The FDA s Mini- Sen*nel Program and the Learning Health System

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1 sen*nel.org 1 The FDA s Mini- Sen*nel Program and the Learning Health System Richard PlaB, MD, MS Harvard Pilgrim Health Care Ins*tute Harvard Medical School October 1, 2014

2 Vision We seek the development of a learning health system that generates and applies the best evidence for the collaborative health care choices of each patient and provider; drives discovery as a natural outgrowth of patient care; and ensures innovation, quality, safety, and value in health care. (Roundtable Charter)

patient and provider; drives discovery as a natural outgrowth of patient care;

3 Learning Healthcare System The increased complexity of health care requires a sustainable system that gets the right care to the right people when they need it, and then captures the results for improvement. The nation needs a healthcare system that learns.

the right people when they need it, and then captures the

4 Every day, patients and doctors face common questions for which we have no solid evidence! For short cervix does bed rest prevent early labor?! Should I take my daily blood pressure medicine in the morning or at night?! How can I help my 87 year old patient with multiple myeloma decide which chemotherapy option is best?! What are the benefits and risks of giving medication to my child with ADHD?

! Should I take my daily blood pressure medicine in the morning or at night?

5 Which Treatment is Best for Whom? High-Quality Evidence is Scarce: < 15% of guideline recommendations are supported by high quality evidence 5 Tricoci P et al. JAMA 2009;301:831-41

6 Collins- Slides- Network.pdf

7 7

8 Influenza- like Illness

9 Quality 9

10 When could we have suspected a link? en.wikipedia.org/wiki/heart_attack IOM Drug Safety Meeting March 12, 2007

12 Digital platform Issues for further attention Functionality standards Governance and coordination The public case

13 sen*nel.org 13 FDA's Mini- Sen-nel Program

14 sen*nel.org 14 Mini- Sen*nel q Congress mandated FDA develop electronic record based safety surveillance system q Mini- Sen*nel: Develops opera*onal capacity for ac*ve medical product safety surveillance in exis*ng automated healthcare data systems Develops and evaluates scien*fic methods Offers FDA the opportunity to evaluate safety issues Assesses barriers and challenges

system q Mini- Sen*nel: Develops opera*onal capacity for ac*ve medical product safety

15 Mini- Sen*nel Partner Organiza*ons Lead HPHC Institute Data and scientific partners Scientific partners sen*nel.org Institute for Health 15

16 sen*nel.org 16 Mini- Sen*nel Distributed Database* q Popula*ons with well- defined person- *me for which most medically- abended events are known q 358 million person- years of observa*on *me q 48 million people currently accruing new data q 4 billion dispensings q 4.1 billion unique encounters 42 million acute inpa*ent stays q 30 million people with >1 laboratory test result *As of July 2014

medically- abended events are known q 358 million person- years of observa*on *me q 48 million people

17 Mini- Sen*nel s Data Sources q Administra*ve data Enrollment Demographics Outpa*ent pharmacy dispensing U*liza*on (encounters, diagnoses, procedures) q EHR data Height, weight, blood pressure, temperature Laboratory test results (selected tests) q Registries Immuniza*on Birth cer*ficates q Full text records (small number to confirm selected exposures and outcomes) info@mini- sen*nel.org 17

pressure, temperature Laboratory test results (selected tests) q Registries Immuniza*on Birth

18 Mini- Sen*nel s Common Data Model Enrollment Demographic Dispensing Encounter Lab Result Vital Signs Person ID Person ID Person ID Person ID Person ID Person ID Enrollment start & end dates Drug coverage Medical coverage Death Birth date Sex Race Etc. Cause of Death Dispensing date Na*onal drug code (NDC) Days supply Amount dispensed Diagnosis Dates of service Provider seen Type of encounter Facility Etc. Procedure Dates of order, collec*on & result Test type, immediacy & loca*on Procedure code & type Test result & unit Abnormal result indicator Etc. Date & *me of measurement Height Weight Diastolic & systolic BP Tobacco use & type BP type & posi*on Person ID Person ID Person ID Person ID Date of death Cause of death Date Dates of service Source Confidence Etc. Diagnosis code & code type Source Confidence Etc. Principal diagnosis flag Encounter type & provider Diagnosis code & type Procedure code & type Encounter type & provider Etc. Also: Vaccine table Birth certificate table Blood components table Etc. info@mini- sen*nel.org 18

Cause of Death Dispensing date Na*onal drug code (NDC) Days supply Amount dispensed Diagnosis Dates of service Provider seen Type of encounter Facility Etc.

19 sen*nel.org 19 Common Data Model Standards q The data set uses the data source s original codes whenever possible q For each variable, the model captures both the value AND coding system, e.g., ICD- 9- CM, SNOMED, CPT, HCPCS, LOINC

20 sen*nel.org Ensuring Data Privacy

21 sen*nel.org 21 Mini- Sen*nel Distributed Analysis 1- User creates and submits query (a computer program) 2- Data partners retrieve query 3- Data partners review and run query against their local data 4- Data partners review results 5- Data partners return results via secure network 6 Results are aggregated

22 sen*nel.org 22 Dabigatran vs warfarin and stroke / bleeding q Goal: Compare ischemic and hemorrhagic stroke and gastrointes*nal bleeding rates among new users of dabigatran or warfarin therapy who have atrial fibrilla*on/ fluber

