Healthcare organizations face provider dissatisfaction, lack of data integration, and excessive clicks to perform basic functions within the EHR. Closed-Loop Analytics™ aggregates data, circulates that data into new or existing workflows, and then surfaces best practice alerts at the decision point for physicians, clinical providers, and financial and operational teams. With clear calls to action throughout the workflow, organizations improve the utilization and effectiveness of analytics tools, yielding simplified workflows, decreased clicks, and improved outcomes.
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To succeed in today’s rapidly evolving business environment, healthcare organizations must have accurate financial data. Approximately 50 percent of CMS payments are now tied to a value component; hospital operating margins are at an all-time low; and consumer demands are rising with their costs. In order to meet these new challenges, health systems must shift their strategy or risk being left behind. This article details the operational, organizational, and financial strategies that drive financial transformation, as well as examples of how to obtain and utilize financial data, find waste reduction opportunities, and much more.
Why would a healthcare data warehousing and analytics company partner with the life sciences industry? Because trust and collaboration across the industry—between life sciences, healthcare delivery systems, and insurance—is the only path to real healthcare transformation. Health Catalyst recognizes an industrywide improvement opportunity in collaborating with life sciences to build mutual trust, integrate data, and leverage analytics insights for a common interest (i.e., patient outcomes). By aligning themselves around human health fulfillment, Health Catalyst, their provider partners, and life sciences will advance important healthcare goals:
- Improving clinical trial design and execution.
- Stimulating clinical innovation.
- Supporting population health.
- Reducing pharmaceutical costs.
- Improving drug safety and pharmacovigilance.
Agnostic Analytics Solutions vs. EHRs: Six Reasons EHRs Can’t Deliver True Healthcare Interoperability
As enterprisewide analytics demands grow across healthcare, health systems that rely on EHRs from major vendors are hitting limitations in their analytics capabilities. EHR vendors have responded with custom and point-solution tools, but these tend to generate more complications (e.g., multiple data stores and disjointed solutions) than analytics interoperability. To get value out of existing EHRs while also evolving towards more mature analytics, health systems must partner with an analytics vendor that provides an enterprise data management and analytics platform as well as deep improvement implementation experience. Vendor tools and expertise will help organizations leverage their EHRs to meet population health management and value-based payment goals, as well as pursue some of today’s top healthcare strategic goals:
The life science industry has historically relied on sanitized clinical trials and commoditized data sources (largely claims) to inform its drug development process—an under-substantiated approach that didn’t reflect how a new drug would affect broader patient populations. In an effort to gain more accurate insight into the patient experience and bring drugs to market more efficiently and safely, the industry is now expanding into extended real-world data (RWD). To access the needed breadth and depth of patient-centric data, life science companies must partner with a healthcare transformation company that has three key qualities:
- A broad and deep data asset.
- Extensive provider partnerships.
- An outcomes-improvement engine to support the next generation of drug development.
A lack of effective technology is impeding the progress of patient safety, according to a 2018 survey of healthcare professionals. Even though most healthcare organizations claim safety as a priority, serious challenges remain to making a significant impact on patient safety outcomes. Survey respondents said ineffective information technology and the related lack of real-time warnings for possible harm events were the top barriers to improving patient safety. They cited a number of key obstacles:
- Lack of resources.
- Organization structure.
- Lack of reimbursement for safety measures.
- Changes in patient population.
Five Reasons Why Health Catalyst Acquired Medicity and What It Means for Interoperability, as Explained by Dale Sanders, President of Technology
Why did Health Catalyst acquire Medicity? Dale Sanders, President of Technology, shares five reasons and what it means for interoperability:
- Medicity has several petabytes of valuable data content.
- Medicity’s data governance expertise.
- Medicity’s 7 x 24 real-time cloud operations expertise.
- Medicity’s expertise in real-time EHR integration.
- Medicity’s presence and expertise in the loosely affiliated, community ambulatory care management space.
Healthcare mergers and acquisitions can involve a lot of EMRs and other IT systems. Sometimes leaders feel like they have to rip and replace these systems to fully integrate organizations. However, this is not the answer, according to Dale Sanders. This report, based upon his July 2017 webinar, outlines the importance of a data-first strategy and introduces the Health Catalyst® Data Operating System (DOS™) platform. DOS can play a critical role in facilitating IT strategy for the growing healthcare M&A landscape.
