Using qualitative research to inform development of a diagnostic algorithm for UTI in children

Isabel de Salis*, Penny Whiting, Jonathan A. C. Sterne, Alastair D. Hay

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)


Diagnostic and prognostic algorithms can help reduce clinical uncertainty. The selection of candidate symptoms and signs to be measured in Case Report Forms (CRFs) for potential inclusion in diagnostic algorithms needs to be comprehensive, clearly formulated and relevant for end users.

To investigate whether qualitative methods could assist in designing CRFs in research developing diagnostic algorithms. Specifically, the study sought to establish whether qualitative methods could have assisted in designing the CRF for the Health Technology Association funded Diagnosis of Urinary Tract infection in Young children (DUTY) study, which will develop a diagnostic algorithm to improve recognition of urinary tract infection (UTI) in children aged <5 years presenting acutely unwell to primary care.

Qualitative methods were applied using semi-structured interviews of 30 UK doctors and nurses working with young children in primary care and a Childrens Emergency Department. We elicited features that clinicians believed useful in diagnosing UTI and compared these for presence or absence and terminology with the DUTY CRF.

Despite much agreement between clinicians accounts and the DUTY CRFs, we identified a small number of potentially important symptoms and signs not included in the CRF and some included items that could have been reworded to improve understanding and final data analysis.

This study uniquely demonstrates the role of qualitative methods in the design and content of CRFs used for developing diagnostic (and prognostic) algorithms. Research groups developing such algorithms should consider using qualitative methods to inform the selection and wording of candidate symptoms and signs.

Original languageEnglish
Pages (from-to)325-331
Number of pages7
JournalFamily Practice
Issue number3
Publication statusPublished - Jun 2013


  • Algorithm
  • methodological study
  • qualitative research
  • urinary tract infections


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