Predictive Modeling and Primary Care: Toward More Personalized Services

1. 2007 European ACG Conference, Karlskrona, Sweden 18-19 Sep, 2007 Predictive Modeling and Primary Care: Toward More Personalized Services Christopher B. Forrest, MD, PhD Senior Vice President and Chief Transformation Officer Children’s Hospital of Philadelphia Professor of Pediatrics University of Pennsylvania School of Medicine 3400 Civic Center Boulevard Abramson Research Center, Room 1335 Philadelphia, PA 19104 USA Email: forrestc@email.chop.edu Anders Halling

2. This presentation will address: • Past Myths: The “marginal” patient • Present Realities: ACG predictive models: DxPM, RxPM, and DxRxPM • Future Promises: Toward personalized services Genomics -- Biology – Psychology -- Environment

3. PAST MYTHS THE MARGINAL PATIENT

4. The “Marginal” Patient • Treatment advances are reported at the average response for patients with disease X • Ex/ Mean change in blood pressure • First line treatment takes this “marginal” approach • Yet, we know that treatment responsiveness varies by many biopsychosocial factors • A marginal approach is inherently wasteful and poor quality • Preventive care is also formulaic • Patient need is very heterogeneous (as the next 2 slides illustrate)

5. When we look at adults with a disease, we see that most have many other chronic conditions

6. When we look at resource consumption, we see that costs are concentrated in a small % of individuals(Data are from the US Population, 1970-2002) Source: ML Berk and AC Monheit, Health Affairs, 2001; Conwell, L. J. and Cohen, J. W. AHRQ. March 2005.

7. Biopsychosocial heterogeneity is the norm • Genetic polymorphisms • Gene expression • Phenotypic variability • Perceptions of stress • Environmental interactions • Life course influences

8. Health Information: AbundantInformation Integration: Rare • Early 21st century, health data is abundant and stored in “silos” and often not in a useful form. • Integrating data into useful summary and clinically actionable information is not occurring. • Such integration can occur as a data model (hardware integration) or statistical model (software integration)—or, ideally, both.

9. PRESENT REALITIES ACG PREDICTIVE MODELS:DxPM, RxPM, and DxRxPM

10. ACG Predictive Models • Statistically integrate large amounts of health information • DxPM: age + sex + diagnostic codes • RxPM: age + sex + medication codes • DxRxPM: age + sex + diagnostic & medication codes • Produce “risk scores” • Higher values  ↑need for healthcare and ↑ opportunity to intervene • Risk scores can be “unpacked” into clinical drivers

11. Uses of ACG Predictive Models • Clinical Tiering / Care Management • “Tier” the population into risk strata and apply services according to risk, rather than at the margin • Forecast resource consumption • Primary health care planning • Morbidity-adjusted payment

12. DxPM:The Risk Factors Age Sex Frailty Marker Total Morbidity Burden (ACGs) DxPM Risk Score Hospital Dominant Condition Makers Selected High Impact Medical Conditions (Expanded Dx Clusters) Optional: Charge Data Pregnancy Without Delivery Marker

13. HOSDOM Diagnoses: Examples

14. The 11 Frailty Clusters

15. Clinical Vignettes For DxPM Risk Scores 1.00 = Average Expected Future Resource Consumption

16. DxPM Risk Score (Baseline Year) as Predictor of Hospital Use and Mortality (Year 2) Overall 1999 Mortality Rate: 7 / 1,000 population Source: British Columbia linked database (n=3,800,000)

17. RxPM:The Risk Factors Sex Age RxPM Risk Score Optional: Charge Data Rx-Defined Morbidity Groups

18. Rx-Defined Morbidity Groups (Rx-MGs) • Based entirely on ATC or NDC codes (medication codes) • Each medication code is assigned to a unique Rx-MG • Assignments are made at the generic drug/route of administration level

19. ~100,000 Medication Codes (ATCs/NDCs) 60 Rx-MGs From Medication Codes to Rx-MGs ~3,000 Generic Drug / Route Of Administration Unique Combinations

20. Administrative/Preventive Allergy/Immunology Cardiovascular Ears, Nose, Throat Endocrine Eye Female Reproductive Gastrointestinal/Hepatic General Signs & Symptoms Genito-urinary Hematologic Infections Malignancies Musculoskeletal Neurologic Psychosocial Respiratory Skin Toxic Effects/ Adverse Reactions Others / non-specific medications The Major Rx-MG Categories

