Drivers of information disclosure on health information exchange platforms: insights from an exploratory empirical study.
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ABSTRACT: Objective
The objective of this research is to empirically explore the drivers of patients' consent to sharing of their medical records on health information exchange (HIE) platforms.Materials and methods
The authors analyze a dataset consisting of consent choices of 20,076 patients in Western New York. A logistic regression is applied to empirically investigate the effects of patients' age, gender, complexity of medical conditions, and the role of primary care physicians on patients' willingness to disclose medical information on HIE platforms.Results
The likelihood of providing consent increases by age (odds ratio (OR)?= 1.055; P < .0001). Female patients are more likely to provide consent (OR = 1.460; P = .0003). As the number of different physicians involved in the care of the patient increases, the odds of providing consent slightly increases (OR = 1.024; P = .0031). The odds of providing consent is significantly higher for the patients whom a primary care physician has been involved in their medical care (OR = 1.323; P < .0001).Conclusion
Individual-level characteristics are important predictors of patients' willingness to disclose their medical information on HIE platforms.
SUBMITTER: Yaraghi N
PROVIDER: S-EPMC7784317 | biostudies-literature | 2015 Nov
REPOSITORIES: biostudies-literature
Publications
Drivers of information disclosure on health information exchange platforms: insights from an exploratory empirical study.
Yaraghi Niam N Sharman Raj R Gopal Ram R Singh Ranjit R Ramesh R R
Journal of the American Medical Informatics Association : JAMIA 20150814 6
<h4>Objective</h4>The objective of this research is to empirically explore the drivers of patients' consent to sharing of their medical records on health information exchange (HIE) platforms.<h4>Materials and methods</h4>The authors analyze a dataset consisting of consent choices of 20,076 patients in Western New York. A logistic regression is applied to empirically investigate the effects of patients' age, gender, complexity of medical conditions, and the role of primary care physicians on pati ...[more]
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Project description:BackgroundMobile health (mHealth) provides a new opportunity for disease prediction and patient health self-management. However, privacy problems in mHealth have drawn significant attention to patients' online health information disclosure and to the possibility that privacy concerns may hinder mHealth development.ObjectivePrivacy calculus theory (PCT) has been widely used to understand personal information disclosure behaviors with the basic assumption of a rational and linear decision-making process. However, cognitive behavior processes are complex and mutual. In an attempt to gain a fuller understanding of information disclosure behavior, we further optimize a PCT-based information disclosure model by identifying the mutual relationship between costs (privacy concerns) and benefits. Social support, which has been proven to be a distinct and significant disclosure benefit of mHealth, was chosen as the representative benefit of information disclosure.MethodsWe examine a structural equation model that incorporates privacy concerns, health information disclosure intention in mHealth, and social support from mHealth, all at the individual level.ResultsA validated questionnaire was completed by 253 randomly selected participants. The result indicated that perceived health information sensitivity positively enhances patients' privacy concern (beta path coefficient 0.505, P<.001), and higher privacy concern levels will decrease their health information disclosure intention (beta path coefficient -0.338, P<.001). Various individual characteristics influence perceived health information sensitivity in different ways. One dimension of social support, informational support, negatively moderates the effect of the relationship between perceived health information sensitivity and privacy concerns (beta path coefficient -0.171, P=.092) and the effect of the relationship between privacy concerns and health information disclosure intention (beta path coefficient -0.105, P=.092). However, another dimension, emotional support, has no direct moderation effect on the relationship between privacy concerns and health information disclosure intention.ConclusionsThe results indicate that social support can be regarded as a disutility reducer. That is, on the one hand, it reduces patients' privacy concerns; on the other hand, it also reduces the negative impact of privacy concerns on information disclosure intention. Moreover, the moderation effect of social support is partially supported. Informational support, one dimension of social support, is significant (beta path coefficient -0.171, P=.092), while the other dimension, emotional support, is not significant (beta path coefficient -0.137, P=.146), in mHealth. Furthermore, the results are different among patients with different individual characteristics. This study also provides specific theoretical and practical implications to enhance the development of mHealth.
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