveness ratios (ICERs). (2) Treatment policies can be devised to take network structure into account. The integration makes it feasible to conduct a cost-benefit evaluation of such policies.