Healthcare Data and Platforms in the News Healthcare data comes from many different types of sources. Could smart speakers be a new healthcare data source? Could the new iPad Pro help streamine both clinical and administrative workflows? This week, our news roundup includes some of the biggest stories about healthcare data-some of which may soon come from unlikely sources. We also share lessons from the last 20 years of enterprise data warehousing, a conversation with Dale Sanders, president of technology at Health Catalyst, about commercial vs. homegrown healthcare data platforms, …
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In the high-pressured world of value-based care, healthcare leadership is more important than ever. Leaders need to make data-driven decisions and respond to the increasing demands of patients while cutting costs. The right tools–ones that support today’s outcome-focused healthcare environment–can help leaders break through the noise to make effective decisions. This article shares three common problems faced by healthcare leadership and how Leading Wisely, an executive decision support tool helps them make informed decisions and guide their organizations forward. Three effective strategies for healthcare leadership include:
- How to prevent communication breakdown.
- How to break through measure madness.
- How to alleviate information overload and siloed reporting.
With a potential industry-wide savings of almost $21 billion and an impact on more than seven million patient lives, preventing harmful medication error is a significant improvement opportunity for health systems. Also known as adverse drugs events (ADEs), harmful medication errors comprise about 37 percent of all medical harm. Approximately 50 percent of ADEs are preventable, making their reduction a highly impactable area of patient safety. Current data and analytics workflow tools are making ADE surveillance, monitoring, and prevention increasingly more effective with four key capabilities:
- Perspective surveillance for ADEs and identification of previously undescribed ADEs.
- Identification of the root cause of many ADEs by drug class.
- Prescription at appropriate doses for patients with compromised kidney or liver functions.
- Identification of different types of harm to find causes.
NLP in healthcare has long been a challenge to gaining a complete analytics picture. This week, we’re featuring stories that show the advancements healthcare has made in NLP; Amazon Comprehend Health has announced a new NLP processing service for unstructured data. And some that show how far we have to go. Finally, we’re also sharing the four essentials of NLP and how text analytics can be used to improve patient outcomes. NLP evolving from legacy tech to interactive apps with text and speech for pop health and …
Healthcare generates an estimated $1 trillion in waste each year, including supply costs, unnecessary tests, and surgeries that aren’t clinically indicated by best practices. One effective way health systems can reduce waste is by centralizing duplicated services into one high-performing center for that service. For example, instead of having a few cardiac catheterization (cath) labs, a health system can consolidate its cath services into one facility, cutting equipment, staffing, and space requirements. Despite its clinical and financial benefits, centralization can be challenging for health system leaders, who may face operational and political challenges when cutting services from certain locations. To navigate these challenges, leadership must use a data- and analytics-driven centralization strategy and a data and analytics system that can measure performance at the surgeon, facility, and program levels.
Healthcare data scientists are in high demand. This shortage limits the ability of healthcare organizations to leverage the power of artificial intelligence (AI). Health systems must better utilize their data analysts, and, where possible, turn some data analysts into data scientists. This report covers the following:
- Healthcare use cases and which ones data analysts can take the lead on.
- Specific steps for turning data analysts into data scientists.
- How to identify the best candidates among your data analysts.
- Recommended resources to get started on an AI journey.
Drs. Allen Frankel and Michael Leonard have developed a framework for creating high-reliability organizations in healthcare. This report, based on their 2018 webinar, covers the components and factors of this frame work, including:
- Improvement and Measurement
- Continuous Learning
- Teamwork and Communication
- Psychological Safety
Patient Safety In the News After another week of news moving at breakneck speed, here are some important stories about patient safety to keep your eye on. A large health system uses 15-minute huddles to keep 23 hospitals aligned; medical device companies are in the news, and no, not in a good way; breast implant manufacturers are allowed to report issues in bulk; and the FDA’s guidelines for reporting problem devices is vague. Then, some positives: a seven-step framework to create a culture of safety; a six-step evaluation procedure to …
Twenty years after Intermountain Healthcare launched its enterprise data warehouse in 1998, industry leaders are looking at what they did right, what they’d do differently, and what the future holds for healthcare data and analytics. While early successes (such as a hiring framework of social, domain, and technical skills; lightweight data governance; and late-binding architecture) continue to hold their value, advanced analytics and technology and innovation in diagnosis and treatment are reshaping the capabilities of and demands on the healthcare data warehouse. Present-day and future healthcare IT leaders will have to revisit approaches to data warehousing people, processes, and technology to understand how they can improve, continue to adapt, and fully leverage emerging opportunities.
