Relying on Rx Data for Predictive Modeling By Richard Lieberman Health Data Services, Inc. Why ACG-Rx? • What if your health plan doesn’t have diagnosis codes, but has NDC codes from Rx transactions? • Are Rx transactions more reliable than claims, especially when providers are paid by capitation? • Will Rx-based risk adjustment address the “lag problem” that can hamper predictive modeling? Some of the Challenges Faced • Drug use is NOT synonymous with morbidity • Multiple indications for same drug – Approved uses – Off-label uses • Patterns of practice can directly influence risk scores • Complexities of working with roughly 90,000 NDC codes that regularly change. Examples of the Rx-Morbidity Groups • Allergy/Immunology / Asthma • Gastrointestinal/Hepatic / Peptic Disease • Allergy/Immunology / Chronic Inflammatory • Genito-Urinary / Acute Minor • Cardiovascular / Vascular Disorders • • Ears, Nose, Throat / Acute Minor • Endocrine / Bone Disorders • Endocrine / Diabetes With Insulin • Endocrine / Diabetes Without Insulin • Endocrine / Thyroid Disorders • Female Reproductive / Contraception • Female Reproductive / Infertility • Female Reproductive / Pregnancy and Delivery • Gastrointestinal/Hepatic / Acute Minor • Gastrointestinal/Hepatic / Chronic Liver Disease • Gastrointestinal/Hepatic / Inflammatory Bowel Disease • • • • • • • • • • • • • • Infections / Acute MajorGenito-Urinary / Chronic Renal Failure Infections / Acute Minor Infections / HIV/AIDS Infections / TB Neurologic / Migraine Headache Neurologic / Seizure Disorder Psychosocial / ADHD Psychosocial / Addiction Psychosocial / Anxiety Psychosocial / Depression Psychosocial / Acute Minor Psychosocial / Unstable Skin / Acne Skin / Acute and Recurrent Skin / Chronic Medical www.health-data-services.com 13 Implementing Diagnosis-Based and Rx-Based Risk Adjustment • Preparing the data for diagnosis-based risk adjustment is more complicated – Need to eliminate diagnoses from invalid places of service. • • • • – – – Diagnostic radiology Clinical laboratory Medical supplies/DME Any service where a clinician did not interact with the patient Carve-out services Paid vs. denied services Temporal issues: measurement period and lag • Rx-based risk adjustment is easier to implement – – – All NDCs are used There are no denied claims (except for reversals) Most health plans use a single PBM • Nonetheless, better predictions results when the systems are used in tandem. www.health-data-services.com 17 Data Timeframes for Dx-based vs. Rxbased • Typically, assignment of ACGs, ADGs and EDCs requires 12 months of medical claims data. • Allowing for a lag of 3-6 months, this means that virtually all of the data used for risk adjustment or predictive modeling is 6-18 months old. • Pharmacy data does not suffer from the typical 3-6 month time lag. – – Pharmacy claims are adjudicated and paid at the point of sale. Most PBMs dump data to their health plan customers on a weekly or biweekly basis. www.health-data-services.com 18 Number of Months to Maximize Risk Score Months of Rx Data 5 6 7 8 9 10 11 12 Percent of Members with Maximum Risk Score 41.9 7.2 7.7 7.4 8.6 9.1 8.8 9.4 www.health-data-services.com Cumulative Percent 41.9 49.0 56.8 64.1 72.8 81.9 90.6 100.0 19 Changes in Prevalence of Conditions from Pre- to Post-Disaster: Conditions Used by Medicare for Capitation Payment Computation Johns Hopkins Rx-Morbidity Groups Prevalence/1,000 members Pre-Disaster Post-Disaster Percent Change 48.5 96.0 +98% Cardiovascular / Vascular Disorders Endocrine / Diabetes With Insulin 154.7 59.8 268.1 132.2 +73% +121% Endocrine / Diabetes Without Insulin Gastrointestinal/Hepatic / Chronic Liver Disease Gastrointestinal/Hepatic / Pancreatic Disorder 177.3 296.8 +67% 1.7 2.2 +31% 2.0 4.5 +127% Genito-Urinary / Chronic Renal Failure Hematologic / Coagulation Disorders Infections / HIV/AIDS Malignancies 1.9 0.3 0.8 20.9 7.7 1.0 3.4 39.6 +310% +200% +305% +90% Musculoskeletal / Rheumatic Diseases 14.3 31.3 +119% Neurologic / Parkinson's Disease 15.7 36.4 +132% Neurologic / Seizure Disorder Psychosocial / Addiction 75.9 4.1 121.3 9.1 +60% +122% Psychosocial / Chronic Unstable 26.4 62.4 +136% Cardiovascular / Congestive Heart Failure www.health-data-services.com 20 Changes in Prevalence1 of Mental Illness Based on Prescription Drugs Coded to RxMGs Johns Hopkins Rx-Morbidity Groups Prevalence/1,000 members Pre-Disaster Post-Disaster Percent Change Psychosocial / ADHD 2.4 7.5 +219% Psychosocial / Addiction 4.1 9.1 +122% 159.8 181.4 96.7 26.4 252.4 270.6 150.6 62.4 +58% +49% +56% +136% Psychosocial / Anxiety Psychosocial / Depression Psychosocial / Acute Minor Psychosocial / Unstable www.health-data-services.com Benefits of Rx-MGs and RxPM • More timely “signal” • Useful building blocks (the Rx-Morbidity Groups) • Easier to implement www.health-data-services.com 22 For More Information Richard N. Lieberman Health Data Services, Inc. www.health-data-services.com 1-866-377-4929