Four Population Health Management Strategies that Help Organizations Improve Outcomes

Population health management (PHM) strategies help organizations achieve sustainable outcomes improvement by guiding transformation across the continuum of care, versus focusing improvement resources on limited populations and acute care. Because population health comprises the complete picture of individual and population health (health behaviors, clinical care social and economic factors, and the physical environment), health systems can use PHM strategies to ensure that improvement initiatives comprehensively impact healthcare delivery. Organizations can leverage four PHM strategies to achieve sustainable improvement: Data transformation Analytic transformation Payment transformation Care transformation

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The Real Opportunity of Precision Medicine and How to Not Miss Out

Precision medicine, defined as a new model of patient-powered research that will give clinicians the ability to select the best treatment for an individual patient, holds the key that will allow health IT to merge advances in genomics research with new methods for managing and analyzing large data sets. This will accelerate research and biomedical discoveries. However, clinical improvements are often designed to reduce variation. So, how do systems balance tailoring medicine to each patient with standardizing care? The answer is precise registries. For example, using registries that can account for the most accurate, specific patients and disease, clinicians can use gene variant knowledge bases to provide personalized care.

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Measuring the Value of Care Management: Five Tools to Show Impact

To earn legitimacy and resources within a healthcare organization, care management programs need objective, data-driven ways to demonstrate their success. The value of care management isn’t always obvious; while these programs may, in fact, be responsible for improvements in critical metrics, such as reducing readmissions, C-suite leaders need visibility into care management’s impact and processes to understand precisely how they’re improving care and lowering costs at their organizations. Five analytics-driven technologies give healthcare leaders a comprehensive understanding of care management performance: The Patient Stratification Application The Patient Intake Tool The Care Coordination Application The Care Companion Application The Care Team Insights Tool

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Advanced Analytics Holds the Key to Achieve the Triple Aim and Survive Value-based Purchasing

Every hospital and health system has to juggle significant IT needs with a limited budget. In the middle of these demands and possibilities, hospital executives have to prioritize and decide which technology solutions are the most critical to the health of their organization. I call these most critical IT solutions “survival software.” Advanced clinical analytics solutions are the survival software of the near future, as they really hold the key to achieving the triple aim and survive value-based purchasing.

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Critical Healthcare M&A Strategies: A Data-driven Approach

Historically technology and talent were primary assets used to weigh the value of M&A activity, but data is an equal pillar. Buyers (the acquiring organizations) face enormous responsibility and risk with M&A transactions. C-suite leaders have a lot to consider—enterprise-wide technology, finances, operations, facilities, talent, processes, workflows, etc.—during the due diligence process. But attention is often heavily weighted toward time-honored balance sheet and facility assets rather than next-generation assets with the long-term strategic value in the M&A process: data. The model for conducting due diligence around data involves four disciplines: Establish the strategic objectives of the M&A with the leadership team. Prioritize data along with the standardization of solutions and the design of a new IT organization (i.e., a co-equal…

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The Top 8 Skills Every Healthcare Process Improvement Leader Must Have

Healthcare process improvement leaders not only have to be a jack-of-all-trades, but they need to be a master, as well. This is one of the most important leadership roles in the healthcare system with responsibilities that can ultimately end up saving lives, improving the patient experience, improving caregiver job satisfaction, and reducing costs. Although there are many others, these eight skills are the most critical for the efficient, and ultimately, successful process improvement leader: Communication Trust Building Coaching Understanding Process Management Understanding Care Management Personnel Constructive Accountability and Constructive Conflict Resiliency and Persistency Seeing the Big Picture Along with the right training, education, and sponsorship, it’s easy to see why this role blends many elements of art and science.

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The Dangers of Commoditized Machine Learning in Healthcare: 5 Key Differentiators that Lead to Success

Many vendors deliver machine learning models with different applications in healthcare. But they don’t all deliver accurate models that are easy to implement, targeted to a specific use case, connected to actionable interventions, and surrounded by a machine learning community and support team with extensive, exclusive healthcare experience. These machine learning qualities are possible only through a machine learning model delivered by a vendor with a unique set of capabilities. There are five differentiators behind effective machine learning models and vendors: Vendor’s expertise and exclusive focus on healthcare. Machine learning model’s access to extensive data sources. Machine learning model’s ease of implementation. Machine learning model’s interpretability and buy-in. Machine learning model’s conformance with privacy standards. These five factors separate the…

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The Key to Healthcare Mergers and Acquisitions Success: Don’t Rip and Replace Your IT

Healthcare mergers and acquisitions can involve a lot of EMRs and other IT systems. Sometimes leaders feel like they have to rip and replace these systems to fully integrate organizations. However, this is not the answer, according to Dale Sanders. This report, based upon his July 2017 webinar, outlines the importance of a data-first strategy and introduces the Health Catalyst® Data Operating System (DOS™) platform. DOS can play a critical role in facilitating IT strategy for the growing healthcare M&A landscape.

