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Related Experiment Video

Updated: May 5, 2026

Block Building Task Identifies Distinct Groups of Left/Right-hand Choice Patterns After Unilateral Peripheral Nerve Injury
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How many patients are "normal"? Only 1.55%.

Griffin M Weber1

  • 1Beth Israel Deaconess Medical Center, Boston, MA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|December 5, 2013
PubMed
Summary
This summary is machine-generated.

Identifying a "normal" control population for clinical studies is difficult. Simple heuristic filters applied to over 2 million patients excluded all but 1.55%, highlighting challenges in defining healthy cohorts.

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Area of Science:

  • Clinical research methodology
  • Health informatics
  • Population health

Background:

  • Defining a "normal" control population is crucial for clinical studies.
  • Identifying healthy individuals in clinical data repositories (CDRs) is challenging due to healthcare access patterns and multi-hospital care.
  • The absence of a diagnosis in one electronic health record (EHR) does not confirm a patient is disease-free.

Purpose of the Study:

  • To develop and apply heuristic filters to identify potential "normal" control populations within a large clinical data repository.
  • To assess the feasibility and yield of identifying healthy cohorts using defined exclusion criteria.

Main Methods:

  • A set of 10 heuristic filters was defined to exclude patients unlikely to be "normal" controls.
  • Filters targeted conditions like chronic diseases, rare diseases, and lack of recent data.
  • The filters were applied to a dataset of 2,019,774 patients from two large academic hospitals.

Main Results:

  • The application of 10 heuristic filters excluded the vast majority of patients.
  • Only 31,352 patients (1.55%) remained after filtering from the initial cohort of 2,019,774.
  • This demonstrates a significant reduction in potential control subjects.

Conclusions:

  • Identifying a "normal" control population for clinical research is exceptionally challenging.
  • The low yield highlights difficulties in defining and isolating healthy cohorts from large electronic health record datasets.
  • Further research is needed to refine methods for identifying truly normal control groups.