Data for adherence decision support

被引：0

作者：

Diemert S. ^{[1
]}

Weber J. ^{[2
,3
]}

Price M. ^{[2
,3
]}

Bannman J. ^{[3
]}

机构：

[1] Critical Systems Labs Inc., Vancouver, BC

[2] Department of Computer Science, University of Victoria

[3] Department of Family Medicine, University of British Columbia

来源：

Studies in Health Technology and Informatics | 2019年 / 257卷

关键词：

decision support; health information technology; Internet of Things; medication adherence;

D O I：

10.3233/978-1-61499-951-5-75

中图分类号：

学科分类号：

摘要：

Technological interventions aimed at addressing medication non-adherence have shown some promise but do not deliver the full potential of an Internet of Things based Adherence Decision Support (ADS) system due, in part, to a lack high-resolution definition and measure of adherence. This paper presents a novel methodology and pilot study aimed at collecting data to support an AI-based measure of adherence. The pilot study results demonstrate the viability of the methodology and that a full-scale study could provide meaningful data to support to an AI-based ADS system. © 2019 American Psychological Association Inc. All rights reserved.

引用

页码：75 / 79

页数：4

共 4 条

[1]

Diemert S., Weber J., Price M., An engagement model for medication management: From prescription to description and conscription, Stud Health Technol. Inform., 234, pp. 81-86, (2017)

[2]

Vlasnik J.J., Aliotta S.L., DeLor B., Medication adherence: Factors influencing compliance with prescribed medication plans, The Case Manager, 16, pp. 47-51, (2005)

[3]

Nieuwlaat R., Interventions for enhancing medication adherence, Cochrane Database of Systematic Reviews, 11, (2014)

[4]

Diemert S., A mathematical basis for medication prescriptions and adherence, Master of Science Thesis, (2017)

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