[{"innerBlocks":[],"name":"core\/freeform","postId":1193,"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":"The University of Pittsburgh Medical Center\u2019s clinical analytics team has leveraged machine learning to develop an algorithm that rates hospital patients for their risks of being readmitted.

Specifically, the ML algorithm identifies patients at highest risk of re-hospitalization within seven and 30 days of discharge. To date, re-hospitalizations have been reduced by about 50 percent at UPMC Presbyterian hospital.

\u201cRight now, the main area of focus is on seven days,\u201d says Oscar Marroquin, MD, chief clinical analytics officer, UPMC Health Services Division. \u201cThe models to predict seven and 30 days are almost identical. The only thing that changes is the statistical weights of each one of the co-variants, which are a little bit different. They are statistically evident but not clinically important.\u201d

Electronic health record data, gleaned from UPMC\u2019s Cerner (inpatient), Epic (ambulatory) and other EHR systems, is analyzed and fed to a dashboard integrated into the clinical workflow that breaks down the findings, helping clinicians to visualize the insights and make it actionable.

The dashboard, which tracks currently hospitalized patients so that UPMC can identify those at high risk of re-hospitalization, allows clinicians to look at the entire patient population by hospital, unit, or condition. According to Marroquin, the dashboard's geo-coding plots where patients live so that the health system can arrange the proper post-hospitalization follow up, as well as track which other hospitals are sending patients to UPMC's facilities.

\u201cWe can use our data to figure out which actions are associated with better outcomes,\u201d adds Marroquin, who notes that the ML model was piloted last year at UPMC Presbyterian hospital in one of its cardiology units.

The results of the model, based on an analysis of 1 million discharges, are now being used by about 17 of 40 hospitals at UPMC\u2014the nation\u2019s largest non-profit academic health system\u2014to better manage care for those patients deemed at risk by providing interventions that prevent hospital readmissions.

\u201cOur charge is to use the data in our EHRs, analyze it and derive insights,\u201d notes Marroquin. \u201cEvery large healthcare system claims to have a lot of data and analytical capabilities. What\u2019s different about us\u2014and perhaps a few other systems\u2014is that unlike the majority who have built infrastructure mostly for research purposes, we have made the investment to have a team dedicated to better deliver care for our patients right now.\u201d

Also See<\/b>: Decision support tool in EHR helps detect acute kidney injury<\/a>

Marroquin, who is a practicing cardiologist, leads UPMC\u2019s 15-member clinical analytics team, which applies different analytical techniques to large data sets to develop predictive and prescriptive models of healthcare delivery that result in improved health outcomes.

\u201cWe\u2019re less concerned with the use of a specific analytic technique\u2014we use different AI-based techniques to try and figure out the best care using patient data,\u201d adds Marroquin. \u201cWe take a pragmatic approach and figure out how to get clinicians answers, regardless of whether we use traditional statistical techniques, tree based models or neural networks.\u201d

According to Marroquin, UPMC is reaping the benefits of a five-year, $100 million investment the health system made starting in late 2012 in the area of enterprise analytics with the creation of a best-in-class data warehouse, bringing together clinical, financial, administrative, genomic and other information that was previously difficult to integrate and analyze.

\u201cWhat differentiates us is that we are an integrated delivery system,\u201d says Ed McCallister, UPMC\u2019s chief information officer. \u201cThere\u2019s an arm of UPMC\u2014the insurance services division and health plan\u2014that have been very deep in data warehousing and analytics.\u201d

Currently, UPMC manages 18 petabytes of online data storage, which comes from sources of information across the 40-hospital enterprise, UPMC Health Plan and outside entities, including labs and pharmacies, according to Chris Carmody, senior vice president of the Information Services Division.

\u201cThe genius of Oscar (Marroquin) is taking very practical approaches to the data,\u201d says Carmody. \u201cHe\u2019s not creating one algorithm to solve one problem. He\u2019s creating a platform to solve many problems. The insights that he and his team are garnering are simply awesome.\u201d

\u201cMy clinical analytics team are the data consumption folks,\u201d concludes Marroquin. \u201cBefore our team came about in 2015, it was very difficult to answer clinical questions about patient care longitudinally.\u201d

According to McCallister, UPMC has just started to scratch the surface when it comes tapping the value of clinical analytics. \u201cWe now have a holistic view because we have the holistic dataset,\u201d he adds.","saveContent":"The University of Pittsburgh Medical Center\u2019s clinical analytics team has leveraged machine learning to develop an algorithm that rates hospital patients for their risks of being readmitted.

