The Healthcare Analytics Adoption Model: A Roadmap to Analytic Maturity

The focus on analytics is contributing to the “EHR problem”—doctors prioritizing the EHR over patients. The Healthcare Analytics Adoption Model (HAAM) walks healthcare organizations through nine levels that lay the framework to fully leverage analytic capabilities to improve patient outcomes:

Level 1. Enterprise Data Operating System
Level 2. Standardized Vocabulary & Patient Registries
Level 3. Automated Internal Reporting
Level 4. Automated External Reporting
Level 5. Waste and Care Variability Reduction
Level 6. Population Health Management & Suggestive Analytics
Level 7. Clinical Risk Intervention & Predictive Analytics
Level 8. Personalized Medicine & Prescriptive Analytics
Level 9. Direct-To-Patient Analytics & Artificial Intelligence

Analytics are crucial to becoming a data-driven organization, but providers and administrators can’t forget about the why behind the data—to improve outcomes. Following the HAAM enables organizations to build a sustainable, analytic platform and empower patients to become data-driven when it comes to their own care.

Healthcare Analytics for Payers: How to Thrive Through Shifting Financial Risk

To stay in sync with healthcare’s transition to value-based care, payers today must develop the analytics capability to support alternative payment models and drive more value to their members. Payers can follow an analytics roadmap to develop a strategy that extends their data, analytics, and risk management expertise to meet growing demands.

The analytics roadmap helps the payer meet these common challenges of establishing a data-driven culture:

• Recruiting and retaining high-quality providers in a competitive market.
• Managing increasing numbers of high-risk/high-cost members with limited resources.
• Efficiently reacting to federal and state legislative and payment changes.
• Controlling the rising costs of healthcare services and pharmaceuticals.

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.

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.

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

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:

1. Healthcare data management and acquisition
2. Integrating data in mergers and acquisitions
3. Enabling a personal health record
4. Scaling existing, homegrown data warehouses
5. Ingesting the human health data ecosystem
6. Providers becoming payers
7. 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 issues, including big data, physician burnout, rising healthcare expenses, and the productivity backfire created by other healthcare technologies.

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.

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

When Healthcare Data Analysts Fulfill the Data Detective Role

There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:

1. They are inquisitive and relentless with their questions.
2. They let the data inform.
3. They drive to the heart of what matters.

Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.

Why Healthcare Requires an EDW, Analytics Applications, and Visualization Tools for Quality Improvement Initiatives

Business intelligence may not sound like something that belongs in a healthcare setting. After all, what role can it possibly play in medical excellence and compassionate care? But federal mandates that require cost and care improvement and reporting on those improvement metrics, are driving the need for business intelligence tools. For healthcare, this means an enterprise data warehouse with the processing power and architecture to handle the vast volumes of data, analytics applications that will effectively unlock the data, and data visualization tools to easily illustrate areas of opportunity.

Anatomy of Healthcare Delivery Model: How a Systematic Approach Can Transform Care Delivery (white paper)

Read about this breakthrough model and framework, developed and refined by Dr. David Burton during his 25 years of executive healthcare experience. This model creates a framework that maps major healthcare processes into common patterns and process flows that can then be used to systematically examine and improve healthcare delivery. By using a systematized framework to reduce variations in clinical and operational processes, health systems can experience sustainable cost and quality gains. This framework won’t eliminate critical thinking, but it will provide a standardized, evidence-based approach to care delivery, which will bring all care up to the same, high standard.

Keys to a Successful Health Catalyst Data Warehouse Platform and Analytics Implementation (Executive Report)

During the process of learning about the Health Catalyst Late-Binding ™ data warehouse platform and analytics solutions, we have found that many customers ask similar questions about how the process really works. So, we thought it would be useful to produce a document that we hope will answer the majority of these and other common questions. The keys for a successful Health Catalyst data warehouse platform and analytics implementation are outlined step-by-step format.

Pre-step (most important): Identify key personnel resources needed on the health system side
Step 1: Implementation Planning
Step 2: Deploy Hardware
Step 3: Technical Kickoff Meeting with the Client and Health Catalyst Deployment Teams
Step 4: Access Source Data
Step 5: Install Platform
Step 6: Load Data
Step 7: Install Foundational Applications
Step 8: Install Discovery Applications
Step 9: Install Advanced Applications
At the beginning of the project, Health Catalyst will begin a collaborative implementation planning process resulting in a timeline tailored to each project. Some projects can be accelerated, with the initial phase completed in 90 days.
Your health system will have questions specific to your organization and your circumstances. We are happy to answer those in person.

5 Reasons BI Tools Can’t Work as a Healthcare Enterprise Data Warehouse (Executive Report)

BI tool and visualization solution vendors recognize the importance of a healthcare enterprise data warehouse, so they sometimes market themselves as cloud data warehouses. Here are 5 things a healthcare BI tool cannot do:
i. Optimize the healthcare data.
ii. Handle large amounts of healthcare data.
iii. Work with healthcare data at different levels of granularity.
iv. Optimize healthcare data for multiple user types.
v. Provide for modularity, understandability, and code reuse.

How to Evaluate a Clinical Analytics Vendor: A Checklist (white paper)

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.the field of vendors and focus on the best solution available for your organization today and for the future.

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.

4 Options for Choosing the Best Healthcare Analytics Solutions (Executive Report)

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.

The Best Approach to Healthcare Analytics (Executive Report)

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) deployment or how teams and work are organized, and 3) content or how evidence/knowledge is gathered, evaluated, and disseminated for adoption.

3 Common Pitfalls in Healthcare Analytics (Executive Report)

Finding a sustainable approach to healthcare analytics can be a challenge and requires a meaningful comparison of some of the more prevalent methods out there. Let’s start by looking at those that seem to fail time and again. These include 1) “the report factory” — this approach uses an analytics platform alone and assumes that “if you build it, they will come.”, 2) the “flavor of the month” — which is usually driven by the “squeaky wheel” or management’s favorite pet project, or 3) point solutions, which have “sub-optimization” and “technology-spaghetti-bowl” challenges.