Professional Patients and Deception in Clinical Research Trials
If your trials rely on research participants with a track record of taking part in a number of clinical trials, it might be worth taking a second look and examining them a bit closer. Building on past research findings that found high fabrication rates by frequent study participants, or “professional patients,” two universities recently collaborated to study the prevalence of deception used by these subjects.
While participating in a number of studies isn’t necessarily a problem — after all, some people may be motivated by adding to scientific knowledge — the bigger issue at hand is the potential deception of some of these participants.
After taking a closer look at the rates of deception among professional patients, the study uncovered some big findings including:
- 75% of frequent participants admitted either withholding or hiding some sort of information at least once to take part in a study.
- Participants who use some sort of deception are also likely to be more motivated by the financial incentives of trial participation.
- Professional patients who make up information may threaten a study validity more than those who hide information, especially when the disease or condition being measured cannot be verified objectively (e.g. depression, anxiety, physical pain, etc.).
- Some frequent participants have even taken an extra step and paid a ‘research kingpin’ for information to pass a phone screening and take part in a study.
So, how do clinical research associates locate professional patients in research trials and keep an eye on their data? Although some may believe that 100% Source Data Verification (SDV) holds the answer, this isn’t the most effective method. In fact, only by taking a step back and looking at all of the data in the aggregate can we spot these study participants.
As a company that has created a fantastic electronic data capture system (and has the market leadership to prove it!), we have a better system in place for detecting professional patients. Our database of unique site IDs lets us take a look at sites in the same region to spot professional patients and other anomalies. In the past, our software has even flagged these outliers and allowed us to alert researchers to the problem.
What do you think of these findings? Have your studies ever been impacted by professional patients? Be sure to share your thoughts in the comment section below!
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