Weekly News Roundup: February 15, 2019

Clinical Workflow News

MedStar Launches EHR Usability Campaign, With Videos Showing UX, Workflow Challenges

Effective software depends on user feedback, with developers rely on a culture of open communication to learn more about bugs and usability issues. That goes electronic health records too, and MedStar Health is showing, not telling, how they could. Read More

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ACOs: Four Ways Technology Contributes to Success

With an increasing emphasis on value-based care, Accountable Care Organizations (ACOs) are here to stay. In an ACO, healthcare providers and hospitals come together with the shared goals of reducing costs and increasing patient satisfaction by providing high-quality coordinated healthcare to Medicare patients. However, many ACOs lack direction and experience difficulty understanding how to use data to improve care. Implementing a robust data analytics system to automate the process of data gathering and analysis as well as aligning data with ACO quality reporting measures. The article walks through four keys to effectively implementing technology for ACO success: Build a data repository with an analytics platform. Bring data to the point of care. Analyze claims data, identify outliers, including successes and…

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Healthcare Data Management: Three Principles of Using Data to Its Full Potential

Author Douglas Laney is now tackling the topic of Infonomics: the practice of information economics. In his 2017 book, Infonomics: How to Monetize, Manage, and Measure Information as an asset for competitive advantage, Laney provides detailed rationale as well as a thoughtful framework for treating information as a modern-day organization’s most valuable asset. This article walks through how healthcare organizations can leverage data to its full potential using this framework and the three principles of infonomics: Measure - How much data does the organization have? What is it worth? Manage - What data does the organization have? Where is it stored? Monetize - How does the organization use data?

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How to Run Your Healthcare Analytics Operation Like a Business

A robust data analytics operation is necessary for healthcare systems’ survival. Just like any business, the analytics enterprise needs to be well managed using the principles of successful business operations. This article walks through how to run an analytics operation like a business using the following five-question framework: Who does the analytics team serve and what are those customers trying to do? What services does the analytics team provide to help customers accomplish their goals? How does the analytics team know they’re doing a great job and how do they communicate that effectively to the leadership team? What is the most efficient way to provide analytics services? What is the most effective way to organize?

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Weekly News Roundup: February 8, 2019

Healthcare Tech Funding in the News

Cloud Unicorn: Health Catalyst Reaches $1 Billion Valuation Sifting Through Hospitals' Data

Health Catalyst, a data analytics provider to hospitals, has raised $100 million in debt and equity financing, tipping the Salt Lake City…

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Four Steps to Effective Opportunity Analysis

Opportunity analysis uses data to identify potential improvement initiatives and quantifies the value of these initiatives—both in terms of patient care benefits and financial impact. This process is an effective way to find unwarranted and costly clinical variation and, in turn, develop strategies to reduce it, improving outcomes and saving costs along the way. Standardizing the opportunity analysis process makes it repeatable and prioritizes actionable opportunities. Quarterly opportunity analysis should follow four steps: Kicking off the analysis by getting analysts together to do preliminary analysis and brainstorm. Engaging with clinicians to identify opportunities and, in the process, get clinician buy in. Digging deeper into the suggested opportunities to prioritize those that offer the greatest benefits. Presenting findings to the decision…

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Weekly News Roundup: February 1, 2019

  Nearly half of all Americans use at least one prescription drug, and with high prescription usage comes medication-related problems. More than 1.5 million preventable medication-related adverse events occur each year, costing more than $177 billion in medication-related morbidity and mortality. This week's news roundup covers medication-related patient safety news including a case for better after-hours palliative care, a success story featuring a significant cost of care reduction at Allina Health, a day in the life of a medication safety officer, and the terrifying story of an Ohio physician who allegedly ordered 28 people potentially fatal doses of opioid painkillers. How Did a Doctor Allegedly Order Fatal Doses of Painkillers Connected to 28 Deaths?

Christine Allison and her husband,…

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The Four Keys to Increasing Hospital Capacity Without Construction

Many health systems have a hospital capacity problem as demand for patient beds rises. When the supply of usable patient beds can’t meet demand, the negative impact on patients and staff can be significant. Hospitals can solve capacity problems with four key concepts: Using data, start with the problem and the ideal solution. Be sure the analytics team works with teams throughout the organization—including leadership. Have leaders spend time with the operations team to understand workflow. Focus on the impact, not the tool.

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Increase Healthcare Analytics ROI Through the Rapid Response Analytics Solution

Health systems feel mounting pressure to demonstrate ROI from analytics investments but are faced with inefficacies and delays. Fortunately, the Rapid Response Analytics Solution delivers a 10x increase in analytics productivity and a 90 percent decrease in the time required to develop new analytic insights. The Rapid Response Analytics Solution solves these tough analytics problems through two primary elements: curated, modular data kits called DOS Marts; and Population Builder, a powerful self-service tools that lets any time of user, from physician executive to frontline nurse, explore data and quality build cohorts of patients without relying on IT staff and with no need for sophisticated and customized SQL and data science coding.

