Qin Jimmy, Chan Carri W, Dong Jing, Homma Shunichi, Ye Siqin
Decision, Risk, and Operations Division, Columbia Business School, New York, USA.
Division of Cardiology, Columbia University Irving Medical Center, New York, USA.
J Telemed Telecare. 2024 Oct;30(9):1507-1515. doi: 10.1177/1357633X231154945. Epub 2023 Mar 27.
The global pandemic caused by coronavirus (COVID-19) sped up the adoption of telemedicine. We aimed to assess whether factors associated with no-show differed between in-person and telemedicine visits. The focus is on understanding how social economic factors affect patient no-show for the two modalities of visits.
We utilized electronic health records data for outpatient internal medicine visits at a large urban academic medical center, from February 1, 2020 to December 31, 2020. A mixed-effect logistic regression was used. We performed stratified analysis for each modality of visit and a combined analysis with interaction terms between exposure variables and visit modality.
A total of 111,725 visits for 72,603 patients were identified. Patient demographics (age, gender, race, income, partner), lead days, and primary insurance were significantly different between the two visit modalities. Our multivariable regression analyses showed that the impact of sociodemographic factors, such as Medicaid insurance (OR 1.23, < 0.01 for in-person; OR 1.03, = 0.57 for telemedicine; < 0.01 for interaction), Medicare insurance (OR 1.11, = 0.04 for in-person; OR 0.95, = 0.32 for telemedicine; = 0.03 for interaction) and Black race (OR 1.36, < 0.01 for in-person; OR 1.20, < 0.01 for telemedicine; = 0.03 for interaction), on increased odds of no-show was less for telemedicine visits than for in-person visits. In addition, inclement weather and younger age had less impact on no-show for telemedicine visits.
Our findings indicated that if adopted successfully, telemedicine had the potential to reduce no-show rate for vulnerable patient groups and reduce the disparity between patients from different socioeconomic backgrounds.
由冠状病毒（COVID - 19）引发的全球大流行加速了远程医疗的采用。我们旨在评估未就诊相关因素在面对面就诊和远程医疗就诊之间是否存在差异。重点在于了解社会经济因素如何影响两种就诊方式下患者的未就诊情况。
我们利用了一家大型城市学术医疗中心2020年2月1日至2020年12月31日门诊内科就诊的电子健康记录数据。采用了混合效应逻辑回归。我们对每种就诊方式进行了分层分析，并对暴露变量与就诊方式之间的交互项进行了综合分析。
共识别出72603名患者的111725次就诊。两种就诊方式在患者人口统计学特征（年龄、性别、种族、收入、伴侣情况）、提前天数和主要保险类型方面存在显著差异。我们的多变量回归分析表明，社会人口统计学因素，如医疗补助保险（面对面就诊时OR为1.23，P < 0.01；远程医疗就诊时OR为1.03，P = 0.57；交互项P < 0.01）、医疗保险（面对面就诊时OR为1.11，P = 0.04；远程医疗就诊时OR为0.95，P = 0.32；交互项P = 0.03）和黑人种族（面对面就诊时OR为1.36，P < 0.01；远程医疗就诊时OR为1.20，P < 0.01；交互项P = 0.03），对远程医疗就诊未就诊几率增加的影响比对面对面就诊的影响小。此外，恶劣天气和较年轻的年龄对远程医疗就诊未就诊的影响较小。
我们的研究结果表明，如果成功采用，远程医疗有可能降低弱势群体的未就诊率，并减少不同社会经济背景患者之间的差距。
