von Kalckreuth Niklas, Feufel Markus A
Department of Psychology and Ergonomics (IPA), Technische Universität Berlin, Berlin, Germany.
Digit Health. 2024 Sep 5;10:20552076241274245. doi: 10.1177/20552076241274245. eCollection 2024 Jan-Dec.
The electronic health record (EHR) is integral to improving healthcare efficiency and quality. Its successful implementation hinges on patient willingness to use it, particularly in Germany where concerns about data security and privacy significantly influence usage intention. Little is known about how specific characteristics of medical data influence patients' intention to use the EHR.
This study aims to validate the privacy calculus model (PCM) regarding EHRs and to assess how personal and disease characteristics, namely disease-related stigma and disease time course, affect PCM predictions.
An online survey was conducted to empirically validate the PCM for EHR, incorporating a case vignette varying in disease-related stigma (high/low) and time course (acute/chronic), with = 241 participants, aged 18 years and older residing in Germany with no previous experience with the diseases mentioned in the respective medical reports. Participants were randomized (single-blinded) into four groups in parallel: high stigma and acute time course ( = 74), high stigma and chronic time course ( = 56), low stigma and acute time course ( = 62) and low stigma and chronic time course ( = 49). The data were analyzed using structural equation modeling with partial least squares.
The model explains ² = 71.8% of the variance in intention to use. The intention to use is influenced by perceived benefits, data privacy concerns, trust in the provider, and social norms. However, only the disease's time course, not stigma, affects this intention. For acute diseases, perceived benefits and social norms are influential, whereas for chronic diseases, perceived benefits, privacy concerns, and trust in the provider influence intention.
The PCM validation for EHRs reveals that personal and disease characteristics shape usage intention in Germany. The need for tailored EHR adoption strategies that address specific needs and concerns of patients with different disease types. Such strategies could lead to a more successful and widespread implementation of EHRs, especially in privacy-conscious contexts.
电子健康记录（EHR）对于提高医疗效率和质量至关重要。其成功实施取决于患者使用它的意愿，尤其是在德国，对数据安全和隐私的担忧显著影响使用意愿。关于医疗数据的特定特征如何影响患者使用电子健康记录的意愿，人们知之甚少。
本研究旨在验证关于电子健康记录的隐私计算模型（PCM），并评估个人和疾病特征，即疾病相关耻辱感和疾病病程，如何影响PCM预测。
进行了一项在线调查，以实证验证电子健康记录的PCM，纳入了一个病例 vignette，其疾病相关耻辱感（高/低）和病程（急性/慢性）各不相同，共有241名年龄在18岁及以上、居住在德国且之前没有各自医学报告中提及疾病经历的参与者。参与者被随机（单盲）平行分为四组：高耻辱感和急性病程（n = 74）、高耻辱感和慢性病程（n = 56）、低耻辱感和急性病程（n = 62）以及低耻辱感和慢性病程（n = 49）。使用偏最小二乘结构方程模型对数据进行分析。
该模型解释了使用意愿方差的R² = 71.8%。使用意愿受到感知利益、数据隐私担忧、对提供者的信任和社会规范的影响。然而，只有疾病病程而非耻辱感会影响这种意愿。对于急性疾病，感知利益和社会规范有影响，而对于慢性疾病，感知利益、隐私担忧和对提供者的信任会影响意愿。
电子健康记录的PCM验证表明，个人和疾病特征塑造了德国的使用意愿。需要制定量身定制的电子健康记录采用策略，以满足不同疾病类型患者的特定需求和担忧。此类策略可能会导致电子健康记录更成功、更广泛地实施，尤其是在注重隐私的环境中。
