How much data is enough? Data prioritisation using analytics

Transcription

1 How much data is enough? Data prioritisation using analytics

2 ABOUT BIZDATA Established in 2005 Experts in Data Management and Business Intelligence Have delivered over 200 projects Provide services to health providers covering: Proactive Data Quality Management Compliance reporting and Performance Management Predictive Analytics Microsoft Gold Specialisations in: Business Intelligence Portals & Collaboration Provide a Data Governance software toolkit

3 ABOUT PREDIXION U.S. based software company with Development offices in Redmond, Washington. Key Microsoft architects and engineers form Predixion s development team and developed a disruptive predictive analytics platform on the Microsoft BI stack. Provides a predictive analytics workbench that is focused on ease of deployment, and accessibility by a wide audience Track record delivering solutions to the Health Care industry, and have developed IP that assists health providers to identify preventable readmissions

4 Competing Interests Researcher We need more data Practitioner We need more time with patients

5 What if You could use history to lower risks in the future? You could make the result of data research actionable for each patient? You could prioritise which data needed to be collected throughout the lifecycle of a patient s stay?

6 Retrospective vs Prospective How did we do? How do we do better?

7 Context Hospital readmission rates proposed as an indicator of care quality US mandate to hospitals Today, one in five Medicare inpatients is readmitted within 30 days. The Centers for Medicare & Medicaid Services (CMS) considers 40%-75% of these readmissions to be preventable. In October 2012, CMS will begin to impose financial penalties on hospitals with higher-than-accepted readmission rates for certain conditions including Heart Failure, Acute Myocardial Infarction and Pneumonia. Other payers (health insurance companies) will likely follow. UK: readmission fines could cost UK hospitals up to 1.5bn

8 Predictive Analytics in action Historical Data, Clinical Variables, Social Factors Today Readmission Study Cohort Test Data Inpatient Data Probability of Readmission Alt Care Strategy Intervention: -Cost -Efficacy Training Data Readmission Model

9 Population Correct % True Lift Proving the model Using a gains/lift chart Predicted Column 'Readmitted' = = Yes Gains Lift Readmit Bal 50 - DT % Readmit Bal 50 - LR % Readmit Bal 50 - NB % Readmit Bal 50 - NN % Readmit Bal 50 FS - DT % Readmit Bal 50 FS - LR % Readmit Bal 50 FS - NB % Readmit Bal 50 FS - NN % % 90 % 80 % 70 % 60 % 50 % 40 % 30 % 20 % 10 % 0 % 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % Overall Population % % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % Overall Population % No Model Ideal Model No Model Ideal Model Readmit Bal 50 - DT Readmit Bal 50 - LR Readmit Bal 50 - DT Readmit Bal 50 - LR Readmit Bal 50 - NB Readmit Bal 50 - NN Readmit Bal 50 - NB Readmit Bal 50 - NN Readmit Bal 50 FS - DT Readmit Bal 50 FS - LR Readmit Bal 50 FS - DT Readmit Bal 50 FS - LR Readmit Bal 50 FS - NB Readmit Bal 50 FS - NN Readmit Bal 50 FS - NB Readmit Bal 50 FS - NN

10 Proving the model Measuring accuracy differently using a c statistic C statistic is a published standard Risk Prediction Models for Hospital Readmission A Systematic Review, JAMA, October 19, 2011 Vol 306, No. 15» A c statistic of 0.50 indicates that the model performs no better than chance» A c statistic of 0.70 to 0.80 indicates modest or acceptable discriminative ability» A c statistic of greater than 0.80 indicates good discriminative ability Deals better with costs identifying false negatives and positives

11 Prioritising data inputs 87 potential input variables Not all are immediately available! Need for organisations to focus on highest priority elements

12 Accuracy growth, as information builds up 01. Fluid and Electrolyte Disorders 02. CharlsonScore 03. Inpat_6m_Count 04. Deficiency Anemia 05. Hypertension Uncomplicated 06. Chronic Pulmonary Disease 07. Depression 08. Other Neurological Disorders hrStay 10. MedPPI_30d 11. AntidepressantsWS_30d 12. ERVisit_6m_Count 13. INDEXServiceCareCenter 14. PriorDC_30dYesNo c-statistic: - at admission: intermediate: at discharge : 0.846

13 Making it actionable Delivery of dashboards that rate each patient Interactive predictors that highlight the affect of each factor

14 Summary Focus the data research on an actionable outcome Make it easy to consume the insights by those that you are asking for more data capture Use data science to prove which data matters most!

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