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.
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Build Versus Buy a Healthcare Enterprise Data Warehouse: How IT Leaders Choose the Best Option for Their Organizations
The public cloud has made IT infrastructure increasingly accessible, influencing some healthcare CIOs and CTOs to try to build an enterprise data warehouse (EDW) in-house versus purchasing a commercial solution. However, a spring 2020 survey indicates that the vast majority of healthcare data platform users purchase their EDWs, citing superior quality, functionality, and security. Meanwhile, homegrown EDW users report high satisfaction with their systems despite common roadblocks including insufficient IT personnel. A deeper dive into survey findings shows which type of organization may be best suited to building or buying an EDW.
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.
The framework must include data-supported surveillance and containment strategies to enhance detection, reduce transmission, and manage capacity and supplies, providing a roadmap to respond to immediate demands and also support a sustainable long-term pandemic response.
With the CMS and ONC March 2020 endorsement of HL7 FHIR R4, FHIR is positioned to grow from a niche application programming interface (API) standard to a common API framework. With broader adoption, FHIR promises to support expanding healthcare interoperability and prepare the industry for complex use cases by addressing significant challenges:
- Engaging consumers.
- Sharing data with modern standards.
- Building a solid foundation for healthcare interoperability.
During the emergency phase of the COVID-19 pandemic, almost half of all U.S. healthcare consumers postponed routine and non-emergent care, leaving organization with significant revenue loss across all care settings. In response to the widespread financial strain on the healthcare industry, Congress has allocated $100 billion in relief funding for hospitals and other healthcare providers. But while providers clearly need the financial relief, using it (including navigating terms and conditions and eligibility) has been less straightforward. Better understanding of these relief programs and compliance requirements will help organizations confidently optimize this assistance.
The focus on analytics is contributing to the “EHR problem”—doctors prioritizing the EHR over patients. The Healthcare Analytics Adoption Model (HAAM) walks healthcare organizations through nine levels that lay the framework to fully leverage analytic capabilities to improve patient outcomes:
Level 1. Enterprise Data Operating System
Level 2. Standardized Vocabulary & Patient Registries
Level 3. Automated Internal Reporting
Level 4. Automated External Reporting
Level 5. Waste and Care Variability Reduction
Level 6. Population Health Management & Suggestive Analytics
Level 7. Clinical Risk Intervention & Predictive Analytics
Level 8. Personalized Medicine & Prescriptive Analytics
Level 9. Direct-To-Patient Analytics & Artificial Intelligence
Analytics are crucial to becoming a data-driven organization, but providers and administrators can’t forget about the why behind the data—to improve outcomes. Following the HAAM enables organizations to build a sustainable, analytic platform and empower patients to become data-driven when it comes to their own care.
The year 2020 marks a decade since the passage of the Affordable Care Act in 2010 and healthcare’s first transitional steps from volume to value. The 10-year progress report is mixed. On one hand, CMS’s emphasis on quality and cost is driving an upward trend for patients and providers, with substantial improvement in readmissions; on the other hand, organizations still need to simplify and consolidate value-based programs for more widespread positive impact. As the industry enters into another decade of value, it’s time for health systems to consider the impacts of these programs so far and make sure they have the processes and tools in place to succeed in an increasingly value-driven industry.
Medicare patients make up the majority of health systems’ revenue; yet, organizations earn only a one percent profit while caring for this population. Despite historically low profit margins, Medicare can be lucrative for health systems, and through the Medicare Shared Savings Program, healthcare organizations can increase revenue with four tools:
- The ability to aggregate and analyze data.
- The ability to align financial incentives between payers and providers.
- The ability to engage patients in behavior or lifestyle modifications.
- The ability to garner support from clinicians and encourage them to lead the shift to VBC.
As the shift from fee-for-service to value-based care continues, health systems can leverage MSSP to deliver the highest level of care while also increasing profit margins.
According to a November 2019 survey, 62 percent of clinicians and other healthcare professionals view burnout as a major problem industrywide. When asked for the best way to address clinician burnout problems, the most popular solution was less-complex workflows, which is the aim of emerging point-of-care analytics solutions.
Responses to additional questions reveal more about clinician burnout experience and views on the technology designed to help:
- At your organization, how big of a problem is clinician burnout?
- What is the best way to solve clinician burnout problems?
- What are the biggest barriers to adopting closed-loop, point-of-care analytics capabilities at your organization
- What are the biggest problems arising from a lack of adopting closed-loop, point-of-care analytics capabilities?
The U.S. healthcare market projects that by 2022 90 million Americans will be in an ACO. The upward trend in population health management (PHM) makes the move towards risk-based contracts increasingly urgent for health systems. The industry has been largely unprepared for the shift, as it hasn’t established a clear definition of population health or solid guidelines on transitioning from volume to value. Organizations can, however, prepare for the demands of PHM by adopting a solution that manages comprehensive population health data, provides advanced analytics from new and complex challenges, and connects them with the deep expertise to thrive in a value-based landscape.