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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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The Best Solution for Declining Medicare Reimbursements

I am one of the brave souls who takes the time to read the report issued each spring by the Medicare Payment Advisory Commission (Medpac). The report shows the numbers of Medicare beneficiaries and claims are growing; healthcare organizations are increasingly losing money on Medicare; payment increases certainly will not keep pace with declining margins; and Medicare policies will continue to incentivize quality and push providers to assume more risk. But the report also reveals that some healthcare organizations—referred to as “relatively efficient”—are making money from Medicare with an average 2 percent margin. How do you become one of these organizations? And how do you target and counter Medicare trends that impact your business?

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Five Data-driven Patient Empowerment Strategies

Data plays a big role toward empowering patients to become more involved in their care. With data, digital tools, and education, patient empowerment can act like a blockbuster drug to produce exceptional outcomes. Data empowers patients five ways: Promotes patient engagement. Produces patient-centered outcomes. Helps patients practice self-care. Improves communication with clinicians. Leads to faster healing and independence. Clinicians using creative, innovative care strategies, and patients with access to the right tools and technology, can produce remarkable results in terms of cost, health outcomes, and experience.

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10 Motivational Interviewing Strategies for Deeper Patient Engagement in Care Management

Care management programs are most successful when patients are deeply engaged in their own care. Using the motivational interviewing technique, care managers work with patients to identify personal care goals and motivators to follow the care management program. Ten strategies guide the motivational interviewing process, each focusing on patient-centered insights (e.g., pros and cons to following care management and barriers to adherence). With mobile technology to support these interactions, motivational interviewing can become a seamless, and vital, part of the care management workflow.

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Health Equity: Why it Matters and How to Achieve it

According to the Robert Wood Johnson Foundation, health equity is achieved when everyone can attain their full health potential and no one is disadvantaged from achieving this potential because of social position of any other socially defined circumstance. Without health equity, there are endless social, health, and economic consequences that negatively impact patients, communities, and organizations. The U.S. ranks last on measures of health equity compared to other industrialized countries. Healthcare contributes to this problem in many ways, including ignoring clinician biases toward certain populations and overlooking the importance of social determinants of health. Fortunately, there are effective, tested steps organizations can take to tackle their health inequities and disparities (e.g., incorporating nonmedical vital signs into their health assessment processes…

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Introducing Touchstone: The Next-Generation Healthcare Benchmarking and Opportunity Prioritization Tool

To do healthcare benchmarking effectively and efficiently, healthcare organizations need to know where they’re underperforming, where they’re performing well, and how to focus and prioritize their improvement efforts. They also need a new approach to benchmarking that isn’t limited to the inpatient setting. The Health Catalyst® Touchstone™ product is the next-generation healthcare benchmarking and prioritization tool that delivers what antiquated benchmarking technologies cannot: Risk-adjusted benchmarking across the full continuum of care. Artificial intelligence-powered recommendations. Ranked lists of improvement opportunities. Detailed analytics and an intuitive user interface that enable the easy exploration of factors driving performance issues. Democratized benchmarking that’s available to as many people as the organization wants. Touchstone was designed with many users and use cases in mind, from…

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Data Science for Healthcare: What Today's Leaders Must Know

Healthcare leaders who understand data science can embrace the significant improvement potential of the industry’s vast data stores, including an estimated $300 billion in annual costs savings. Executives must know the value of data science to understand the urgency in investing and supporting the technology and data scientists to fully leverage data’s capabilities. Today’s data science-savvy executives will lead the healthcare transformation by enabling faster, more accurate diagnoses and more effective, lower-risk treatments.

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The Impact of FDA Digital Health Guidance on CDS, Medical Software, and Machine Learning

The FDA recently released guidance documents on the use of clinical decision support (CDS) and medical software that may be of concern to forward-thinking healthcare innovators who rely on these technologies to deliver exceptional care and improve outcomes. What will be the impact of this guidance on machine learning and predictive analytics efforts? How will the guidance affect timelines, costs, and effectiveness of ongoing machine learning implementation? As healthcare delivery increasingly relies on digital innovation and support, more questions emerge about the governance of the accompanying tools and technology. This article provides a summary of the FDA guidance on CDS, how CDS is defined, whether or not CDS is exempt from regulation, and how the FDA intends to enforce compliance.…

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Five Lessons for Building Adaptive Healthcare Data Models that Support Innovation

