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Enterprise Data Warehouse / Data Operating system

Agnostic Analytics Solutions vs. EHRs: Six Reasons EHRs Can’t Deliver True Healthcare Interoperability

As enterprisewide analytics demands grow across healthcare, health systems that rely on EHRs from major vendors are hitting limitations in their analytics capabilities. EHR vendors have responded with custom and point-solution tools, but these tend to generate more complications (e.g., multiple data stores and disjointed solutions) than analytics interoperability.

To get value out of existing EHRs while also evolving towards more mature analytics, health systems must partner with an analytics vendor that provides an enterprise data management and analytics platform as well as deep improvement implementation experience. Vendor tools and expertise will help organizations leverage their EHRs to meet population health management and value-based payment goals, as well as pursue some of today’s top healthcare strategic goals:

  1. Growth.
  2. Innovation.
  3. Digitization.

The Six Biggest Problems With Homegrown Healthcare Analytics Platforms (Executive Reports)

Most healthcare systems have been building, improving, and maintaining proprietary healthcare analytics platforms since the early 2000s and have invested heavily in the people and resources required to do so. As the demands of today’s healthcare environment continue to increase, it’s becoming more difficult for analytic teams to keep up.

This article deals with the six biggest problems to maintaining a homegrown healthcare analytic platform today:

  1. Inability to keep pace with analytic demands.
  2. Difficult to support and scale for the future.
  3. Difficulty finding and keeping talent.
  4. Use of point solutions to fill gaps.
  5. Analytic teams must also support third-party vendors and affiliated groups.
  6. Difficulty keeping abreast of rapidly changing regulatory requirements.

The Homegrown Versus Commercial Digital Health Platform: Scalability and Other Reasons to Go with a Commercial Solution

Public cloud offerings are making homegrown digital platforms look easier and more affordable to health system CTOs and CIOs. Initial architecture and cost, however, may be the only real benefit of a do-it-yourself approach. These homegrown systems can’t scale at the level of commercial vendor systems when it comes to long-term performance and expense, leaving organizations with a potentially costly and undereffective platform for years to come.

Over his 25 years as a health system CIO, Dale Sanders, President of Technology for Health Catalyst, has observed both the tremendous value of healthcare-specific vendor platforms, as well as the shortcomings of homegrown solutions. He shares his insights in a question-and-answer session that addresses pressing issues in today’s digital healthcare market.

Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growing Health Data Demands (Executive Report)

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.

Database vs. Data Warehouse: A Comparative Review (Executive Report)

What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use. The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.

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.

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

Comparing the Three Major Approaches to Healthcare Data Warehousing: A Deep Dive Review (White Paper)

The task to improve healthcare presents a significant challenge to providers, health systems, and payers. But according to the Institute for Healthcare Improvement, if health systems focus on achieving the objectives of the Triple Aim, they will be able to meet the ongoing government mandates to improve care. A key component for meeting the Triple Aim will require the ability to overcome the current data warehouse challenges the healthcare industry faces. Because of constantly changing business rules and definitions, health systems need to choose a data warehouse that’s able to bind volatile and nonvolatile data at different stages rather than the early binding approach that’s inherent with traditional data warehouses. The best type of healthcare data warehouse should offer a late-binding approach, which will provide the following critical characteristics: data modeling flexibility, data flexibility, a record of changes saved, an iterative approach, and granular security.

Getting the Best Business-Intelligence Solution for Healthcare (Executive Report)

In today’s reality, healthcare is more dependent than ever upon business intelligence to survive and ultimately thrive. Get a review of the available solutions, a summary of what each does as well as the pros and cons. Also discover how a Late-Binding™ data warehouse stacks up against other solutions in its ability to aggregate data and make it accessible and foster a truly data-driven culture.

Build vs. Buy a Healthcare Enterprise Data Warehouse: Which is Best for You? (Executive Report)

Chances are, if you are reading this blog, you have heard some flavor of the “build vs. buy” question in the context of data warehousing. For example, here are two conflicting ways that I’ve personally heard this question posed:

“Do we need to buy [a data warehouse], or can we build it?”
“Are there any vendors we can buy this from, or will we have to build this?”

As you can imagine, both approaches resonate differently with different people, cultures, and strategies, and the same basic questions sound very different depending on who is asking it.

The Late-Binding Data Warehouse Explained (white paper)

You have options when it comes to data warehouses – but which one is right for your healthcare organization? Discover the difference of the Late-Binding (TM) data warehouse architecture. And see why this unique system offers quick time-to-value and the agility necessary to meet the changing demands of the healthcare industry.

6 Reasons Why Healthcare Data Warehouses Fail (Executive Report)

It’s no secret that the failure rate of data warehouses across all industries is high – Gartner once estimated as many as 50 percent of data warehouse projects would have only limited acceptance or fail entirely. So what makes the difference between a healthcare data warehouse project that fails and one that succeeds? As a former co-founder of HDWA, Steve details six common reasons: 1) a solid business imperative is missing, 2) executive sponsorship and engagement is weak or non-existent., 3) frontline healthcare information users are not involved from start to finish, 4) boil-the-ocean syndrome takes over, 5) the ideal trumps reality, and 6) worrying about getting governance “perfect” immobilizes the project.