Analytics

Insights

Jeff Selander

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:

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

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Ann Tinker, MSN, RN
Dan Hopkins

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:

Establish a data-driven culture.
Acquire and access data.
Establish data stewardship.
Establish data quality.
Spread data use.

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Russ Staheli, MPH

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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Mike Dow

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 to solve healthcare’s toughest problems.

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Imran Qureshi

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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John Wadsworth

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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Dale Sanders

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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Health Catalyst

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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Jeffrey Wu

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:

Data must be transformed from its raw, obscure form into actionable insights.
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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Eric Just

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:

Optimize text search (display, medical terminologies, and context).
Enhance context and extract values with an NLP pipeline.
Always validate the algorithm.
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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Bobbi Brown, MBA
Leslie Falk

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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Bryan Hinton
Sean Stohl

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:

Archiving
Streaming
Machine learning

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Eric Just

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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Eric Just

Questions You Should Ask When Selecting a Healthcare Analytics Platform

As vice president of technology for a healthcare IT company, I’m often asked what should be considered when selecting a solution for healthcare analytics. Healthcare organizations have many choices when selecting a healthcare data warehouse and analytics platforms. I advise them to consider the following fundamental criteria: 1) time-to-value (measured in months, not years), 2) experience as a predictor of future success, and 3) extensibility to meet your needs.

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Paul Horstmeier

Factoids Reveal Healthcare Trends in Analytics and Technology

We hand-picked the most interesting, useful, credible factoids from 2015 (including the plethora of facts that came out of the 2nd Healthcare Analytics Summit) to create an easy-to-share presentation. The 32 factoids included in this presentation revealed several interesting healthcare trends:

Trend #1: Healthcare analytics continue to improve outcomes and save money. For example, OSF’s predictive readmission model reduced its all-cause readmission rate to less than 10%.
Trend #2: New technologies are improving patient engagement. For example, 73 percent of health executives surveyed see positive ROI from personalization technologies, and 76 percent of doctors say patient use of wearable health devices improves engagement.
Trend #3: Patients and providers agree on data is useful but have security and interoperability concerns. For example, 83 percent of patients don’t trust EHR safety and security, and 83 percent of physicians are frustrated by EHR interoperability.

Although a majority of healthcare leaders understand the importance of using analytics to improve outcomes and reduce costs, only 15% of hospitals use predictive analytics. We hope to see analytics use increase in 2016, and we’re excited to see how technology will continue to engage patients and lead to better health outcomes.

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Russ Staheli, MPH

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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Leslie Falk

Improving Patient Safety and Quality through Culture, Clinical Analytics, Evidence-Based Practices, and Adoption

According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents.  Learn key best practices in patient safety and quality including:  patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.

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John Wadsworth

Analytics in Community Hospitals: Embracing Data to Thrive in the New Era of Value-Based Care

Value-based care has remade the healthcare landscape for small hospitals. Many are struggling to compete with the larger, better-funded medical centers in the communities they serve. Embracing data and analytics is no longer a luxury for these organizations if they are to succeed and remain competitive. Data analysis can assist senior leaders in identifying opportunities for improvement while balancing long-term goals with short-term pressures. Incorporating data in to the culture and making it a part of everyday decision making will enable smaller hospitals to not only survive, but thrive in the new era of value-based care.

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Anne Marie Bickmore

Prospective Analytics: The Next Thing in Healthcare Analytics

Retrospective and predictive analytics are familiar terms for practitioners of clinical outcomes improvement, but the new kid on the block is prospective analytics. This is the next level that uses findings from its predecessors to not only identify the best clinical routes, but also what the results might be of each choice. Prospective analytics gives bedside clinicians an expanded, branching view of operational and clinical options in a type of decision support that can lead to not only improving surgical and medical outcomes, but to making a positive financial contribution, as well. But, as expected with any new process or new way of thinking, prospective analytics requires careful introduction and stewardship to help drive its adoption within the organization.

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Paul Horstmeier

Deloitte Report Reveals Why Health Systems Lack Integrated Healthcare Analytics Strategy; Recommends Adoption Framework

A 2015 Deloitte Report investigates why health systems lack integrated healthcare analytics strategies (despite acknowledging the myriad of benefits analytics-driven insights offer):

Lack clarity on current analytics spending.
Culture, operating models, and fragmented oversight.
Lack of access to funding and skilled resources.
Numerous confusing vendor product offerings.
Inconsistent industry definitions of analytics.

The report concludes by recommending analytics adoption guidelines, from engaging committed leaders across the enterprise and implementing a structured data governance model to emphasizing data and technology standards to promote interoperability.

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Dan Soule

Why Most Analytic Applications Will Never Be Able to Significantly Improve Healthcare Outcomes

The availability of healthcare IT solutions can be overwhelming and all promise to solve an organization’s most pressing issues. While typical data and analytic applications are excellent at exposing opportunities for improvement that are impacting the bottom line, most are not effective at helping the organization determine what to do to address them and improve outcomes. However, a new approach to creating analytics applications is emerging. Analytics applications that incorporate best practices clinical content along with the best practices visualizations help everyone understand the problem and the solution. These applications also enable clinicians to better understand, adopt, roll out, and execute outcome improvement initiatives with healthcare systems. Health Catalyst has deliberately created a comprehensive, dynamic suite of applications that integrate clinical content and facilitate the orderly implementation of action plans.

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Jared Crapo

The Practical Use of the Healthcare Analytics Adoption Model

When an analyst from another health system asked our resident analytics expert about the practical value of the Analytics Adoption Model, our expert had a lot to say. Specifically, he elaborated on the results the organization would realize, especially if they used the Adoption Model as a roadmap on their journey to become data driven. But first, they would need to adopt a late-binding data warehouse and analytics applications. With both solutions, they would be able to confidently deliver evidence-based care.

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Greg Miller

Healthcare analytics applications to the rescue — or not?

Better analytics technology alone will not achieve the healthcare industry’s desired improvements in life, efficiency, effectiveness, or provider and patient satisfaction. That all will change, however, when the technology is combined with a deployment system. With such a system, health systems learn from experienced healthcare experts about how to use the information from their analytics applications to transform from the old world to the new.

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Brian Eliason, MIS
Roopa Foulger

Outsourced vs. In-house Healthcare Analytics: Pros and Cons

Healthcare analytics are essential for organizations to thrive in the new healthcare environment. Using analytics, systems can evaluate efficiency, effectiveness, and find improvement opportunities. There are two principal approaches: outsourcing the analytics function to benchmarking companies and providers of software-as-a-service; and doing analytics in-house with a system’s own data warehouse. The pros of outsourcing include gaining benchmarking access to how health system peers are performing. The cons to outsourcing include focusing too much high-level outcomes with no insight in how to effect change. The pros of in-house analytics include having quick access to fine-grained details of the data and being able to include clinicians in the implementation and development of the analytics process. A con is that in-house analytics can require significant resources – an investment in the right personnel and right technology.
 

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Paul Horstmeier

Cut Through the Confusion: The 5 Types of Healthcare Analytics Solutions

As healthcare leaders look into ways to bend the cost curve in today’s value-based reimbursement environment, they are faced with many vendors offering analytics solutions. The key is to determine which product best fits the organization’s needs, not just now but for the long term. This article discusses the five primary options available and the pros and cons of each one. The five analytics options include the following: (1) Buy from a hosted analytics service provider. (2) Buy from a large non-healthcare-specific technology vendor. (3) Buy “best of breed” point solutions. (4) Buy from the EMR vendor. (5) Buy and build an analytics solution from a healthcare data warehouse platform vendor.

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