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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.

Healthcare Analytics Platform: DOS Delivers the 7 Essential Components (Executive Report)

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 (Executive Report)

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

The Best Way to Maximize Healthcare Analytics ROI (White Paper)

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 (White Paper)

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

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.

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.