PCORnet Coordinating Center Site Visit: GPC March 19, 2015 Call-in:1 (571) 317-3122 Access code: 863-993-413 Agenda Welcome & Introductions Vision and Goals for PCORnet DSSNI Discussion Sentinel-PCORnet workgroup Data Characterization Complete Data Common Data Model-Updates and Maintenance IRB and ADAPTABLE Use Cases: Health Systems Demonstration Project & Obesity Complete Data Demonstration Open Session for Questions and Wrap Up GPC Global Call 2 Welcome & Introductions Vision and Goals for PCORnet Rich Platt, Adrian Hernandez PCORnet Opportunities and Challenges Rich Platt PCORnet: the National Patient-Centered Clinical Research Network PCORnet’s goal is to improve the nation’s capacity to conduct CER efficiently, by creating a large, highly representative, national patient-centered clinical research network for conducting clinical outcomes research. The vision is to support a learning US healthcare system, which would allow for large-scale research to be conducted with enhanced accuracy and efficiency. 6 Guiding principle: Make research easier Analysis ready data Reusable analysis tools Administrative simplicity Simple, pragmatic studies integrated into routine care Multiple Networks Sharing Infrastructure Health Plan 1 Health Plan 4 Health Plan 7 Hospital 1 Hospital 4 Outpatient clinic 1 Patient network 1 Health Plan 2 Health Plan 5 Health Plan 8 Hospital 2 Hospital 5 Outpatient clinic 2 Patient network 2 Health Plan 3 Health Plan 6 Health Plan 9 Hospital 3 Hospital 6 Outpatient clinic 3 Patient network 3 Each organization can participate in multiple networks Each network controls its governance and coordination Other networks can participate Networks share infrastructure, data curation, analytics, lessons, security, software development Multiple Networks Sharing Infrastructure Health Plan 1 Health Plan 4 Health Plan 7 Hospital 1 Hospital 4 Outpatient clinic 1 Patient network 1 Health Plan 2 Health Plan 5 Health Plan 8 Hospital 2 Hospital 5 Outpatient clinic 2 Patient network 2 Health Plan 3 Health Plan 6 Health Plan 9 Hospital 3 Hospital 6 Outpatient clinic 3 Patient network 3 Each organization can participate in multiple networks Each network controls its governance and coordination Other networks can participate Networks share infrastructure, data curation, analytics, lessons, security, software development PCORnet Common Data Model, Draft v2.0 Modifications DEMOGRAPHIC PATID BIRTH_DATE BIRTH_TIME SEX HISPANIC RACE BIOBANK_FLAG Fundamental basis ENROLLMENT PATID ENR_START_DATE ENR_END_DATE CHART ENR_BASIS DISPENSING PATID RX_DATE NDC RX_SUP RX_AMT Data captured from processes associated with healthcare delivery VITAL PATID ENCOUNTERID (optional) MEASURE_DATE MEASURE_TIME VITAL_SOURCE HT WT DIASTOLIC SYSTOLIC ORIGINAL_BMI BP_POSITION TOBACCO TOBACCO_TYPE CONDITION PATID ENCOUNTERID (optional) REPORT_DATE RESOLVE_DATE CONDITION_STATUS CONDITION CONDITION_TYPE CONDITION_SOURCE PRO_CM PATID ENCOUNTERID (optional) CM_ITEM CM_LOINC CM_DATE CM_TIME CM_RESPONSE CM_METHOD CM_MODE CM_CAT Data captured within multiple contexts: healthcare delivery, registry activity, or directly from patients ENCOUNTER PATID ENCOUNTERID SITEID ADMIT_DATE ADMIT_TIME DISCHARGE_DATE DISCHARGE_TIME PROVIDERID FACILITY_LOCATION ENC_TYPE FACILITYID DISCHARGE_DISPOSITION DISCHARGE_STATUS DRG DRG_TYPE ADMITTING_SOURCE DIAGNOSIS PATID ENCOUNTERID ENC_TYPE (replicated) ADMIT_DATE (replicated) PROVIDERID (replicated) DX DX_TYPE DX_SOURCE PDX LAB_CM_RESULT PATID ENCOUNTERID (optional) LAB_NAME SPECIMEN_SOURCE LAB_LOINC STAT RESULT_LOC LAB_PX LAB_PX_TYPE LAB_ORDER_DATE SPECIMEN_DATE vv SPECIMEN_TIME RESULT_DATE RESULT_TIME RESULT_QUAL RESULT_NUM RESULT_MODIFIER RESULT_UNIT NORM_RANGE_LOW MODIFIER_LOW NORM_RANGE_HIGH MODIFIER_HIGH ABN_IND PROCEDURE PATID ENCOUNTERID ENC_TYPE (replicated) ADMIT_DATE (replicated) PROVIDERID (replicated) PX_DATE PX PX_TYPE Data captured from healthcare delivery, direct encounter basis New New to to v2.0 v2.0 PCORnet DRN Coordinating Center 1 6 1. User creates and submits query (a computer program) PCORnet Secure Network Portal CDRN 1 Review & Run Query 2 3 Review & Return Results Demographics Utilization Etc 4 3 Review & Return Results Demographics Utilization Etc 3. CDRNs/PPRNs review and run query against their local data 4. CDRNs/PPRNs review results CDRN 11 Review & Run Query 5 2. Individual CDRNs/PPRNs retrieve