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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The Four Essential Zones of a Healthcare Data Lake

The role of a data lake in healthcare analytics is essential in that it creates broad data access and usability across the enterprise. It has symbiotic relationships with an enterprise data warehouse and a data operating system. To avoid turning the data lake into a black lagoon, it should feature four specific zones that optimize the analytics experience for multiple user groups: Raw data zone. Refined data zone. Trusted data zone. Exploration zone. Each zone is defined by the level of trust in the resident data, the data structure and future purpose, and the user type. Understanding and creating zones in a data lake behooves leadership and management responsible for maximizing the return on this considerable investment of human, technical,…

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Five Solutions to Controlling Healthcare’s Cost Problem

When expenses exceed revenue, business has a financial problem. In healthcare, the focus has been on revenue for so long, we’ve lost sight of runaway costs brought about by high labor and technology expenses, inefficient use of resources, and supply waste. Recognizing the cost problem is a big first step toward solving it. Five expense-controlling strategies can play a significant role in returning healthcare systems to a stronger financial position: Refocus on labor management. Manage employed physicians. Change the patient encounter environment. Augment standard approaches with technology. Manage patient access and flow through the healthcare system. With new, value-based payment structures, shrinking margins, and decreasing reimbursements, this insight offers some new ways to think about expense inefficiency and how to…

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Hospital Revenue Cycle Management: 5 Ways to Improve

Besides improving your information systems and educating your staff on the ins and outs of managing revenue, there are many more opportunities for improvement. Here are five suggestions to help health systems improve their revenue cycle management: Trend and benchmark your healthcare data. Use DOS to Mine Your Healthcare Data. Constantly ask frontline staff for suggestions. Monitor all payer contracts. Maintain convenient and caring touch points with patients.

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The Healthcare Analytics Ecosystem: A Must-Have in Today’s Transformation

Healthcare organizations seeking to achieve the Quadruple Aim (enhancing patient experience, improving population health, reducing costs, and reducing clinician and staff burnout), will reach their goals by building a rich analytics ecosystem. This environment promotes synergy between technology and highly skilled analysts and relies on full interoperability, allowing people to derive the right knowledge to transform healthcare. Five important parts make up the healthcare analytics ecosystem: Must-have tools. People and their skills. Reactive, descriptive, and prescriptive analytics. Matching technical skills to analytics work streams. Interoperability.

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Saving Lives: Effective Healthcare Communication Empowers Care Management

With an estimated 80 percent of medical errors resulting from miscommunication among healthcare teams, organizations can significantly improve outcomes with better communication. A communication methodology outlines the essential information clinicians need to share, giving care teams the knowledge they need, when they need it, to make informed treatment decisions. One communication toolkit, SBAR (Situation, Background, Assessment, Recommendation), defines the essential information clinicians must share when they hand off patient care from the inpatient to the ambulatory setting: S (situation): The patient’s current situation. B (background): Information about the current situation. A (assessment): Assessment of the situation and background and potential treatment options. R (recommendation): Recommended action.

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In Healthcare Predictive Analytics, Big Data Is Sometimes a Big Mess

Those in Big Data and Healthcare Analytics circles will seldom hear the phrase “less is more.” In a clinical setting however, there is an important lesson to learn in regards to the effective execution of predictive analytics. We should not confuse more data with more insight. More data is simply more—as in more tables, more lists, more replicates, more clinics, more controls, more rows, tables of tables and lists of lists, etc. You get the idea. In short, for predictive analytics to be effective in a clinical venue, a specific focus will always trump global utility.

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Sepsis Treatment: Target Five Key Areas to Improve Sepsis Outcomes

More people in the U.S. die from sepsis than from prostate cancer, breast cancer, and AIDS…combined. Although health systems continue working to improve outcomes for septic patients, there is tremendous room for improvement. Preparing health systems to most effectively tackle sepsis starts with an awareness of consensus definitions of sepsis and continues with following evidence-based recommendations from credible organizations, such as the Surviving Sepsis Campaign and the Sepsis Alliance. Distilling ever-evolving recommendations and best practices for sepsis is time intensive. This article facilitates healthcare’s distillation effort by highlighting the five key areas health systems can target to improve sepsis outcomes (based on evidence-based guidelines and Health Catalyst’s first-hand experience with healthcare partners): Early ED recognition Three-hour sepsis bundle compliance Six-hour…

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Five Deming Principles That Help Healthcare Process Improvement

Dr. John Haughom explains 5 key Deming processes that can be applied to healthcare process improvement. These include 1) quality improvement as the science of process management, 2) if you cannot measure it, you cannot improve it, 3) managed care means managing the processes of care (not managing physicians and nurses), 4) the importance of the right data in the right format at the right time in the right hands, and 5) engaging the “smart cogs” of healthcare.

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Combatting the Opioid Epidemic with Next-Generation Risk Assessment Tools

The opioid-related death rate in the U.S. has quadrupled since 1999, making more effective ways to predict opioid misuse a healthcare priority. A new generation of machine learning-enabled risk assessment tools promises to deliver broader and more relevant insight into a patient’s risk. With more comprehensive insight (including comorbidities, other substance abuse, the amount of medication prescribed, and the duration of opioid use), clinicians can make informed decisions when prescribing opioids and reduce the risk that patients will misuse, abuse, or overuse the pain killers. Clinicians will also be able to identify which patients might benefit from alternatives to opioid pain management (non-pharmacologic, multi-modal therapies, or care management programs).

