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The Best Way to Maximize Healthcare Analytics ROI

When it comes to maximizing analytics ROI in a healthcare organization, the more domains, the merrier. Texas Children’s Hospital started their outcomes improvement journey by using an EDW and analytics to improve a single process of care. It quickly realized the potential for more savings and improvement by applying analytics to additional domains, including:

  • Analytics efficiencies
  • Operations/Finance
  • Organization-wide clinical improvement

The competencies required to launch and sustain such an organizational sea change are all part of a single, defining characteristic: the data-driven culture. This allows fulfillment of the analytics strategy, ensures data quality and governance, encourages data and analytics literacy, standardizes data definitions, and opens access to data from multiple sources.

This article highlights the specifics of how Texas Children’s has evolved into an outcomes improvement leader, with stories about its successes in multiple domains.

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4 Ways Healthcare Data Analysts Can Provide Their Full Value

Analysts are most effective when they have the right tools. In healthcare, that means providing data analysts with a means of accessing and testing ALL of the available data and using it to discover more insights. To do this, analysts need guidance more than they need a detailed set of instructions. And, equally as important, they need a data warehouse and access to a testing environment and data discovery tools, so they can truly do the work they were hired to do: analyze.

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Powering Medical Research With Data: The Research Analytics Adoption Model (Webinar)

Analytics are becoming imperative to researchers in recruiting patients into studies, making breakthrough discoveries, as well as monitoring the clinical implementation of these discoveries. This webinar will be for organizations that want to leverage their enterprise data to power more effective research.

Join Eric Just, Vice President of Technology at Health Catalyst, as he presents a Research Analytics Adoption Model that outlines ways that a research organization can leverage data and analytics to achieve greater speed and ROI on research.The Adoption Model walks through analytics competencies starting with basic data usage and culminating with using analytics to incorporate the latest research discoveries into clinical practice.

Content presented and discussed:

  • A summary of some of the challenges in using data and analytics for research
  • A research analytics adoption framework for all organizations interested in using clinical data for research
  • What is needed from a workflow and organizational perspective to power research with data

We hope you enjoy.

 

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How to Avoid the 3 Most Common Healthcare Analytics Pitfalls and Related Inefficiencies

Analytics are supposed to provide data-driven solutions, not additional healthcare analytics pitfalls and other related inefficiencies. Yet such issues are quite common. Becoming familiar with potential problems will help health systems avoid them in the future. The three common analytics pitfalls are point solutions, EHRs, and independent data marts located in many different databases. An EDW will counter all three of these problems. The two inefficiencies include report factories and flavor of the month projects. The solution that best overcomes these inefficiencies is a robust deployment system.

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What Is Data Mining in Healthcare?

This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the best practice system (and systematically applying evidence-based best practices to care delivery), and the adoption system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.

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4 Options for Choosing the Best Healthcare Analytics Solutions

Analytics is a buzzword in healthcare today. You hear it often: “What does an organization need to succeed in a value-based care environment? Robust analytics.” But what exactly does that mean? Anyone who has looked into implementing “analytics” for their organization knows that a multitude of options for healthcare analytics are available—and each vendor touts its approach to analytics as the best. I’d like to take a moment here to summarize four primary analytics options available to healthcare organizations today: 1) hosted analytics service providers, 2) “best of breed” point solutions, 3) EMR vendors, 4) healthcare data warehouse platform providers.

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How to Avoid the 3 Most Common Healthcare Analytics Pitfalls and Related Inefficiencies

Analytics are supposed to provide data-driven solutions, not additional healthcare analytics pitfalls and other related inefficiencies. Yet such issues are quite common. Becoming familiar with potential problems will help health systems avoid them in the future. The three common analytics pitfalls are point solutions, EHRs, and independent data marts located in many different databases. An EDW will counter all three of these problems. The two inefficiencies include report factories and flavor of the month projects. The solution that best overcomes these inefficiencies is a robust deployment system.

