PCORnet Coordinating Center Site Visit: GPC March 19, 2015 Call-in:1 (571) 317-3122 Access code:...

PCORnet Coordinating Center Site Visit: GPC

March 19, 2015

Call-in:1 (571) 317-3122 Access code: 863-993-413

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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

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.

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Guiding principle: Make research easier

Analysis ready data

Reusable analysis tools

Administrative simplicity

Simple, pragmatic studies integrated into routine care

Outpatient clinic 2

Outpatient clinic 3

Each organization can participate in multiple networksEach network controls its governance and coordinationOther networks can participateNetworks share infrastructure, data curation, analytics, lessons, security, software development

Health Plan 2

Health Plan 1

Health Plan 5

Health Plan 4

Health Plan 7

Hospital 1

Health Plan 3

Health Plan 6

Health Plan 8

Hospital 3Health Plan 9

Hospital 2

Hospital 4

Hospital 6

Hospital 5

Outpatient clinic 1

Patient network 1

Patient network 3

Patient network 2

Multiple Networks Sharing Infrastructure

Outpatient clinic 2

Outpatient clinic 3

Health Plan 2

Health Plan 1

Health Plan 5

Health Plan 4

Health Plan 7

Hospital 1

Health Plan 3

Health Plan 6

Health Plan 8

Hospital 3Health Plan 9

Hospital 2

Hospital 4

Hospital 6

Hospital 5

Outpatient clinic 1

Patient network 1

Patient network 3

Patient network 2

Multiple Networks Sharing Infrastructure

Each organization can participate in multiple networksEach network controls its governance and coordinationOther networks can participateNetworks share infrastructure, data curation, analytics, lessons, security, software development

Data captured from healthcare delivery, direct encounter basis

Data captured from processes associated with healthcare delivery

Data captured within multiple contexts: healthcare delivery,

registry activity, or directly from patients

Fundamental basis

PATIDBIRTH_DATEBIRTH_TIMESEXHISPANICRACEBIOBANK_FLAG

DEMOGRAPHIC

PATIDENR_START_DATEENR_END_DATECHARTENR_BASIS

ENROLLMENT

PATIDENCOUNTERIDSITEIDADMIT_DATEADMIT_TIMEDISCHARGE_DATEDISCHARGE_TIMEPROVIDERIDFACILITY_LOCATIONENC_TYPEFACILITYIDDISCHARGE_DISPOSITIONDISCHARGE_STATUSDRGDRG_TYPEADMITTING_SOURCE

ENCOUNTER

PATIDENCOUNTERID (optional)MEASURE_DATEMEASURE_TIMEVITAL_SOURCEHTWTDIASTOLICSYSTOLICORIGINAL_BMIBP_POSITIONTOBACCOTOBACCO_TYPE

VITAL

PATIDENCOUNTERIDENC_TYPE (replicated)ADMIT_DATE (replicated)PROVIDERID (replicated)DXDX_TYPEDX_SOURCEPDX

DIAGNOSIS

PATIDENCOUNTERIDENC_TYPE (replicated)ADMIT_DATE (replicated)PROVIDERID (replicated)PX_DATEPXPX_TYPE

PROCEDURE

PATIDRX_DATENDCRX_SUPRX_AMT

DISPENSING

PATIDENCOUNTERID (optional)LAB_NAMESPECIMEN_SOURCELAB_LOINCSTATRESULT_LOCLAB_PXLAB_PX_TYPELAB_ORDER_DATESPECIMEN_DATESPECIMEN_TIMERESULT_DATERESULT_TIMERESULT_QUALRESULT_NUMRESULT_MODIFIERRESULT_UNITNORM_RANGE_LOWMODIFIER_LOWNORM_RANGE_HIGHMODIFIER_HIGHABN_IND

LAB_CM_RESULT

PATIDENCOUNTERID (optional)REPORT_DATERESOLVE_DATECONDITION_STATUSCONDITIONCONDITION_TYPECONDITION_SOURCE

CONDITION

PATIDENCOUNTERID (optional)CM_ITEMCM_LOINCCM_DATECM_TIMECM_RESPONSECM_METHODCM_MODECM_CAT

PRO_CM

vv

New to v2.0New to v2.0

PCORnet Common Data Model, Draft v2.0 Modifications

CDRN 1

PCORnet DRN Coordinating Center

PCORnet Secure Network Portal

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Demographics Utilization

Etc

Review & Run Query

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Review & Return Results

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CDRN 11

Demographics Utilization

Etc

Review & Run Query

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Review & Return Results

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1. User creates and submits query (a computer program)

2. Individual CDRNs/PPRNs retrieve query

3. CDRNs/PPRNs review and run query against their local data

4. CDRNs/PPRNs review results

5. CDRNs/PPRNs return results via secure network

6. Results are aggregated

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Toh Arch Intern Med.2012;172:1582-1589.

Used data for 3.9 million new users of anti-hypertensives in 18 organizationsPropensity score matched stratified analysisNo person-level data was sharedFive months and $250,000 required for programming and analysis – compared to 1-2 years and $2 million without analysis-ready distributed dataset

Reusable Program: Propensity Score Matched Cohort Study

Specify:Population (age/sex/etc), time periodExposuresOutcomes 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 criteriaExclusion criteriaCovariatesPropensity score matching options Comorbidity, utilization, high dimensional propensity

score Matching ratio Caliper size

Angioedema: Table 1. Unmatched Cohort

3.9 million new users

www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf

Diabetes 21% vs 10%Heart failure 2% vs 4%Ischemic heart disease 5% vs 13%

Propensity Scores Before Match

www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdfDP3

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%

Propensity Scores After Match

www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdfDP3

Angioedema: Table 3. Results

ACEI vs β-blocker 1:1 matched analysis:• HR = 3.1

(95% CI, 2.9-3.4)


Toh et al findings: • HR = 3.0

(95% CI, 2.8-3.3)

Trial Logistics: Taking Advantage of PCORnet Infrastructure

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Screening, Enrollment & Data Flow

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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

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Getting Informed Consent

Randomization & ASA dose assignment

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.