Healthcare organizations know that they need to an effective clinical data analytics strategy to improve and survive in today’s challenging environment. In order to make these necessary improvements, healthcare leaders need to establish clear goals for their clinical data analytics initiatives. Achieving these goals requires clinical teams to clearly identify problems and plan for how to achieve them. This article walks improvement teams through sometimes confusing process of identifying problems, setting clear, achievable goals, and common pitfalls along the way. Topics covered include:
- Six categories of clinical data.
- Three types of goals: outcome, process, and balance.
- How to write an outcome goal.
- Internal vs. External Benchmarks.
- Mitigation strategies.
- Getting clinical buy-in.
It might sound surprising, but the world of surfing just might hold key observations about the world of healthcare analytics. After watching the Pipeline Masters in Oahu, John Wadsworth, Technical Operations VP at Health Catalyst, took away three key principles from the world of surfing that are important for healthcare analysts:
- Understand the Changing Environment.
- Know When to Say No, So You Can Say Yes to the Right Opportunity.
- Get Good at Positioning.
The Homegrown Versus Commercial Digital Health Platform: Scalability and Other Reasons to Go with a Commercial Solution
Public cloud offerings are making homegrown digital platforms look easier and more affordable to health system CTOs and CIOs. Initial architecture and cost, however, may be the only real benefit of a do-it-yourself approach. These homegrown systems can’t scale at the level of commercial vendor systems when it comes to long-term performance and expense, leaving organizations with a potentially costly and undereffective platform for years to come. Over his 25 years as a health system CIO, Dale Sanders, President of Technology for Health Catalyst, has observed both the tremendous value of healthcare-specific vendor platforms, as well as the shortcomings of homegrown solutions. He shares his insights in a question-and-answer session that addresses pressing issues in today’s digital healthcare market.
Data is everywhere. But without a plan to extract meaning from data and turn insights into action, data can’t impact outcomes. Generating value from data takes work, but it can be done. To create compelling data insights that promote action, health systems can follow three guiding principles for actionable healthcare data analytics as well as hire analysts with seven important skills. Three principles form the foundation for actionable healthcare data analytics:
- Balance investments.
- Hire generalists over specialists.
- Develop a team that’s highly aligned and loosely coupled.
How prepared are healthcare organizations to enter into value-based care? Many may not be ready. While early value-based care adopters have focused on improving and measuring quality, they’ve often overlooked steps to bear the associated financial risk. Now that health systems can enter into alternative payment models and risk-based contracts, they need to ensure that cost is as much a priority as quality. Health systems can achieve sustainable value-based care success by optimizing the five core competencies of population health management:
- Governance that educates, engages, and energizes.
- Data transformation that addresses clinical, financial, and operational questions.
- Analytic transformation that aligns information and identifies populations.
- Payment transformation that drives long-term sustainability.
- Care transformation as a key intervention in value-based contracts.
Many health systems are eager to embrace the capability of natural language processing (NLP) to access the vast patient insights recorded as unstructured text in clinical notes and records. Many healthcare data and analytics teams, however, aren’t experienced in or prepared for the unique challenges of working with text and, specifically, don’t have the knowledge to transform unstructured text into a usable format for NLP. Data engineers can follow four need-to-know principles to meet and overcome the challenges of making unstructured text available for advanced NLP analysis:
- Text is bigger and more complex.
- Text comes from different data sources.
- Text is stored in multiple areas.
- Text user documentation patterns matter.
Based on a 2018 Healthcare Analytics Summit presentation, this report details the four phases necessary for successful healthcare data governance:
- Elevate a vision and agenda that align with organizational priorities.
- Establish an organizational structure to fulfill the data governance mandate.
- Execute with prioritized data governance projects, people and resource assignment, and disciplined focus on the work.
- Extend data governance investments and efforts through established practices.