1. Laboratory Data Integrity Ashraf Mozayani, PharmD, PhD Texas Southern University Barbara Jordan-Mickey Leland School of Public Affairs Forensic Science Learning Lab mozayania@tsu.edu 713-313-7332

2. Objectives Terminology associated with data integrity and data manipulation Why should we have a Plan to prevent and detect data manipulation/fraud? Situation that contribute to unethical behavior Steps to add to detect/avoid data manipulation

3. What is Data Integrity?  Data that follows:  Ethical Standards  Be Acceptable in Scientific Community  Be Traceable  Be Defensible

4. Terms Laboratory fraud: The falsification of analytical and quality assurance results, where failed method requirements are made to appear acceptable during reporting: Sampling Receipt, Preparation, Analysis Report writing Testifying

5. Terms  Intentional misrepresentation of lab data to hide existing or potential problems.  Intent to deceive – making data look better than it really is.  Dry Labbing – Reporting data for samples or procedures not actually analyzed or performed.  Data deletion – removal of bad/undesirable data.  Backlogging – In this context, is the entry of data into a laboratory document at a time later than the time indicated on the document  Time Travel- Changing times and dates

6. Why Should We Have A Plan?  When it happens it causes :  Tainting our Lab Staff  Distrust by our Clients  Cost us  Ruin Careers

7. Root Causes that Might Contribute to Unethical Behavior  Analyst  Lack of Knowledge of testing  Lack of understanding the impact  Lack of courage  Desire for perfect record  To complete cases quickly in an effort to meet deadlines

8. Root Causes that Might Contribute to Unethical Behavior  Management  Ineffective Oversight  Impractical Expectation  Emphasis on Production over Quality

9. Situations that Might Contribute to Laboratory Fraud  To encourage boosting productivity with obtaining better merit increases or bonuses  Conflict of interest with regards to a case  Lack of factual/ thorough technical and administrative review

10. Examples of Improper Lab Practices  PT  Batch QC  Blank  Testimony

11. The Good News  The forensic scientists are good at what they do however we need to address this perceived problem:  realize that NO system is error-free  design and implement a program that ACTIVELY looks for weak spots and corrects them  And this leads to a better system – never perfect, but always better.

12. Suggestions to improve Data Integrity!$$$

13. Laboratory Data Integrity Policy  A systematic approach by laboratory to assure:  Accurate data reflecting the test follows:  Analysis using accepted scientific practice and principles  Produce data that are traceable and defensible  Bias free environment  Ethical Professional  Comply with all regulations associated with tasks, responsibilities, testimonies

14. Data Integrity Foundation  Thorough training the staff  Monitoring  Confidential reporting  Investigation  Correction

15. Staff Training  Training Program  All analysts should be competency tested  Prior qualification and authorization should be required to perform casework  Mock Trials should be conducted  Court Testimony Evaluation should be performed for testifying analysts  Mandatory Ethics Training of Staff  Certification

16. Monitoring Data Integrity  Staff  Analytical Instruments  Testing Processes  Oversight and Review

17. Monitoring Data Integrity: Staff  Experienced data reviewers  Thorough data review by outside expert  Including chain of custody, time stamps, signatures, and an evaluation of control data  Monitoring of computer updates  Random, periodic observation  Blind samples  Detailed review of each analyst data

18. Monitoring Data Integrity: Instrumentation  Analytical Instrumentation Preparation of the sample and operation of the instrument should be performed by different individuals  Instrument maintenance should be performed by individuals other than the analyst if it is possible  Calibration and tuning of instruments should be performed by an individual other than the analyst.  All operations of the instrument should be recorded by the operator and reviewed by a qualified reviewer prior to release of the final report  All tuning, calibration and run data should be stored as PDF files immediately upon generation

19. Monitoring Data Integrity – The Testing Process  Testing Process  Batching: multiple people work on a case and multiple cases should worked together  Implementing the use of unique barcode on all samples  Internal Chain of Custody: should be verified to track the evidence. Everyone should be indicated on the chain of custody  Log Sheets, Derivative Evidence Log, and paper trail in case files  100 % Technical Reviews/Admin Reviews on all cases  Taking photographs of evidence before and after sampling  Photographing all subjective tests like microscopic tests, color tests

20. Data Integrity Standard Operating Procedure- Purpose:  to describe the laboratory’s data integrity system  to emphasize the importance of integrity in the performance of all analytical work  to acquire the commitment of laboratory staff to the principle that all analyses shall be performed in a controlled and documented manner  to confirm that laboratory staff consistently meet the specific ethical requirements defined in this data integrity plan.

21.  Dry-labbing, back filling or data manipulation/fraud is not wide spread  It can happen in any laboratory with any analyst  Learn from misfortune of other labs  Have a strong Data Integrity Policy Summary

22.  All these programs require self- assessment  A painful process, but necessary if we want to improve  This is NOT about not trusting our colleagues. This IS about improving our system

23. Thank you for your time. mozayaniA@tsu.edu 713-313-7332