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Knowledge Center

HAS 17 Session Shares Strategy for Achieving Triple Aim

Like many U.S. health systems, Allina Health understands the importance of the Triple Aim in today’s healthcare environment—as the industry confronts rising costs along with inadequate quality of care. With the move toward value-based care, these factors will increasingly impact market share. Allina is responding with its Clinical Value Process initiative. The organization uses this fully integrated multidisciplinary strategy to make outcomes improvement decisions and measure both the quality and fiscal performance of improvement projects. Key elements of Clinical Value Process include:

  1. Reducing variation.
  2. Organizing for a systemwide improvement plan.
  3. Using data to identify improvement opportunities and monitor progress.

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Closed-Loop Analytics Approach: Making Healthcare Data Actionable

Healthcare organizations rely on data to support informed decisions. To be truly valuable, data must be high quality and meet two criteria for end-users:

  1. Data must be transformed from its raw, obscure form into actionable insights.
  2. Data-driven insights must be immediately accessible at the point of care (versus in static dashboards or buried on the intranet).
Closed-Loop Analytics™ methodology transforms raw data into actionable, accessible insight—providing physicians and nurses with critical insight into their patients’ situation and how they can effectively intervene. A Closed-Loop Analytics approach will become increasingly essential as healthcare becomes more systems dependent.

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Why Healthcare Decision Support Is No Longer Optional for Chief Operating Officers

Without daily access to healthcare decision support, health system COOs struggle to make rapid, meaningful decisions. Healthcare decision support systems are no longer optional for these highly visible leaders, who play critical roles in their organizations’ success, for many reasons:

  • Aggregates reliable, up-to-date information from all available sources.
  • Presents information in user-friendly, user-configurable ways.
  • Makes trends and important conclusions more recognizable and understandable.
  • Enhances C-Suite’s ability to drill down into data in search of a problem’s root cause.
  • Improves C-Suite communication and collaboration.
  • Unites C-suites around a common vision and strategy.
Healthcare COOs (and other C-Suites) need healthcare decision support to be data-driven problem solvers and collaborative leaders who achieve clinical, financial, and operational success for their systems. Given the industry’s increasing complexity, healthcare decision support is now an industrywide imperative.

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Four Effective Opioid Interventions for Healthcare Leaders

The crisis of opioid abuse in the U.S. is well known. What may not be so well known are the ways for clinicians and healthcare systems to minimize misuse of these addictive drugs. This article describes the risks for patients when they are prescribed opioids and the need for opioid intervention. It offers four approaches that healthcare systems can take to tackle the crisis while still relieving pain and suffering for the patients they serve:

  1. Use data and analytics to inform strategies that reduce opioid availability
  2. Adopt prescription drug monitoring programs to prevent misuse
  3. Adopt evidence-based guidelines
  4. Consider promising state strategies for dealing with prescription opioid overdose
Opioid misuse is a public health epidemic, but treatments are available and it’s time for those involved in the delivery of healthcare to change practices.

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A Landmark, 12-Point Review of Population Health Management Companies (Executive Report)

Population health management (PHM) is in its early stages of maturity, suffering from inconsistent definitions and understanding, overhyped by vendors and ill-defined by the industry. Healthcare IT vendors are labeling themselves with this new and popular term, quite often simply re-branding their old-school, fee-for-service, and encounter-based analytic solutions.  Even the analysts —KLAS, Chilmark, IDC, and others—are also having a difficult time classifying the market. In this paper, I identify and define 12 criteria that any health system will want to consider in evaluating population health management companies.  The reality of the market is that there is no single vendor that can provide a complete PHM solution today.  However there are a group of vendors that provide a subset of capabilities that are certainly useful for the next three years.  In this paper, I discuss the criteria and try my best to share an unbiased evaluation of sample of the PHM companies in this space.

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HAS 17 Keynote, Eric Topol, Signals New Era in Digital Healthcare

Healthcare is poised for groundbreaking change, in which the fusion of unprecedented access to mobile technology and advanced understanding of disease brings in the era of truly individualized medicine. Eric Topol, M.D., a leader in digital medicine, will share his vision for the wireless medicine revolution at the 2017 Healthcare Analytics Summit™ conference. In the next data-driven generation of democratized healthcare, consumers will monitor personal health information—from vital signs to fetal heartrate—directly on their smartphones. Real-time access to health status will make consumers true partners in their own care, enabling them to make proactive decisions to improve their health and prevent or respond to critical events.