query 4 5. CDRNs/PPRNs return results via secure network 6. Results are aggregated 11 Used data for 3.9 million new users of antihypertensives in 18 organizations Propensity score matched stratified analysis No person-level data was shared Five months and $250,000 required for programming and analysis – compared to 1-2 years and $2 million without analysis-ready distributed dataset Toh Arch Intern Med.2012;172:1582-1589. Reusable Program: Propensity Score Matched Cohort Study Specify: Population (age/sex/etc), time period Exposures Outcomes ICD-9-CM code 995.1 in any position during outpatient, inpatient, or emergency department encounter Washout period (days before first dispensing): 183 days Inclusion criteria Exclusion criteria Covariates Propensity score matching options Comorbidity, utilization, high dimensional propensity score Matching ratio Caliper size Angioedema: Table 1. Unmatched Cohort 3.9 million new users Diabetes 21% vs 10% Heart failure 2% vs 4% Ischemic heart disease 5% vs 13% www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf Propensity Scores Before Match DP3 www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf Angioedema: Table 2. Matched Cohort 2.6 million new users Diabetes 10% vs 10% Heart failure 3% vs 3% Ischemic heart disease 8% vs 8% www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf Propensity Scores After Match DP3 www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf Angioedema: Table 3. Results ACEI vs βblocker 1:1 matched analysis: • HR = 3.1 (95% CI, 2.93.4) Toh et al findings: • HR = 3.0 (95% CI, 2.83.3) www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf Trial Logistics: Taking Advantage of PCORnet Infrastructure 19 Screening, Enrollment & Data Flow 20 Computable phenotype History of CAD • Past MI OR • Past cath showing significant CAD OR • Revascularization (PCI/CABG) At least one of the following: • age > 65 years • Creatinine > 1.5 • Diabetes, • Known 3 vessel coronary artery disease • Current cerebrovascular disease and/or peripheral artery disease • Known ejection fraction <50%, • Current smoker Getting consent 21 Getting Informed Consent Clinician reviews and decides on participation Email to potential patient with trial introduction and link to consent Letter to potential pt. with trial intro and paper consent for non-Internet accessible pt. Consent Form Contacts: Local contact info for any site issues Local contact info for withdrawal from trial Contact info for questions about the trial Contact info for reporting adverse events Randomization & ASA dose assignment PCORnet’s opportunities Be a national/regional resource to answer questions important to patients, clinicians, and delivery system leaders (be a foundation of the Learning Health System) Researchers embedded in clinical environments, who are able to Engage patients, providers, health plan leaders, and Use both EHR and claims data when needed Data Develop validated data domains/elements for national use Use, publish standard analytic tools for the CDM Trials Develop efficient methods for pragmatic, multi-center trials Methods Create novel analytical tools, e.g., privacy preserving regression Our Challenges Funders will increasingly expect multi-site studies to be better, faster, and cheaper than our investigators are accustomed to Using data that comes directly from delivery systems and from patients We need to develop trust and systems to allow PCORnet-wide coordination to guide external investigators and funders, manage projects, and implement efficiencies DSSNI Discussion Rich Platt, DSSNI Team: Jeff Brown, Lesley Curtis, and Jessica Sturtevant Mini-Sentinel/PCORnet Data Linkage Workgroup Rich Platt 26 About the Workgroup Workgroup Lead: Rich Platt Project Period: January – August 2015 Meetings held first and third Fridays of the month at 9am ET Next meeting: April 3, 2015 Deliverable: White paper Lead author: Kevin Haynes Address governance and technical aspects of several data linkage scenarios Provide guidance for implementation of select scenarios 27 Scenarios for potential collaboration Scenario 0 Characterize within network and outside network utilization for each CDRN by providing CDRN NPI/TIN information to MiniSentinel Data Partners with which they have meaningful overlap. 