Clinician reviews and decides on participation

Consent Form Contacts:Local contact info for any site issuesLocal contact info for withdrawal from trialContact info for questions about the trialContact info for reporting adverse events

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

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Mini-Sentinel/PCORnet Data Linkage Workgroup

Rich Platt

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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

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Scenarios for potential collaboration

Scenario 0

Characterize within network and outside network utilization for each CDRN by providing CDRN NPI/TIN information to Mini-Sentinel Data Partners with which they have meaningful overlap.

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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.

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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.

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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.

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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 4BCreate 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.

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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.

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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

PCORnet Data Characterization Overview

Jeff Brown and Lesley Curtis

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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

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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

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Data Characterization Cycle

Create DataMart and

ETL ADD

DSSNI sends DC queries

DC query output to DSSNI

DSSNI review; summary to

PCORI

DSSNI and CDRN site discuss DC

results

DSSNI approves

Datamart for querying

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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)

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Data Characterization Queries

PCORnet CDM 1.0

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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

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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

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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

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Data Characterization: Diagnosis


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)

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Data Characterization: Procedure


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)

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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

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Query Results Processing

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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)

Sturtevant,Jessica

Second Bullet - complete cycle by the end of may. Recommend modifying this to something like: High level Metric review and response by the end of May

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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

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Questions?

MS Data QA Overview

Jeff Brown

[email protected] 52

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


Why check after every refresh?

Green: records from prior refreshRed: 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.


Why check after every refresh?

Green: records from prior refreshRed: 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.


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)


MS QA: example

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 year-

month) 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 year-

month) 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

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"


Standardizing clinical lab data

Blank%% A1C% A1c% NGSP% OF TOTAL% TOTAL HGB% of Hgb% of total%A1C%AIC

%Hb

%HbA1c%NGSP%T.Hgb%THbHbA1c%MG/DLNULLPERCENTPercentg/dLmmol/mol

Observed Result Units for Hba1c



0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3 3.6 3.9 4.2 4.5 4.8 5.1 5.4 5.7 6.0 6.3 6.6 6.9 7.2 7.5 7.8 8.1 8.4 8.7 9.0 9.3 9.6 9.90%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

Result Value

Perc

ent o

f Tot

al IN

R Re

sults

Percent of INR Results by Data Partner


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

Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness (ADAPTABLE)Trial

PCORnet’s First Pragmatic Clinical Trial

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Case ScenarioSaul 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.

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Leading causes of death worldwide

Ischemic heart diseases

Cerebrovascular diseases

Lower respiratory infections

Diarrheal diseases

Perinatal conditions

COPD

Trachea, bronchus, lung cancers

1990 2020Ischemic heart diseases

Cerebrovascular diseases

Lower respiratory infections

—Murray CJL, Lopez AD. The Global Burden of Disease. 1996.

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Platelets are critical in acute cardiovascular events

Quiescent plaqueProcess

Plaque formation

InflammationMultiple factors? Infection

Plaque rupture? MacrophagesMetalloproteinases

ThrombosisPlatelet ActivationThrombin

Marker

CholesterolLDL

C-reactive proteinAdhesion moleculesInterleukin 6, TNFa,sCD-40 ligand

MDA modified LDL

D-dimerFibrinogenTroponin

Vulnerable plaque

MacrophagesFoam Cells

Collagen platelet activation

TF Clotting cascade

Lipid core

Metalloproteinases

Inflammation

Platelet-thrombin micro-emboliPlaque rupture

Aspirin: A “wonder” drugProven 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

Gastrointestinal bleeding

Likely dose-dependent relationship

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

81 mg36%

162 mg3%

325 mg61%

Other0.01%

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 all-cause 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)

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To develop and refine the infrastructure for PCORnet to conduct multiple comparative effectiveness trials in the future

Study DesignPatients 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 eConsentTreatment assignment will be provided directly to patient

ASA 81 mg QD ASA 325 mg QD

Electronic F/U Q 4 monthsSupplemented 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

*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

Trial Logistics: Taking Advantage of PCORnet Infrastructure

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Screening, Enrollment & Data Flow

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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

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Getting Informed Consent

Randomization & ASA dose assignment

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.

Clinician reviews and decides on participation

Consent Form Contacts:Local contact info for any site issuesLocal contact info for withdrawal from trialContact info for questions about the trialContact info for reporting adverse events

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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 activationAugust 2015

First patient randomizedAugust 2015

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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

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Next steps….

Let’s make a plan for each of the following areas:

Clinician engagement

Recruitment plan

Patient engagement

eConsent/IRB

Follow-up

Use Cases: Health Systems Demonstration Project & Obesity Complete Data Demonstration

Rich Platt & Adrian Hernandez

Open Session for Questions and Wrap Up

All

PCORnet Coordinating Center Site Visit: GPC March 19, 2015 Call-in:1 (571) 317-3122 Access code:...

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