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Dr. David Wild to Present on Reducing Systemwide Readmissions

Human decision making…ultimately, this is the component that drives outcomes improvement in healthcare. But the pathway to this decision making involves technology—machine learning and predictive analytics—as well as processes and people. Dr. David Wild, the Vice President of Lean Promotion at The University of Kansas Health System, along with Chris Harper, the system’s Director of Business Architecture and Analytics, will lead a case study breakout session at the 2017 Healthcare Analytics Summit to discuss this topic. Of particular note is this healthcare system’s results in reducing readmissions across the board in a very short period, which also contributed significantly to financial improvement. It will be an inside look at the successful blend of human and technological resources needed for broadly reducing readmissions and improving outcomes within a large healthcare system.

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Healthcare Total Cost of Care Analysis: A Vital Tool

How can healthcare organizations set themselves up for success as the industry shifts from fee-for-service to value-based reimbursement? They need to understand risk of their patients and population to identify ways to reduce healthcare costs and improve quality of care. This makes total cost of care (TCOC) analysis a necessary skillset in this time of transition. TCOC analysis leverages key elements of the healthcare analytics infrastructure to understand how money is being spent at the organization and identify the drivers of high cost:

  • An integrated EDW.
  • Payer reporting tools.
  • Claims and membership data.
  • Predictive capabilities.
  • Risk scores.
  • Scorecards and dashboards.
  • Analyst support.

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Duncan Gallagher—HAS 2017 Keynote—Leads Healthcare into Value-Based Era

Duncan Gallagher—a Keynote Speaker at the 2017 Healthcare Analytics Summit™ conference—is healthcare’s top expert when it comes to transitioning to value-based care. As Allina Health’s CFO, Executive Vice President, and Chief Administrative Officer, Gallagher has first-hand experience leading a four-billion-dollar system’s transition out of fee-for-service gridlock into what’s quickly becoming healthcare’s next era: value-based care. Gallagher argues that, while fee-for-service is the status quo, it’s not a long-term viable option. With accolades such as 2016 CFO of the year, Gallagher has the deep industry experience required to help health systems achieve VBC-driven financial viability while prioritizing (and relentlessly pursuing) disease prevention and outcomes improvement.

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An Inside Look at Building Machine Learning for Healthcare

Machine learning is a term that crops up often in healthcare lately, but it’s important to understand what really constitutes learning in this context. What some call machine learning is actually unintentional programming, but true learning is derived during the process of building a predictive model. This article delves into the nuts and bolts of a healthcare machine learning model and describes the training process a model undergoes to impact outcomes for patients. The key ingredient is data and the key deliverable is to complete the feedback loop so those responsible for managing care have actionable information at their disposal. Machine learning is beyond conceptual; it’s incorporated into a growing list of predictive models for various disease classifications.

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Dr. David Nash Presents Keynote: Leadership for Population Health

Dr. David Nash is the Founding Dean of the Jefferson College of Population Health on the campus of Thomas Jefferson University in Philadelphia. His keynote presentation at the 2017 Healthcare Analytics Summit, titled “Leadership for Population Health,” will address several formative topics, including:

  • The state of the U.S. healthcare economy
  • Moving from volume-based to value-based care
  • The people and technology issues involved in this transition
Even the most knowledgeable population health adherents attending HAS™ will learn new strategic pointers as Dr. Nash describes his checklist for managing population health and delves into the analytics tools best suited for today’s healthcare landscape.

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The Healthcare Data Warehouse: Evolved for Today’s Analytics Demands

What do health systems risk when they hold onto an older enterprise data warehouse (EDW) perspective? By thinking about the EDW as a tool for only historic data that’s not highly reliable and can’t support important decisions, organizations miss out on near real-time (NRT) reporting and valuable decision-making resources. Far from an outdated tool, today’s EDW is capable of meeting rising demands for timely, quality data. Health systems can ensure their EDW reaches its full potential by prioritizing it among their technology and properly supporting it—with the best equipment and human resources. The well maintained EDW is not stuck in the past, but rather, an invaluable tool to move healthcare analytics forward.

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Tackle These 8 Challenges of MACRA Quality Measures

The Medicare Access and CHIP Reauthorization Act (MACRA) appears to be a reporting challenge for many healthcare provider systems with few resources for managing the menagerie of measures. Indeed, with more than 270 measures in play, many systems have yet to jump in, but the deadline is inevitable. A plan of action is possible by recognizing and acting on these eight challenge areas:

Challenge #1: High-level performance insight

Challenge #2: Defining measure specifications

Challenge #3: Data quality reporting requirements

Challenge #4: Benchmarking data

Challenge #5: Proactively increasing measures surveillance to enhance outcomes

Challenge #6: Strategically aligning measures on which to base risk

Challenge #7: Identifying measures with the largest financial impact

Challenge #8: Taking risk in multi-year, value-based contracts

Mid-to-large size provider groups need a strategy around MACRA quality measures and a tool to help them make sense of all the reporting requirements.

