Clinical Research CoordinatorCourse 5 of 8

Clinical Research Coordinator

Full course · Participant Management: Screening to Retention

Database and registry-based identification strategies — Participant Management: Screening to Retention

Database and registry-based identification strategies

Translate eligibility criteria into EHR queries, manage false positives, leverage research registries, and apply data governance principles for population-level participant identification.

24 min

1.2 million records and 50 needles

The site's electronic health record system contains 1.2 million patient records. Somewhere in that population are approximately 50 people who meet every eligibility criterion for the Phase III diabetes trial that just received IRB approval. The protocol requires adults aged 40 to 75 with a confirmed diagnosis of Type 2 diabetes mellitus, HbA1c between 7.5% and 10.5% within the past 90 days, on stable metformin monotherapy for at least 12 weeks, with an estimated glomerular filtration rate above 45 mL/min/1.73m2. And that is before the exclusion criteria — no recent cardiovascular events, no active malignancy, no current insulin use, no pregnancy or nursing.

The coordinator could, in theory, open charts one by one. At three minutes per chart, reviewing just the cardiology and endocrinology panels — perhaps 40,000 records — would take 2,000 hours. Roughly 50 weeks of full-time work, assuming no other responsibilities. Which, of course, is absurd.

This is why database-driven identification exists. It is the difference between searching for 50 people by walking door to door across a city and querying the city's records to find addresses that match specific criteria. Both approaches can find the same people. One takes weeks; the other takes an afternoon. But the database approach introduces its own problems — problems that, if you do not understand them, will waste your time in a different and more insidious way than chart-by-chart review ever could.

The previous two lessons taught you to review individual records (Lesson 1) and to conduct telephone pre-screening with callers who present themselves (Lesson 2). This lesson teaches the third identification strategy: going to the data proactively, at the population level, to find people who do not yet know they might be eligible.

What you will learn4 objectives

By the end of this lesson, you will be able to:

Translate protocol eligibility criteria into searchable EHR parameters including ICD-10 codes, medication lists, laboratory value ranges, and demographic filters
Explain why database matches require clinical verification and describe the false positive problem inherent in population-level screening
Evaluate research registries and opt-in databases as recruitment tools, including advantages of pre-consented contact and limitations of selection bias and data currency
Apply data governance principles including data use agreements, appropriate use policies, and the distinction between clinical data and research data

Progress saves itself — finish reading and this lesson completes

This is just the beginning

1.2 million records and 50 needles