[{"innerBlocks":[],"name":"core\/freeform","postId":1169,"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":"Faced with the reality that 80 percent of healthcare data is unstructured, the University of Pittsburgh Medical Center is investing in natural language processing to leverage this untapped resource.

UPMC Enterprises, the venture arm of the Pittsburgh-based health system, is focused on solving problems in healthcare and driving innovation by harnessing big data.

The organization sees NLP technology as having the potential for making the abundance of unstructured data in healthcare accessible and actionable.

At the epicenter of NLP activities at UPMC Enterprises is Rebecca Jacobson, MD, vice president of analytics, who spent 16 years as a professor of biomedical informatics at the University of Pittsburgh before joining UPMC.

\u201cThere are different areas of NLP, but the one we primarily work in is information extraction,\u201d <\/i>says Jacobson. \u201cAt least three of our portfolio companies that we work with closely are using natural language processing, including medCPU, which is a company that is doing real-time clinical decision support using NLP.\u201d

In 2016, UPMC took majority ownership in New York-based medCPU, whose Context Engine is touted by the vendor as solving the \u201ctraditional NLP accuracy limitation\u201d by providing \u201c95-plus percent accuracy of content extracted from free text\u2014recovering 30 percent to 90 percent of information not retrievable by other systems.\u201d

In addition, according to the company, its medCPU Reader \u201caccurately and continuously acquires all information including free-text notes, dictations, and structured documentation from electronic health records and ancillary systems without the need for high-level interfaces or integration on the client\u2019s part.\u201d

\u201cThey have an innovative model where they are able to see what is happening on a clinician\u2019s terminal as they are typing and ordering things\u2014without directly integrating into the EHR,\u201d adds Jacobson, who notes that medCPU\u2019s clinical decision support tools take the data entered by clinicians in real time, analyzes it and provides point-of-care alerts, as well as shares best practice recommendations.

Also See<\/b>: UPMC files federal trademark registration for cloud solution<\/a>

San Mateo, Calif.-based Health Fidelity, another of UPMC Enterprises\u2019 portfolio companies, is focused on clinical NLP technology.

\u201cThat is a company that\u2019s doing risk adjustment for healthcare payers,\u201d comments Jacobson. \u201cSpecifically, they use NLP to make (Hierarchical Condition Categories) coding much more accurate.\u201d

According to Health Fidelity, it offers the \u201conly solution on the market that utilizes NLP to extract valuable insights from medical charts, changing the way risk is identified, quantified and managed\u201d at health plans.

Before UPMC became an investor in Health Fidelity, UPMC\u2019s Health Plan tested the NLP approach to optimizing risk adjustment on the payer side and in the process increased coder productivity and added about $200 million in revenue.

Last year, UPMC had selected Health Fidelity\u2019s HF360 Provider, which it contends is the industry\u2019s first NLP-powered, EHR-integrated physician workflow solution for risk and quality gap closure. UPMC is using HF360 to standardize the risk capture process across all populations and increase the efficiency of that process through targeted patient interventions.

When it comes to technology that has not yet been commercialized, Jacobson points to an ongoing project between UPMC Enterprises and UPMC\u2019s Wolff Center designed to create a new NLP-based system for rapidly abstracting clinical quality metrics.

\u201cThere are so many different quality programs on the payer and provider sides that are difficult to compute, are costly and not so accurate,\u201d <\/i>contends Jacobson. \u201cYou have to do them manually using human abstractors. But, over the last year and a half, we\u2019ve been developing technology to be able to do that abstraction in a partially automated way that makes it much faster. Now, we\u2019re looking to take some parts of the process and make it automatic so we can see things in the data earlier.\u201d

Another area that Jacobson sees as potentially benefitting from NLP is the identification of social determinants of health within EHRs.

\u201cIt\u2019s been argued that 60 percent to 70 percent of health outcomes are driven by factors such as your social support network, access to stable housing, food and transportation, and the degree to which you are exposed to violence or substance abuse,\u201d she says. \u201cBut this information is hardly ever captured by EHRs within explicitly structured fields. Identifying and extracting this information from the clinical notes could help to improve predictive analytics and eventually help us to intervene more effectively.\u201d","saveContent":"Faced with the reality that 80 percent of healthcare data is unstructured, the University of Pittsburgh Medical Center is investing in natural language processing to leverage this untapped resource.

