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Latest Executive Reports

Extended Real-World Data: The Life Science Industry’s Number One Asset

The life science industry has historically relied on sanitized clinical trials and commoditized data sources (largely claims) to inform its drug development process—an under-substantiated approach that didn’t reflect how a new drug would affect broader patient populations. In an effort to gain more accurate insight into the patient experience and bring drugs to market more efficiently and safely, the industry is now expanding into extended real-world data (RWD).

To access the needed breadth and depth of patient-centric data, life science companies must partner with a healthcare transformation company that has three key qualities:

  1. A broad and deep data asset.
  2. Extensive provider partnerships.
  3. An outcomes-improvement engine to support the next generation of drug development.

Pairing HIE Data with an Analytics Platform: Four Key Improvement Categories

Population health and value-based payment demand data from multiple sources and multiple organizations. Health systems must access information from across the continuum of care to accurately understand their patients’ healthcare needs beyond the acute-care setting (e.g., reports and results from primary care and specialists). While health system EHRs have a wealth of big-picture data about healthcare delivery (e.g., patient satisfaction, cost, and outcomes), HIEs add the clinical data (e.g., records and transactions) to round out the bigger picture of patient care, as well as the data sharing capabilities needed to disseminate the information.

By pairing HIE capability with an advanced analytics platform, a health system can leverage data to improve processes in four important outcomes improvement areas:

  1. Workflow
  2. Machine learning
  3. Professional services
  4. Data governance

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.

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.