Thibodaux Regional Health System had improved sepsis mortality rates, yet it recognized the challenges associated with sustaining and further improving care outcomes. Driven by a mission of patient-centered excellence that starts with the chief executive officer, the Board, and leadership, Thibodaux Regional’s sepsis care transformation team utilized its data platform and analytics applications to help facilitate data-driven, sustained improvements.
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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?
Patient comments such as “I feel dizzy” or “my stomach hurts” can tell clinicians a lot about an individual’s health, as can additional background, including zip code, employment status, access to transportation, and more. This critical information, however, is captured as free text, or unstructured data, making it impossible for traditional analytics to leverage. Machine learning tools (e.g., NLP and text mining) help health systems better understand the patient and their circumstances by unlocking valuable insights residing unstructured data:
- NLP analyzes large amounts of natural language data for human users.
- Text mining derives value through the analysis of mass amounts of text (e.g., word frequency, length of words, etc.).
Waste is a $3 trillion problem in the U.S. Fortunately, quality improvement theory (per W. Edwards Deming) intrinsically links high-quality care with financial performance and waste reduction. According to Deming, better outcomes eliminate waste, thereby reducing costs. To improve quality and process and ultimately financial performance, an industry must first determine where it falls short of its theoretic potential. Healthcare fails in five critical areas:
- Massive variation in clinical practices.
- High rates of inappropriate care.
- Unacceptable rates of preventable care-associated patient injury and death.
- A striking inability to “do what we know works.”
- Huge amounts of waste.
UnityPoint Health evaluated its percutaneous coronary intervention (PCI) performance and identified the opportunity to further improve. The health system decided to identify ways to improve its PCI outcomes. With its data operating system and a robust suite of analytics tools, UnityPoint Health took a data-driven approach to improving its PCI outcomes.
The Data Operating System (DOS™) is a vast data and analytics ecosystem whose laser focus is to rapidly and efficiently improve outcomes across every healthcare domain. DOS is a cornerstone in the foundation for building the future of healthcare analytics. This white paper from Imran Qureshi details the seven capabilities of DOS that combine to unlock data for healthcare improvement:
As the need for data-driven improvement becomes more urgent, health systems are finding that their current approaches to data management and analytics are failing to keep up. Organizations must evolve towards a broad data management solution to satisfy healthcare's increasing demands for data storage and near-real time insights.
Join Stephen as he looks in the rearview mirror at these important issues and how they impacted the healthcare industry in 2019 and then gazes into the crystal ball to predict the trends that will most impact healthcare in 2020. Stephen will discuss the following topics and more:
- The continued focus on price transparency.
- Congress’ efforts to control prescription drug costs.
- Policies that may change the future of ACOs.
- What to expect going into the 2020 election year.
While over 90 percent of organizations in industries worldwide now use cloud computing in their operations, healthcare still lags behind. As health systems grow their ability to capture data, they still have only a fraction of the data they need to achieve today’s population health and precision medicine goals. Organizations looking to migrate to more agile cloud-based platforms and leverage data for measurable improvements can learn the fundamentals of this critical transformation in an e-book about the Health Catalyst Data Operating System (DOS™).
Colorectal cancer (CRC) accounts for $16 billion in healthcare costs, and with 142,250 new cases annually, it’s the second leading cause of cancer deaths in the U.S. Thibodaux Regional Health System had implemented evidence-based screening and oncology treatment guidelines for colon cancer, yet it still needed to meet organizational goals for early diagnosis and colon cancer survival. With support from the CEO and senior executive leadership, a collaborative approach to tackling CRC diagnosis rates, and a robust suite of analytics applications to deliver accurate data, Thibodaux Regional improved CRC outcomes and patient satisfaction.
In the digital age, cloud computing is an essential part of more effective healthcare and precision medicine. But healthcare organizations themselves are still facing challenges in migrating to the cloud. Currently, only 8 percent of the EHR data needed for precision medicine and population health is being effectively captured and used. As a primer on the Data Operating System (DOS™), this e-book, Bring Data Together for Value-Based Outcomes, describes how Health Catalyst’s platform brings healthcare organizations the benefits of a more flexible computing infrastructure in the cloud. Key lessons include fresh approaches to the healthcare cloud, starting with the three key organizational questions to ask before beginning the journey, and seven capabilities necessary to maximize an analytics investment, founded on Health Catalyst’s deep expertise in healthcare data and analytics technologies. Finally, Bring Data Together includes a data analytics capabilities assessment to pinpoint where an organization stands and determine the critical next steps.
