Abstract
Background
Patients make various healthcare decisions on a daily basis. Such day-to-day decision making can have significant consequences on their own health, treatment, care, and costs. While Decision Aids (DAs) provide effective support in enhancing patient's decision making, to date there have been few studies examining patient's decision making process or exploring how the understanding of such decision processes can aid in extracting requirements for the design of DAs.
Objective
This paper applies Cognitive Work Analysis (CWA) to analyse patient's decision making in order to inform requirements for supporting self-care decision making.
Method
This study uses focus groups to elicit information from elderly Cardiovascular Disease (CVD) patients concerning a range of decision situations they face on a daily basis. Specifically, the focus groups addressed issues related to the decision making of CVD in terms of medication compliance, pain, diet and exercise.
Results
The results of these focus groups are used to develop high level views using CWA. CWA framework decomposes the complex decision making problem to inform three approaches to DA design: one design based on high level requirements; one based on a normative model of decision-making for patients; and the third based on a range of heuristics that patients seem to use.
Conclusion
CWA helps in extracting and synthesizing decision making from different perspectives: decision processes, work organization, patient competencies and strategies used in decision making. As decision making can be influenced by human behaviour like skills, rules and knowledge, it is argued that patients require support to different types of decision making. This paper also provides insights for designers in using CWA framework for the design of effective DAs to support patients in self-management.
Patients make various healthcare decisions on a daily basis. Such day-to-day decision making can have significant consequences on their own health, treatment, care, and costs. While Decision Aids (DAs) provide effective support in enhancing patient's decision making, to date there have been few studies examining patient's decision making process or exploring how the understanding of such decision processes can aid in extracting requirements for the design of DAs.
Objective
This paper applies Cognitive Work Analysis (CWA) to analyse patient's decision making in order to inform requirements for supporting self-care decision making.
Method
This study uses focus groups to elicit information from elderly Cardiovascular Disease (CVD) patients concerning a range of decision situations they face on a daily basis. Specifically, the focus groups addressed issues related to the decision making of CVD in terms of medication compliance, pain, diet and exercise.
Results
The results of these focus groups are used to develop high level views using CWA. CWA framework decomposes the complex decision making problem to inform three approaches to DA design: one design based on high level requirements; one based on a normative model of decision-making for patients; and the third based on a range of heuristics that patients seem to use.
Conclusion
CWA helps in extracting and synthesizing decision making from different perspectives: decision processes, work organization, patient competencies and strategies used in decision making. As decision making can be influenced by human behaviour like skills, rules and knowledge, it is argued that patients require support to different types of decision making. This paper also provides insights for designers in using CWA framework for the design of effective DAs to support patients in self-management.
| Original language | English |
|---|---|
| Pages (from-to) | 430-443 |
| Journal | International Journal of Medical Informatics |
| Volume | 84 |
| Early online date | 24 Jan 2015 |
| DOIs | |
| Publication status | Published - 2015 |
Access to Document
- Dhukaram_Baber_Modelling_elderly_cardiac_patients_International_Journal_Medical_Informatics_2015
NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Medical Informatics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Medical Informatics, DOI: 10.1016/j.ijmedinf.2015.01.001. Eligibility for repository checked March 2015.