Improving Health Outcomes Using Big Data

Transcription

1 Improving Health Outcomes Using Big Data Approach and Methodology to Achieve Success Ahmed Ghouri, M.D. Founder and CEO, Anvita Health March 2013 Anvita Health is a Subsidiary of Humana Inc.

2 Big Data is Not a Destination New Understanding and New Interpretations are the Purpose of Big Data. Insights that Avoid: Misdiagnoses and delayed diagnoses, leading to complications Use of invasive measures before conservative measures Unsafe prescribing leading to adverse drug events Lack of preventative services, leading to disease formation, worsening, and complications Use of expensive tertiary and emergency services vs. affordable PCP and homebased care The Holy Grail of Medicine What is the Best Treatment for a patient - based on thousands to millions of observations of Like-Patients 2

3 Anvita: One Engine, Many Capabilities Humana Harvard teaching hospital Condition management ADE and Adherence opportunities Humana Large BCBS plan Top 2 national claims clearinghouse Top 3 PBM Top 5 Disease management company Nationwide EMR, erx Top 3 HIE Harvard teaching hospital Clinical decision support, MU Real time and Batch Quality reporting Humana Inc. Large BCBS plan Top 5 Disease management company Humana Top 3 BCBS plan MRA analysis Consumer facing CDS Available for PHRs Cost savings Opportunities, epa Top 2 lab company (laboratory benefits management) 3

4 Where Insights Need to Be Distributed 300+ EMRs (tablets and PCs) HIEs dashboards Consumer Portals Eligibility Portals Nursing Applications Revenue Cycle Management Apps Handheld Devices 4

5 Anvita Delivers Interpretations via an Infrastructure-Independent Platform Any HIT Application Service Oriented Architecture Single Patient Real Time CDS Arbitrary number of clinical questions Population Analyses Arbitrary number of outcomes analyses What to Do Right Now Real-time, continuous guidance 5

6 Keys to Interpretation of Big Data Data Understanding + Performance Data Volume Overload (must use next-generation query tools) Data Type Overload (must know how to reconcile different dictionaries) 6

7 Data Volume Overload Single Patient Trajectory Billions 5 4 Clinical Data Points in a Single Patient Continuous Bio-sensors (1 meg / min) Sophisticated Semantic Search (Symbolic Reasoning) will be essential due to combinatorics: (1) data explosion 3 Gene Sequencing: 3 Billion data points per member (2) knowledge explosion and (3) terminological explosion EMR & Claims: 10,000+ transactions per member

8 Data Type Overload Tower of Babel More data becomes less usable Pathways Gene Directory Proteins Small Molecules Sequences Inheritance Lab Values Sensor Data POC Data 8

9 Big Data Requires Transformation NOT Just Storage and Real-time Retrieval Meaning Transforms To Compute Data Transformation Changes Everything Which patients have rapidly worsening kidney function that is correctable? 9

10 Examples of Important Data Transformations Rosetta Stone operations, Information Gap Resolution, and Signal-to-Noise Analysis Fill in Data Gaps Crosswalk CPT [2D Echocardiogram] + RxClass [Diuretic] + CPT [Lab: B-Natriuretic Peptide] + (ICD-9 [Edema or Shortness of Breath]) INFERS ICD Congestive Heart Failure Lab Value Standardization Signal to Noise Data Veracity A TRUE, COMPUTABLE PATIENT VIEW Patient is Allergic to Cephalosporin FDB Medispan 027 Multum 002 SnoMed RxNorm 2235 HRA 4242 POC HRA Self-Reported Data Biometrics, Labs, PBMs, Claims Understanding of Time 10

11 Lab Value Standardization in Real-time Solves Multiple Equations in Multiple Unknowns Simultaneous Variability in Multiple Dimensions has made the problem almost intractable Quantity Type Variability (pick 1 of 3) Moles (number of molecules) specific to analyte Charge (e.g., milliequivalents) specific to analyte Mass (e.g., grams) Mathematical Factor Variability (pick 1 of 20) Milli, Micro, Femto, Pico, etc sender choice Ratio of quantities (numerator and denominator choices arbitrary to lab system) Representative LOINC Code (pick 1 of 10) Arbitrary Choice of code to transmit/store 11

12 Single Lab Result Identical Variants Serum Calcium = 9 mg/dl (molar vs. mass vs. charge units of measure) milligrams per deciliter MEQ/L milligrams per liter mceq/ml micrograms per milliliter ueq/ml mg/dl milligrams per dl mg/l milligrams per liter mcg/ml micrograms per ml ug/ml mg/dl millimoles per liter milligrams per liter micromoles per milliliter mcg/ml mmol/l ug/ml mm millimoles per liter mcmol/ml micromoles per milliliter umol/ml mmol/l milliequivalents per liter mm microequivalents per milliliter mcmol/ml meq/l umol/ml meq/l milliequivalents per liter MEQ/L microequivalents per milliliter mceq/ml meq/l ueq/ml meq/l millimoles per liter MEQ/L micromoles per milliliter mceq/ml mmol/l ueq/ml mm Business Intelligence milligrams per deciliter mcmol/ml will Not work on this Data 90 milligrams per liter umol/ml micrograms per milliliter milliequivalents per liter mg/dl microequivalents per milliliter mg/l meq/l mcg/ml meq/l ug/ml 12

13 Anvita Issued Patents Pertaining to Big Data Variable Copay of Medications Using EMR Data - Allows for personalized increase/decrease of copay based on efficacy and safety of a medication. Increases the use of drugs of choice for conditions (e.g., chemotherapy with tumor response genes and receptors, antibiotics with highest efficacy for specific infections, etc) and decreases use of dangerous drugs (e.g., Beer s criteria in elderly) by financial versus educational means alone. Key value is evidence-based adherence to best practices resulting in lower total medical costs and superior outcomes. Secondary value is market differentiation and leadership by innovation. Comparative Efficacy of Medications Using EMR Data POC Identifying, ranking, and scoring of medication choices using patient data. Enables direct, head-tohead comparison of treatment options that are scored independently by safety, efficacy, and cost at the Point of Care, at the moment of decision making. Value drives competition for outcomes by providing transparency into safety, efficacy, and price head to head. Reduces inappropriate decisions, increases cost-effective and safe use of medications, leading to medical cost avoidance and superior outcomes. 13

14 Patient-Specific Co-Pay U.S. Patent Couples Incentives with Evidence-Based Medicine derived from Big Data vs. passive physician education 14

15 Signal to Noise Information Overload Realtime Biological Waveforms in Context Comparative Efficacy of Medications US Patent 2010 Personalized Head-to-Head Comparisons 15

16 Demonstration Real-time Population Analyses (who needs to be contacted) Real-time, Single Patient CDS (what to do Right Now) 16

17 Thank You Ahmed Ghouri, M.D. Founder and CEO, Anvita Health A Subsidiary of Humana Inc. aghouri@anvitahealth.com 17