The State University of New York at Buffalo, School of Management, Buffalo, NY, United States.
J Med Internet Res. 2021 Oct 28;23(10):e30637. doi: 10.2196/30637.
Patients' access to and use of electronic medical records (EMRs) places greater information in their hands, which helps them better comanage their health, leading to better clinical outcomes. Despite numerous benefits that promote health and well-being, patients' acceptance and use of EMRs remains low. We study the impact of predictors that affect the use of EMR by patients to understand better the underlying causal factors for the lower use of EMR.
This study aims to examine the critical system (eg, performance expectancy and effort expectancy) and patient characteristics (eg, health condition, issue involvement, preventive health behaviors, and caregiving status) that influence the extent of patients' EMR use.
We used secondary data collected by Health Information National Trends Survey 5 cycle 3 and performed survey data analysis using structural equation modeling technique to test our hypotheses. Structural equation modeling is a technique commonly used to measure and analyze the relationships of observed and latent variables. We also addressed common method bias to understand if there was any systematic effect on the observed correlation between the measures for the predictor and predicted variables.
The statistically significant drivers of the extent of EMR use were performance expectancy (β=.253; P<.001), perceived behavior control (β=.236; P<.001), health knowledge (β=-.071; P=.007), caregiving status (β=.059; P=.013), issue involvement (β=.356; P<.001), chronic conditions (β=.071; P=.016), and preventive health behavior (β=.076; P=.005). The model accounted for 32.9% of the variance in the extent of EMR use.
The study found that health characteristics, such as chronic conditions and patient disposition (eg, preventive health behavior and issue involvement), directly affect the extent of EMR use. The study also revealed that issue involvement mediates the impact of preventive health behaviors and the presence of chronic conditions on the extent of patients' EMR use.
患者访问和使用电子病历（EMR）使他们能够获得更多的信息，这有助于他们更好地共同管理自己的健康，从而带来更好的临床结果。尽管 EMR 带来了许多促进健康和福祉的好处，但患者对 EMR 的接受度和使用率仍然很低。我们研究了影响患者使用 EMR 的预测因素，以更好地理解 EMR 使用率较低的潜在因果因素。
本研究旨在检查影响患者 EMR 使用程度的关键系统（例如，绩效预期和努力预期）和患者特征（例如，健康状况、问题参与度、预防性健康行为和照护状态）。
我们使用了健康信息国家趋势调查 5 周期 3 收集的二级数据，并使用结构方程建模技术对调查数据进行了分析，以检验我们的假设。结构方程建模是一种常用的技术，用于测量和分析观测变量和潜在变量之间的关系。我们还解决了共同方法偏差问题，以了解观察到的预测变量和因变量之间的测量值是否存在任何系统性影响。
EMR 使用程度的统计学显著驱动因素是绩效预期（β=.253；P<.001）、感知行为控制（β=.236；P<.001）、健康知识（β=-.071；P=.007）、照护状态（β=.059；P=.013）、问题参与度（β=.356；P<.001）、慢性疾病（β=.071；P=.016）和预防性健康行为（β=.076；P=.005）。该模型解释了 EMR 使用程度的 32.9%的方差。
研究发现，健康特征，如慢性疾病和患者状况（如预防性健康行为和问题参与度），直接影响 EMR 使用程度。研究还表明，问题参与度中介了预防性健康行为和慢性疾病对患者 EMR 使用程度的影响。
