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Latest Executive Reports

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.

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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.

Healthcare Safety Culture: A Seven-Step Success Framework (Executive Report)

Preventable patient harm costs healthcare billions annually, making strategies to improve patient safety an imperative for health systems. To improve patient safety, organizations must establish a safety culture that prioritizes safety throughout the system, supports blame-free reporting of safety events, and ensures that healthcare IT solutions functions and accessibility align with safety goals.

A sociotechnical framework gives health systems a seven-part roadmap to improving patient safety culture:

  1. Leverages qualitative and quantitative data.
  2. Doesn’t rely on HIMSS stage levels to tell the complete safety picture.
  3. Gives frontline clinicians a voice in decision making.
  4. Makes IT solutions accessible to non-technical users.
  5. Encourages frontline clinicians to report safety and quality issues.
  6. Treats a safety issue in one area as a potential systemwide risk.
  7. Performs thorough due diligence before taking safety IT solutions live.

Lean Healthcare: 6 Methodologies for Improvement from Dr. Brent James (Executive Report)

The survival of healthcare organizations depends on applying lean principles. Organizations that adopt lean principles can reduce waste while improving the quality of care. By applying stringent clinical data measurement approaches to routine care delivery, healthcare systems identify best practice protocols and incorporate those into the clinical workflow. Data from these best practices are applied through continuous-learning loop that enables teams across the organization to update and improve protocols–ultimately reducing waste, lowering costs, and improving access to care. This executive report based on a presentation by Dr. Brent James at a regional medical center, covers the following:

  1. How lean healthcare principles can help improve the quality of care.
  2. The steps healthcare organizations need to take to create a continuous-learning loop.
  3. How a lean approach creates financial leverage by eliminating waste and improving net operating margins and ROI.

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.

Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it Effectively in Their Organizations (Executive Report)

Machine learning (ML) is gaining in popularity throughout healthcare. ML’s far-reaching benefits, from automating routine clinical tasks to providing visibility into which appointments are likely to no-show, make it a must-have in an industry that’s hyper focused on improving patient and operational outcomes.

This executive report—co-written by Microsoft Worldwide Health and Health Catalyst—is a basic guide to training machine learning algorithms and applying machine learning models to clinical and operational use case. This report shares practical, proven techniques healthcare organizations can use to improve their performance on a range of issues.

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.

Precision Medicine: Four Trends Make It Possible (Executive Report)

When realized, the promise of precision medicine (to specifically tailor treatment to each individual) stands to transform healthcare for the better by delivering more effective, appropriate care. To date, to achieve precision medicine, health systems have faced financial, data management, and interoperability barriers. Current trends in healthcare, however, will give researchers and clinicians the quality and breadth of health data, biological information, and technical sophistication to overcome the challenges to achieving precision medicine.

Four notable trends in healthcare will bolster to growth of precision medicine in the coming years:

  1. Decision support methods harness the power of the human genome.
  2. Healthcare leverages big data analytics and machine learning.
  3. Reimbursement methods incentivize health systems to keep patients well.
  4. Emerging tools enable more data, more interoperability.

The Key to Healthcare Mergers and Acquisitions Success: Don’t Rip and Replace Your IT (Executive Report)

Healthcare mergers and acquisitions can involve a lot of EMRs and other IT systems. Sometimes leaders feel like they have to rip and replace these systems to fully integrate organizations. However, this is not the answer, according to Dale Sanders. This report, based upon his July 2017 webinar, outlines the importance of a data-first strategy and introduces the Health Catalyst® Data Operating System (DOS™) platform. DOS can play a critical role in facilitating IT strategy for the growing healthcare M&A landscape.

Critical Healthcare M&A Strategies: A Data-driven Approach (Executive Report)

Historically technology and talent were primary assets used to weigh the value of M&A activity, but data is an equal pillar. Buyers (the acquiring organizations) face enormous responsibility and risk with M&A transactions. C-suite leaders have a lot to consider—enterprise-wide technology, finances, operations, facilities, talent, processes, workflows, etc.—during the due diligence process. But attention is often heavily weighted toward time-honored balance sheet and facility assets rather than next-generation assets with the long-term strategic value in the M&A process: data. The model for conducting due diligence around data involves four disciplines:

  • Establish the strategic objectives of the M&A with the leadership team.
  • Prioritize data along with the standardization of solutions and the design of a new IT organization (i.e., a co-equal effort for data, tools, and talent).
  • Identify the near-term data strategic priorities, stakeholders, and tools.
  • Assess the talent and consider creating an analytics center of excellence (ACOE) to harness organizational capabilities.