Health systems increasingly recognize data as one of their top strategic assets, but how many organization have the processes and frameworks in place to protect their data? Without effective data governance, organizations risk losing trust in their data and its value in process and outcomes improvement; a 2018 survey indicated less than half of healthcare CIOs have strong trust in their data. By following five steps towards data governance, health systems can effectively steward data and grow and maintain trust in it as a critical asset:
- Identify the organizational priorities.
- Identify the data governance priorities.
- Identify and recruit the early adopters.
- Identify the scope of the opportunity appropriately.
- Enable early adopters to become enterprise data governance leaders and mentors.
What can healthcare learn from Formula One racing? According to Dr. Sadiqa Mahmood, SVP of medical affairs and life sciences for Health Catalyst, race support teams leverage about 30TB of baseline data to create a digital twin of the car, track, and racer for simulation models that drive decisions at each race. Applied in the healthcare setting, a digital twin can help clinicians better understand each patient and their health conditions and circumstances in real time and make comprehensive, informed care decisions. But for the healthcare digital twin to happen, the industry must move away from data silos and towards a digital learning healthcare ecosystem.
UnityPoint Health created a task force to develop and implement a plan for maximizing blood management. The task force incorporated decision support to improve transfusion ordering in alignment with the transfusion standards. An analytics platform has also been leveraged, which monitors the utilization of blood products, including predictive modeling to risk-adjust blood utilization specific to patient case-mix, and data down to the ordering provider level.
Improving the future of global healthcare requires a shift towards a real-time, digital learning healthcare ecosystem—a goal that data-driven action will help achieve. Elia Stupka, Health Catalyst senior vice president and general manager, life sciences business, shared his insights with HealthManagement.org on the structure of this ecosystem and its power to improve individual patient health around the world. According to Stupka, “If we can all shift towards the massively transformational purpose of a real-time, connected, digital learning healthcare ecosystem, our children and grandchildren will hopefully see a world where most diseases will be prevented, diagnosed and treated for all citizens and hospital stays will be a thing of the past for most patients.”
In this new, data-rich healthcare environment, clinicians have greater abilities than ever to provide tailored treatments for specific patients's needs. However, personalized healthcare is easier said than done. In this week's news roundup: how real-world data can help advance clinical trials; the power of analytics and AI in the new era of personalized medicine; four trends to make precision medicine possible; and more.
Our goal is to support leaders in driving systemwide outcomes improvement—do we have more opportunity in readmission or depression, how should we staff the ED on weekends, how long does a nurse manager need to improve safety culture, and so on. There is an opportunity to include AI to assist in decision making in new and innovative ways. In this webinar, you will see specific frameworks and tools to use AI to close the information gap for leaders to drive outcomes improvement.
Healthcare is looking towards an era of personalized medicine in which providers customize treatments for the individual patient. Realizing this tailored level of care s a new level of data volume and analytics and AI capabilities that, while novel to healthcare, other industries are thriving in. Choosing the right role models as healthcare works towards the analytics- and AI-driven territory of personalized medicine will guide informed strategies and establish best practices. With experience and expertise in these key areas, the military, aerospace, and automotive industries can serve as healthcare’s best examples:
- The human cognitive processes of complex decision making.
- The digitization of their industries, with the “health” of their assets as key drivers.
- Operating in a “big data” ecosystem.
The healthcare industry is under scrutiny for its treatment of different groups including residents, women in leadership (or lack thereof), and the LGBTQ+ community. In this week's news roundup: how health systems can leverage data to improve care delivery to the LGBTQ+ community; female residents report higher rates of discrimination leading to higher rates of burnout; how to improve the skewed ratio of entry level female employees to the number of women at the top in the healthcare industry; and a new alliance in the Northwest focuses on achieving health equity in seven states over the next two years.