背景:随着数字技术,尤其是人工智能(AI)在医疗保健中变得越来越重要,必须确定潜在用户是否以及为什么打算使用相关的健康信息系统(HIS)。有几种理论存在,但是他们主要关注医疗保健或信息系统的方面,除了一般的心理学理论,因此提供了少量的变量来解释未来的行为。因此,通过结合医疗保健的几种理论提供更多变量的研究,信息系统,心理学是必要的。
目的:本研究旨在调查使用新的HIS进行短期和长期医学治疗决策的意图,该研究使用具有多个变量的综合方法来解释未来行为。
方法:我们开发了一个基于医疗保健理论的综合理论模型,信息系统,和心理学,使我们能够分析适应性和非适应性评估的二元性方法及其对使用HIS意图的影响。我们将综合理论模型应用于使用基于AI的HIS进行手术的短期治疗和使用结构化方程模型的调查数据进行糖尿病跟踪的长期治疗。为了区分一定程度的人工智能参与,我们使用了几种方案,包括仅由医生进行治疗,有人工智能支持的医生,和人工智能只是为了了解个人如何感知人工智能的影响。
结果:我们的结果表明,对于短期和长期治疗,感知威胁的变量,恐惧(疾病),感知功效,态度(HIS),在确定使用基于AI的HIS的意图时,感知规范是重要的考虑因素。此外,结果显示,感知效能和态度(HIS)是决定所有治疗和方案使用意向的最重要变量.相比之下,能力(HIS)仅对短期治疗很重要。对于我们的9种情况,适应性和非适应性评估对于确定使用意图都很重要,取决于治疗是否已知。此外,我们确定的R²值在57.9%和81.7%之间变化,这表明我们的模型的解释能力是中等到良好的。
结论:我们通过强调整合疾病和技术相关因素的重要性并提供整合的理论模型来为HIS文献做出贡献。因此,我们展示了如何安排适应性和非适应性评估来报告未来的医疗决策,尤其是短期和长期。医师和HIS开发人员可以利用我们的见解来确定采用HIS的短期和长期治疗方法的有希望的理由,并相应地适应和开发HIS。具体来说,HIS开发人员应确保未来的HIS在HIS功能方面采取行动,正如我们的研究表明,有效的HIS会导致对HIS的积极态度,并最终导致更高的使用意图。
BACKGROUND: As digital technologies and especially artificial intelligence (AI) become increasingly important in health care, it is essential to determine whether and why potential users intend to use related health information systems (HIS). Several theories exist, but they focus mainly on aspects of health care or information systems, in addition to general psychological theories, and hence provide a small number of variables to explain future behavior. Thus, research that provides a larger number of variables by combining several theories from health care, information systems, and psychology is necessary.
OBJECTIVE: This
study aims to investigate the intention to use new HIS for decisions concerning short- and long-term medical treatments using an integrated approach with several variables to explain future behavior.
METHODS: We developed an integrated theoretical model based on theories from health care, information systems, and psychology that allowed us to analyze the duality approach of adaptive and nonadaptive appraisals and their influence on the intention to use HIS. We applied the integrated theoretical model to the short-term treatment using AI-based HIS for surgery and the long-term treatment of diabetes tracking using survey data with structured equation modeling. To differentiate between certain levels of AI involvement, we used several scenarios that include treatments by physicians only, physicians with AI support, and AI only to understand how individuals perceive the influence of AI.
RESULTS: Our results showed that for short- and long-term treatments, the variables perceived threats, fear (disease), perceived efficacy, attitude (HIS), and perceived norms are important to consider when determining the intention to use AI-based HIS. Furthermore, the results revealed that perceived efficacy and attitude (HIS) are the most important variables to determine intention to use for all treatments and scenarios. In contrast, abilities (HIS) were important for short-term treatments only. For our 9 scenarios, adaptive and nonadaptive appraisals were both important to determine intention to use, depending on whether the treatment is known. Furthermore, we determined R² values that varied between 57.9% and 81.7% for our scenarios, which showed that the explanation power of our model is medium to good.
CONCLUSIONS: We contribute to HIS literature by highlighting the importance of integrating disease- and technology-related factors and by providing an integrated theoretical model. As such, we show how adaptive and nonadaptive appraisals should be arranged to report on medical decisions in the future, especially in the short and long terms. Physicians and HIS developers can use our insights to identify promising rationale for HIS adoption concerning short- and long-term treatments and adapt and develop HIS accordingly. Specifically, HIS developers should ensure that future HIS act in terms of HIS functions, as our
study shows that efficient HIS lead to a positive attitude toward the HIS and ultimately to a higher intention to use.