28 Scenarios for potential collaboration Scenario 1 Create a list of overlapping populations between networks and health plans. Options include a master list of all individuals in common between two networks, or lists could be created as needed. 29 Scenarios for potential collaboration Scenario 2 Identify outcomes of interest in Mini-Sentinel Data Partners’ data for specific cohorts of people in the PCORnet networks. Example with informed consent: A Mini-Sentinel data partner identifies hospitalizations for acute myocardial infarction or GI bleeding among PCORnet network's patients who are participating in a randomized trial of different aspirin doses. Example without informed consent: A PCORnet Clinical Data Research Network shares INR values for Mini-Sentinel health plan individuals exposed to different anticoagulants. 30 Scenarios for potential collaboration Scenario 3 Create a merged dataset tailored to address a single question. Examples: the PCORnet randomized trial on aspirin dosing and the observational studies based on the PCORnet weight cohorts • Identify outcomes of different kinds of bariatric surgery. – CDRNs and health plans create a merged longitudinal dataset for their shared patient-members who had bariatric surgery at a CDRN site. The dataset would include all of the data from both organizations that are in the Common Data Model, for the entire period during which the individual was a member of the health plan. Both organizations that contribute data to the merged dataset would need to approve each use of the data. 31 Scenarios for potential collaboration Scenario 4 Create a multipurpose merged dataset capable of rapidly answering many questions Example: CDRNs and Mini-Sentinel Data Partners create a merged longitudinal dataset for their shared patient-members. The dataset would include all of the data from both organizations that are in the Common Data Model. The data set would be used for rapid querying using modular programs. Both organizations that contribute data to the merged dataset would need to approve each use of the data. Scenario 4B Create a merged dataset for a cohort of individuals with a condition or treatment of interest, e.g., diabetes, heart failure, hip arthroplasty. This dataset would be suitable for addressing a variety of questions about the population or condition of interest. 32 Scenarios for potential collaboration Scenario 5 Transfer all the data about a PPRN participant from a Sentinel Data Partner to the PPRN – with the individual’s request/authorization. Data would be in Common Data Model format. 33 Timeline Deliverable Due Date First draft of Scenario 0 ready for workgroup discussion March 27, 2015 First drafts of 1, 2, 3, 4, 4B, and 5 ready for discussion at two week intervals April 10, 2015 – June 19, 2015 Second drafts ready for review within one month of initial discussion May 11, 2015 – July 20, 2015 Final scenario report completed 3 weeks after second draft is released June 1, 2015 – August 10, 2015 Final white paper submitted to PCORI and FDA August 24, 2015 34 PCORnet Data Characterization Overview Jeff Brown and Lesley Curtis Data Characterization: Purpose and Process Ensure consistency with PCORnet Common Data Model (CDM) Identify major data gaps or issues for discussion ETL Annotated Dictionaries describe how each data table was created and identifies local issues Data characterization queries will include basic checks of each data domain and data element in the PCORnet CDM ETL ADDs and data characterization query output will be reviewed by the DSSNI Team and PCORI 36 Data Characterization: Tools and Resources ETL Annotated Data Dictionary – description of mapping and data transformation from source data Functional Specifications – summary of the data characterization approach and process Technical Specifications – detailed descriptions of the data characterization design and output, to inform SQL code development Work Plans – outline data characterization query and output 37 Data Characterization Cycle Create DataMart and ETL ADD DSSNI approves Datamart for querying DSSNI sends DC queries DSSNI and CDRN site discuss DC results DC query output to DSSNI DSSNI review; summary to PCORI 38 Data Characterization Query Distribution Native SQL programs distributed via the DRN Query Tool File distribution query type Query and workplan sent to DataMart Administrator via the Query Tool DataMart Administrator receives query and work plan with DataMart