Preventing Medication Errors: A $21 Billion Opportunity

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…

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Weekly News Roundup: December 7, 2018

 

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 precision medicine

Natural language processing is a useful technology that's become so commonplace in recent years it's almost hard to remember how miraculous it once seemed. Hospitals are leveraging NLP…

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Addressing Healthcare Waste Through Centralization

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…

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How to Turn Data Analysts into Data Scientists

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.

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A Framework for High-Reliability Organizations in Healthcare

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: Leadership Transparency Reliability Improvement and Measurement Continuous Learning Negotiation Teamwork and Communication Accountability Psychological Safety

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Weekly News Roundup: November 30

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 manage an infection control breach; and lastly, 10 ways to reduce heart failure and COPD readmissions.   How a U.S. Healthcare System Uses 15-Minute Huddles to Keep 23 Hospitals Aligned…

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The Healthcare Data Warehouse: Lessons from the First 20 Years

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.

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How to Achieve Your Clinical Data Analytics Goals

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.

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What Healthcare Analysts Can Learn About Data Analytics From the World of Surfing

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. This article also offers insights on moving from reactive to prescriptive analytics, the top five technical skills data analysts need, and a four step model for problem solving.

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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.

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Three Principles for Making Healthcare Data Analytics Actionable

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.

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Value-Based Care: Four Key Competencies for Success

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.

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Healthcare NLP: Four Essentials to Make the Most of Unstructured Data

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.

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Four Critical Phases for Effective Healthcare Data Governance

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. Each step must follow the core principles of stakeholder engagement, shared understanding, alignment, and focus. Effective healthcare data governance is not a one-time event and requires ongoing and iterative efforts.

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The Top Seven Healthcare Outcome Measures and Three Measurement Essentials

Healthcare outcomes improvement can’t happen without effective outcomes measurement. Given the healthcare industry’s administrative and regulatory complexities, and the fact that health systems measure and report on hundreds of outcomes annually, this article adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples. The top seven categories of outcome measures are: Mortality Readmissions Safety of care Effectiveness of care Patient experience Timeliness of care Efficient use of medical imaging CMS used these seven outcome measures to calculate overall hospital quality and arrive at its 2018 hospital star ratings. This article also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement.

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DSRIP in 2018: Continuing Efforts for Medicaid Reform

As a performance-based incentive program, DSRIP (the Delivery System Reform Incentive Payment) is designed to help participating states reform Medicaid. To date, 13 states have implemented DSRIP and received a Section 1115 waiver from CMS to transform their Medicaid programs and align them with value-based reimbursement. These states have agreed to budget neutrality, transparency, statewide quality metrics, and frequent reporting of outcomes. While each state’s program structure and objectives are unique, under DSRIP, participating states share three key goals: Reducing the total medical spend. Improving patient outcomes. Establishing a direct link between provider performance and payment.

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The Secret to Patient Compliance: An Application of The Four Tendencies Framework

Every day, healthcare professionals face the challenge of determining how to get patients to make good healthcare decisions and follow recommendations. The Four Tendencies framework, developed by The New York Times bestselling author Gretchen Rubin, can make this task easier and improve patient compliance by revealing how each person responds to expectations. By asking this question, healthcare practitioners can gain exciting insights into how patients respond to expectations to in order to help them achieve their goals. This report covers the following: An overview of each of the Four Tendencies. An understanding of how these tendencies can affect behavior in a healthcare setting. Practical tips for working with patients and colleagues that fall into different tendencies.

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Meaningful Machine Learning Visualizations for Clinical Users: A Framework

Health systems can leverage the predictive potential of machine learning to improve outcomes, lower costs, and save lives. Machine learning, however, doesn’t inherently produce insights that are actionable in the clinical setting, and frontline clinicians need information that’s accessible and meaningful at the point of care. Thoughtfully designed visualizations of machine learning insights are a powerful way to give clinical users the information they need, when and how they need it, to support informed decision making. A design framework for machine learning visualizations addresses three key questions about who will use the decision-support insights and how: People: who are the targeted users? Context: in what context or environment do they work? Activities: what activities do they perform?

