[{"innerBlocks":[],"name":"core\/freeform","postId":2511,"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":"Atrius Health, a large multi-specialty ambulatory group practice serving eastern and central Massachusetts, is leveraging natural language processing to tap into a treasure trove of unstructured patient data used to support its value-based care initiatives.

By implementing an NLP platform from Linguamatics, Atrius is able to mine critical clinical information hidden within unstructured text to improve Medicare Advantage reimbursement, streamline accountable care organization reporting and close gaps in care delivery.

\u201cThe majority of clinically useful information is unstructured text that includes physician notes and radiology reports, which impacts our patients and our practice,\u201d says Craig Monsen, MD, Atrius Health\u2019s medical director of analytics. \u201cI\u2019d estimate that probably 50 percent to 80 percent is unstructured data.\u201d

According to Monsen, there are several of the healthcare organization\u2019s workflows within which NLP is being used to identify and extract critical clinical data that exists as unstructured text.

\u201cWe found for our heart failure patients that their heart pump function is not captured as structured data,\u201d he says. \u201cWe\u2019ve integrated natural language processing into our quality metrics reporting pipeline so that we can figure out who are these high-risk heart failure patients.\u201d

Monsen adds that if an organization is trying to find that kind of information\u2014such as the ejection fraction, which measures how well a heart is pumping\u2014on a population level, they will \u201cneed to hire a small army\u201d to manually extract it. However, he says the NLP tool enables Atrius to automate some of that process.

When it comes to closing care gaps, Atrius is also using the platform to identify patients with difficult-to-track conditions, such as pulmonary nodules, which are small growths in the lungs. \u201cMost often, pulmonary nodules are benign, but in some cases, they\u2019re an early sign of lung cancer,\u201d according to Monsen. \u201cFrequently, that information is only captured in free text, so that creates a situation where serious concerns can fall through the cracks.\u201d

However, Atrius has invested in a series of \u201cpatient safety net\u201d initiatives for several types of cancer designed to improve outcomes by leveraging NLP. \u201cIn the case of lung cancer, for example, the technology helps us to identify pulmonary nodules from radiology reports, making sure that we have followed up with those patients and have carried out the necessary interventions,\u201d he notes.

Monsen also points out that NLP is ensuring the accuracy of clinical documentation workflows so that Atrius has sufficient resources to close gaps in care.

\u201cWe\u2019re about 900 physicians across 30 practice locations, and we are heavily committed to value-based care, with something like 75 percent of our revenue coming from risk-based contracts,\u201d says Monsen. \u201cFor us, it\u2019s not just upside risk, but it\u2019s also downside risk, so it\u2019s important to make sure that we are resourced to provide the care, quality and safety nets.\u201d

Physician burnout, a growing epidemic among provider organizations characterized by exhaustion, cynicism and feelings of ineffectiveness, has been linked to decreased quality of care and medical errors. However, Monsen believes that NLP will help Atrius alleviate the \u201cdata overload problem\u201d and reduce physician burnout among its ranks because of data-entry tasks.

\u201cThe answer to closing information gaps can\u2019t be to put everything into a structured field,\u201d he observes. \u201cClinicians are already spending half their time in front of the electronic health record. This time is better spent with patients and having conversations that will contribute to their care rather than documenting what\u2019s been done. NLP can help in the closure of information gaps without asking clinicians to make dramatic changes to their workflows.\u201d

Also See<\/b>: Atrius Health looks to analytics to reduce physician burnout<\/a>

Going forward, Monsen says Atrius is looking to expand the use of NLP to support other care initiatives, including identifying social determinants of health and behavioral health conditions. \u201cThere is strong evidence to suggest that those are perhaps some of the greatest contributors to patient wellness, or the lack thereof,\u201d he adds.","saveContent":"Atrius Health, a large multi-specialty ambulatory group practice serving eastern and central Massachusetts, is leveraging natural language processing to tap into a treasure trove of unstructured patient data used to support its value-based care initiatives.

By implementing an NLP platform from Linguamatics, Atrius is able to mine critical clinical information hidden within unstructured text to improve Medicare Advantage reimbursement, streamline accountable care organization reporting and close gaps in care delivery.

\u201cThe majority of clinically useful information is unstructured text that includes physician notes and radiology reports, which impacts our patients and our practice,\u201d says Craig Monsen, MD, Atrius Health\u2019s medical director of analytics. \u201cI\u2019d estimate that probably 50 percent to 80 percent is unstructured data.\u201d

According to Monsen, there are several of the healthcare organization\u2019s workflows within which NLP is being used to identify and extract critical clinical data that exists as unstructured text.

\u201cWe found for our heart failure patients that their heart pump function is not captured as structured data,\u201d he says. \u201cWe\u2019ve integrated natural language processing into our quality metrics reporting pipeline so that we can figure out who are these high-risk heart failure patients.\u201d

Monsen adds that if an organization is trying to find that kind of information\u2014such as the ejection fraction, which measures how well a heart is pumping\u2014on a population level, they will \u201cneed to hire a small army\u201d to manually extract it. However, he says the NLP tool enables Atrius to automate some of that process.

When it comes to closing care gaps, Atrius is also using the platform to identify patients with difficult-to-track conditions, such as pulmonary nodules, which are small growths in the lungs. \u201cMost often, pulmonary nodules are benign, but in some cases, they\u2019re an early sign of lung cancer,\u201d according to Monsen. \u201cFrequently, that information is only captured in free text, so that creates a situation where serious concerns can fall through the cracks.\u201d

However, Atrius has invested in a series of \u201cpatient safety net\u201d initiatives for several types of cancer designed to improve outcomes by leveraging NLP. \u201cIn the case of lung cancer, for example, the technology helps us to identify pulmonary nodules from radiology reports, making sure that we have followed up with those patients and have carried out the necessary interventions,\u201d he notes.

Monsen also points out that NLP is ensuring the accuracy of clinical documentation workflows so that Atrius has sufficient resources to close gaps in care.

\u201cWe\u2019re about 900 physicians across 30 practice locations, and we are heavily committed to value-based care, with something like 75 percent of our revenue coming from risk-based contracts,\u201d says Monsen. \u201cFor us, it\u2019s not just upside risk, but it\u2019s also downside risk, so it\u2019s important to make sure that we are resourced to provide the care, quality and safety nets.\u201d

Physician burnout, a growing epidemic among provider organizations characterized by exhaustion, cynicism and feelings of ineffectiveness, has been linked to decreased quality of care and medical errors. However, Monsen believes that NLP will help Atrius alleviate the \u201cdata overload problem\u201d and reduce physician burnout among its ranks because of data-entry tasks.

\u201cThe answer to closing information gaps can\u2019t be to put everything into a structured field,\u201d he observes. \u201cClinicians are already spending half their time in front of the electronic health record. This time is better spent with patients and having conversations that will contribute to their care rather than documenting what\u2019s been done. NLP can help in the closure of information gaps without asking clinicians to make dramatic changes to their workflows.\u201d

Also See<\/b>: Atrius Health looks to analytics to reduce physician burnout<\/a>

Going forward, Monsen says Atrius is looking to expand the use of NLP to support other care initiatives, including identifying social determinants of health and behavioral health conditions. \u201cThere is strong evidence to suggest that those are perhaps some of the greatest contributors to patient wellness, or the lack thereof,\u201d he adds.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

NLP enables Atrius Health to gain insights from unstructured data

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