Client DataMart Administrator runs SQL code locally against their DataMart and review the output Results are approved and uploaded to Query Tool via the DataMart Client DSSNI downloads results for review and tracking Queries produce aggregate data only (no patient-level data) 39 Data Characterization Queries PCORnet CDM 1.0 40 Data Characterization: Demographics Count of unique PATIDs Frequency of records by SEX RACE HISPANIC AGE_GROUP(calculated as of data characterization date) Summary statistics for age in years 41 Data Characterization: Enrollment Counts of unique PATIDs and records (enrollment periods) Distribution of records by ENR_START ENR_END Frequency of records By enrollment months per PATID By enrollment years per PATID By year and month of enrollment By ENR_BASIS 42 Data Characterization: Encounter Counts of unique PATIDs and ENCOUNTERID Frequency of records by ENC_TYPE ADMIT_DATE year ADMIT_DATE year month ADMITTING_SOURCE Distribution and frequency of records by ADMITTING_SOURCE and ENC_TYPE DISCHARGE_DATE by year DISCHARGE_DATE by year month DISCHARGE_DISPOSITION DISCHARGE_DISPOSITION and ENC_TYPE DISCHARGE_STATUS DISCHARGE_STATUS and ENC_TYPE 43 Data Characterization: Diagnosis Counts of unique PATIDs and ENCOUNTERID Distribution and frequency of records by ADMIT_DATE by year ADMIT_DATE by year month Principal discharge diagnosis (PDX) DX_SOURCE DX_SOURCE and DX_TYPE Distribution of records by Diagnosis Code (DX) 44 Data Characterization: Procedure Counts of unique PATIDs and ENCOUNTERID Distribution and frequency of records by ADMIT_DATE by year ADMIT_DATE by year month PX_TYPE Distribution of records by PX and ENC_TYPE Distribution of records by Procedure Code (PX) 45 Data Characterization: Vital Counts of unique PATIDs and ENCOUNTERID Distribution and frequency of records by MEASURE_DATE by year MEASURE_DATE by year month VITAL_SOURCE HEIGHT WEIGHT DIASTOLIC SYSTOLIC BMI 46 Query Results Processing 47 Data Characterization Timeline DSSNI Liaisons will work with networks to determine Data Characterization schedules/timelines Prioritization 1 DataMart per Network complete cycle by end of May ADAPTABLE sites/DataMarts Obesity Observational Studies CDM v1.0 DC queries ready for distribution by early April (SQL Server, Postgres, Oracle) 48 Initial Metrics for PCORI Reporting Days from distribution to response Number of unique patients in each table Trend in unique encounters by encounter type Month-year of first and last encounter Number of diagnoses per encounter by encounter type (eg, diagnoses per ambulatory visit) Number of procedures per encounter by encounter type (eg, procedures per inpatient visit) Frequency of missingness and “out of range” values for critical data elements (eg, % of missing values for DOB) DOB Sex Race Dates Encounter type 49 Questions? 50 MS Data QA Overview Jeff Brown Why check after every refresh? Underlying data sources are dynamic Verify compliance with CDM Identify changes in Data Partners’ data sources or transformation processes Identify problems and/or differences in Data Partners’ data transformation methods info@mini-sentinel.org 52 Why check after every refresh? Green: records from prior refresh Red: record from new refresh under review Problem: Enrollment data from 2010 was archived between refreshes and not included in latest refresh. Outcome: Data Partner was asked to recreate the MS refresh including 2010 data. info@mini-sentinel.org 53 Why check after every refresh? Green: records from prior refresh Red: record from new refresh under review Problem: Loss of 2010 also observed in the Diagnosis table. Outcome: The Data Partner was asked to recreate the MS refresh including 2010 data. info@mini-sentinel.org 54 Data QA & Characterization Four “levels” • Level 1: checks basic compliance with CDM (completeness, validity, accuracy) • Level 2: checks cross-variable and cross-table integrity (integrity) • Level 3: characterizes trends within and across data partners (consistency) • Level 4: characterizes implausible (and illogical) data and variation in data capture and care practices (plausibility, convergence) Standardized error check codes Err code: Table, Level, Variable Number, and Check Number – Err Code “DEM1.3.2” denotes the second level 1 check performed on the variable SEX in the Demographic table – QA Data Model (now queryable) info@mini-sentinel.org 