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Interactive Healthcare Dashboards Are Gaining Momentum

Workers in today’s healthcare systems need dashboards with more power, interactivity, and visual feedback than traditional static reports are able to provide. Users also need to understand how and where to make improvements based on the dashboard’s information. To provide such deep insight to the data, a healthcare dashboard should have the following characteristics: be easily accessible, display reliable data, contain relevant data, be up-to-date for the task at hand, and include trends and/or benchmarks. When the right type of dashboard is combined with a late-binding data warehouse, users will gain access to the knowledge their data holds to drive lasting and effective improvement initiatives.

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Health Catalyst Named One of Best Places to Work in IT

Digital magazine and website Computerworld has named Health Catalyst to its 2017 Best Places to Work in IT list. Health Catalyst joins 100 IT companies that are leading the way in employee satisfaction and engagement with generous salaries, exceptional benefits, ongoing learning, and more. According to Ken Mingis, executive editor of Computerworld, IT employee satisfaction is increasingly vital: “As technology moves to the strategic center of every business, the ability of the enterprise to attract and retain skilled IT talent has become critically important.” Some of the team member-reported attributes that make Health Catalyst a best place to work include:

  1. Above-market compensation.
  2. Great work-life balance, thanks to unlimited PTO, company holidays, a work-from-home policy, and maternity and paternity leave.
  3. Companywide bonus structure.
  4. Fitness benefits, including onsite gym with fitness classes.
  5. Education/training reimbursement.

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Introducing the Health Catalyst® CORUS Suite: Activity-Based Costing and Cost Insights

Outdated technology and antiquated costing methodologies have left health system CFOs unable to see the true cost of the services they provide and impacts on patient outcomes. The move from fee-for-service to value-based contracts, however, means that CFOs need this information more than ever. Health Catalyst® has partnered with industry-leading health systems to develop a next-generation costing system: the CORUS Suite. Two integrated products comprise the suite:

  • Activity-Based Costing delivers accurate and actionable data from across the continuum of care in a scalable and maintainable tool.
  • Cost Insights analyzes and delivers early insights through a customizable dashboard powered by embedded logic and access to the most granular level of activity and costing data.
CORUS leverages Health Catalyst’s analytics platform and best-of-breed activity-based costing models to help users manage the true cost of care.

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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:

  1. Describe why visualization is important
  2. Recognize commonly accepted presentation rules
  3. Identify weakness in existing visualizations
  4. Execute the critical steps for effective chart creation
This article provides a sneak peek into details of this workshop and the team who will be leading it.

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Is Your Care Management Program Working: A Guide to ROI Challenges and Solutions

Care management programs play a large part in many health systems’ population health strategies. However, these programs can consume a lot of resources. It is important to know if a care program is effective, and eventually, to show a positive ROI. Many roadblocks stand in the way:

  • Complexity of Environment
  • Prolonged Time to ROI
  • Lack of Access to Disparate Data
  • Difficulty Engaging the Patient
A thoughtful approach and a robust analytics platform can help organizations overcome these challenges. Care management ROI should be a long-term strategy, but cost savings and quick wins are possible using the Health Catalyst® Cost Management Suite.

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How Machine Learning in Healthcare Saves Lives

There are limitless opportunities for machine learning in healthcare. Defined as, “a field of computer science that uses pattern recognition to identify historical relationships in large data sets using an algorithm to create a generalized model of behavior,” machine learning is one of the most important life-saving technologies ever introduced to healthcare for several reasons:

  • Reduces readmissions.
  • Prevents hospital acquired infections (HAIs).
  • Reduces hospital length of stay (LOS).
  • Predicts chronic disease.
  • Reduces one-year mortality.
  • Predicts propensity-to-pay.
  • Predicts no-shows.
  • Improves sepsis outcomes.
It’s no wonder health systems are eager to start leveraging machine learning to save lives, improve outcomes, and make systemwide enhancements. They can do so by understanding machine learning basics, the importance of customized machine learning (not one-size-fits-all models) and historical data (a requirement for answering basic machine learning questions), and how machine learning helps their patients, clinical teams, and bottom lines.

