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Designing a mHealth Clinical Decision Support System for Parkinson’s Disease: A Theoretically Grounded User Needs Approach

Timotijevic, Lada, Banks, Carys, Rusconi, Patrice, Egan, Bernadette, Peacock, Matthew, Seiss, Ellen, Touray, Morro, Gage, Heather, Pellicano, C., Spalletta, G. , Assogna, F., Giglio, M., Marcante, A., Gentile, G., Cikajilo, I., Gatsios, D., Konitsiotis, S. and Fotiadis, D. (2020) Designing a mHealth Clinical Decision Support System for Parkinson’s Disease: A Theoretically Grounded User Needs Approach BMC Medical Informatics and Decision Making, 20 (34).

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Abstract

Background: Despite the established evidence and theoretical advances explaining human judgments under uncertainty, developments of mobile health (mHealth) Clinical Decision Support Systems (CDSS) have not explicitly applied the psychology of decision making to the study of user needs. We report on a user needs approach to develop a prototype of a mHealth CDSS for Parkinson’s Disease (PD), which is theoretically grounded in the psychological literature about expert decision making and judgement under uncertainty. Methods: A suite of user needs studies was conducted in 4 European countries (Greece, Italy, Slovenia, the UK) prior to the development of PD_Manager, a mHealth-based CDSS designed for Parkinson’s Disease, using wireless technology. Study 1 undertook Hierarchical Task Analysis (HTA) including elicitation of user needs, cognitive demands and perceived risks/benefits (ethical considerations) associated with the proposed CDSS, through structured interviews of prescribing clinicians (N=47). Study 2 carried out computational modelling of prescribing clinicians’ (N=12) decision strategies based on social judgment theory. Study 3 was a vignette study of prescribing clinicians’ (N=18) willingness to change treatment based on either self-reported symptoms data, devices-generated symptoms data or combinations of both. Results: Study 1 indicated that system development should move away from the traditional silos of ‘motor’ and ‘non-motor’ symptom evaluations and suggest that presenting data on symptoms according to goal-based domains would be the most beneficial approach, the most important being patients’ overall Quality of Life (QoL). The computational modelling in Study 2 extrapolated different factor combinations when making judgements about different questions. Study 3 indicated that the clinicians were equally likely to change the care plan based on information about the change in the patient’s condition from the patient’s self-report and the wearable devices. 3 Conclusions: Based on our approach, we could formulate the following principles of mHealth design: 1) enabling shared decision making between the clinician, patient and the carer; 2) flexibility that accounts for diagnostic and treatment variation among clinicians; 3) monitoring of information integration from multiple sources. Our approach highlighted the central importance of the patient-clinician relationship in clinical decision making and the relevance of theoretical as opposed to algorithm (technology)-based modelling of human judgment.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Psychology
Authors :
NameEmailORCID
Timotijevic, LadaL.Timotijevic@surrey.ac.uk
Banks, Carysc.banks@surrey.ac.uk
Rusconi, Patricep.rusconi@surrey.ac.uk
Egan, BernadetteM.Egan@surrey.ac.uk
Peacock, MatthewM.Peacock@surrey.ac.uk
Seiss, EllenE.Seiss@surrey.ac.uk
Touray, Morrom.touray@surrey.ac.uk
Gage, HeatherH.Gage@surrey.ac.uk
Pellicano, C.
Spalletta, G.
Assogna, F.
Giglio, M.
Marcante, A.
Gentile, G.
Cikajilo, I.
Gatsios, D.
Konitsiotis, S.
Fotiadis, D.
Date : 19 February 2020
Funders : University of Surrey, European Commission under the Framework Programme for Research and Innovation Horizon 2020
DOI : 10.1186/s12911-020-1027-1
Copyright Disclaimer : © The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Uncontrolled Keywords : mHealth; m-health; digital health; Clinical Decision Support System; CDSS; user needs; Parkinson’s; mobile devices; wearables
Depositing User : James Marshall
Date Deposited : 30 Jan 2020 13:50
Last Modified : 05 Jun 2020 15:25
URI: http://epubs.surrey.ac.uk/id/eprint/853502

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