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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The 2018 Healthcare Analytics Summit: Thursday Recap

In the final day of the 2018 Healthcare Analytics Summit in Salt Lake City, we were treated a continuation of the highest-rated keynote lineup in the event’s 5-year history. Dr. Penny Wheeler shared some important tips about improvement. Three digital innovators showed mind-blowing technology and approaches that will forever change healthcare, and Kim Goodsell showed us all why she’s the only the first of her kind—the data-empowered, genomified patient of the future.

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The 2018 Healthcare Analytics Summit: Wednesday Recap

The first full-day of the 2018 Healthcare Analytics Summit (HAS 18) featured keynotes from Marc Randolph (Co-Founder, Netflix), Dr. Brent James, Dr. Daniel Kraft, Dr. Toby Cosgrove, Dr. Jill Hoggard Green, and Dr. Robert Wachter. Two waves of breakout sessions covered success stories from organizations all over the nations, complete with countless lessons learned.

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The Number One Secret of Highly Effective Healthcare Data Analysts

Data-driven quality improvement is propelling healthcare transformation. The ability to strategically leverage healthcare data is essential, making highly effective data analysts more valuable than ever. So, what attributes differentiate a good data analyst from a great analyst? Stephen Covey’s well-known book “The 7 Habits of Highly Effective People,” has long had far-reaching impacts in the business world. These same principles are relevant today and applicable in the world of healthcare analytics. Learn how Covey’s second habit, “Begin With the End in Mind,” drives great healthcare data analysts.

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Three Ways Evidence-Based Medicine Improves Machine Learning

As health systems continue to adopt machine learning to impact significant outcomes (e.g., reducing readmissions, preventing hospital-acquired infections, and reducing length of stay), they must also leverage evidence-based medicine. Evidence adds critical insight to machine learning models, ensuring that models incorporate all necessary variables in their risk prediction, and builds credibility among clinicians. Evidence-based medicine brings three essential elements to healthcare machine learning: Boosts machine learning model credibility. Engages data experts around healthcare projects. Saves time and money and increases ROI.

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Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growing Health Data Demands

Smartphone applications, home monitoring equipment, genomic sequencing, and social determinants of health are adding significantly to the scope of healthcare data, creating new challenges for health systems in data management and storage. Traditional on-premises data warehouses, however, don’t have the capacity or capabilities to support this new era of bigger healthcare data. Organizations must add more secure, scalable, elastic, and analytically agile cloud-based, open-platform data solutions that leverage analytics as a service (AaaS). Moving toward cloud hosting will help health systems avoid the five common challenges of on-premises data warehouses: Predicting future demand is difficult. Infrastructure scaling is lumpy and inelastic. Security risk mitigation is a major investment. Data architectures limit flexibility and are resource intensive. Analytics expertise is misallocated.

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Survey Shows the Role of Technology in the Progress of Patient Safety

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. This survey of more than 400 healthcare professionals tackles a big question many hospital leaders are asking: Why aren’t we seeing improvements in patient safety despite our efforts?

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Four Essential Ways Control Charts Guide Healthcare Improvement

Control charts are a critical asset to any health system seeking effective, sustainable improvement. With a simple three-line format, control charts show process change over time, including the average of the data, upper control limit, and lower control limit. This insight helps improvement teams monitor projects, understand opportunities and the impact of initiatives, and sustain improved processes. Also known as Shewhart charts or statistical process control charts, control charts drive effective improvement by addressing three fundamental questions: What is the goal of the improvement project? How will the organization know that a change is an improvement? What change can the organization make that will result in improvement?

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Employer Health Plans: Keys to Lowering Cost, Boosting Benefits

Employers that offer robust employee health plans at affordable costs are more likely to attract and retain a great workforce. Healthcare, however, is often a top expense for organizations, making balancing attractive benefits with attractive costs a complex undertaking. Employers need a deep understanding of employee populations and opportunities to manage health plan costs without sacrificing quality. An analytics-driven approach to employee population health management gives employers insight into two key steps to lower healthcare costs and enhance benefits: Manage easily fixed cost issues. Use healthcare cost savings to fund expanded benefits.

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Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it Effectively in Their Organizations

Machine learning (ML) is gaining in popularity throughout healthcare. ML’s far-reaching benefits, from automating routine clinical tasks to providing visibility into which appointments are likely to no-show, make it a must-have in an industry that’s hyper focused on improving patient and operational outcomes. This executive report—co-written by Microsoft Worldwide Health and Health Catalyst—is a basic guide to training machine learning algorithms and applying machine learning models to clinical and operational use case. This report shares practical, proven techniques healthcare organizations can use to improve their performance on a range of issues.

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Identifying Vulnerable Patients and Why They Matter

The vulnerable individuals in a health system’s patient population are at risk of becoming some of the organization’s most complex and costly members. Because vulnerability can be determined by long-term health status and social determinants of health (versus acute episodes), managing risk for these patients relies on a whole-person approach to care. Fee-for-service reimbursement hasn’t incentivized this comprehensive approach to care, but, under value-based payment models, health systems are increasingly rewarded for care that keeps patients well. The first challenge in addressing the needs of vulnerable patients is identifying those patients. Analytics-driven technologies can help health systems understand who is vulnerable in their populations and take actions to control risk for these patients.

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EHR Integration: Achieving this Digital Health Imperative

As the digital trajectory of healthcare rises, health systems have an array of new resources available to make more effective and timely care decisions. However, to use these data analytics, machine learning, predictive analytics, and wellness applications to gain real-time, data-driven insight at the point of care, health systems must fully integrate the tools with their EHRs. Integration brings technical and administrative challenges, requiring organizations to coordinate around standards, administrative processes, regulatory principles, and functional integration, as well as develop compelling integration use cases that drive demand. When realized, full EHR integration will allow clinicians to leverage data from across the continuum of care (from health plan to patient-generated data) to improve patient diagnosis and treatment.

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How UPMC and Health Catalyst Improve Outcomes Using Innovation in Activity-based Costing

UPMC and Health Catalyst created a great business partnership focused on sharing risks and rewards to innovate how activity-based costing (ABC) is done in healthcare. The partners relied on complementary intellectual property, complementary talent, and complementary risks and rewards to drive benefits that extend beyond either organization’s borders. Health Catalyst licensed UPMC’s activity-based costing software, which served as the foundation for the Health Catalyst CORUS suite. Together, the partners will continue to work for innovations in ABC to drive outcomes improvements in healthcare.

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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.

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