One of the biggest challenges health systems have faced since the onset of COVID-19 is the disruption to routine care. These care disruptions, such as halted routine checkups and primary care visits, place some patients at a higher risk for adverse outcomes. Health systems can rely on data science, based on past care disruption, to identify vulnerable patients and the short- and long-term effects these care disruptions could have on their health. Data science can also inform the care team which care disruptions to address first. With comprehensive information about care disruption on patients, health systems can apply the right interventions before it’s too late.
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COVID-19 has given data-driven healthcare the opportunity to prove its value on the national and global stages. Health systems, researchers, and policymakers have leveraged data to drive critical decisions from short-term emergency response to long-term recovery planning. Five areas of pandemic response and recovery stand out for their robust use of data and measurable impact on the course of the outbreak and the individuals and frontline providers at its center:
- Scaling the hospital command center to pandemic proportions.
- Meeting patient surge demands on hospital capacity.
- Controlling disease spread.
- Fueling global research.
- Responding to financial strain.
With an ever-changing understanding of COVID-19 and a continually fluctuating disease impact, health systems can’t rely on a single, rigid plan to guide their response and recovery efforts. An effective solution is likely a flexible framework that steers hospitals and other providers through four critical phases of a communitywide healthcare emergency:
- Prepare for an outbreak.
- Prevent transmission.
- Recover from an outbreak.
- Plan for the future.
Healthcare providers knew that COVID-19 would threaten the lives of their patients, but few understood the greater ripple effects across their business and industry as a whole. For providers, two significant COVID-19-induced challenges arose: analytic strain and resource limitations. These challenges highlighted the critical importance of data quality. Healthcare leaders can improve data quality throughout their organizations by understanding the data quality lessons learned from COVID-19. Five guidelines from these lessons will help organizations prepare for the next pandemic or significant analytic use case:
- Assess data quality throughout the pipeline.
- Do not leave analysts to firefight.
- Look outside the four walls of the organization.
- Data context and purpose matters.
- Use a singular vision to scale data quality.
Many health systems have realized they lack the data and analytics infrastructure to guide a sustainable reactivation plan and recover lost revenue from months of halted procedures due to COVID-19. However, with operational, clinical, and financial data, augmented by analytics tools, leaders have the visibility into hospital and resource capacity to guide a safe, sustainable elective surgery restart plan. The first step on the road to recovery for health systems is access to robust analytics to understand the full impact of COVID-19 on clinical, financial, and operational outcomes. Second, organizations need data-sharing tools, like data displays and dashboards, allowing leaders to make decisions based on consistent data that support the organization’s reactivation goals. Leaders can even take the data one step further with predictive models and forecast procedure count, staff, and resources.
With limited information about the novel coronavirus, industries are scrambling to create an effective response to more quickly and safely return to life before the pandemic. Data has proven to be the best way to capture information about the developing virus. With access to the latest, comprehensive COVID-19 data, decision makers in any industry—from education to healthcare—can develop a sustainable, viable approach to pandemic-era operations. In the education sector, leaders can use accurate, up-to-date COVID-19 data to make decisions about implementing in-person or virtual learning. When states across the country instituted virtual learning as a stopgap until it was safe to resume in-person education, the most vulnerable students experienced the greatest disadvantages. As these disparities grow with continued virtual learning, it is an imperative that leaders have access to the latest coronavirus data to rapidly return to face-to-face learning.
Shifting to Virtual Care in the COVID-19 Era: Analytics for Financial Success and an Optimized Patient Experience
The COVID-19 era has seen a decline in visits to ambulatory care practices by 60 percent and an estimated financial loss for primary care of over $15 billion. Shutting down elective care is financially unsustainable for health systems and for patients, who continue to need non-pandemic-related care. While virtual medicine has emerged as a viable and mutually beneficial solution for patients and providers, the shift from in-person to virtual health is logistically and financially complicated. Processes and workflows from in-person care don’t directly translate to the virtual setting, and a financially successful shift requires deep understanding of the factors driving patient engagement and revenue in the new normal. As such, meeting patient needs and financial goals requires robust enterprisewide analytics that drill down to the provider level.
Health systems have always faced bad debt—from charity care to insurance claim denials—and COVID-19 has exacerbated its impact on revenue. While hospitals and clinics are responsible for providing care to populations, they can still generate revenue from care delivery without compromising care accessibility or quality. An effective bad debt management approach provides the patient with every financial resource possible and allows the health systems to focus less on payment and more on delivering the best care. With four tactics, health system leadership can identify bad debt and implement effective processes to minimize it without undue burden on patients:
- Identify bad debt exposure early.
- Educate patients about alternative payment options.
- Leverage technology within the workflow.
- Understand the true cost of care.
