Longitudinal symptom and clinical outcome analysis of hospitalized COVID-19 patients

COVID-19 pandemics increased patient hospitalization impacting the hospital operations and patient care beyond COVID-19 patients. Although longitudinal symptom analysis may provide prognostic utility about clinical outcomes and critical hospitalization events of COVID-19 patients, such analysis is still missing. Here, we have analyzed over 10,000 hospitalized COVID-19 patients in the Houston Methodist Hospital at the Texas Medical Center from the beginning of pandemics till April of 2020. Our study used statistical and regression analysis over symptoms grouped into symptom groups based on their anatomical locations. Symptom intensity analysis indicated that symptoms peaked at the time of admission and subsided within the first week of hospitalization for most of the patients. Patients surviving the infection (n=9,263), had faster remission rates, usually within the first days of hospitalization compared to sustained symptom for the deceased patient group (n=1,042). The latter had also a longer hospitalization stay and more comorbidities including diabetes, cardiovascular, and kidney disease. Inflammation-associated systemic symptoms (Systemic) such as fever and chills, and lower respiratory system specific symptoms (Lower Respiratory System) such as shortness of breath and pneumonia, were the most informative for the analysis of longitudinal symptom dynamics. Our results suggest that the symptom remission rate could possess prognostic utility in evaluating patient hospitalization stay and clinical outcomes early in hospitalization. We believe knowledge and information about symptom remission rates can be used to improve hospital operations and patient care by using common and relatively easy to process source of information.

Quality and Safety in Hospital Pediatrics During COVID-19: A National Qualitative Study

OBJECTIVE: To describe challenges in inpatient pediatric quality and safety during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: In a previous qualitative study, our team sought to broadly describe changes in pediatric inpatient care during the pandemic. For both that study and this ancillary analysis, we purposefully sampled participants from community and children’s hospitals in the 6 US states with the highest COVID-19 hospitalization rates from March to May 2020. We recruited 2 to 3 participants from each hospital (administrators, front-line physicians, nurses, caregivers) for semistructured interviews. We used constant comparative methods to identify themes regarding quality and safety challenges during the pandemic. RESULTS: We interviewed 30 participants from 12 hospitals. Participants described several impacts to clinical workflows, including decreased direct clinician-patient interactions and challenges to communication, partly addressed through innovative use of telehealth technology. Participants reported changes in the discharge and transfer process (eg, discharges, difficulties accessing specialized facilities). Participants also described impacts to hospital operations, including changes in quality monitoring and operations (eg, decreased staff, data collection), increased health risks for clinicians and staff (eg, COVID-19 exposure, testing delays), and staff and supply shortages. Participants voiced concerns that negative quality and safety impacts could include increased risk of preventable safety events and hospital readmissions, and decreased patient engagement, education, and satisfaction. CONCLUSIONS: We identified several impacts to clinical workflows and hospital operations during the pandemic that may have affected inpatient pediatric care quality and safety. Our findings highlight potentially important areas of focus for planning pandemic recovery, preparing for future pandemics, and conducting future research on inpatient pediatric quality and safety.

COVID-19 Intelligence-Driven Operational Response Platform: Experience of a Large Tertiary Multihospital System in the Middle East

The COVID-19 pandemic has resulted in global disruptions within healthcare systems, leading to quick dynamic fluctuations in hospital operations and supply chain management. During the early months of the pandemic, tertiary multihospital systems were highly viewed as the go-to hospitals for handling these rapid healthcare challenges caused by the rapidly increasing number of COVID-19 cases. Yet, this pandemic has created an urgent need for coordinated mechanisms to alleviate increasing pressures on these large multihospital systems and ensure services remain high-quality, accessible, and sustainable. Digital health solutions have been identified as promising approaches to address these challenges. This case report describes results for developing multidisciplinary visualizations to support digital health operations in one of the largest tertiary multihospital systems in the Middle East. The report concludes with some lessons and insights learned from the rapid development and delivery of this user-centric COVID-19 multihospital operations intelligent platform.

IMPACT OF COVID 19 PANDEMIC ON ACUTE GENERAL SURGICAL EMERGENCY MANAGEMENT- A TERTIARY CARE HOSPITAL BASED STUDY

BACKGROUND: The COVID-19 pandemic is standing as a never before threat to the healthcare systems and hospital operations worldwide.Transmission of coronavirus (COVID-19) is a considerable risk during the perioperative period of surgery. Treatment algorithms have changed in general surgery clinics, as in other medical disciplines providing emergency services. OBJECTIVES: This study was aimed to evaluate the changes in approach to management and the perioperative outcome of patients with acute surgical emergency during COVID-19 pandemic. STUDY DESIGN AND METHODS:We performed a retrospective observational study in patients presented with acute surgical emergency between April 2020 to June 2021. RESULTS: A total of 298 patients were included, among whom 12 (3.4%) were COVID 19 positive. 274 non-COVID patients and 8 COVID-19 positive patients underwent emergency surgery.While 12 non-COVID (4.1%) and 4 COVID-19 positive patients (40%) underwent conservative management. None of the hospital staff involved in the surgeries of COVID-19 positive patients developed any symptoms related to COVID-19. CONCLUSION: This study showed that the patients with surgical emergency both with or without COVID-19 infection were successfully treated,without influencing each other,through appropriate isolation measures,although managed in the same hospital. Importance can also be given towards conservative management particularly for COVID-19 positive patients presenting with surgical emergency selectively with proper monitoring. So it can be concluded that, although the management of surgical patients during the COVID-19 pandemic is a global challenge,adequate preparedness and strategic plan to adjust the surgical services can reduce the exposures to this highly contagious virus.

