Big Data and Healthcare Information. Ed Reiner Quintiles Transnational

Transcription

1 Big Data and Healthcare Information Ed Reiner Quintiles Transnational

2 About Me 30 years in the information sector McGraw-Hill Information Division Elsevier Science (Cardiosource, ScienceDirect) Medical Economics GE Healthcare Healthcare IT Quintiles Transnational

3 The demand and the challenge The U.S. healthcare economy is driven by overwhelming market forces Cost of care Access to care Quality of care Affordable Care Act CMS(Centers for Medicare and Medicaid)

4 Challenges facing the healthcare industry Science/research continues Costs are growing Population is aging Technology is expensive Society wants to extend life Innovation in drug discovery is slowing (success/failure rates)

5 The challenge Healthcare one of the largest drivers of our GNP at nearly 18% -1 (largest in the world) Pharmaceutical industry represents $1t in annual revenue worldwide NEED TO ANSWER REALLY IMPORTANT QUESTIONS How do drug companies plan for drug discovery? How do health insurers decide how much to reimburse? (if at all) How do brands compare to generics in effectiveness? What influences doctors to prescribe which products? What drug discoveries are innovative vs. me too How do products compare to each other? How does government (CMS) decide how to manage Medicare Part D drug plans? Why do pharmacies switch prescriptions from what the doctor wrote? What is the cost of care / burden of illness for treatment of various diseases? Does age of the patient and the cost of care impact what insurance companies are willing to pay? How does treatment impact the long term cost of care? -1 World Bank

6 The answers lie in big data The Solution Big data is the emerging business of aggregating some of the largest data sets of: EMR and EHR databases (over 100 providers covering about 80% of the patients in the US) Pharmacy databases Claims and administrative databases Originally used for claims processing and reimbursement as well as physician payment, these databases have become the greatest asset to healthcare.

7 Welcome to the REAL WORLD Using real world data assets to figure out the answers Create insight Dispel myths Prove it scientifically/mathematically Defend hypotheses

8 RWD Fundamentals Two-by-Two Typology Retrospective Prospective Primary Data Medical Charts RCT Piggybacks Pragmatic Trials Registries Health Surveys Secondary Data Administrative Claims EMRs EMR to EDC Autofeed

9 Matching Needs to Solutions Real World Data Research Portfolio of solutions: Administrative claims EMR EMR-claims linkage Distributed data networks Retro-to-Pro hybrids Prospective observational Prospective interventional Needs & constraints to consider: Time & cost Geography (US only or international?) Study objectives (hypothesis generation or testing?) Study measures (outcomes, costs, PROs, treatment patterns, comparative effectiveness)

10 Emergence of health economics The ability to translate big raw data sets into analytical chunks for study Creates the ability to inform decision makers Creates the ability to document OUTCOMES based on real world evidence Creates the ability for decision makers to put their investments in the right buckets

11 What products perform better? Cost/benefit comparisons? Outcomes comparisons? Commercial Opportunities The financial implications are profound to: Pharma companies Government Payers Employers Hospital Healthcare providers

12 The reveal Case studies: Synthroid vs. generic levothyroxine in hypothyroid patients Brand vs. generic? Actively switched at the pharmacy to generic Nonetheless, nearly $700 million annually for AbbVie But analysis of big data outcomes>>>>>

13 Synthroid Obese and hypertensive patients on generics have higher prevalence of cardiovascular events Patients who have select co-morbid conditions have a higher propensity for strokes while on generics Many doctors also prescribe a lipid agent, negating the cost savings of a generic which would be unnecessary if a brand were prescribed

14 Osteoporosis and Prolia Under diagnosed disease (requires a BMD test) Most women on a bisphosphonate (can you name them?) which is cheap, totaling > $8b in annual sales Aging/frail patients are prone to fractures (wrist, hip, etc) Is it worth an expensive preventative treatment to avoid a fracture? Which is more expensive the medication or the treatment of a fracture? Prolia rebuilds bone density in frail patients but it is expensive what does the data say about the health economics of osteoporosis in frail patients?

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16 Big data applications Establishing cost effectiveness models Comparative effectiveness Looking at outcomes clinically, financially, ethically Supporting decision-making to support marketing, commercial/market access, product launches, medical education

17 Rx data from IMS New disclosures from data broker IMS Health reveal how much pharmaceutical firms will pay to know what your doctor is prescribing Need another reminder of how much drugmakers spend to discover what doctors are prescribing? IMS Health Holdings Inc. says it pulled in nearly $2 billion in the first nine months of 2013, much of it from sweeping up data from pharmacies and selling it to pharmaceutical and biotech companies. The firm s revenues in 2012 reached $2.4 billion, about 60 percent of it from selling such information.

18 IMS really big data The data maintained by the industry is huge. IMS, based in Danbury, Conn., says its collection includes over 85 percent of the world s prescriptions by sales revenue, as well as comprehensive, anonymous medical records for 400 million patients. All of this adds up to 10 petabytes worth of material or about 10 million gigabytes

19 The Mandate over the use of big data Surveillance Safety Labeling and packaging Monitoring "Data has become the new raw material for businesses to make their decisions on

20 The benefit Proper access and utilization of big data assets in healthcare will: Allow access to measuring outcomes Improve financial investments by payers and pharmacy Improve the science behind managing big data Increase the value of big data to stakeholders and hopefully reduce cost Allow for better clinical decisions Increase the value of big data assets Force better outcomes

21 Contact information Edward Reiner (o) (c)