Harnessing the Power of Data Analytics to Transform Care for Vulnerable Populations

Transcription

1 Harnessing the Power of Data Analytics to Transform Care for Vulnerable Populations Fred Cerise, MD, MPH President and CEO, Parkland Health and Hospital System Ruben Amarasingham, MD, MBA, President and CEO, PCCI December

2 The work comes in many forms. Sometimes it is life changing, life sustaining and lifesaving. And sometimes it is little understood, little remembered and little noticed... unless it goes undone. Parkland Health & Hospital System: 1.3 million patient visits a year. 770 staffed adult inpatient beds and 65 staffed neonatal beds. First Level I Trauma Center in North Texas. A regional burn unit, second largest civilian burn unit in the nation. A network of community-oriented primary care health centers. The Dallas County Jail health system. Primary teaching hospital for UT Southwestern Medical School. 2

3 Support for Parkland Parkland has served the residents of Dallas County well and has relied on the taxpayers to continue to invest in its growth and development. The last major expansion in 1979 was funded by an $80 million bond issue. In November 2008, Dallas County voters 82 percent in favor of a $747 million bond issue for construction of a new Parkland. The New Parkland 3

4 PCCI Organizational Background A 501c(3) non-profit research and development corporation specializing in the development of clinical prediction and surveillance software for U.S. hospitals and health systems Mission To Help Save A Life. 4

5 Why was PCCI created? Safety Net Systems have a unique view of the world. A vision of the power and promise of a large EMR data repository for use in safety net settings. Early indications in 2009 that the software and analytics developed and deployed at PCCI and Parkland could be shared with other hospitals. Revenue could fund further research and development for issues that matter to safety net hospitals. 5

6 PCCI Impact at Parkland: Some Highlights Pieces prediction software has been involved >200,000 patient and resource allocation decisions since 2009 Sustained reduction in heart failure readmissions since Pieces live in 2009 All Cause readmission reduction since go-live this year $3.2M penalty and 1,421 readmissions avoided 100% increase in sepsis bundle compliance Early results in sepsis mortality relative reduction of 17% Received $19 M in 1115 revenue capture from PCCI services 1 FTE saved due to PCCI infection prevention mobile apps 6

7 PCCI and Parkland Scientific Funding > $32M in Funding for Predictive Analytics 7

8 PCCI s Technologies Are Moving from Parkland into Hospital and Community Settings Across the Country San Francisco, CA Dallas-Fort Worth, TX San Antonio, TX 8

9 Parkland and PCCI: Shared Goals 1. Greater exploration on how we can impact population health with more robust real-time predictive systems 2. Developing novel shared savings programs between Parkland and Community Based Organizations in Dallas 3. Deploying what we have learned to safety net systems nationally 9

10 Dr. Amarasingham (Video) 10/24/2014 Proprietary and Confidential, 2014 PCCI 10

11 PCCI Vision To Deploy Predictive and Surveillance Solutions Around the World that Make Healthcare Safer, Simpler, and Less Stressful. 11

12 What We Do in Medicine: Prediction 1. What does this patient have? 2. What will this patient develop? 3. What will be the effect of a given therapy? 12

13 Doubling Time of Medical Knowledge Doubling Time of Medical Knowledge : 150 years Year 13

14 Doubling Time of Medical Knowledge Doubling Time of Medical Knowledge We are here: 1 year Year 14

15 Doubling Time of Medical Knowledge Doubling Time of Medical Knowledge Staggering increase in total medical knowledge Increasing volume and rapidity of decision-making Fragmentation and specialization of care Increasing capacity for error Scarce resources : 2.2 months Year 15

16 What is Electronic Clinical Predictive Modeling and its Purpose? Using electronic data to predict future clinical events so that one can: 1. Discriminate between high and low risk patients 2. Prevent adverse events 3. Allocate scarce clinical resources under real-time demands 4. Suggest actions 16

17 Every Adverse Event has a Timeline Hours 30 days 90 days Years Cardio-Pulmonary Arrest Sepsis Readmissions Asthma Complications Short-Term Diabetic Complications Preventable Admissions Triad: diabetes, hypertension, CKD 17

