The Impact of Inaccurate Patient Data Analytics

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The Cost of Poor Quality

By http://www.MCOL.com

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The-Psychology-of-Analytics-When-Working-is-Not-Working

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4 Responses

  1. Big-data initiatives have the potential to transform health care

    http://www.mckinsey.com/insights/health_systems_and_services/the_big-data_revolution_in_us_health_care

    Stakeholders that are committed to innovation, willing to build their capabilities, and open to a new view of value will likely be the first to reap the rewards of big data and help patients achieve better outcomes.

    Donald

    Like

  2. Mainframe systems and analytics

    Most applicable to large hospitals, such a configuration is highly centralized. A large and powerful computer performs basically all the information processing for the institution and connects to multiple terminals that communicate with the mainframe to display the information at the user sites.

    Hospital IT departments usually use in-house programmers to modify the core operating systems or applications programs such as billing and scheduling programs.

    Brent Metfessl MD MS

    Like

  3. The Healthcare Data Sharing Conundrum

    An essay by DAN HOUSMAN

    People are more likely to avoid loss than to seek gains. HIPAA creates a framework where it rewards risk adverse behavior for data sharing even when data sharing would ultimately be beneficial to the enterprise, the mission, and the patients. This is a general issue at the heart of making progress in healthcare regarding data sharing and interoperability. I have some new thoughts on how to bridge this divide.

    Recently I read the book ‘Thinking, Fast and Slow’ by the Nobel Prize winning economist Daniel Kahneman PhD. This book discusses the concept of Prospect Theory.

    In reading through it I could see a hint of why our industry has so much trouble trying to share medical records and in general has trouble sharing almost anything among trading partners and competitors. If you haven’t read about Prospect Theory, the following tests provide some of the basics into how humans make decisions about risk.

    Decision 1: Which do you choose? Get $900 for sure OR 90% chance to get $1,000

    Decision 2: Which do you choose? Lose $900 for sure OR 90% chance to lose $1,000

    The common answer to #1 is to take the $900. The common answer to #2 is to take the 90% chance to avoid the loss.

    As a result, we take risks to avoid danger but avoid risks when we see certain rewards. This behavior is relevant to data sharing and access to PHI and can be instructive on how people will approach risk.

    http://thehealthcareblog.com/blog/2016/03/28/the-healthcare-data-sharing-conundrum/

    Caleb

    Like

  4. Hardest part of population health and precision medicine?

    First-moving hospitals are thinking about how to transform themselves from data-driven to information-driven organizations, able to offer drastically improved patient experience akin to Amazon and Google.

    But it’s not easy.

    http://www.govhealthit.com/news/big-data-hardest-part-population-health-and-precision-medicine?topic=34&mkt_tok=3RkMMJWWfF9wsRonuaXPe%2B%2FhmjTEU5z16u8sUKK%2Fh4kz2EFye%2BLIHETpodcMTcZnMbrYDBceEJhqyQJxPr3MLtINwNlqRhPrCg%3D%3D

    Simon

    Like

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