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. 2021 May 20;4(2):ooab027.
doi: 10.1093/jamiaopen/ooab027. eCollection 2021 Apr.

A mobile app for delirium screening

Brett Armstrong  1 , Daniel Habtemariam  2 , Erica Husser  3 , Douglas L Leslie  3 , Marie Boltz  3 , Yoojin Jung  4 , Donna M Fick  3 , Sharon K Inouye  2 , Edward R Marcantonio  4 , Long H Ngo  4
Affiliations

A mobile app for delirium screening

Brett Armstrong et al. JAMIA Open. .

Abstract

Objective: The objective of this study is to describe the algorithm and technical implementation of a mobile app that uses adaptive testing to assess an efficient mobile app for the diagnosis of delirium.

Materials and methods: The app was used as part of a NIH-funded project to assess the feasibility, effectiveness, administration time, and costs of the 2-step delirium identification protocol when performed by physicians and nurses, and certified nursing assistants (CNA). The cohort included 535 hospitalized patients aged 79.7 (SD = 6.6) years enrolled at 2 different sites. Each patient was assessed on 2 consecutive days by the research associate who performed the reference delirium assessment. Thereafter, physicians, nurses, and CNAs performed adaptive delirium assessments using the app. Qualitative data to assess the experience of administering the 2-step protocol, and the app usability were also collected and analyzed from 50 physicians, 189 nurses, and 83 CNAs. We used extensible hypertext markup language (XHTML) and JavaScript to develop the app for the iOS-based iPad. The App was linked to Research Electronic Data Capture (REDCap), a relational database system, via a REDCap application programming interface (API) that sent and received data from/to the app. The data from REDCap were sent to the Statistical Analysis System for statistical analysis.

Results: The app graphical interface was successfully implemented by XHTML and JavaScript. The API facilitated the instant updating and retrieval of delirium status data between REDCap and the app. Clinicians performed 881 delirium assessments using the app for 535 patients. The transmission of data between the app and the REDCap system showed no errors. Qualitative data indicated that the users were enthusiastic about using the app with no negative comments, 82% positive comments, and 18% suggestions of improvement. Delirium administration time for the 2-step protocol showed similar total time between nurses and physicians (103.9 vs 106.5 seconds). Weekly enrollment reports of the app data were generated for study tracking purposes, and the data are being used for statistical analyses for publications.

Discussion: The app developed using iOS could be easily converted to other operating systems such as Android and could be linked to other relational databases beside REDCap, such as electronic health records to facilitate better data retrieval and updating of patient's delirium status.

Conclusion: Our app operationalizes an adaptive 2-step delirium screening protocol. Its algorithm and cross-plat formed code of XHTML and JavaScript can be easily exported to other operating systems and hardware platforms, thus enabling wider use of the efficient delirium screening protocol that we have developed. The app is currently implemented as a research tool, but with adaptation could be implemented in the clinical setting to facilitate widespread delirium screening in hospitalized older adults.

Keywords: 2-step delirium protocol; API; JavaScript; REDCap; XHTML; app; delirium diagnosis.

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Figures

Figure 1.
Figure 1.
Data flow of the READI Project: from Staff to iPad to REDCap and to SAS for analysis.
Figure 2.
Figure 2.
Flow diagram of the content of the delirium screening assessment app.
See this image and copyright information in PMC

References

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