The FDA recently released guidance documents on the use of clinical decision support (CDS) and medical software that may be of concern to forward-thinking healthcare innovators who rely on these technologies to deliver exceptional care and improve outcomes. What will be the impact of this guidance on machine learning and predictive analytics efforts? How will the guidance affect timelines, costs, and effectiveness of ongoing machine learning implementation? As healthcare delivery increasingly relies on digital innovation and support, more questions emerge about the governance of the accompanying tools and technology. This article provides a summary of the FDA guidance on CDS, how CDS is defined, whether or not CDS is exempt from regulation, and how the FDA intends to enforce compliance. It also summarizes the FDA guidance on medical software, what software is exempt from regulation, and helps to answer some of the questions surrounding the digital health space.
Health Catalyst Data Operating System (DOS) is a revolutionary architecture that addresses the digital and data problems confronting healthcare now and in the future. It is an analytics galaxy that encompasses data platforms, machine learning, analytics applications, and the fabric to stitch all these components together. DOS addresses these seven critical areas of healthcare IT:
- Healthcare data management and acquisition
- Integrating data in mergers and acquisitions
- Enabling a personal health record
- Scaling existing, homegrown data warehouses
- Ingesting the human health data ecosystem
- Providers becoming payers
- Extending the life and current value of EHR investments
Learn from the Best in Healthcare Data Visualization at Health Catalyst University™ During HAS™ 2017
Too often, the hard work of collecting and transforming data into meaningful insights is betrayed by a critical step in the journey: the visualization. Data visualizations should always make data easily consumable and digestible and accelerate outcomes improvement. This is where the Health Catalyst University Visualization Track comes into play. It’s one of four tracks available leading up to the 2017 Healthcare Analytics Summit. Class attendees will learn how to:
- Describe why visualization is important
- Recognize commonly accepted presentation rules
- Identify weakness in existing visualizations
- Execute the critical steps for effective chart creation
Healthcare information systems are integral to hospital operations and clinical care for patients. In the 1960s healthcare was driven by Medicare and Medicaid and HIT developed shared hospital accounting systems. In the 1970s communication between departments and individual transactional systems became important. DRGs drove healthcare in the 1980s and HIT needed to find ways to pull both clinical and financial data in order for reimbursements. The 1990s saw competition and consolidation drive technology to create IDN-like integration. In the 2000s outcomes-based reimbursement became the drive behind developing real-time clinical decision support. For the future, ACOs and value-based purchasing means that CIOs will need to implement data warehouses and analytics application to provide the insights to drive performance improvement necessary for hospital survival.
According to statistician W. Edwards Deming, “Uncontrolled variation is the enemy of quality.” The statement is particularly true of outcomes improvement in healthcare, where variation threatens quality across processes and outcomes. To improve outcomes, health systems must recognize where and how inconsistency impacts their outcomes and reduce unwanted variation. There are three key steps to reducing unwanted variation:
- Remove obstacles to success on a communitywide level.
- Maintain open lines of communication and share lessons learned.
- Decrease the magnitude of variation.
Establishing a healthcare improvement initiative is just the first step toward transformation. The real work of improvement lies in sustaining it, which is why qualified change agent are essential to meaningful progress. Change agents are trained to lead organizations in:
- Case for change
- Data management
- Change management concepts
- Cost Benefit Analysis
U.S. healthcare is one of the most technologically advanced industries in the world, yet it has such a difficult time transforming some of its most mundane problems (cost, quality, and service). With these problems, we are not so different from many other industries, so we should be able to learn from the individuals and industries that have succeeded in finding answers. At the same time, we need to recognize that healthcare is incredibly complex, so we need to search within for barriers that prevent disruption and innovation. The future of healthcare lies in technology, but more importantly, in our ability to pave the way for its implementation starting right now.
Healthcare organizations need to make lasting, systemwide improvements to make the transition to value-based care models. Starting this work is tough, but a new tool from Health Catalyst will show the way. This 25-question assessment based on an integrate literature review of outcomes improvement research, will show how organizations are performing in five main categories:
- Adaptive leadership and culture
- Best Practices
- Financial Alignment
Transforming healthcare takes more than just dashboards and data. It takes an entirely new approach combining best practices, analytics, and adoption of the improvement program throughout the entire organization. Which is why Health Catalyst Clinical Improvement Applications offer tools to help organizations with all three of those systems. The applications contain starter content (best practices), which includes a knowledge brief, a care process improvement map, and an outcomes improvement packet. Of course, analytics is also part of the applications in the form of precise patient registries, outcomes and process metrics, and visualizations. And finally, Health Catalyst includes deployment services to drive adoption of improvement work. This includes engagement with health system teams and sharing of insights based on work from a variety of healthcare organizations across the country and the world. Armed with a Clinical Improvement Application, a health system is in a better position to make real, meaning changes resulting in outcomes improvement for patients and itself.