Clinical decision support system to enhance quality control of spirometry using information and communication technologies

Felip Burgos, Umberto Melia, Montserrat Vallverdú, Filip Velickovski, Magí Lluch-Ariet, Pere Caminal, Josep Roca, Felip Burgos, Umberto Melia, Montserrat Vallverdú, Filip Velickovski, Magí Lluch-Ariet, Pere Caminal, Josep Roca

Abstract

Background: We recently demonstrated that quality of spirometry in primary care could markedly improve with remote offline support from specialized professionals. It is hypothesized that implementation of automatic online assessment of quality of spirometry using information and communication technologies may significantly enhance the potential for extensive deployment of a high quality spirometry program in integrated care settings.

Objective: The objective of the study was to elaborate and validate a Clinical Decision Support System (CDSS) for automatic online quality assessment of spirometry.

Methods: The CDSS was done through a three step process including: (1) identification of optimal sampling frequency; (2) iterations to build-up an initial version using the 24 standard spirometry curves recommended by the American Thoracic Society; and (3) iterations to refine the CDSS using 270 curves from 90 patients. In each of these steps the results were checked against one expert. Finally, 778 spirometry curves from 291 patients were analyzed for validation purposes.

Results: The CDSS generated appropriate online classification and certification in 685/778 (88.1%) of spirometry testing, with 96% sensitivity and 95% specificity.

Conclusions: Consequently, only 93/778 (11.9%) of spirometry testing required offline remote classification by an expert, indicating a potential positive role of the CDSS in the deployment of a high quality spirometry program in an integrated care setting.

Keywords: information communication technologies; primary care; quality control; spirometry; telemedicine.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Flow of the process followed to elaborate and validate the Clinical Decision Support System (CDSS). ATS=American Thoracic Society; FS=forced spirometry.
Figure 2
Figure 2
Equations for data analysis. F=flow; V=volume; i=1,…,N; N=length of the sequence; true positive (TP) corresponds to curves classified as grade 0 by both CDSS and the evaluator; true negative (TN) corresponds to curves classified as grade 1 by the CDSS and the by the evaluator; false positive (FP) indicates curves classified as grade 0 by the CDSS, but classified in grade 1 by the evaluator; and, false negative (FN) corresponds to curves classified as grade 1 by the CDSS, but as grade 0 by the evaluator.

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Source: PubMed

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