The first COVID-19 case in the US was reported on January 20, 2020. As the first cases were being reported in the US, Washington State became a reliable source not just for hospital bed demand based on incidence and community spread but also for modeling the impact of skilled nursing facilities and assisted living facilities on hospital bed demand. Various hospital bed demand modeling efforts began in earnest across the United States in university settings, private consulting and health systems. Nationally, the University of Washington Institute of Health Metrics and Evaluation seemed to gain a footing and was adopted as a source for many states for its ability to predict the epidemiological curve by state, including the peak.
This book therefore addresses a compelling need for documenting what has been learned by the academic and professional healthcare communities in healthcare analytics and disaster preparedness to this point in the pandemic. What is clear, at least from the US perspective, is that the healthcare system was unprepared and uncoordinated from an analytics perspective. Learning from this experience will only better prepare all healthcare systems and leaders for future crisis.
Both prospectively, from a modeling perspective and retrospectively from a root cause analysis perspective, analytics provide clarity and help explain causation and data relationships. A more structured approach to teaching healthcare analytics to students, using the pandemic and the rich dataset that has been developed, provides a ready-made case study from which to learn and inform disaster planning and preparedness. The pandemic has strained the healthcare and public health systems. Researchers and practitioners must learn from this crisis to better prepare our processes for future pandemics, at minimum. Finally, government officials and policy makers can use this data to decide how best to assist the healthcare and public health systems in crisis.
Author(s): Edward M. Rafalski, Ross M. Mullner
Publisher: CRC Press
Year: 2022
Language: English
Pages: 303
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Foreword
Contributors
Introduction
What would Alexis de Tocqueville have observed?
What did we learn from the last flu pandemic in 1918, or not?
Section 1: Epidemiology and analytics
Section 2: State case studies
Section 3: Topics
References
Section 1: Epidemiology and analystics
Chapter 1: What is an epidemic, a pandemic?
Definition(s) of epidemics and pandemics
Why do pandemics occur?
Perspective for the healthcare system
References
Chapter 2: A brief history of pandemics
References
Chapter 3: The healthcare continuum
Introduction
Public health
Healthcare systems
The evolution of the healthcare continuum
Screening and testing
Primary care
Urgent and emergent care
Hospital acute care and the re-establishment of the field hospital
Post-acute care
Transition hotel to home, Hospital at Home®, home health and the physician house call
Palliative care and hospice care
Conclusion
References
Chapter 4: The fog of war and data
References
Chapter 5: Sources of data/modeling
Introduction
Constants and assumptions
Early public data modeling – SIR
Health system experiential modeling
Publicly available models
Proprietary models
Institutional proprietary models
Conclusion
References
Chapter 6: Quantifying and responding to COVID’s financial and operational impact
An approach to modeling
The state of play
Strategic goals for an uncertain future
Live to fight another day
Rethink the future in a post-COVID world
Manage the shift to outpatient settings
Aggressively pursue no-regrets strategies
Conclusion: Regaining lost balance
Section 2: State case studies
Chapter 7: Measuring and addressing healthcare employee well-being in an Alabama health system during COVID-19
Introduction
Before the crisis
Measuring well-being
Maintaining well-being in crisis
Assessing well-being during the pandemic
Data collection
Results
Employee considerations for pandemic preparedness
Staffing
Well-being
Healthy teams
Perceived organizational support
Ability to solicit rapid and frequent feedback
The recovery period
References
Chapter 8: Colorado state case study
Introduction
COVID-19 case summary, Colorado, United States
COVID-19 response in Colorado, United States
Geographic patterns of COVID-19 cases in Colorado
Mask protests in Colorado
Differences in COVID compliance and outcomes by race
Reports of cardiac events decrease amidst rise of the coronavirus pandemic in Colorado and nationally
Denver EMS runs decreased amidst coronavirus pandemic, while out-of-hospital cardiac arrests increased
Cardiac alert activations decrease in Denver amidst coronavirus pandemic
Clinicians urge patients not to delay medical interventions
Responses among different hospital systems in the Denver metro area
Telehealth
The impact of elective procedure cancellation on cardiac morbidity, sequela and mortality in Colorado
Heart disease and COVID-19 – A rocky tale from the rocky mountains
Conclusion
References
Chapter 9: Case study: A Florida COVID-19 dashboard
Introduction
Summary of case study
Common concerns with COVID-19 dashboards
Can the public trust the COVID-19 data from the FDOH?
Were the rising COVID-19 cases in the summer of 2020 simply due to increased testing?
Which date should be used for reflecting the temporal progression of COVID-19 cases on an epidemic curve?
Are COVID-19 case positivity rates enough to fully convey the changes pandemic severity?