18 Every Adverse Event has a Timeline Admission Discharge 30 Days 90 Days 24 hours 18

19 Every Adverse Event has a Timeline Admission Discharge 30 Days 90 Days 24 hours 7 days ID Risk List Orders Inpatient Intervention 5 5 Outpatient Intervention Evaluation & Improvement 6 EMR Pieces 19

20 Identification of HF patients in Real-Time Using Natural Language Processing and Data Mining 20

21 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 Chronic Heart Failure current and primary historic Systolic, significant depression in ejection fraction; Diastolic dysfunction, grade 2 Non-ischemic 21

22 System calculates risk for readmission 22

23 Identifying High-Risk Patients in Real-Time Derivation Samples Validation Samples * Very Low Low Intermediate High Very High Predicted Readmission Risk Category Amarasingham et al, Medical Care,

24 Pieces provides list of targeted high risk patients 24

25 Activation of Clinical Pathways in the EMR 25

26 Pieces tracks interventions in the EMR 26

27 Pieces monitors outcomes 27

28 Real-Time Failure Analysis Examples Hospital Factors PIECES Performance Patient Selection 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 Applies not only for readmissions, but for all of Pieces e-models. 28

29 Real-Time Failure Analysis Examples Hospital Factors PIECES Performance Patient Selection Follow-Through Intervention Effectiveness CHF Volumes Up PIECES Down High Risk Patient Missed Non-CHF Patient Enrolled Inpatient Intervention Not Ordered / Completed Early Early Discharge Pattern Pattern Noticed 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 Scheduled / Completed / Completed within within TF 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 Applies not only for readmissions, but for all of Pieces e-models. 29

30 Amarasingham et al, BMJ, 2013 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 30

31 A Different Hospital: Readmission Performance 31

33 Sepsis: Bundle Compliance and Mortality Results Length of Stay: FY 13 (Pre) Patients 1, Mean LOS 10.4 days 8.1 days -2.2 days -21.5% Median LOS 6.6 days 5.8 days -0.8 days -12.6% Post Pieces FY 13 (Pre) Live Relative Improvement POA Performance: Post Pieces Live Patients 1, Lactate within 3 Hr. 54.6% 64.2% IV Abx within 3 Hr. 27.1% 50.0% Bundle Compliance 14.0% 29.2% Post Pieces FY 13 (Pre) Live Mortality: All 6.9% 5.7% Absolute Reduction % 85.0% 2x relative improvement Relative Improvement 17.4% Relative Reduction 33

35 The Complexities of Predictive Modeling Cohen G, Amarasingham R et al, Health Affairs, 2014 Amarasingham et al, Health Affairs,

36 The Complexities of Predictive Modeling 1. Interventions for highest risk patients * 2. Considering clinical vs. social risk 3. Explanation vs. Prediction 4. Non-health care data sources * 5. Changing EMR data models 6. Changing clinical interventions 7. Changing populations Amarasingham et al, Health Affairs,

37 Connecting the Community 37

38 Pieces : Analytics and Intelligence Layer Leverages predictive and prescriptive analytics on medical and social data to identify at risk individuals Enhances population health, preventive care, and disaster response initiatives Informs allocation of healthcare and community resources 38

39 Partnerships 39

40 Community driven connections

41 Novel shared savings: Pilot starting at Parkland and DFW Services Communitybased organizations Hospitals Shared Savings ($) 41

42 Implementation Status Pieces Iris 70+ scheduled implementations in ,000 expected lives to be touched Diverse social service organizations (e.g. homeless shelters, food distribution centers, transportation, counseling, job skills training, financial assistance, clothing, and many more) Pieces Plexus Pieces Plexus Go Live in Q

43 Conclusions Predictive analytics are a promising way to help improve timeliness, safety and quality in health care. Predictive analytics may be particularly useful in resource constrained environments. There are many ways to approach predictive analytics at any given institution. 43

44 Thank You!