[{"innerBlocks":[],"name":"core\/freeform","postId":1412,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Health information technology is enabling healthcare organizations to analyze the data they generate during the process of taking care of patients to create new \u201clocal\u201d evidence to improve outcomes.

Combining this locally generated evidence with evidence from peer-reviewed medical literature is critical to creating a learning health system that can continuously study and improve itself while rapidly integrating this knowledge into best practice models, moving research out of journals and into the clinical environment.

So says Charles Friedman, chair of the University of Michigan Medical School\u2019s Department of Learning Health Sciences and the Josiah Macy Jr. Professor of Medical Education.

\u201cWe need to begin to think about what evidence means in an age where academic and large health centers, in particular, are routinely collecting data in electronic form as a byproduct of their patient care,\u201d contends Friedman. \u201cThe idea that any practitioner might be able to keep up with the latest knowledge in his or her field by reading journals is simply not realistic. Too much of it is changing too quickly.\u201d

\u201cThe value of big data is to generate big knowledge,\u201d he observes. \u201cThe power of big data is to provide better models. If all those models do is sit in journal articles, no one\u2019s going to be any healthier.\u201d

Writing in an article<\/a> in the December issue of the Journal of General Internal Medicine<\/i>, Friedman and his co-authors note that while evidence has traditionally been represented in human readable form\u2014in text, tables, and figures\u2014and published in books and journals, knowledge in this format cannot support its rapid and routine translation into practice. They contend that evidence of all types must be available in standardized, computable formats to be leveraged for the curation, representation, dissemination and application of knowledge.

Towards that end, the University of Michigan Medical School\u2019s Department of Learning Health Sciences has created an open computer platform called the Knowledge Grid<\/a>, which is designed to make medical best practice knowledge computable and readily available for widespread use in digital libraries that generate patient-specific advice.

Also See<\/b>: Harvard offers digital repository of medical evidence to providers<\/a>

\u201cA lot of scientific studies result in some kind of model\u2014an equation, a guideline, a statistical relationship, or an algorithm\u2014all of these kinds of models can be expressed as computer code that can automatically generate advice about a specific patient,\u201d according to Friedman, who notes that when both \u201clocal\u201d models and published models are available in computable forms, it is suddenly possible to generate advice that reflects both kinds of sources.

\u201cTo achieve optimal use of knowledge in support of care decisions and choices, the main theme of the paper is how to combine the two kinds of knowledge,\u201d says Friedman. \u201cWe need both but we need a way to put them together.\u201d

As part of a preliminary initiative, Friedman reveals that a Knowledge Grid activator\u2014a system for deploying Knowledge Objects in a computable form to process health data\u2014has been installed in the Michigan Health Information Network, which facilitates information sharing among the state\u2019s HIEs and providers, so that as clinical data moves through MiHIN the network has the capability to apply Knowledge Objects to the data and draw conclusions about individual patients.

\u201cWith all this information and data available to us, the field of biomedicine and particularly healthcare is in a new era,\u201d adds Friedman. \u201cThere are knowns that we don\u2019t know because we haven\u2019t done the analyses yet.\u201d

At the same time, he emphasizes that the privacy of patients is paramount and that \u201cthere are a whole set of ethical, legal, and social issues that bear on these processes that must be taken into account\u2014this is not just a technical exercise.\u201d","saveContent":"Health information technology is enabling healthcare organizations to analyze the data they generate during the process of taking care of patients to create new \u201clocal\u201d evidence to improve outcomes.

Combining this locally generated evidence with evidence from peer-reviewed medical literature is critical to creating a learning health system that can continuously study and improve itself while rapidly integrating this knowledge into best practice models, moving research out of journals and into the clinical environment.

So says Charles Friedman, chair of the University of Michigan Medical School\u2019s Department of Learning Health Sciences and the Josiah Macy Jr. Professor of Medical Education.

\u201cWe need to begin to think about what evidence means in an age where academic and large health centers, in particular, are routinely collecting data in electronic form as a byproduct of their patient care,\u201d contends Friedman. \u201cThe idea that any practitioner might be able to keep up with the latest knowledge in his or her field by reading journals is simply not realistic. Too much of it is changing too quickly.\u201d

\u201cThe value of big data is to generate big knowledge,\u201d he observes. \u201cThe power of big data is to provide better models. If all those models do is sit in journal articles, no one\u2019s going to be any healthier.\u201d

Writing in an article<\/a> in the December issue of the Journal of General Internal Medicine<\/i>, Friedman and his co-authors note that while evidence has traditionally been represented in human readable form\u2014in text, tables, and figures\u2014and published in books and journals, knowledge in this format cannot support its rapid and routine translation into practice. They contend that evidence of all types must be available in standardized, computable formats to be leveraged for the curation, representation, dissemination and application of knowledge.

Towards that end, the University of Michigan Medical School\u2019s Department of Learning Health Sciences has created an open computer platform called the Knowledge Grid<\/a>, which is designed to make medical best practice knowledge computable and readily available for widespread use in digital libraries that generate patient-specific advice.

Also See<\/b>: Harvard offers digital repository of medical evidence to providers<\/a>

\u201cA lot of scientific studies result in some kind of model\u2014an equation, a guideline, a statistical relationship, or an algorithm\u2014all of these kinds of models can be expressed as computer code that can automatically generate advice about a specific patient,\u201d according to Friedman, who notes that when both \u201clocal\u201d models and published models are available in computable forms, it is suddenly possible to generate advice that reflects both kinds of sources.

\u201cTo achieve optimal use of knowledge in support of care decisions and choices, the main theme of the paper is how to combine the two kinds of knowledge,\u201d says Friedman. \u201cWe need both but we need a way to put them together.\u201d

As part of a preliminary initiative, Friedman reveals that a Knowledge Grid activator\u2014a system for deploying Knowledge Objects in a computable form to process health data\u2014has been installed in the Michigan Health Information Network, which facilitates information sharing among the state\u2019s HIEs and providers, so that as clinical data moves through MiHIN the network has the capability to apply Knowledge Objects to the data and draw conclusions about individual patients.

\u201cWith all this information and data available to us, the field of biomedicine and particularly healthcare is in a new era,\u201d adds Friedman. \u201cThere are knowns that we don\u2019t know because we haven\u2019t done the analyses yet.\u201d

At the same time, he emphasizes that the privacy of patients is paramount and that \u201cthere are a whole set of ethical, legal, and social issues that bear on these processes that must be taken into account\u2014this is not just a technical exercise.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

Med school platform puts knowledge in computable format

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