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
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
What is Data Integrity?
 Data that follows:
Ethical Standards
Be Acceptable in Scientific Community
 Be Traceable
 Be Defensible
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
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
Why Should We Have A Plan?
When it happens it causes :
Tainting our Lab Staff
Distrust by our Clients
Cost us
Ruin Careers
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
Root Causes that Might
Contribute to Unethical Behavior
Management
Ineffective Oversight
Impractical Expectation
Emphasis on Production over
Quality
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
Examples of Improper Lab
Practices
PT
Batch QC
Blank
 Testimony
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.
Suggestions to improve
Data Integrity!$$$
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
Data Integrity Foundation
Thorough training the staff
Monitoring
Confidential reporting
Investigation
Correction
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
Monitoring Data Integrity
Staff
Analytical Instruments
Testing Processes
Oversight and Review
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
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
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
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.
Summary
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
All these programs require selfassessment
A painful process, but necessary if
we want to improve
This is NOT about not trusting our
colleagues. This IS about improving
our system
Thank you for your time.
mozayaniA@tsu.edu
713-313-7332