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Why Clinical Quality Should Drive Healthcare Business Strategy

Healthcare today is in the midst of a massive transformation. The opportunities for improvement are great if healthcare systems can do the following: Reduce clinical variation. Reduce rates of inappropriate care and care-associated patient injury and death. Follow accepted best care practices. Eliminate waste. This article covers the different types of waste in healthcare systems, ways to reduce them, financial alignment around waste reduction opportunities, and the importance of reducing clinical variation. The core driver of healthcare systems must be improving clinical quality. Almost always, with proper clinical management, better care is cheaper care through waste management.

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Weekly News Roundup: January 25, 2019

Addressing the Opioid Epidemic With Analytics

How Top Healthcare Executives Can Use IT to Stem the Opioid Crisis

The sheer scope of Americas opioid crisis more than 40,000 opioid overdose deaths just in 2017, and more than 2 million addicted almost defies comprehension. Many of us know one or…

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How to Evaluate Emerging Healthcare Technology With Innovative Analytics

As healthcare systems are pressured to cut costs and still provide high-quality care, they will need to look across the care continuum for answers, reduce variation in care, and look to emerging technologies. This article walks through how to evaluate the safety and effectiveness and of emerging healthcare technology and prioritize high-impact improvement projects using a robust data analytics platform. Topics covered include: The importance of identifying variation in innovation. Ways to improve outcomes and decrease costs. The value of an analytics platform. The reliable information that produce sparks for innovation. Identifying and evaluating emerging healthcare technology. Knowing what data to use. The difference between efficacy and effectiveness in evaluation of emerging healthcare technology.

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Reducing Hospital Readmissions: A Case for Integrated Analytics

Health systems continue to prioritize reducing hospital readmissions as part of their value-based payment and population health strategies. But organizations that aren’t fully integrating analytics into their readmission reduction workflows struggle to meet improvement goals. By embedding predictive models across the continuum of care, versus isolated them in episodes of care, health systems can leverage analytics for meaningful improvement. Organizations that integrate predictive models into readmissions reduction workflows have achieved as much as a 40 percent reduction in risk-adjusted readmissions indexes. Effective analytics integration strategies use a multidisciplinary development approach to meet the needs of a patient’s entire care team and deliver common tools for all involved in the patient’s healthcare journey.

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Weekly News Roundup: January 18, 2019

Healthcare Project Management in the News

  Healthcare Project Management Techniques: A Pragmatic Approach to Outcomes Improvement

Healthcare leaders are working hard to continuously refine and advance their processes in order to improve patient care, reduce costs, and improve the patient's overall experience and satisfaction-the Triple Aim. Healthcare project management skills have become increasingly important to businesses, including the healthcare industry, because they help control costs, manage risk,…

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The Number One Skill for a Healthcare Data Analyst

In today’s high-pressured world of healthcare, health systems don’t need report writers. They need highly valuable healthcare data analysts. A top healthcare data analyst becomes a partner for clinical and operational improvement by using a five-step method for solving complex problems. This article walks through this step-by-step approach and demonstrates its application using the real-world example of building a diabetes registry. In addition to this specialized approach to solving problems, the article discusses the five essential skills for data analysts needed in the diabetes registry example: Data query Data movement Data modeling Data analysis Data visualization

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Weekly News Roundup: January 11, 2019

Healthcare Trends to Watch in 2019 After ringing in the new year, it's time to look forward to what 2019 will bring. This week we're sharing news and resources that examine trends to watch out for this year, such as drivers of digital transformation in the next 12 months, an upcoming webinar that tackles such questions as what to expect from the new congress, as well as a look back to see how well we did with our predictions last year. Digital Healthcare Growth Drivers In 2019

The digital transformation of healthcare will see significant growth in the next 12 months fueled by institutional interest in driving down costs and improving patient engagement. Expect increased pharma investment, improving regulatory…

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Emergency Department Quality Improvement: Transforming the Delivery of Care

Overcrowding in the emergency department has been associated with increased inpatient mortality, increased length of stay, and increased costs for admitted patients. ED wait times and patients who leave without seeing a qualified medical provider are indicators of overcrowding. A data-driven system approach is needed to address these problems and redesign the delivery of emergency care. This article explores common problems in emergency care and insights into embarking on a successful quality improvement journey to transform care delivery in the ED, including an exploration of the following topics: A four-step approach to redesigning the delivery of emergency care. Understanding ED performance. Revising High-Impact Workflows. Revising Staffing Patterns. Setting Leadership Expectations. Improving the Patient Experience.

