Welcome to Durham Smart CRF Leonard Sacks

Welcome to Durham!

Smart CRF Leonard Sacks MD Deputy director Office of Critical Path Programs FDA

How familiar are you with the CDISC CDASH initiative? 1. 2. 3. 4. Not familiar Somewhat familiar Familiar Very Familiar

Do you believe FDA accepts paperless trials? n 1) Yes n 2) No

If your organization works with clinical trials, what approximate percentage of CRFs are fully electronic? 1. 0% 2. 1 -50% 3. 51%-99% 4. 100%

How effective would you estimate your current systems are in detecting investigator fraud (e. g. fictitious patients)? 1. <25% 2. 26 -50% 3. 51 -75% 4. 76 -99% 5. 100%

What percentage of clinical monitoring is spent correcting and completing the CRF (e. g. missing concomitant meds, missing AE report, missing investigator signature or date)? 1. <25% 2. 26 -50% 3. 51 -75% 4. 76 -99% 5. 100%

What percentage of clinical monitoring is spent educating study staff? 1. <25% 2. 26 -50% 3. 51 -75% 4. 76 -99% 5. 100%

Hey what’s this?

And this….

Old system n Huge bulky paper CRF n Tedious for investigator- repetition, data transfer, n n n consistency Tedious for monitors Tedious to transport and archive All data must be transcribed Data cannot be evaluated in real-time Unable to capture images etc Numerous ancillary functions are needed e. g. randomization, informed consent, monitoring

Borrow experience n Paper records antiquated in clinical practice n At FDA, electronic submissions n SPL- searchable labels n Electronic Adverse Event reports n Electronic health records in clinical practice

Borrow experience n Security and privacyn Experience from the financial world-online banking n Financial privacy and tax returns

Paper CRF – ? the dinosaur n Electronic CRF ~50% submissions, limited capabilities. n Smart CRF becomes the platform for clinical study

Smart CRF – Two functional tiers Information entry/archival- 1. n Interface with point of data entry n n investigator, laboratory, patient (in the case of a Patient Reported Outcome tool) “smart” function n n auto-audits for inconsistencies in matching fields checks for missing data prompts for additional data fields e. g. SAE form automated carry over of information that does not change data assembly

Smart CRF – Two functional tiers 2. Data trafficking n n Input from: n investigator, n lab, n imaging, n patient (PRO’s) Output to: n Sponsor n IRB/DSMB n CRO n FDA

Smart CRF-trial components n Envisaged to cater for all the components of a clinical trial n n n n n Electronic informed consent/video interface Inclusion and exclusion Randomization Recording study procedures Recording clinical findings Laboratory data trafficking Auto monitoring Safety reporting Interactive with investigator Archival of data, images etc

Smart CRF – the stakeholders/users n Envisaged to cater for the needs of all the many users/stakeholders in a trial: n n n n n Patient Health care provider/investigator Laboratory services Imaging services CRO Monitors IRB/DSMB Sponsor FDA

Functions tailored for user/stakeholder n Patientn informed consent, real-time results, safety alerts-from lab, from sponsor n Investigatorn interactive-informs on data trends n prompts e. g. SAE forms n auto transfer of fixed data n automatic population of lab fields n auto-monitoring n consistency e. g. inclusion and exclusion criteria, n completeness e. g. stop dates for concomitant meds n Sponsorn remote access real-time e. g. overseas sites, n Immediate data analysis without waiting for transcription n CROn monitoring function, archiving, communication n IRBn automatic submission and aggregation of SAE reports n FDAn fraud monitoring, investigator training

Other functions n Fraud detection n n Safety warningn n n n Automatic generation of SAE form Integration of all SAE reports Algorithms for iterative lab safety analysis e. g. threshold number of LFT elevations, threshold differences between arms Notification of IRB and Sponsors They can trigger immediate alerts to investigator and patient Investigator interactive reporting of safety trends-e. g. . subcritical rise in LFTs, Creatinine Auto-monitoringn n n Variance algorithms- e. g. date of birth, variance of lab data, BP measurement Corroborative data entry-independent entry from lab- eliminates transcription fraud (cannot eliminate fraudulent specimens), independent imaging data entry Electronic date and time stamp, unable to change data without an electronic record Prompts for missing data-e. g. . stop dates, start dates informed consent- won’t allow investigator to continue Consistency check on all dependent fields- e. g. concomitant meds, pill counts Imaging