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Healthcare Decision Support Helps CFOs Achieve Their Top Goal: Timely, Accurate, Agile Decision Making

Supporting decision making is a top goal for CFOs today, according to a 2017 Kaufmann Hall CFO survey. Healthcare decision support empowers CFOs and their finance teams to make accurate, agile, and timely decisions, from rolling forecasts of future trends to risk-adjusted scenario modeling. In addition to helping CFOs make good decisions, healthcare decision support helps CFOs lead their teams and organizations improve in four key ways:

  1. Data-driven growth and practice expansion.
  2. Improved ability to negotiate favorable risk-based contracts with payers.
  3. Effectively and fairly address important physician compensation issues.
  4. Improve population health management.
With healthcare decision support, CFOs and their health systems have a distinct competitive advantage (e.g., shortened planning cycles and more accurate cost measurement). They can adjust to unexpected challenges and take advantage of new opportunities.

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Five Strategies for Easing the Burden of Clinical Quality Measures

Healthcare systems need to view regulatory measures in a different light. Rather than approaching them as required processes that burden the system, they should be viewed as quality improvement opportunities that lead to best practices. It helps to have a strategy to get there:

  1. Prioritize measures that truly impact patient care
  2. Have a line-of-sight to reimbursement
  3. Understand measure alignment across programs
  4. Involve the right people
  5. Get involved in measure development upstream
The right tools also help, but a plan for success is advised for healthcare system administrators and clinicians who need to ease the reporting burden and take advantage of every measure in a positive way.

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From Surviving to Arriving: A Road Map for Transitioning to Value-Based Reimbursement

When it comes to transitioning to value-based reimbursement, health systems consistently ask two questions:

  1. Why should I invest in reducing utilization when 90+ percent of my business is still fee-for-service (FFS)?
  2. Where do I start?
This value-based reimbursement road map can help systems transition from barely surviving to successfully arriving (while respecting both shared-risk and FFS worlds): Stop #1: Surviving— If you don’t get paid for the risk you take on, then you can’t survive long term. Stop #2: Sustaining—Numerous clinical interventions occur in hospitals that systems can focus on to help improve the bottom line. Stop #3: Succeeding—Build out competencies on a smaller population with aligned incentives so you can negotiate deeper alignment with key payers. Stop #4: Arriving— The ultimate destination, where the lines between traditional healthcare delivery and public health are blurred. Although healthcare is far from arriving at the value-based reimbursement destination, it can use this road map’s pragmatic strategies for heading down the right road.

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How Healthcare Text Analytics and Machine Learning Work Together to Improve Patient Outcomes

Healthcare organizations that leverage both text analytics and machine learning are better positioned to improve patient outcomes. Used in tandem, text analytics and machine learning can significantly improve the accuracy of risk scores, used widely in healthcare to help clinicians identify patients at high risk for certain conditions and, therefore, intervene. Health systems can run machine learning models with input from text analytics to provide tailored risk predictions on both unstructured and structured data. The result? More accurate risk scores and the ability to identify every patient’s level of risk in time to inform decisions about their care.

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Analytics Pioneer Tom Davenport Is HAS17 Opening Keynote

Tom Davenport, one of the top three business/technology analysts in IT and one of Fortune’s top 50 business school professors, pioneered the concept of “competing on analytics” and is the opening keynote speaker for the 2017 Healthcare Analytics Summit. Having written/edited 18 books and more than 100 articles, Tom is cofounder of the International Institute for Analytics and senior advisor to Deloitte Analytics. Tom is known for making the most complex concepts accessible—a critical skill an industry as complex as healthcare. And when it comes to big data, Tom is dedicated to equipping healthcare leaders with a clear, useful understanding of what it means from a technical, consumer, and management perspective so health systems can make fact-based, data-driven decisions that lead to outcomes improvement.

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Three Affordable Care Act Questions Everyone in Healthcare Is Asking

Trump/Republican rhetoric recently met reality when it comes to the Affordable Care Act (ACA). The latest version of the bill that passed in the House is far from a complete repeal and replacement of the ACA. However, the bill includes significant changes to healthcare policy and coverage, from severe Medicaid cuts to shifting financial accountability. ACA uncertainty has healthcare leaders concerned about how to plot a path forward, with three questions on the top of their minds:

  1. What will the final bill look like?
  2. How do I plan for the changes?
  3. What should happen next to fix the problems with the ACA?
Answers to these questions, although helpful, distract the industry from the ultimate goal: delivering on healthcare’s longstanding mission to provide quality, affordable healthcare. In short, health systems need to continue prioritizing patients until the ACA dust settles in Washington.

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