Healthcare data models are the backbone of innovation in healthcare, without which many new technologies may never come to fruition, so it’s important to build models that focus on relevant content and specific use cases. Health Catalyst has been continuously refining its approach to building concise yet adaptive healthcare data models for years. Because of our experience, we’ve learned five key lessons when it comes to building healthcare data models: Focus on relevant content. Externally validate the model. Commit to providing vital documentation. Prioritize long-term planning. Automate data profiling. These lessons are essential to apply when building adaptive healthcare data models (and their corresponding methodologies, tools, and best practices) given the prominent role they play in fueling the technologies designed…

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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: Use data and analytics to inform strategies that reduce opioid availability Adopt prescription drug monitoring programs to prevent misuse Adopt evidence-based guidelines Consider promising state strategies for dealing with prescription opioid overdose Opioid misuse is a public health epidemic, but treatments are available and it’s…

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How to Apply Machine Learning in Healthcare to Reduce Heart Failure Readmissions

One large healthcare system in the Pacific Northwest is moving machine learning technology from theory to practice. MultiCare Health System is using machine learning to develop a predictive model for reducing heart failure readmissions. Starting with 88 predictive variables applied to data from 69,000 heart failure patient encounters, the machine learning team has been able to quickly develop and refine a predictive model. The output from the model has guided resource allocation efforts and pre-discharge decision making to significantly improve patient care management activities. And the data has engendered trust among clinicians who rely on it the most for clinical decision making. This inside look at the application of advanced technology offers lessons for any healthcare system planning to ramp…

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Custom Care Management Algorithms that Actually Reveal Risk

Care management is a tool for population health that focuses scarce healthcare resources on the sickest patients. Care management leaders need to know who those sickest patients are (or may be). The static risk models typically used for stratifying patients into risk categories only paint a partial picture of health and are ineffective for modern care management programs. Custom algorithms are now capable of predicting risk based on multiple risk models and multiple data sources. They help care management teams confidently stratify patient populations to paint a complete picture of care needs and efficiently deliver care to those who need it most. This article explains how custom algorithms work on static risk models to normalize risk scores and improve patient…

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Diversity in the Workplace: A Principle-Driven Approach to Broadening the Talent Pool

Improving diversity and inclusivity in healthcare, and any industry, is more than just the right thing to do: it’s an intelligent business decision with impacts on productivity, sales, and innovation. Organizations committed to addressing the lack of diversity and inclusivity in healthcare should start by thinking about the principles and values that underlie their cultures. At Health Catalyst, every diversity initiative is founded in one of the core principles that motivates our work and is embodied by every team member: Respect Humility Transparency Advocacy But turning the tide on monumental challenges, like closing the gender gap in technology (women hold less than 26 percent of U.S. technology jobs), requires more than a return to values; it requires initiatives, from equitable…

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Improving Patient Safety: Machine Learning Targets an Urgent Concern

With over 400,000 patient-harm related deaths annually and costs of more the $1 billion, health systems urgently need ways to improve patient safety. One promising safety solution is patient harm risk assessment tools that leverage machine learning. An effective patient safety surveillance tool has five core capabilities: Identifies risk: provides concurrent daily surveillance for all-cause harm events in a health system population. Stratifies patients at risk: places at-risk patients into risk categories (e.g., high, medium, and low risk). Shows modifiable risk factors: by understanding patient risk factors that can be modified, clinicians know where to intervene to prevent harm. Shows impactability: helps clinicians identify high-risk patients and prioritize treatment by patients who are most likely to benefit from preventive care.…

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Healthcare Analytics Platform: DOS Delivers the 7 Essential Components

The Data Operating System (DOS™) is a vast data and analytics ecosystem whose laser focus is to rapidly and efficiently improve outcomes across every healthcare domain. DOS is a cornerstone in the foundation for building the future of healthcare analytics. This white paper from Imran Qureshi details the seven capabilities of DOS that combine to unlock data for healthcare improvement: Acquire Organize Standardize Analyze Deliver Orchestrate Extend These seven components will reveal how DOS is a data-first system that can extract value from healthcare data and allow leadership and analytics teams to fully develop the insights necessary for health system transformation.

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Resolving Uncompensated Care: Artificial Intelligence Takes on One of Healthcare's Biggest Costs

Uncompensated care can cost large health systems billions of dollars annually, making outstanding balances one of their biggest costs. Propensity-to-pay tools help organizations target unpaid accounts by using artificial intelligence (AI) to leverage external and internal financial and socioeconomic data and identify the likelihood that patients in a population will pay their balances (propensity to pay). With propensity-to-pay insight, financial teams can focus their efforts on patients most likely to pay, and connect patients who are unable to pay with charity care or government assistance. Both health systems and patients benefit, as patients can avoid bad debt and organizations receive compensation for care they’ve delivered.

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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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