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Published on: August 1, 2019
Attitudes of Patients and Health Professionals Regarding Screening Algorithms: Qualitative Study
Christina Oxholm1, Anne-Marie Soendergaard Christensen1, Regina Christiansen1
1Department for the Study of Culture, University of Southern Denmark, Odense, Denmark.
Background:
As a preamble to an attempt to develop a tool that can aid health professionals at hospitals in identifying whether the patient may have an alcohol abuse problem, this study investigates opinions and attitudes among both health professionals and patients about using patient data from electronic health records (EHRs) in an algorithm screening for alcohol problems.
Objective:
The aim of this study was to investigate the attitudes and opinions of patients and health professionals at hospitals regarding the use of previously collected data in developing and implementing an algorithmic helping tool in EHR for screening inexpedient alcohol habits; in addition, the study aims to analyze how patients would feel about asking and being asked about alcohol by staff, based on a notification in the EHR from such a tool.
Methods:
Using semistructured interviews, we interviewed 9 health professionals and 5 patients to explore their opinions and attitudes about an algorithm-based helping tool and about asking and being asked about alcohol usage when being given a reminder from this type of tool. The data were analyzed using an ad hoc method consistent with a close reading and meaning condensing.
Results:
The health professionals were both positive and negative about a helping tool grounded in algorithms. They were optimistic about the potential of such a tool to save some time by providing a quick overview if it was easy to use but, on the negative side, noted that this type of helping tool might take away the professionals' instinct. The patients were overall positive about the helping tool, stating that they would find this tool beneficial for preventive care. Some of the patients expressed concerns that the information provided by the tool could be misused.
Conclusions:
When developing and implementing an algorithmic helping tool, the following aspects should be considered: (1) making the helping tool as transparent in its recommendations as possible, avoiding black boxing, and ensuring room for professional discretion in clinical decision making; and (2) including and taking into account the attitudes and opinions of patients and health professionals in the design and development process of such an algorithmic helping tool.
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