Using cognitive task analysis to facilitate the integration of decision support systems into the neonatal intensive care unit

Gordon D. Baxter, Andrew F. Monk, Kenneth Tan, Peter R F Dear, Simon J. Newell

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31 Citations (Scopus)


Objective: New medical systems may be rejected by staff because they do not integrate with local practice. An expert system, FLORENCE, is being developed to help staff in a neonatal intensive care unit (NICU) make decisions about ventilator settings when treating babies with respiratory distress syndrome. For FLORENCE to succeed it must be clinically useful and acceptable to staff in the context of local work practices. The aim of this work was to identify those contextual factors that would affect FLORENCE's success. Methods: A cognitive task analysis (CTA) of the NICU was performed. First, work context analysis was used to identify how work is performed in the NICU. Second, the critical decision method (CDM) was used to analyse how staff make decisions about changing the ventilator settings. Third, naturalistic observation of staff's use of the ventilator was performed. Results: A. The work context analysis identified the NICU's hierarchical communication structure and the importance of numerous types of record in communication. B. It also identified important ergonomic and practical requirements for designing the displays and positioning the computer. C. The CDM interviews suggested instances where problems can arise if the data used by FLORENCE, which is automatically read, is not manually verified. D. Observation showed that most alarms cleared automatically. When FLORENCE raises an alarm staff will normally be required to intervene and make a clinical judgement, even if the ventilator settings are not subsequently changed. Conclusions: FLORENCE must not undermine the NICU's hierarchical communication channels (A). The re-design of working practices to incorporate FLORENCE, reinforced through its user interface, must ensure that expert help is called on when appropriate (A). The procedures adopted with FLORENCE should ensure that the data the advice is based upon is valid (C). For example, FLORENCE could prompt staff to manually verify the data before implementing any suggested changes. FLORENCE's audible alarm should be clearly distinguishable from other NICU alarms (D); new procedures should be established to ensure that FLORENCE alarms receive attention (D), and false alarms from FLORENCE should be minimised (B, D). FLORENCE should always provide the data and reasoning underpinning its advice (A, C, D). The methods used in the CTA identified several contextual issues that could affect FLORENCE's acceptance. These issues, which extend beyond FLORENCE's capability to suggest changes to the ventilator settings, are being addressed in the design of the user interface and plans for FLORENCE's subsequent deployment.

Original languageEnglish
Pages (from-to)243-257
Number of pages15
JournalArtificial Intelligence in Medicine
Issue number3
Publication statusPublished - Nov 2005
Externally publishedYes


  • Artificial respiration
  • Decision making
  • Expert systems
  • Neonatal intensive care
  • Respiratory distress syndrome

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