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Healthcare Analytics Adoption Model: A Framework and Roadmap (white paper)

Outlined for practical use in the healthcare industry, this eight-level, industry-specific roadmap acts as a guide for organizations striving to be truly data driven. Developed by an independent, cross-industry group, this white paper explains the Analytics Adoption Model, its history and purpose. Organizations can then use the model to determine direction, highlight goals and gauge progress, as well as see the next steps needed to achieve goals associated using data to drive and assess clinical improvements.

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How to Evaluate a Clinical Analytics Vendor: A Checklist

Based on 25 years of healthcare IT experience, Dale outlines a detailed set of criteria for evaluating clinical analytic vendors. These criteria include 1) completeness of vision, 2) culture and values of senior leadership, 3) ability to execute, 4) technology adaptability and supportability, 5) total cost of ownership, 6) company viability, and 7) nine elements of technical specificity including data modeling, master data management, metadata, white space data, visualization, security, ETL, performance and utilization metrics, hardware and software infrastructure.

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The Best Approach to Healthcare Analytics

Healthcare has remained entrenched in its cottage industry-style of operation, even within huge medical centers and significant medical innovation. The result, as documented by Dr. John Wennberg’s Dartmouth Atlas of Health Care project , is unwarranted variation in the practice of medicine and in the use of medical resources including underuse of effective care, misuse of care, and overuse of care provided to specific patient populations. The root of the problem, Wennberg concludes, is that there is no healthcare “system.” At Health Catalyst, we agree. Healthcare needs to be systematized and standardized in three key areas:1) healthcare analytics or measurement, 2) adoption or how teams and work are organized, and 3) best practice or how evidence/knowledge is gathered, evaluated, and disseminated for adoption.

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Analytics - Additional Content

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Healthcare NLP: Four Essentials to Make the Most of Unstructured Data

Many health systems are eager to embrace the capability of natural language processing (NLP) to access the vast patient insights recorded as unstructured text in clinical notes and records. Many healthcare data and analytics teams, however, aren’t experienced in or prepared for the unique challenges of working with text and, specifically, don’t have the knowledge to transform unstructured text into a usable format for NLP. Data engineers can follow four need-to-know principles to meet and overcome the challenges of making unstructured text available for advanced NLP analysis:

  1. Text is bigger and more complex.
  2. Text comes from different data sources.
  3. Text is stored in multiple areas.
  4. Text user documentation patterns matter.

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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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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:

  1. Predicting future demand is difficult.
  2. Infrastructure scaling is lumpy and inelastic.
  3. Security risk mitigation is a major investment.
  4. Data architectures limit flexibility and are resource intensive.
  5. Analytics expertise is misallocated.

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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:

  1. Manage easily fixed cost issues.
  2. Use healthcare cost savings to fund expanded benefits.

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Transforming Healthcare Analytics: Five Critical Steps

By committing to transforming healthcare analytics, organizations can eventually save hundreds of millions of dollars (depending on their size) and achieve comprehensive outcomes improvement. The transformation helps organizations achieve the analytics efficiency needed to navigate the complex healthcare landscape of technology, regulatory, and financial challenges and the challenges of value-based care. To achieve analytics transformation and ROI within a short timeframe, organizations can follow five phases to become data driven:

  1. Establish a data-driven culture.
  2. Acquire and access data.
  3. Establish data stewardship.
  4. Establish data quality.
  5. Spread data use.

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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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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:

  1. Focus on relevant content.
  2. Externally validate the model.
  3. Commit to providing vital documentation.
  4. Prioritize long-term planning.
  5. 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 to solve healthcare’s toughest problems.

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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:

  1. Acquire
  2. Organize
  3. Standardize
  4. Analyze
  5. Deliver
  6. Orchestrate
  7. 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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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:

  1. Must-have tools.
  2. People and their skills.
  3. Reactive, descriptive, and prescriptive analytics.
  4. Matching technical skills to analytics work streams.
  5. Interoperability.

