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Five Reasons Why Health Catalyst Acquired Medicity and What It Means for Interoperability, as Explained by Dale Sanders, President of Technology

Why did Health Catalyst acquire Medicity? Dale Sanders, President of Technology, shares five reasons and what it means for interoperability:

  1. Medicity has several petabytes of valuable data content.
  2. Medicity’s data governance expertise.
  3. Medicity’s 7 x 24 real-time cloud operations expertise.
  4. Medicity’s expertise in real-time EHR integration.
  5. Medicity’s presence and expertise in the loosely affiliated, community ambulatory care management space.

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Data Warehousing in Healthcare: A Guide to Success

Looking for a way to share his extensive experience with data warehousing in healthcare, in 2002 Dale Sanders wrote what many consider to be the “EDW Bible.” It’s a document with guidance that, if followed, will drive value and utilization from a data warehouse. We’ve made that report available now.

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Introducing the Health Catalyst Monitor™ Patient Safety Suite: Surveillance Module

Unlike the standard post-event reporting process, the Patient Safety Monitor Suite: Surveillance Module is a trigger-based surveillance system, enabled by the unique industry-first technological capabilities of the Health Catalyst Data Operating System platform, including predictive analytic models and AI. Additionally, once listed, the Health Catalyst PSO will create a secure and safe environment where clients can collect and analyze patient safety events to learn and improve, free from fear of litigation. Coupled with patient safety services, an organization’s active all-cause harm patient safety system is fully enabled to deliver measurable and meaningful improvements.

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Five Deming Principles That Help Healthcare Process Improvement

Dr. John Haughom explains 5 key Deming processes that can be applied to healthcare process improvement. These include 1) quality improvement as the science of process management, 2) if you cannot measure it, you cannot improve it, 3) managed care means managing the processes of care (not managing physicians and nurses), 4) the importance of the right data in the right format at the right time in the right hands, and 5) engaging the “smart cogs” of healthcare.

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Six Challenges to Becoming a Data-Driven Payer Organization

As healthcare transitions from fee-for-service to value-based payment, payer organizations are increasingly looking to population health management strategies to help them lower costs. To manage individuals within their populations, payers must become data driven and establish the technical infrastructure to support expanding access to and reliance on data from across the continuum of care. To fully leverage the breadth and depth of data that an effective health management strategy requires, payers must address six key challenges of becoming data driven:

  1. Data availability.
  2. Data access.
  3. Data aggregation.
  4. Data analysis.
  5. Data adoption.
  6. Data application.

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Database vs Data Warehouse: A Comparative Review

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.

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Precision Medicine: Four Trends Make It Possible

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.

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Linking Clinical and Financial Data: The Key to Real Quality and Cost Outcomes

Since accountable care took the healthcare industry by a storm in 2010, health systems have had to move from their predictable revenue streams based on volume to a model that includes quality measures. While the switch will ultimately improve both quality and cost outcomes, health systems now need the capability of tracking and analyzing the data from both clinical and financial systems. A late-binding enterprise data warehouse provides the flexible architecture that makes it possible to liberate both kinds of data to link it together to provide a full picture of trends and opportunities.

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Transforming Healthcare Analytics: Five Critical Steps

By committing to transforming healthcare analytics, organizations can eventually save hundreds of millions of dollars (depending on their size) and achieve comprehensive outcomes improvement. The transformation helps organizations achieve the analytics efficiency needed to navigate the complex healthcare landscape of technology, regulatory, and financial challenges and the challenges of value-based care. To achieve analytics transformation and ROI within a short timeframe, organizations can follow five phases to become data driven:

  1. Establish a data-driven culture.
  2. Acquire and access data.
  3. Establish data stewardship.
  4. Establish data quality.
  5. Spread data use.

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The Future of Healthcare AI: An Honest, Straightforward Q&A

Health Catalyst President of Technology, Dale Sanders, gives straightforward answers to tough questions about the future of AI in healthcare. He starts by debunking a common belief: We are awash in valuable data in healthcare as a consequence of EHR adoption. The truth involves a need for deeper data about a patient.

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