55 MS QA: example Completeness: • ADate variable has missing values Validity: • ADate variable is not SAS date value of numeric data type • ADate variable is not of length 4 • DDate variable is not SAS date value of numeric data type • DDate variable is not of length 4 Accuracy: • ADate is after DDate (for IP and IS only) • ADate and DDate variables have values before DP_MinDate Integrity: • DDate variable is missing for EncType value "IP" • DDate variable is populated for records with EncType values other than "IP" or "IS" info@mini-sentinel.org Consistency: • Problem with distribution of ADate (i.e. total number of records per year) within the ETL • Problem with distribution of ADate (i.e. total number of records per yearmonth) within the ETL • Significant change in number of records per ADate (year) across ETLs • Significant change in number of records per ADate (year-month) across ETLs • Problem with distribution of ADate (overall) within the ETL • Problem with distribution of ADate (overall) across ETLs • Problem with distribution of DDate (i.e. total number of records per year) within the ETL • Problem with distribution of DDate (i.e. total number of records per yearmonth) within the ETL • Significant change in number of records per DDate (year) across ETLs • Significant change in number of records per DDate (year-month) across ETLs • Problem with distribution of DDate (overall) within the ETL • Problem with distribution of DDate (overall) across ETLs • Problem with distribution of DDate variable by EncType per year • Problem with distribution of DDate variable by EncType per year-month • Problem with distribution of length of stay (DDate-ADate + 1) by EncType • Problem with distribution of length of stay (DDate-ADate + 1) by EncType per year 56 Standardizing clinical lab data Observed Result Units for Hba1c Blank %Hb % %HbA1c % A1C %NGSP % A1c %T.Hgb % NGSP %THb % OF TOTAL HbA1c% % TOTAL HGB MG/DL % of Hgb NULL % of total PERCENT %A1C Percent %AIC g/dL mmol/mol info@mini-sentinel.org 57 Standardizing clinical lab data info@mini-sentinel.org 58 Standardizing clinical lab data Percent of INR Results by Data Partner 20% 18% Percent of Total INR Results 16% 14% 12% 10% 8% 6% 4% 2% 9.9 9.6 9.3 9.0 8.7 8.4 8.1 7.8 7.5 7.2 6.9 6.6 6.3 6.0 5.7 5.4 5.1 4.8 4.5 4.2 3.9 3.6 3.3 3.0 2.7 2.4 2.1 1.8 1.5 1.2 0.9 0.6 0.3 0.0 0% Result Value info@mini-sentinel.org 59 Quality Assurance Statistics # of QA output files to review: 264 # of error codes to evaluate: 1,493 # of analysts who review each refresh: 2 Average number of analyst hours per data refresh: 16 # of Data Partners: 18 # of refreshes/QA reviews per year: ~55 % of refreshes that undergo QA review: 100% Pages of documentation: >100 SAS code: available online http://www.mini-sentinel.org/data_activities/distributed_db_and_data/details.aspx?ID=131 info@mini-sentinel.org 65 Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness (ADAPTABLE) Trial PCORnet’s First Pragmatic Clinical Trial Case Scenario Saul had chest pain while working and was taken to the emergency room where he learned he was having a heart attack. Saul’s doctors told him that plaque was building up in his arteries. Upon discharge from the hospital Saul was advised to take 325mg of aspirin each day. Saul compared notes with another friend who said his doctor has him on a baby aspirin because it causes less bleeding and bruising. Saul is confused about what dose he should take. He does a lot of work outdoors and carpentry. He is worried about bleeding while working but doesn’t want another heart attack either. Saul now wonders what he should do. 67 Leading causes of death worldwide 1990 2020 Ischemic heart diseases 1 1 Ischemic heart diseases Cerebrovascular diseases 2 2 Cerebrovascular diseases Lower respiratory infections 3 3 COPD Diarrheal diseases 4 4 Lower respiratory infections Perinatal conditions 5 5 Trachea, bronchus, lung cancers 6 11 10 16 —Murray CJL, Lopez AD. The Global Burden of Disease. 