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Lean Healthcare: 6 Methodologies for Improvement from Dr. Brent James

The survival of healthcare organizations depends on applying lean principles. Organizations that adopt lean principles can reduce waste while improving the quality of care. By applying stringent clinical data measurement approaches to routine care delivery, healthcare systems identify best practice protocols and incorporate those into the clinical workflow. Data from these best practices are applied through continuous-learning loop that enables teams across the organization to update and improve protocols–ultimately reducing waste, lowering costs, and improving access to care. This executive report based on a presentation by Dr. Brent James at a regional medical center, covers the following: How lean healthcare principles can help improve the quality of care. The steps healthcare organizations need to take to create a continuous-learning loop.…

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Healthcare Safety Culture: A Seven-Step Success Framework

Preventable patient harm costs healthcare billions annually, making strategies to improve patient safety an imperative for health systems. To improve patient safety, organizations must establish a safety culture that prioritizes safety throughout the system, supports blame-free reporting of safety events, and ensures that healthcare IT solutions functions and accessibility align with safety goals. A sociotechnical framework gives health systems a seven-part roadmap to improving patient safety culture: Leverages qualitative and quantitative data. Doesn’t rely on HIMSS stage levels to tell the complete safety picture. Gives frontline clinicians a voice in decision making. Makes IT solutions accessible to non-technical users. Encourages frontline clinicians to report safety and quality issues. Treats a safety issue in one area as a potential systemwide risk.…

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Unleashing Patient’s Power in Improving Health and Care

We know that patient engagement has a powerful effect on outcomes, but we haven’t yet truly harnessed patient’s power. Maureen Bisognano, former president and CEO of the Institute for Healthcare Improvement (IHI) discusses the effect of patient engagement across the IHI Triple Aim: improving the experience of patient care, improving the health of populations, and lowering costs. She shares examples of how increased patient engagement can help improve healthcare outcomes and deliver a better care experience while reducing costs. Such examples from her experience in the field include how lessons from the “flipped classroom” can be translated to healthcare, how technology can improve patient accountability and decision making, and other impactful stories.

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Six Steps to Managing an Infection Control Breach

Despite widespread efforts to improve patient safety, infection control breaches still happen at an alarming rate. In order to improve patient safety and prevent infections, healthcare organizations need to have infection control procedures in place and regularly assess protocols and adherence to these policies. In the case of an infection control breach, organizations need to be prepared to act quickly and follow a six-step evaluation procedure outlined by the CDC: Identify the infection control breach. Gather additional data. Notify and involve key stakeholders. Perform a qualitative assessment. Make decisions about patient notification and testing. Handle communications and logistical issues.

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Clinical Data Management: 3 Improvement Strategies

Most health systems suffer from data clutter and efficiency problems. As a result, analysts spend most of their time searching for data, not performing high value work.  There are three steps that can help you address your data management issues: 1) find all your dispersed analysts in the organization, 2) assess your analytics risks and challenges, 3) champion the creation of an EDW as the foundation for clinical data management.

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Why Patient-Reported Outcomes Are the Future of Healthcare—and the Key to Ruth’s Independence

Patient-reported outcomes (PROs), defined as “any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else,” are the future of healthcare. In addition to helping people like 80-year-old-Ruth continue to live interpedently, PROs—interchangeable with the term patient-generated health data (PGHD)—have several benefits: Effectively supplement existing clinical data, filling in gaps in information and providing a more comprehensive picture of ongoing patient health. Provide important information about how patients are doing between medical visits. Gather information on an ongoing basis—rather than just one point in time—and provide information relevant to preventive and chronic care management. The new technologies that enable PROs and PGHD (e.g.,…

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