Reduced turnaround times through multi-sectoral community collaboration during the first surge of SARS-CoV-2 and associated effect on patient care and hospital operations

Background In March 2020, an influx of admissions in COVID-19 positive patients threatened to overwhelm healthcare facilities in East Baton Rouge Parish, Louisiana. Exacerbating this problem was an overall shortage of diagnostic testing capability at that time, resulting in a delay in time-to-result return. An improvement in diagnostic testing availability and timeliness was necessary to improve the allocation of resources and ultimate throughput of patients. The management of a COVID-19 positive patient or patient under investigation requires infection control measures that can quickly consume personal protective equipment (PPE) stores and personnel available to treat these patients. Critical shortages of both PPE and personnel also negatively impact care in patients admitted with non-COVID-19 illnesses. Methods A multisectoral partnership of healthcare providers, facilities and academicians created a molecular diagnostic lab within an academic research facility dedicated to testing inpatients and healthcare personnel for SARS-CoV-2. The purpose of the laboratory was to provide a temporary solution to the East Baton Rouge Parish healthcare community until individual facilities were self-sustaining in testing capabilities. We describe the partnership and the impacts of this endeavor by developing a model derived from a combination of data sources, including electronic health records, hospital operations, and state and local resources. Findings Our model demonstrates two important principles: the impact of reduced turnaround times (TAT) on potential differences in inpatient population numbers for COVID-19 and savings in PPE attributed to the more rapid TAT.

Modern communications technology: An essential tool for optimizing hospital operations and improving outcomes

An ability to rapidly convert data from multiple different sources into actionable information is embodied in a concept called Real-time Health Systems (RTHS). The foundational component of RTHS is a modern Clinical Communication and Collaboration (CC&C) Platform, which translates organizational knowledge into action. Effective communication is the key. A CC&C Platform that can receive data from multiple hospital systems, analyze the data, arbitrate any resulting actions and determine the relative priorities to distribute work to the right person or teams–can lead to improved operational efficiencies and better patient outcomes.

Multicentre methodological study to create a publicly available score of hospital financial standing in the USA

ObjectivesTo create a straightforward scoring procedure based on widely available, inexpensive financial data that provides an assessment of the financial health of a hospital.DesignMethodological study.SettingMulticentre study.ParticipantsAll hospitals and health systems reporting the required financial metrics in the USA in 2017 were included for a total of 1075 participants.InterventionsWe examined a list of 232 hospital financial indicators and used existing models and financial literature to select 30 metrics that sufficiently describe hospital operations. In a set of hospital financial data from 2017, we used principal coordinate analysis to assess collinearity among variables and eliminated redundant variables. We isolated 10 unique variables, each assigned a weight equal to the share of its coefficient in a regression onto Moody’s Credit Rating, our predefined gold standard. The sum of weighted variables is a single composite score named the Yale Hospital Financial Score (YHFS).Primary outcome measuresAbility to reproduce both financial trends from a ‘gold-standard’ metric and known associations with non-fiscal data.ResultsThe validity of the YHFS was evaluated by: (1) cross-validating it with previously excluded data; (2) comparing it to existing models and (3) replicating known associations with non-fiscal data. Ten per cent of the initial dataset had been reserved for validation and was not used in creating the model; the YHFS predicts 96.7% of the variation in this reserved sample, demonstrating reproducibility. The YHFS predicts 90.5% and 88.8% of the variation in Moody’s and Standard and Poor’s bond ratings, respectively, supporting its validity. As expected, larger hospitals had higher YHFS scores whereas a greater share of Medicare discharges correlated with lower YHFS scores.ConclusionsWe created a reliable and publicly available composite score of hospital financial stability.

Predicting Inpatient Flow at a Major Hospital Using Interpretable Analytics

Problem definition: Translate data from electronic health records (EHR) into accurate predictions on patient flows and inform daily decision making at a major hospital. Academic/practical relevance: In a constrained hospital environment, forecasts on patient demand patterns could help match capacity and demand and improve hospital operations. Methodology: We use data from 63,432 admissions at a large academic hospital (50% female, median age 64 years old, median length of stay 3.12 days). We construct an expertise-driven patient representation on top of their EHR data and apply a broad class of machine learning methods to predict several aspects of patient flows. Results: With a unique patient representation, we estimate short-term discharges, identify long-stay patients, predict discharge destination, and anticipate flows in and out of intensive care units with accuracy in the 80%+ range. More importantly, we implement this machine learning pipeline into the EHR system of the hospital and construct prediction-informed dashboards to support daily bed placement decisions. Managerial implications: Our study demonstrates that interpretable machine learning techniques combined with EHR data can be used to provide visibility on patient flows. Our approach provides an alternative to deep learning techniques that is equally accurate, interpretable, frugal in data and computational power, and production ready.