优化预约提醒软件,减少农村门诊缺勤率

Varun Malik, AL Volo, Monica L. Cales, Joseph Sonner, Christina Scartozzi
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摘要

背景:门诊病人无显示率(N/S)对全国医疗保健系统有很大影响。在许多情况下,调度提醒软件等技术的使用已被用来抑制N/S率。关于这类软件的有效性和影响N/S比率的人口因素的数据仍然缺乏。目的:分析TelevoxTM软件优化对N/S率的影响。研究设计:前瞻性队列研究。环境:家庭医学住院医师实习地点位于一个小城市的市中心(人口5万)。研究人群:所有预约的患者,至少提前24小时预约。干预措施:前瞻性患者病历数据修改一个月,确保联系信息字段更新,减少未联系患者数量。在每次预约结束时,对患者进行有关TelevoxTM系统的教育。主要指标:比较未启动TelevoxTM时、启动TelevoxTM后和我们干预后的N/S率。其次:协变量对N/S概率的影响;N/S对我们诊所的经济影响。结果:原始模型显示,就诊的患者显着(16%)增加[OR 1.16, p=。[031]干预后;这在软件启动后不明显,但在干预组之前不明显[OR 1.09, p=.1195]。年龄在调整后的模型中显示出显著性——年龄增加1岁,显示访问的机会增加1%。作为一个没有保险的病人,出现的机会减少了66%。从统计数据来看,12月份的节目播放率是最低的。按27%的平均捕获损失率计算,我们诊所每月平均损失11400.00美元的账单资金。结论:N/S率可能显著降低
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing appointment reminder software to reduce the no-show rate in a rural outpatient setting
Context: Patient no show (N/S) rates in the outpatient setting have a large effect on healthcare systems nationwide. The use of technology, such as scheduling-reminder software, has been used to curb the N/S rate in many settings. There remains a paucity of data in both the effectiveness of such software and the demographic factors contributing to the N/S rate. Objective: Analyze the effects of TelevoxTM software optimization on N/S rate. Study Design: Prospective cohort study. Setting: Family Medicine residency practice site located in downtown of a small city (population 50,000). Population Studied: All scheduled patients who had an appointment scheduled at least 24 hours prior. Intervention: Prospective patient chart data modified for one month, ensuring updated fields in contact information, decreasing number of uncontacted patients. Patients were provided education regarding TelevoxTM system at the end of each appointment. Outcome Measures: Primary: The comparison of N/S rates from: when TelevoxTM was NOT Launched, after TelevoxTM was launched, and after our intervention. Secondary: the effect of a covariate on N/S probability; economic impact of N/S to our clinic. Results: Crude modeling shows a significant (16%) increase in a patient showing for visit [OR 1.16, p=.0310] after the intervention; this is not apparent after the launch of the software, but before intervention group [OR 1.09, p=.1195]. Age showed significance in the adjusted model – with a 1% increase in chance of showing for a visit correlating to a 1-year increase in age. Being an uninsured patient led to a 66% decrease in the chance of showing. December was statistically the lowest month of those captured regarding show rate. At an average captured N/S rate of 27%, our clinic missed an average of $11,400.00 of billable funds a month. Conclusions: N/S rates may have decreased significantly by our
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