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How to Evaluate a Clinical Analytics Vendor: A Checklist

Based on 25 years of healthcare IT experience, Dale outlines a detailed set of criteria for evaluating clinical analytic vendors. These criteria include 1) completeness of vision, 2) culture and values of senior leadership, 3) ability to execute, 4) technology adaptability and supportability, 5) total cost of ownership, 6) company viability, and 7) nine elements of technical specificity including data modeling, master data management, metadata, white space data, visualization, security, ETL, performance and utilization metrics, hardware and software infrastructure.

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Chilmark Report Studies the 2017 Healthcare Analytics Market Trends and Vendors

Chilmark’s 2017 Healthcare Analytics Market Trends Report is a trove of insights to the analytics solutions driving the management of population health and the transition to new reimbursement models. The report reviews the analytics market forces at work, such as:

  • The need to optimize revenue under diverse payment models.
  • The increasing importance of analytics in general, and a platform in specific, that can aggregate all data.
  • Continuing confusion about how to react to MIPS and APMs.
  • The growing importance of providing a comprehensive set of open and standard APIs.
  • The need for better tools to create analytics-ready data stores.
The report is also a succinct guide to the 17 leading analytics vendors (which represent EHR, HIE, payer, and independent categories) with the most promising products, technology, and services offerings in the market.

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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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How to Use Text Analytics in Healthcare to Improve Outcomes—Why You Need More than NLP

Given the fact that up to 80 percent of clinical data is stored in unstructured text, healthcare organizations need to harness the power of text analytics. But, surprisingly, less than five percent of health systems use it due to resource limitations and the complexity of text analytics. But given the industry’s necessity to use text analytics to create precise patient registries, enhance their understanding of high-risk patient populations, and improve outcomes, this executive report explains why systems must start using it—and explains how to get started. Health systems can start using text analytics to improve outcomes by focusing on four key components:

  1. Optimize text search (display, medical terminologies, and context).
  2. Enhance context and extract values with an NLP pipeline.
  3. Always validate the algorithm.
  4. Focus on interoperability and integration using a Late-Binding approach.
This broad approach with position health systems for clinical and financial success.

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The Best Way to Maximize Healthcare Analytics ROI

When it comes to maximizing analytics ROI in a healthcare organization, the more domains, the merrier. Texas Children’s Hospital started their outcomes improvement journey by using an EDW and analytics to improve a single process of care. It quickly realized the potential for more savings and improvement by applying analytics to additional domains, including:

  • Analytics efficiencies
  • Operations/Finance
  • Organization-wide clinical improvement
The competencies required to launch and sustain such an organizational sea change are all part of a single, defining characteristic: the data-driven culture. This allows fulfillment of the analytics strategy, ensures data quality and governance, encourages data and analytics literacy, standardizes data definitions, and opens access to data from multiple sources. This article highlights the specifics of how Texas Children’s has evolved into an outcomes improvement leader, with stories about its successes in multiple domains.

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Hadoop in Healthcare: Getting More from Analytics

Healthcare data is positioned for momentous growth as it approaches the parameters of big data. While more data can translate into more informed medical decisions, our ability to leverage this mounting knowledge is only as strong as our data strategy. Hadoop offers the capacity and versatility to meet growing data demands and turn information into actionable insight. Specific use cases where Hadoop adds value data strategy include:

  1. Archiving
  2. Streaming
  3. Machine learning

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Understanding Risk Stratification, Comorbidities, and the Future of Healthcare

Risk stratification is essential to effective population health management. To know which patients require what level of care, a platform for separating patients into high-risk, low-risk, and rising-risk is necessary. Several methods for stratifying a population by risk include: Hierarchical Condition Categories (HCCs), Adjusted Clinical Groups (ACG), Elder Risk Assessment (ERA), Chronic Comorbidity Count (CCC), Minnesota Tiering, and Charlson Comorbidity Measure. At Health Catalyst, we use an analytics application called the Risk Model Analyzer to stratify patients into risk categories. This becomes a powerful tool for filtering populations to find higher-risk patients.

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