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Top Five Elements of an Effective Readmission Risk Score

Under value-based healthcare and the 2012 Hospital Readmission Reduction Program, healthcare organizations are more motivated than ever to reduce their incidence of preventable readmissions. Health systems can reduce risk of hospital readmissions by developing readmission risk scores tailored specifically to their populations. A risk model that meets the following five requirements will have significant predictive value and is most likely to achieve systemwide adoption: Identifies at-risk patients early. Separates patients relevant to the disease-specific identification method and intervention strategy from all other in-hospital patients. Uses organization-specific data to train a disease-specific model. Exceeds performance of existing models. Is developed in collaboration with domain experts.

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Ten Essential Steps for Your Readmission Reduction Program

Effective care management is essential during the first 30 days after discharge to prevent unnecessary readmission and associated costs. Care managers can follow a 10-step readmission reduction program to help patients stay on track with recovery and avoid acute care: Call the patient within two days of discharge. Assess the patient’s self-care capacity. Frontload homecare and ensure patient 'touches', if appropriate. Conduct a home safety evaluation. Order and install durable medical equipment prior to discharge. Order an emergency alert/medication reminder system and preprogram important phone numbers on patient’s phone. Implement fall prevention program, intervention, and education. Provide in-home education on new diagnoses or unmanaged chronic conditions. Connect the patient with community resources. Establish a best practice for follow-up phone calls…

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Three Approaches to Predictive Analytics in Healthcare

Predictive analytics in healthcare must be timely, role-specific, and actionable to be successful. There are also three  common types of healthcare predictive analytics: Risk scores (risk stratification using CMS-HCC or other models), What-if scenarios (simulations of specific outcomes given a certain combination of events, and Geo-spatial analytics (mapping a geographical location’s patient disease burden). The common thread in all of these is the element of action, or specifically, the intervention that really matters in healthcare predictive analytics.

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Seven Ways DOS™ Simplifies the Complexities of Healthcare IT

Health Catalyst Data Operating System (DOS) is a revolutionary architecture that addresses the digital and data problems confronting healthcare now and in the future. It is an analytics galaxy that encompasses data platforms, machine learning, analytics applications, and the fabric to stitch all these components together. DOS addresses these seven critical areas of healthcare IT: Healthcare data management and acquisition Integrating data in mergers and acquisitions Enabling a personal health record Scaling existing, homegrown data warehouses Ingesting the human health data ecosystem Providers becoming payers Extending the life and current value of EHR investments This white paper illustrates these healthcare system needs detail and explains the attributes of DOS. Read how DOS is the right technology for tackling healthcare’s big…

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The Top 7 Outcome Measures and 3 Measurement Essentials

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 blog adds much-needed clarity by reviewing the top seven outcome measures, including definitions, important nuances, and real-life examples: Mortality Readmissions Safety of care Effectiveness of care Patient experience Timeliness of care Efficient use of medical imaging CMS used these exact seven outcome measures to calculate overall hospital quality and arrive at its 2016 hospital star ratings. This blog also reiterates the importance of outcomes measurement, clarifies how outcome measures are defined and prioritized, and recommends three essentials for successful outcomes measurement: Transparency Integrated care Interoperability

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What Is a Healthcare Data Lake and Why Do You Need One? Imagine a Supermarket

Using a supermarket analogy, this article helps healthcare leaders understand what data lakes are (open reservoirs for vast amounts of data), why they’re essential (they reduce the time and resources required to map data), and how they integrate with three common analytic architectures: Early-Binding Data Warehouse Late-Binding Data Warehouse Map-Reduce Hadoop System Data lakes are useful parts of all three platforms, but deciding which platform to integrate a data lake with depends heavily on a health system’s resources and infrastructure. Once understood and appropriately integrated with the optimal analytics platform, data lakes save health systems time, money, and resources by adding structure to data only as use cases arise.

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The Key to Transitioning from Fee-for-Service to Value-Based Reimbursement

The shift from fee-for-service to value-based reimbursements has good and bad consequences for healthcare. While the shift will ultimately help health systems provide higher quality lower cost care, the transition may be financially disastrous for some. In addition, the shifting revenue mix from commercial payers to Medicare and Medicaid is creating its own set of challenges. There are, however, three keys to surviving the transition: 1) Effectively manage shared savings programs to maximize reimbursement. 2) Improve operating costs. 3) Increase patient volumes. With an analytics foundation, health systems will be able to meet and survive today’s healthcare challenges.

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Healthcare Reform: Is Bipartisan Legislation Possible?

The effort to repeal and replace the ACA in 2017 failed, leaving the industry wondering if bipartisan healthcare reform is possible in today’s political climate. This article explains why it is possible, by taking a close look at why repeal and replace failed, and why the 21st Century Cures Act and MACRA have been successful. To stand a chance of being successful, proposed bipartisan healthcare legislation will most likely have one (or more) of five features: Driven by practical need rather than politics. Focuses on cost control/cost reduction. Targets areas that are expected to save money. Doesn’t involve creating new programs. Stabilizes the ACA. There are many bipartisan healthcare legislation opportunities, from expanding the use of HSAs to innovation waivers;…

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