23 sen*nel.org 23 New Need all User dispensings Cohort from any Design pharmacy in the prior X months Need all dispensings and days supply from any pharmacy to determine treatment dura*on Start Date Start of new treatment episode End Date Look back XX days Inclusion/exclusion condi*on Need all inpa*ent, ED, ambulatory diagnoses / procedures from every provider Outcome(s) Op*onal: blackout days Op*onal: extension days Need all diagnoses at any ED or hospital Index Date Time

24 sen*nel.org 24 Dabigatran vs warfarin and stroke / bleeding q Analysis: A standard, reusable, SAS program q Inputs: Popula*on: Pa*ents with pre- exis*ng atrial fibrilla*on, Exposures: New users of dabigatran or warfarin (no prior exposure to either in preceding 183 days). Outcomes: First diagnoses of gastrointes*nal (GI) or intracerebral hemorrhage in inpa*ent or ED sejngs. (No event in the 183 days prior to ini*a*ng therapy.) Period: 10/19/2010 to 12/31/2011 q Results: Counts of eligible pa*ents and days under observa*on Counts of new users of dabigatran and warfarin, dispensings, total days supplied, treatment episodes, Counts of first GI or intracerebral hemorrhage diagnoses sen*nel.org/work_products/assessments/mini- Sen*nel_Modular- Program- Report_MSY3_MPR31- Part- 2_Dabigatran- Warfarin- GIH- ICH.pdf

25 sen*nel.org Dabigatran vs warfarin: Data sources q Administra*ve files: Demographic data Eligible person *me (periods when both presence and absence of events is reliably known) q Dispensing data: Outpa*ent medica*ons, including dosage form and days supply q Claims: Diagnoses, ambulatory and inpa*ent Procedures, ambulatory and inpa*ent

26 sen*nel.org Dabigatran vs Warfarin: Data sharing q No person- level data is shared! q Count data only is shared

27 GI bleeding aoer warfarin or dabigatran Figure 1a. New Events of GIH per 100k Days at Risk in the MSDD between October 19, 2010 and December 31, 2011, by Drug, Incidence Criteria, and Washout Period for Individuals with a Pre- Existing Condition of Atrial Fibrillation New GIH Events per 100k Days at Risk 4.0 Drug Incidence with respect to: Washout Period New GIH Events/100k Days at Risk 3.5 Dabigatran Incident with respect to Dabigatran 183- Day Washout Day Washout days at risk Incident with respect to Dabigatran and Warfarin 183- Day Washout Day Washout 1.4 Warfarin 2.5 Incident with respect to Warfarin 183- Day Washout Day Washout 3.7 Incident with respect to Dabigatran and Warfarin 183- Day Washout Day Washout Rate per 100, Day Washout 365- Day Washout 183- Day Washout 365- Day Washout 183- Day Washout 365- Day Washout 183- Day Washout 365- Day Washout Incident with respect to Dabigatran Incident with respect to Dabigatran and Warfarin Dabigatran Incident with respect to Warfarin Incident with respect to Dabigatran and Warfarin Warfarin hbp:// sen*nel.org/work_products/assessments/mini- Sen*nel_Modular- Program- Report_%20MSY3_MPR41_Dabigatran- Warfarin- GIH- ICH_Part- 1.pdf info@mini- sen*nel.org 27

28 Drugs Bleeding rates associated with new use of Pradaxa do not appear to be higher than bleeding rates associated with new use of warfarin. sen*nel.org Nov 2,

29 we are now conduc+ng two protocol- based assessments, using claims data from Mini- Sen+nel and other claims databases, in which adjustments will be made for confounding factors Southworth N Engl J Med 2013; 368:1272

30 30

31 PCORnet s Goal Improve the na*on s capacity to conduct rapid, efficient, and economical compara*ve effec*veness research 31

32 11 Clinical Data Research Networks and 18 Patient Powered Research Networks Numbers indicate the number of networks active in each state 32

33 11 Clinical Data Research Networks Integrated Delivery Systems Clinical & Translational Science Awardees Health Information Exchanges Safety Net Clinics Academic Health Centers 33

34 18 Patient Powered Research Networks 34

35 Goals for Patient-Powered Research Networks (PPRNs)! Enroll >0.5% of those with the condition in the U.S. (~50 to 50,000)! Patient-reported data for >80% of cohort! Patients involved in governance! Standardized data able to respond to queries 35

36 36

37 The NIH Collaboratory: Complementary development of health care systems research capabilities Millions of people. Strong collabora*ons. Privacy first.

38 sen*nel.org Mul*ple Networks Sharing Infrastructure Health Plan 1 Health Plan 4 Health Plan 7 Hospital 1 Hospital 4 Outpa*ent clinic 1 Pa*ent network 1 Health Plan 2 Health Plan 5 Health Plan 8 Hospital 2 Hospital 5 Outpa*ent clinic 2 Pa*ent network 2 Health Plan 3 Health Plan 6 Health Plan 9 Hospital 3 Hospital 6 Outpa*ent clinic 3 Pa*ent network 3 q Each organiza*on can par*cipate in mul*ple networks q Each network controls its governance and coordina*on q Networks share infrastructure, data cura*on, analy*cs, lessons, security, sooware development

39 Thank you!

Distributed Networking

Distributed Networking Millions of people. Strong collaborations. Privacy first. Jeffrey Brown, Lesley Curtis, Richard Platt Harvard Pilgrim Health Care Institute and Harvard Medical School Duke Medical