21. Chemical Structure: Thiazide Mechanism of Action: Diuretic Therapeutic Class: Antihypertensive Cardiovascular/Hypertension Example of Medication Classification Route of Administration Therapeutic Goal Duration & Severity Common Morbidity *HCTZ - hydrochlorothiazide

22. Medications Assigned to Gastrointestinal/Hepatic Major Rx-MGs

23. Most Common Rx-MGs for Patients with Diabetes (defined by ICD codes) • Endocrine / Diabetes without insulin • Cardiovascular / Hyperlipidemia • Infections / Acute Minor—Curative • Endocrine / Diabetes with insulin • Respiratory / Acute Minor—Palliative • General signs and symptoms / Pain • General signs and symptoms / Pain and inflammation • Psychosocial / Depression • Allergy/Immunology / Acute minor—palliative • Gastrointestinal/Hepatic / Peptic Disease Sample: All patients with the Diabetes EDC (using ICD codes to select patients)

24. Primary Care Visits in Year-2 For Persons with Selected Chronic Conditions divided into Top versus Bottom RxPM Risk Quintiles,

25. Proportion of Variance Explained (R2)Year 1 = Predictive ModelYear 2 = Resource Use Source: Pharmetrics Validation Data-set, n=939,013, 2000-01; total costs truncated At $50K, and Rx costs truncated at $20K.

26. Classification Accuracy (c-statistic)Year 1 = Predictive ModelYear 2 = Top 1% of Resource Use (yes/no) Source: Pharmetrics Validation Data-set, n=939,013, 2000-01; total costs truncated At $50K, and Rx costs truncated at $20K.

27. Using Predictive Models to Forecast “Polypharmacy”Percentage of Top 1% Risk Groups (Year 1) with 10+ Types of Medications Next Year (Year 2)

28. FUTURE PROMISES Toward personalized services Genomics -- Biology – Psychology – Environment The Institute to Transform and Advance Children’s Healthcare (iTACH)

29. Pediatric Data Trust:Data Transformation and Integration

30. Creating a Biopsychosocial Profile Community: Physical and Social Environment (Public Health dbs, EMR, PHR) Genomics, Gene Expression, Biomarkers (Research dbs) Body Structures and Physiological Functions (EMR) Self-Assessed Well-Being (PHR) Conditions: Symptoms and Disorders (EMR) Family and Relationships (EMR & PHR) Behavior (EMR & PHR) Preferences (PHR)

31. Center for Applied Genomics: Present • Highly Automated • High Throughput • Terabytes of Data Produced Daily

32. Integration of Genomics with Patient Care • Define genetic sub-types within a condition • Specify treatment pathways and key outcomes • Correlate pathways with outcomes by genetic sub-type • Specify pathways by genetic sub-type (and possibly other data) • Link genetic sub-type to EMR by calling specific medication order sets and clinical documentation templates for a given a specific sub-type • Iterate pathways according to real-world learnings (rapid learning organization)

33. Personalizing Otitis Media Carewith Health Information Technlogy • Shared EMR between primary care and ENT • Cluster services into an “episode-of-care” • Clinical decision support for medication use, referral, and surgery • Quality and cost feedback • Training

34. Distribution of Clinical-Level Adherence to Use of Amoxicillin as a First-Line Antibiotic Choice (n=35,000 Episodes of Otitis Media Care) Mean

35. Otitis Media Template: Opened by Any Clinician in CHOP Network

36. Source: Kristen Feemster, MD N=120,000 children in the 5 counties

37. Overweight, 2 to 4 year olds in year 2005, by Zip Code, Philadelphia Prevalence Overweight > 20% 15 to 20% 10 to 15% < 10% Chestnut Hill Source: Drs. Grundmeier, Haecker, Stettler

38. Overweight, 5 to 11 year olds in year 2005, by Zip Code, Philadelphia Prevalence Overweight > 20% 15 to 20% 10 to 15% < 10% Chestnut Hill Source: Drs. Grundmeier, Haecker, Stettler

39. Conclusions • Healthcare will be more effective and efficient as we “personalize” services. Predictive modeling will be essential for this transformation. • Right now, ACG predictive models provide primary health care with tools to “tier” delivery models • The future holds more data integration and data linkage from the genome to environmental data, which moves us toward a vision of “personalized primary health care”