Specifically, the ML algorithm identifies patients at highest risk of re-hospitalization within seven and 30 days of discharge. To date, re-hospitalizations have been reduced by about 50 percent at UPMC Presbyterian hospital.

\u201cRight now, the main area of focus is on seven days,\u201d says Oscar Marroquin, MD, chief clinical analytics officer, UPMC Health Services Division. \u201cThe models to predict seven and 30 days are almost identical. The only thing that changes is the statistical weights of each one of the co-variants, which are a little bit different. They are statistically evident but not clinically important.\u201d

Electronic health record data, gleaned from UPMC\u2019s Cerner (inpatient), Epic (ambulatory) and other EHR systems, is analyzed and fed to a dashboard integrated into the clinical workflow that breaks down the findings, helping clinicians to visualize the insights and make it actionable.

The dashboard, which tracks currently hospitalized patients so that UPMC can identify those at high risk of re-hospitalization, allows clinicians to look at the entire patient population by hospital, unit, or condition. According to Marroquin, the dashboard's geo-coding plots where patients live so that the health system can arrange the proper post-hospitalization follow up, as well as track which other hospitals are sending patients to UPMC's facilities.

\u201cWe can use our data to figure out which actions are associated with better outcomes,\u201d adds Marroquin, who notes that the ML model was piloted last year at UPMC Presbyterian hospital in one of its cardiology units.

The results of the model, based on an analysis of 1 million discharges, are now being used by about 17 of 40 hospitals at UPMC\u2014the nation\u2019s largest non-profit academic health system\u2014to better manage care for those patients deemed at risk by providing interventions that prevent hospital readmissions.

\u201cOur charge is to use the data in our EHRs, analyze it and derive insights,\u201d notes Marroquin. \u201cEvery large healthcare system claims to have a lot of data and analytical capabilities. What\u2019s different about us\u2014and perhaps a few other systems\u2014is that unlike the majority who have built infrastructure mostly for research purposes, we have made the investment to have a team dedicated to better deliver care for our patients right now.\u201d

Also See<\/b>: Decision support tool in EHR helps detect acute kidney injury<\/a>

Marroquin, who is a practicing cardiologist, leads UPMC\u2019s 15-member clinical analytics team, which applies different analytical techniques to large data sets to develop predictive and prescriptive models of healthcare delivery that result in improved health outcomes.

\u201cWe\u2019re less concerned with the use of a specific analytic technique\u2014we use different AI-based techniques to try and figure out the best care using patient data,\u201d adds Marroquin. \u201cWe take a pragmatic approach and figure out how to get clinicians answers, regardless of whether we use traditional statistical techniques, tree based models or neural networks.\u201d

According to Marroquin, UPMC is reaping the benefits of a five-year, $100 million investment the health system made starting in late 2012 in the area of enterprise analytics with the creation of a best-in-class data warehouse, bringing together clinical, financial, administrative, genomic and other information that was previously difficult to integrate and analyze.

\u201cWhat differentiates us is that we are an integrated delivery system,\u201d says Ed McCallister, UPMC\u2019s chief information officer. \u201cThere\u2019s an arm of UPMC\u2014the insurance services division and health plan\u2014that have been very deep in data warehousing and analytics.\u201d

Currently, UPMC manages 18 petabytes of online data storage, which comes from sources of information across the 40-hospital enterprise, UPMC Health Plan and outside entities, including labs and pharmacies, according to Chris Carmody, senior vice president of the Information Services Division.

\u201cThe genius of Oscar (Marroquin) is taking very practical approaches to the data,\u201d says Carmody. \u201cHe\u2019s not creating one algorithm to solve one problem. He\u2019s creating a platform to solve many problems. The insights that he and his team are garnering are simply awesome.\u201d

\u201cMy clinical analytics team are the data consumption folks,\u201d concludes Marroquin. \u201cBefore our team came about in 2015, it was very difficult to answer clinical questions about patient care longitudinally.\u201d

According to McCallister, UPMC has just started to scratch the surface when it comes tapping the value of clinical analytics. \u201cWe now have a holistic view because we have the holistic dataset,\u201d he adds.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

UPMC cuts hospital readmission rates with ML algorithm

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