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HealthCatalyst Recommends

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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Bundled Payment Changes: Learn What’s New and How to Succeed

In January, CMS announced the Bundled Payment for Care Improvement Advanced “BPCI Advanced” program, initiating renewed interest in a total cost of care payment model for specific episodes of care. Regardless of your organization’s current decision to participate, it’s important to understand how bundled payment programs have the ability to significantly decrease your internal costs, broaden your revenue opportunities, and improve patient outcomes across specific populations.

 

The Center for Medicare and Medicaid Innovation’s newest iteration of bundled payments provides another tightly-defined program that allows organizations to scale Population Health Management. Best practice suggests that tactical interventions to assess clinical variation, implement strategic care redesign programs, and to adjust care management-facilitated patient stratification models are important to be successful with bundled payments – so knowing how to implement them is crucial.

 

One organization’s savings is another’s income and without making overhead allocation changes, bundled payments may reduce revenue that has been critically important to maintain hospital profitability.

 

Join this webinar to learn:

  • What i­s new with bundled payments
  • The ramifications bundles can have across organizations
  • Leveraging data and strategic analysis to identify opportunities for bundled payment success
  • Operationalizing successful care program tactics to be successful in bundled payment contracts
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Part 2 — 20 Years in Healthcare Analytics & Data Warehousing: What did we learn? What’s the future?

In Part 2 we will discuss technology lessons learned over 20 years. If you missed Part 1 that discussed people and processes lessons learned, you can view the Part 1 On-Demand.

The enterprise data warehouse (EDW) at Intermountain Healthcare went live in 1998. The EDW at Northwestern Medicine went live in 2006. Dale Sanders was the chief architect and strategist for both. The business inspiration behind Health Catalyst was, in essence, to create the commercial availability of the technology, analytics, and data utilization skills associated with these systems at Intermountain and Northwestern. Lee Pierce assumed leadership of the Intermountain EDW in 2008. Andrew Winter assumed leadership of the Northwestern EDW in 2009, and transitioned leadership of the EDW to Shakeeb Akhter in 2016. This webinar is a fireside chat among friends and colleagues as they look back across their healthcare IT decisions to answer these questions:

  • What did we do right and what did we do wrong?
  • What advice do we have for others in this emerging era of Big Data?
  • What does the future of analytics and Big Data look like in healthcare?

Please join Dale, Lee, and Shakeeb for an insightful and spirited conversation. You’ll leave better understanding the following:

  • Why data modeling has been overrated and how it is changing
  • The arrival of hybrid architecture data lakes
  • The potential to process text, images, and discrete data together
  • Whether the EHR vendors can deliver against future needs
  • How to optimally use data governance
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Integrated Healthcare Data Quickly Enables Adaptive, Purpose Driven Analytics

Changes in payment models are putting pressure on clinicians to close gaps in care. To do this, they need instant access to actionable information about their patients and their own performance. However, many electronic health records and business intelligence systems are still grappling with how to deliver the insights necessary to revolutionize the way providers work.

Orlando Health, a Florida-based, not-for-profit health system made up of eight hospitals and 50 clinics, found its enterprise data model difficult to scale, making it challenging to gain insights from its healthcare data. Building upon its analytics platform, Orlando Health recognized the value of immediate access to adaptive, integrated healthcare data that could be rapidly deployed in consumable, actionable visualizations to address a wide spectrum of business needs and use cases, and embraced a next-generation data model.

Results:

  • Ten data sources loaded into the platform in under six months.
  • As little as one week to deploy dashboards, visualizations, and analytic insights.
  • 95 percent reduction in work hours required to incorporate system enhancements.
  • 88 days saved in the amount of time required to implement system enhancements.
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Improving Transitions of Care for Patients with Pneumonia

Nationally, the readmission rate for patients over age 65 with pneumonia is 15.8 percent. Though not all hospital readmissions are preventable, high readmission rates may reflect performance on care quality, effectiveness of discharge instructions, and smooth transitioning of patients to their home or other setting.

Piedmont Healthcare wanted to standardize pneumonia care across its entire system but lacked the data it needed to identify patients who could benefit from additional transition support. Piedmont convened a care management steering committee and deployed analytics tools to generate actionable data for appropriate and effective transitions of care for its Medicare patients with pneumonia. In less than one year, it reduced its readmission rate for patients with pneumonia by 26 percent.

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