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6 Essential Data Analyst Skills for Your Healthcare Organization

Healthcare organizations are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy. But simply implementing an EDW doesn’t guarantee an organization’s success. One obstacle organizations come up against is that their analytics team members don’t have the right skills to maximize the effectiveness of the EDW. The following six skills are essential for analytics team members: structured query language (SQL); the ability to perform export, transform, and load (ETL) processes; data modeling; data analysis; business intelligence (BI) reporting; and the ability to tell a story with data.

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Social Determinants of Health: Tools to Leverage Today’s Data Imperative

Social determinants of health (SDOH) data captures impacts on patient health beyond the healthcare delivery system. Traditional health data (e.g., from healthcare encounters) only tells a portion of the patient and population health story. To understand the full spectrum of health impacts (e.g., from environment to relationship and employment status), organizations need data from their patient’s daily lives. The urgency for SDOH data is particularly strong today, as value-based payment increasingly presses health systems to raise quality and lower cost. Without fuller insight into patient health (what happens beyond healthcare encounters) organizations can’t align with community services to help patients meet needs of daily living—prerequisites for maintaining good health. Standardizing SDOH data into healthcare workflows, however, requires an informed strategy.…

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Improving Quality Measures Can Lead to Better Outcomes

Current quality measures are expensive and time consuming to report, and they don’t necessarily improve care. Many health systems are looking for better ways to measure the quality of their care, and they are using data analytics to achieve this goal. Data analytics can be helpful with quality improvement. There are four key considerations to evaluate quality measures: Organizations must develop measures that are more clinically relevant and better represent the care provided. Clinician buy-in is critical. Without it, quality improvement initiatives are less likely to succeed. Investment in tools and effort surrounding improvement work must increase. Tools should include data analytics. Measure improvement must translate to improvement in the care being measured. When the right measures are in place…

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Healthcare Project Management Techniques: A Pragmatic Approach to Outcomes Improvement

Project management skills and good project managers are increasingly important to the healthcare industry because they can help control costs, manage risk, and speed improvement project outcomes. By applying project management techniques, from waterfall to agile methodologies, organizations can plan, organize, and execute a set of tasks efficiently in order to maximize resources and achieve specific goals. This article explores project management techniques and offers considerations for healthcare leaders when adapting these techniques for clinical, financial, and operational process improvement. The author also shares a pragmatic application and practical tips for implementing these project management techniques in a healthcare environment.

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The Digitization of Healthcare: Why the Right Approach Matters and Five Steps to Get There

While many industries are leveraging digital transformation to accelerate their productivity and quality, healthcare ranks among the least digitized sectors. Healthcare data is largely incomplete when it comes to fully representing a patient’s health and doesn’t adequately support diagnoses and treatment, risk prediction, and long-term health care plans. But even with the obvious urgency for increased healthcare digitization, the industry must raise this trajectory with sensitivity to the impacts on clinicians and patients. The right digital strategy will not only aim for more comprehensive information on patient health, but also leverage data to empower and engage the people involved. Health systems can follow five guidelines to digitize in a sustainable, impactful way: Achieve and maintain clinician and patient engagement. Adopt…

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Weekly News Roundup: December 21, 2018

  With the holiday season suddenly upon us, we wanted to share some of the biggest news stories from 2018. Amazon made headlines as it leapt further into the world of healthcare, healthcare became more precise, and payers made the news with mergers, acquisitions, and other shenanigans. We also share the six biggest problems with homegrown healthcare analytics platforms and a recent ONC report that examines the many different forms of exchanging healthcare data that hospitals are wrestling with. Five Ways Healthcare Became More Precise in 2018

From immunotherapy to NASA-like hospital Command Centers, advances over the past 12 months set the stage for more breakthroughs in health care With global healthcare expenditure now exceeding $7 trillion per year,…

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The Missing Ingredient in Healthcare Analytics: The Executive Sponsor

Despite the complexity of healthcare analytics, one key strategy for effective, sustainable analytics stands out: designating an executive sponsor to oversee the program. This sponsor is a C-suite level leader who’s committed to championing analytics throughout the organization and has the influence and relationships to drive widespread outcomes improvement. Healthcare executives can use four criteria to identify a great executive sponsor for their analytics programs: Have a single accountable leader. Find a sponsor with passion for and knowledge about data. Choose organizational clout and a vision for analytics over a specific title. Build a partnership with the CIO.

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The Six Biggest Problems With Homegrown Healthcare Analytics Platforms

Most healthcare systems have been building, improving, and maintaining proprietary healthcare analytics platforms since the early 2000s and have invested heavily in the people and resources required to do so. As the demands of today’s healthcare environment continue to increase, it’s becoming more difficult for analytic teams to keep up. This article deals with the six biggest problems to maintaining a homegrown healthcare analytic platform today: Inability to keep pace with analytic demands. Difficult to support and scale for the future. Difficulty finding and keeping talent. Use of point solutions to fill gaps. Analytic teams must also support third-party vendors and affiliated groups. Difficulty keeping abreast of rapidly changing regulatory requirements.

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