Smart CRF – Trial Components: Do you agree thus far with the Smart CRF as an objective? 1. 2. 3. Yes No Somewhat

Are you currently using a ‘Smart CRF’? 1. 2. 3. Yes No Don’t know

An interesting corollaryn Study results can be evaluated without un- blinding n Automated pre-specified analyses can be reviewed without knowing which arm is which n Conclusions can be made on non-inferiority or superiority prior to breaking the blind

Complementary initiatives n Embedding the case report form in the clinical electronic health record so that patients can be studied during the course of their clinical care (Electronic health Records/Clinical research) n Data standards- streamlining the format and content of data elements so they can be shared between different users e. g. laboratory, health care provider, investigator, sponsor, pharmacovigilance investigator n Ethical initiatives to ensure patient privacy and protect patient identity

More on data standards n CDASH Project n Clinical Data Acquisition Standards Harmonization n CDISC n Study Data Tabulation Model (SDTM) n Standard for Exchange of Nonclinical Data (SEND) n Operational Data Model (ODM)

Has your organization taken any steps to implementing any CDISC standards? 1. 2. 3. Yes No Don’t know

If you have implemented a CDISC standard, which one is most valuable to your organization? 1. 2. 3. 4. Lab Standard SDTM (Study Data Tabulation Model) ODM (Operational Data Model) SEND (Standard for Exchange of Non-clinical Data)

Clinical Data Interchange Standards Consortium n Principles n Lead development of standards that improve process efficiency while supporting the scientific nature of clinical research n Creating flexible, intelligible and navigable submissions n Importance of data quality structure and content n Global multidisciplinary functionality n Emphasize data sharing and minimize duplication n Provide education on CDISC n Avoid promoting individual organizations or vendors

CDISC

CDISC

Challenges n Addressing source documentation as required by regulation n Preventing automatic (default) carryover of changeable data e. g. concomitant meds n Ensure investigator ownership of data- they take responsibility

Regulations and Guidances n 21 CFR 11 “Electronic records, electronic signatures” n Procedures n to ensure signer cannot repudiate the signed record as genuine n For validation of accuracy, ability to detect alterations, time stamped audit trails n For limitation of access to, and generation of accurate copies of electronic records n Checks on authority to sign and access records n To verify identity of electronic signature

Regulations and Guidances n Guidance: Part 11, electronic records; Electronic signatures-scope and application (August 2003) n Enforce n n n n n Enforcement discretion n n Limiting system access to authorized individuals Use of operational system checks Use of authority checks Determination that persons who develop, maintain or use electronic systems have the education, training and experience to perform their assigned tasks Establishment of, and adherence to written policies that hold individuals accountable for actions initiated under their electronic signatures Appropriate controls over systems documentation Controls for open systems corresponding to controls for closed systems Requirement related to electronic signatures Validation of computerized systems Requirements related to computer-generated time-stamped audit traisl Legacy systems (those in place prior to Aug 97) Copies of records and record retention Computerized systems used in clinical investigations (May 2007)

DSI- Examples of problems n Backdating of monitoring reports n Gaps in performance of source verification n On site source data not retained at closeout of study n Procedures recorded prior to actual patient visit n Delays in signing electronic data capture

Conclusions n Smart CRFs offer enormous opportunities to expedite clinical trials n They can be tailored to the needs of all parties involved in the clinical trial n They can perform numerous trial functions n They can simplify study monitoring n They can improve safety monitoring through real-time surveillance n They save trees, and space and gasoline…. . and so on.

Questions n What still needs to be done to move forward? n How to we set about determining electronic reliability? n Are there standards that need to be articulated for the myriad functions of the ECF? n Other ideas?

How many years away is a Smart CRF? 1. 2. 3. 4. 5. <3 3 -7 7 -10 >10 Not going to happen

Is there market value for the Smart CRF? 1. 2. 3. Yes No Not Sure

Is SCDM the forum for discussion on the development and adoption of Smart CRF objective? 1. 2. 3. Yes No Not Sure