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“ High Precision Analytics for Healthcare: Promises and Challenges” by Sriram Vishwanath

This talk by Sriram Vishwanath was a part of The Hive Annual Summit 2017. You can watch Vishwanath’s talk on YouTube HiveMumble channel.

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“ High Precision Analytics for Healthcare: Promises and Challenges” by Sriram Vishwanath

  1. 1. High Precision Analytics for Healthcare: Promises and Challenges Sriram Vishwanath Professor, UT Austin Cofounder, Accordion Health President, Brilliant.MD
  2. 2. Problems with Predictive Analytics Where Are My Actionable Insights? “… Software X is a black box. I put my data, and it gives me some sort of risk scores. I know that high risk scores are bad. So, what should I do next? …” “… I purchased Software Y, and it gives me a report that there have been thirty preventable readmissions in the last month. But I want to know what to do to prevent them in the future … “
  3. 3. Wait! All those people said that they do “predictive” analytics
  4. 4. A Good Approach • Population Health  Personalized Health • Identify High Risk Patients  Predict Change of Risk • I can Predict it all  Based on Measured Precision Key Insight Provider is as critical as patient in determining outcomes
  5. 5. The Importance of Right Methodology Claims Rx Labs EHR transform into tensors feature extraction apply algorithms (ML and traditional) model Input Actionable Insight Intervention GLM kNN RF *courtesy Accordion Health
  6. 6. Forecast the Future ● ●● ●●●● ● ● ●● ●● ●● ●● ● ● ●●● ● ●● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ● ●●●● ● ● ● ●● ● ● ●● ● ●●● ● ● ●●● ●●● ● ● ●●●● ● ● ● ●● ●● ●● ● ●●●●● ●●●● ●● ●●● ● ●● ●● ● ●● ● ●● ●● ● ●●● ● ● ●● ● ● ●● ● ●●●● ●● ● ●● ● ●● ●●● ●●●●●●●●● ●●● ● ● ●● ● ●●● ●● ●● ●●● ● ●● ● ● ●● ● ● ● ● ● ●●● ● ● ●●● ● ●● ● ●● ●● ●● ●● ●● ●●● ● ●● ● ● ● ●● ● ● ● ●● ●● ● ● ● ●● ●● ●● ● ● ●●● ●● ●● ● ●● ●● ● ● ● ●● ● ● 0 25 50 75 100 0 25 50 75 100 Days After Knee Replacement PatientID
  7. 7. Example – Joe S. • 69 y/o man with COPD & h/o acute exacerbations • Tend to occur annually with seasonal triggers • Also has DM, HTN which are relatively poorly- controlled • He does not always take his COPD meds • PCP: Dr. Alvarez (and other members of healthcare ecosystem) • Risk score: Medium
  8. 8. Example – Joe S. Joe had a COPD exacerbation last spring… So, it’s not surprising that he will likely have another exacerbation next spring Difficulty in Prediction : Easy Associated Costs: High Intervention: Medication Reminder Intervention: Home-visit Efficacy: Low Efficacy: High
  9. 9. Example – Linda R. • 76 y/o woman with h/o well-controlled Hypertension • Family h/o of CVD • Recently seen for palpitations, but otherwise asymptomatic • Mostly adherent to medication • PCP: Dr. Lin • Risk score: Low
  10. 10. Example – Linda R. Although palpitations are asymptomatic We predict severe cardiac dysrhythmia, like atrial fibrillation And the likelihood of a stroke is high Difficulty in Prediction : Hard Associated Costs: Extremely High Intervention: PCP-visit, additional medication prescribed Efficacy: High
  11. 11. Measured Precision *courtesy Accordion Health
  12. 12. Predicted Superutilizers Alice S. Bob W. Cindy N. Doug D. Eve A. Frank L. George B. Hank T. Ivana M. Jack K. Alice S. Cindy N. Keith L. Larry L. Mary W. Nancy S. Olivia Z. Patrick W. Quincy A. Robert S.
  13. 13. Case Study 13
  14. 14. BUNDLING: POST-ACUTE RISK PREDICTION Post Acute Pathways Discharge Date Day 0 CJR Period Day 90 Home Health SNF Inpatient Good Decision: Patient A (blue) placed in a Skilled Nursing Facility (SNF), then goes home. Bad Decision: Patient B (red) placed in (HHA) after discharge, resulting in readmission due to surgical complications. Patient A Patient B *courtesy Accordion Health
  15. 15. Post Discharge Facilities Determine Overall Costs *courtesy Accordion Health
  16. 16. Micro Targeting and Forecasting for Care Intervention *courtesy Accordion Health
  17. 17. Targeted  Predictive  Prescriptive Sriram Vishwanath sriram@utexas.edu

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