9/23/2014. Mission To improve the health of the people in the communities we serve.

Transcription

1 Big Data & Reduced Readmissions Chris Myers, MS, RN, CCM, Director of Care Transitions, Texas Health Harris Methodist Hospital Hurst-Euless- Bedford Anand Shah, MD, Vice President, Parkland Center for Clinical Innovation Background One of the largest faith-based, nonprofit health care delivery systems in the United States and the largest in North Texas in terms of patients served. The system's primary service area consists of 16 counties in north central Texas, home to more than 6.2 million people. Mission To improve the health of the people in the communities we serve. 1

2 Background A 501c(3) non-profit specializing in the research and development of clinical prediction systems. Mission To Help Save A Life. 2

3 Electronic Health Predictive (e-hpa) Analysis Electronic data can be used to predict future clinical events for purposes of: 1. Stratifying patients according to risk level for a specific outcome 2. Allocating scarce clinical resources in a maximally effective manner under real-time demands 3. Preventing adverse or harmful events Parkland Results Demonstrated a 26% Relative Reduction in Odds of Readmission using e-hpa Results Demonstrated: Concentrated care management efforts on ¼ of the patients 26% relative reduction in odds of readmission Absolute reduction of 5 readmissions per 100 index admissions 3

4 Scientific Collaboration: Predictive Modeling PCCI, Parkland and THR are collaborating on more than $2 million in predictive modeling grants. Hospital Readmission Prediction $396,03 IEP $980,000 Anonymous donation $100,000 PROSPR $949,195 Chronic Kidney Disease $194,254 $2 Million UT-STAR $109,000 Ruben Amarasingham, MD, MBA selected for Young Leader Award $40,000 National Predictive Analytics Symposium $182,000 Clinical Analytics, Patient Safety, and Risk Surveillance $2.9 Million PCOR/CER Philanthropic Gift $68,896 $5 Million THR-HEB Pieces CHF Go Live Data Mining $59,636 Center for Translational Medicine $822,980 IEP $12M Peerreviewed Scientific Publication in Progress THR-HEB Pieces All Cause Go Live with Disease IDs UH2/UH3 Improving Chronic Disease Management with Pieces $5.8 Million Readmission Predictive Modeling Workflow Using Epic and Pieces at TH-HEB 4

5 Readmission Predictive Modeling Workflow Using Epic and Pieces at TH-HEB Evaluation & Improvement Inpatient Intervention Outpatient Intervention Electronic Medical Record Readmission Predictive Modeling Workflow Using Epic and Pieces at TH-HEB Inpatient Intervention 5 5 Outpatient Intervention Evaluation & Improvement 6 Electronic Medical Record 5

6 Identification of HF patients in Real-Time Using Natural Language Processing and Data Mining 1 Evaluation & Improvement Inpatient Intervention Outpatient Intervention Electronic Medical Record Natural Language Processing 68 yo WF presents with acute on chronic non ischemic systolic and diastolic chf, severely depressed ef and grade ii diastolic dysfunction. Disease/ Symptom Time Attribute Acute Heart Failure current and primary Systolic, significant depression in ejection fraction; Diastolic dysfunction, grade 2 Non-ischemic Chronic Heart Failure historic 6

7 System calculates risk for all-cause readmission 1 2 Evaluation & Improvement Inpatient Intervention Outpatient Intervention Electronic Medical Record Identifying High-Risk Patients in Real-Time 0 60 Derivation Samples Validation Samples D a y R e a d m is s io n (% ) Very Low Low Intermediate High Very High Predicted Readmission Risk Category Amarasingham et al, Medical Care, 2010

8 System provides list of targeted high risk patients to intervention coordination teams Evaluation & Improvement Inpatient Intervention Outpatient Intervention Electronic Medical Record Pieces Intervention Process Flow THR-HEB 8

9 Intervention teams orders inpatient and outpatient interventions in EMR Evaluation & Improvement Inpatient Intervention Outpatient Intervention Electronic Medical Record Intervention teams conduct interventions Evaluation & Improvement Inpatient Intervention 5 5 Outpatient Intervention Electronic Medical Record 9

10 System monitors inpatient and outpatient interventions and other activities Inpatient Intervention 5 5 Outpatient Intervention Evaluation & Improvement 6 Electronic Medical Record Real-Time Failure Analysis Examples Hospital Factors Pieces Performance Patient Selection and Enrollment Intervention Follow- Through Intervention Effectiveness CHF Volumes Up Pieces Down High Risk Patient Missed Non-CHF Patient Enrolled Inpatient Intervention Not Ordered / Completed Early Discharge Pattern Noticed CHF Patient Missed Patient Incorrectly Scored CHF Patient Excluded Phone Call Not Placed / Completed within TF Clarity Down High Risk CHF Patient Missed Missing Data Skewing Risk Calculation Low Risk Patient Enrolled Outpatient Visit Not Scheduled / Completed within TF Clarity Run-Time Slow Incorrect CHF Evaluation Daily Census of High Risk Patients Inconsistent Excluded Patient Enrolled Appointments Not Prioritized by Risk Improper Disease Threshold Modeling of Risk Distribution Incorrect Effect of the Weekend Quality of Outpatient Visit Diminished Model Feeds Broken Model Feeds Broken Screening Protocol Adherence CHF Clinic Overrun 10

11 THR-HEB Readmission Rate THR Outcomes Relative and Absolute Reduction of Admissions 11

12 How did this work at THR-HEB? 2013 CHF Only One nurse responsible for reviewing PIECES list, placing referrals, providing education on CHF during admission 2014 All Cause: CHF, COPD, AMI, PNA, DM Implemented CHF Nurse Navigator Program Developed process for all case managers & social workers to utilize PIECES work list Began referring patients to ED Nurse Navigators How did this work at THR-HEB? Challenges: 2012/2013 One dedicated CHF Case Management FTE Limited resources to identify successful interventions EMR customizations (e.g. order set builds) required support of limited IT resources 2014 All Cause implemented during concurrent restructuring and centralization of case management departments across the system Required IT involvement Incorporating work flow of all staff 12

13 How did this work at THR-HEB? Required Workflow Changes: Increase staff utilization from one RN Navigator to all case managers/social workers Incorporate PIECES work list into daily work flow Documentation of identified problems and interventions Inclusion of readmission risk score into new case management model How did this work at THR-HEB? CHF readmissions significantly decreased All Cause Readmission Risk score utilized by case managers, social workers, and bedside nurses September, Patients with a high readmission risk score now have a full psychosocial evaluation completed by a social worker 13

14 THR Outcomes User Feedback Pieces real-time risk scoring allows us to be notified of cases earlier. Task management is simplified with integration in Epic system list and order sets. Appreciate ability to enroll medium risk patients. No need for separate manual process. Pieces identification of heart failure patients was accurate. The Complexities of Big Data 14

15 Challenges and Limitations of Real-Time Predictive Modeling Activities 1. Interventions for highest risk patients 2. Changing EMR data models 3. Changing clinical interventions 4. Changing populations 5. Considering clinical vs. social risk 6. Inpatient and outpatient integration Predictive Model Expansion to Focus on Other Clinical Quality & Outcomes Metrics 15

16 Future Outlook Transition from focus on heart failure to All-cause readmission reduction model Expansion beyond initial pilot hospital Exploration of other predictive models Consideration of other high- cost, burden, and priority disease areas and conditions Thank You! For questions, please contact: Chris Myers Anand Shah, MD, MS 16