UPMC Enterprises, the venture arm of the Pittsburgh-based health system, is focused on solving problems in healthcare and driving innovation by harnessing big data.

The organization sees NLP technology as having the potential for making the abundance of unstructured data in healthcare accessible and actionable.

At the epicenter of NLP activities at UPMC Enterprises is Rebecca Jacobson, MD, vice president of analytics, who spent 16 years as a professor of biomedical informatics at the University of Pittsburgh before joining UPMC.

\u201cThere are different areas of NLP, but the one we primarily work in is information extraction,\u201d <\/i>says Jacobson. \u201cAt least three of our portfolio companies that we work with closely are using natural language processing, including medCPU, which is a company that is doing real-time clinical decision support using NLP.\u201d

In 2016, UPMC took majority ownership in New York-based medCPU, whose Context Engine is touted by the vendor as solving the \u201ctraditional NLP accuracy limitation\u201d by providing \u201c95-plus percent accuracy of content extracted from free text\u2014recovering 30 percent to 90 percent of information not retrievable by other systems.\u201d

In addition, according to the company, its medCPU Reader \u201caccurately and continuously acquires all information including free-text notes, dictations, and structured documentation from electronic health records and ancillary systems without the need for high-level interfaces or integration on the client\u2019s part.\u201d

\u201cThey have an innovative model where they are able to see what is happening on a clinician\u2019s terminal as they are typing and ordering things\u2014without directly integrating into the EHR,\u201d adds Jacobson, who notes that medCPU\u2019s clinical decision support tools take the data entered by clinicians in real time, analyzes it and provides point-of-care alerts, as well as shares best practice recommendations.

Also See<\/b>: UPMC files federal trademark registration for cloud solution<\/a>

San Mateo, Calif.-based Health Fidelity, another of UPMC Enterprises\u2019 portfolio companies, is focused on clinical NLP technology.

\u201cThat is a company that\u2019s doing risk adjustment for healthcare payers,\u201d comments Jacobson. \u201cSpecifically, they use NLP to make (Hierarchical Condition Categories) coding much more accurate.\u201d

According to Health Fidelity, it offers the \u201conly solution on the market that utilizes NLP to extract valuable insights from medical charts, changing the way risk is identified, quantified and managed\u201d at health plans.

Before UPMC became an investor in Health Fidelity, UPMC\u2019s Health Plan tested the NLP approach to optimizing risk adjustment on the payer side and in the process increased coder productivity and added about $200 million in revenue.

Last year, UPMC had selected Health Fidelity\u2019s HF360 Provider, which it contends is the industry\u2019s first NLP-powered, EHR-integrated physician workflow solution for risk and quality gap closure. UPMC is using HF360 to standardize the risk capture process across all populations and increase the efficiency of that process through targeted patient interventions.

When it comes to technology that has not yet been commercialized, Jacobson points to an ongoing project between UPMC Enterprises and UPMC\u2019s Wolff Center designed to create a new NLP-based system for rapidly abstracting clinical quality metrics.

\u201cThere are so many different quality programs on the payer and provider sides that are difficult to compute, are costly and not so accurate,\u201d <\/i>contends Jacobson. \u201cYou have to do them manually using human abstractors. But, over the last year and a half, we\u2019ve been developing technology to be able to do that abstraction in a partially automated way that makes it much faster. Now, we\u2019re looking to take some parts of the process and make it automatic so we can see things in the data earlier.\u201d

Another area that Jacobson sees as potentially benefitting from NLP is the identification of social determinants of health within EHRs.

\u201cIt\u2019s been argued that 60 percent to 70 percent of health outcomes are driven by factors such as your social support network, access to stable housing, food and transportation, and the degree to which you are exposed to violence or substance abuse,\u201d she says. \u201cBut this information is hardly ever captured by EHRs within explicitly structured fields. Identifying and extracting this information from the clinical notes could help to improve predictive analytics and eventually help us to intervene more effectively.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

UPMC turns to NLP to make sense of unstructured data

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