Were vaccinations effective at decreasing COVID-19 cases, hospitalizations and death in Florida?
Why would the FDOH change the way it reports cases and deaths to CDC?
Lessons learned
Looking ahead
Conclusion
References
Chapter 10: State case study: Illinois
Introduction
COVID-19: First signs
Chicago’s COVID-19 experience, March 1, 2020 to July 6, 2021
Five periods of the COVID-19 pandemic
Citywide variations in COVID-19 burden
Neighborhood-level variations in COVID-19 burden
Data to action
Racial Equity Rapid Response Team (RERRT)
Making data accessible and usable
Progressive collaboration
Lessons learned
Looking forward
Notes
References
Chapter 11: Tennessee case study
Introduction
Shelby county (demographics, poverty, chronic disease)
Poverty
Chronic disease
COVID-19
COVID-19 task force
Establishing community-based COVID-19 testing
Creating an Urban Population Health Observatory (UPHO) for Western Tennessee to more equitably manage the COVID-19 emergency and ongoing public health priorities
Digital interventions as cost-effective and scalable means for healthcare planning and delivery
Social Determinants of Health (SDoH) indicators
Bringing it all together: Deploying an integrated COVID-19 UPHO knowledgebase in Western Tennessee to promote an equitable future
References
Chapter 12: Regional modeling
Early case-based hospitalization and critical care data
Sample COVID-19 data published online from Italy’s National Health System
Using known data from Italian experience to estimate infected population COVID-19 hospitalization rates
Sg2 COVID-19 surge demand model evolves to dynamic SIR modeling
Impact of social distancing measures in Italy
Comparing frequently cited models for early US COVID-19 surge
Sg2’s tracking of state-mandated stay-at-home orders by date of implementation for local transmissibility mitigation inputs
Section 3: Topics
Chapter 13: Healthcare analytics: The effects of the pandemic on behavioral health
Introduction
Telehealth/telepsychiatry/tele-mental health and COVID-19
Mental health and COVID-19
Economic downturn and mental health
Loneliness and isolation and mental health
Mental health and substance abuse/substance use disorder
Issue to address
One: Use of electronic medical records
Two: Linking data systems
Three: Big data and policy in the era of COVID-19 pandemic
Four: Collecting socio-demographic data
Conclusion
References
Chapter 14: Digital transformation in healthcare: How COVID-19 was an agent for rapid change
Introduction
Rush pre-COVID: innovation pilot in telemedicine
Telemedicine pilot for movement disorders
Business model immaturity
Lack of regulatory clarity
Acceptability to a wider population of patients
Public health as a laggard in data sharing
Rush and COVID-19
Telemedicine growth
Transfers and clinical outcomes
Chicago Department of Health (CDPH) and data hub
Future directions
Acknowledgments
References
Chapter 15: Telehealth
Introduction
Telehealth before
Telehealth’s watershed moment
Telehealth’s new normal
Percentage of in-person appointments From May 2020 to May 2021
Conclusion
References
Chapter 16: The COVID-19 pandemic and development of drugs and vaccinations
COVID-19 pandemic
Challenges created by COVID-19 pandemic
Preventative measures
Social distancing
Isolation
Quarantine
Contact tracing
Drugs and vaccines
The basics
Key similarities between drugs and vaccines
Key differences between drugs and vaccines
Herd immunity and its significance
Vaccine development process
Traditional vaccine development process
COVID-19 vaccine development process
Drug development – stages in clinical trials 11
Emergency Use Authorization (EUA) 12
COVID-19 vaccines
Types of vaccines
COVID-19 vaccines approved in the United States
Pfizer/BioNTech/Fosun Pharma 13
Moderna/National Institutes of Health 13
Johnson & Johnson 13
Other COVID-19 vaccine projects 12
COVID-19 treatments 12
Conclusion
References
Chapter 17: Value of health information exchanges to support public health reporting
Learning objectives
Setting the context
Data as the institutional asset
Governmental support
Healthcare data: Utility and challenges
Gaps in transitions of care
Public health is more than about saving money
Public health surveillance
Health information exchange (HIE)
The focal points of HIE and services offered
Health information exchange models
Federated models
Non-federated models
Common data models (CDMs): Definition, history, utility and steps in the process
Key principles related to CDMs
How do the CDMs foster public health surveillance/population health efforts?
Examples of HIEs work during the COVID-19 pandemic 10, 11
San Diego Health Connect (SDHC)
Nebraska Health Information Initiative (NEHII)
Indiana Health Information Exchange (IHIE)
Reliance eHealth Collaborative
Arizona’s statewide HIE
California’s HIE
Other use cases
Conclusion
References
Conclusion
Epilogue
Index