As data in healthcare becomes more available than ever before, so does the need to apply that data to the unique challenges facing health systems, especially in a pandemic. Even with massive amounts of data, health systems still struggle to move data from spreadsheets to drive change in a clinical setting. These six methods allow health systems to transform data into real-world analytics, going beyond basic data usage and maximizing actionable insight:
- Create effective information displays.
- Add context to data.
- Ensure data processes are sustainable.
- Certify data quality.
- Provide systemwide access to data.
- Refine the approach to knowledge management.
Research projects that 2020 healthcare industry losses due to COVID-19 will total $323 billion. As patient volumes fall and pandemic-related expenses rise, health systems need a strategy for both immediate and long-term financial recovery. An effective approach will rely on a deep, nuanced understanding of how the pandemic has altered and reshaped care delivery models. One of the COVID-19 era’s most impactful changes has been the shift from in-person office visits to virtual care (e.g., telehealth). Though patients and providers initially turned to remote delivery to free up facilities for COVID-19 care and reduce disease transmission, the benefits of virtual care (e.g., circumventing the time and resource drain of patients traveling to appointments) position telehealth as lasting model in the new healthcare landscape. As a result, healthcare financial leaders must fully understand the revenue and reimbursement implications of virtual care.
While federal COVID-19 relief funding for health systems sounds good in theory, many organizations have found accessing and using these monies overwhelming and frustrating. Federal guidance has been inconsistent or incomplete, and continued changes to relief packages and policies challenge organizations to develop pragmatic financial recovery strategies. Financial leaders who are confronting more questions than answers need a simple framework to move confidently into recovery. The following five expert financial- and healthcare-based guidelines will help organizations navigate and optimize COVID-19 relief funding:
- Regularly review legislative and regulatory updates and agency activity.
- Make the most of what’s available.
- Use required reporting as a decision-making tool.
- Prepare now for the inevitable audit.
- Test compliance now to eliminate headaches (or litigation) later.
Despite pandemic-driven limitations, health systems can still find ways to optimize revenue cycle and generate income. When health systems improve and prioritize patient access through a patient-centered access center, they can improve the revenue cycle performance through decreased referral leakage, better patient trust, and optimum communication across hospital departments. Rather than relying on traditional revenue cycle improvement tactics, health systems should consider three ways a patient-centered access center can positively impact revenue cycle performance:
- Advance access.
- Optimize resources.
- Engage stakeholders.
With COVID-19 challenges continuing in the near-future, health systems must continue delivering quality care in the midst of the pandemic, without compromising financial well-being. Historical approaches to revenue cycle add value but fail to leverage data to drive financial sustainability in a time of crisis. To financially survive tumultuous economic times, health systems must leverage data to drive a more comprehensive revenue cycle strategy. Five best practices generate the actionable data that allows health system leaders to understand financials at a nuanced level, promoting effective processes that lead to financial sustainability and optimum revenue cycle management:
- Identify and measure the right metrics.
- Define clear lines of accountability.
- Create consistent workflows.
- Define key performance indicators.
- Understand the right metrics at the right place at the right time.
In addition to driving COVID-19 understanding within the United States, a national disease registry is informing research beyond U.S. borders. Clinicians with the Singapore Ministry of Healthcare Office for Healthcare Transformation (MOHT) have used Health Catalyst Touchstone® COVID-19 data to develop a machine learning tool that helps predict the likelihood of COVID-19 mortality. With this national data set that leverages deep aggregated EHR data, the MOHT accessed the research-grade data it needed to build a machine-learning algorithm that predicts risk of death from COVID-19. The registry-informed prediction model was accurate enough to stand up to comparisons in the published literature and promises to help inform vaccine research and, ultimately, allocation of vaccines within populations.
COVID-19 response and recovery is pushing healthcare to operate at an unprecedented level. To meet these demands and continue to improve outcomes and lower costs, healthcare analytics must perform more actionably and with broader organizational impact than ever. Health systems can follow four strategies to produce high-value analytics to withstand the pandemic and make healthcare better in the long term:
- Minimize benchmarking.
- Outsource regulatory reporting.
- Grow risk-based stratification capabilities.
- Run activity-based costing plus at-risk contracting.
Comprehensive COVID-19 understanding is a critical asset for adapting to pandemic needs, directing resources, developing vaccines, and planning for surges in a timely, informed manner. Because common barriers have impeded the progress of comprehensive data repositories, researchers have relied on surveillance data from population-level viral testing, which has proven insufficient. To significantly advance COVID-19 understanding, the medical community needs a digital patient registry that captures national-level data on how the virus impacts individuals differently according to comorbidities, lifestyle factors, and more. These essential insights lie in real-world evidence, which a registry can only deliver when it applies value sets to leverage clinical and claims data from health systems across the United States.