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HealthCatalyst Recommends

The Four Essential Zones of a Healthcare Data Lake

The role of a data lake in healthcare analytics is essential in that it creates broad data access and usability across the enterprise. It has symbiotic relationships with an enterprise data warehouse and a data operating system.

To avoid turning the data lake into a black lagoon, it should feature four specific zones that optimize the analytics experience for multiple user groups:

  1. Raw data zone.
  2. Refined data zone.
  3. Trusted data zone.
  4. Sandbox data zone.

Each zone is defined by the level of trust in the resident data, the data structure and future purpose, and the user type.

Understanding and creating zones in a data lake behooves leadership and management responsible for maximizing the return on this considerable investment of human, technical, and financial resources.

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Five Solutions to Controlling Healthcare’s Cost Problem

When expenses exceed revenue, business has a financial problem. In healthcare, the focus has been on revenue for so long, we’ve lost sight of runaway costs brought about by high labor and technology expenses, inefficient use of resources, and supply waste. Recognizing the cost problem is a big first step toward solving it.

Five expense-controlling strategies can play a significant role in returning healthcare systems to a stronger financial position:

  1. Refocus on labor management.
  2. Manage employed physicians.
  3. Change the patient encounter environment.
  4. Augment standard approaches with technology.
  5. Manage patient access and flow through the healthcare system.

With new, value-based payment structures, shrinking margins, and decreasing reimbursements, this insight offers some new ways to think about expense inefficiency and how to get costs under control.

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Unleashing Data: The Key To Driving Massive Improvements

Tom shares how investing in analytics training and infrastructure will help prepare for massive improvements in healthcare outcomes leading to sustained and distributed improvements throughout entire organizations.

Attendees will learn:

    1. The key team roles and skillsets required for driving and sustaining massive improvements.
    2. How to assess improvement opportunities from an effort and value perspective.
    3. The most common mistakes in leveraging analytics and how to avoid them.
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Enhanced Recovery Program Improves Elective Colorectal Surgical Outcomes

Contemporary colorectal surgery is often associated with long LOS, high costs, and surgical site infections (SSI) approaching 20 percent. Much of the LOS variation is not attributable to patient illness or complications, but most likely represents differences in practice style. Successfully reducing SSI requires a multimodal strategy under the supervision of numerous providers with high compliance across the spectrum.

Allina Health was using established, evidence-based clinical guidelines, yet clinical variation remained high across pre-arrival, preoperative, intraoperative, and postoperative care areas, leading to substantial variation in LOS, cost of care, and the patient experience. To ensure greater consistency, Allina Health developed an enhanced recovery program (ERP) for patients undergoing elective colorectal surgery, which built standard protocols into the EHR to address elements of care from pre-arrival through post-discharge.

To facilitate the program and monitor performance, Allina Health developed an ERP analytics application with an administrative dashboard to easily visualize first-year results:

  • 78 percent relative reduction in elective colorectal SSI rate.
  • 19 percent relative reduction in LOS for patients with elective colorectal surgery.
  • 82.4 percent utilization of preoperative and postoperative order sets, increasing the consistency of care and reducing unwarranted variation.
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Opportunity Analysis Permits Successful Execution of At-Risk Contracts

Growth in the government payer mix and an increased cost burden to the commercial population, decreases in the private payer population, and programs like the Medicare Shared Services Program, have caused joint ventures, partnerships, and co-branding efforts, better known as at-risk contracts, between payers and providers to increase.

Allina Health has three Integrated Health Partnership (IHP) contracts, an accountable care model that incentivizes healthcare providers to take on more financial accountability for the cost of care for Medicaid patients, which cover approximately 90,000 members. To achieve success in its IHP contracts, and avoid losses, Allina Health needed to reduce healthcare costs while improving patient outcomes and experience.

Allina Health has integrated several data sources, including claims and developed the infrastructure required to perform opportunity analysis. Using data and analytics for opportunity analysis has given Allina Health insight into its IHP patient population, supporting the development of interventions to decrease the total cost of care and improve outcomes.

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