1996. Platelets are critical in acute cardiovascular events Quiescent plaque Lipid core Process Marker Plaque formation Cholesterol LDL Inflammation Multiple factors ? Infection C-reactive protein Adhesion molecules Interleukin 6, TNFa, sCD-40 ligand Vulnerable plaque TF Clotting cascade Inflammation Collagen platelet activation Foam Cells Macrophages Metalloproteinases Plaque rupture Platelet-thrombin micro-emboli Plaque rupture ? Macrophages Metalloproteinases Thrombosis Platelet Activation Thrombin MDA modified LDL D-dimer Fibrinogen Troponin Aspirin: A “wonder” drug Proven clinical benefit in reducing ischemic vascular events Cost effective Benefit with combination antiplatelet therapies But there are issues: Emerging evidence for dose modifiers (ASA resistance, genetics, P2Y12 inhibitors) Equal efficacy across patients? Intolerance Most effective dose uncertain Risks of aspirin therapy Intracranial hemorrhage 0.04% per year Sanjay Gupta; CNN Risks of aspirin therapy Intracranial hemorrhage Likely dose-dependent relationship Gastrointestinal bleeding 75mg 2.3 OR 150mg 3.2 OR 300mg 3.9 OR Risk of death from GI bleed 0.5–10% Distribution of aspirin dosing at discharge Other 0.01% 81 mg 36% 325 mg 61% 162 mg 3% Main Objectives of ADAPTABLE Trial To compare the effectiveness and safety of two doses of aspirin (81 mg and 325 mg) in high-risk patients with coronary artery disease. Primary Effectiveness Endpoint: Composite of allcause mortality, nonfatal MI, nonfatal stroke Primary Safety Endpoint: Major bleeding complications To compare the effects of aspirin in subgroups of patients: Women vs men Older vs younger Racial and ethnic minorities Diabetics Chronic kidney disease (CKD) To develop and refine the infrastructure for PCORnet to conduct multiple comparative effectiveness trials in the future 74 Study Design Patients with known Coronary Artery Disease (MI, or CAD or Revasc) + >1 “Enrichment Factor”* Identified through EHR/Direct pt. consenting in clinics and hospitals through CDRNs/PPRNs (PPRN pts. would need to connect through a CDRN to participate) Pts. contacted electronically with trial information and eConsent Treatment assignment will be provided directly to patient ASA 81 mg QD *Enrichment Factors Age > 65 years, Creatinine > 1.5, Diabetes, Known 3 vessel coronary artery disease, Current cerebrovascular disease and/or peripheral artery disease, known ejection fraction <50% Current smoker ASA 325 mg QD Electronic F/U Q 4 months Supplemented with EHR/CDM/Claims Data Duration: Enrollment over 24 months; maximum f/u of 30 months Primary Endpoint: Composite of all-cause mortality, nonfatal MI, nonfatal stroke Primary Safety Endpoint: Major bleeding complications Trial Logistics: Taking Advantage of PCORnet Infrastructure 76 Screening, Enrollment & Data Flow 77 Computable phenotype History of CAD • Past MI OR • Past cath showing significant CAD OR • Revascularization (PCI/CABG) At least one of the following: • age > 65 years • Creatinine > 1.5 • Diabetes, • Known 3 vessel coronary artery disease • Current cerebrovascular disease and/or peripheral artery disease • Known ejection fraction <50%, • Current smoker Getting consent 78 Getting Informed Consent Clinician reviews and decides on participation Email to potential patient with trial introduction and link to consent Letter to potential pt. with trial intro and paper consent for non-Internet accessible pt. Consent Form Contacts: Local contact info for any site issues Local contact info for withdrawal from trial Contact info for questions about the trial Contact info for reporting adverse events Randomization & ASA dose assignment Timeline for ADAPTABLE Trial Application deadline: February 13, 2015 Merit review: March 2015 Award announced: April 2015 Earliest project start: April 2015 ADAPTABLE SC approval May 2015 DSMB approval June 2015 First system/site activation August 2015 First patient randomized August 2015 80 ADAPTABLE Mindset & Community This is a novel project employing novel methods It is being built by a large group of dedicated networks and people leveraging different experiences, skills and expertise dedicated to the Mission If funded: Modifications will be needed per the Review Committee and DMC The Steering Committee will need to modify details based on additional review especially on pragmatism We need pioneers, willing to work together to solve the challenge to create a reusable infrastructure ADAPTABLE needs to be adaptable 81 Next steps…. Let’s make a plan for each of the following areas: Clinician engagement Recruitment plan Patient engagement eConsent/IRB Follow-up 82 Use Cases: Health Systems Demonstration Project & Obesity Complete Data Demonstration Rich Platt & Adrian Hernandez Open Session for Questions and Wrap Up All