PDF(Pubmed)
Abstract：
BACKGROUND: In many large health centers, patients face long appointment wait times and difficulties accessing care. Last-minute cancellations and patient no-shows leave unfilled slots in a clinician\'s schedule, exacerbating delays in care from poor access. The mismatch between the supply of outpatient appointments and patient demand has led health systems to adopt many tools and strategies to minimize appointment no-show rates and fill open slots left by patient cancellations.
OBJECTIVE: We evaluated an electronic health record (EHR)-based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on the ability to fill cancelled appointment slots, patient access to earlier appointments, and clinical revenue from visits that may otherwise have gone unscheduled.
METHODS: In this retrospective cohort study, we extracted Fast Pass appointment offers and scheduling data, including patient demographics, from the EHR between June 18, 2022, and March 9, 2023. We analyzed the outcomes of Fast Pass offers (accepted, declined, expired, and unavailable) and the outcomes of scheduled appointments resulting from accepted Fast Pass offers (completed, canceled, and no-show). We stratified outcomes based on appointment specialty. For each specialty, the patient service revenue from appointments filled by Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer.
RESULTS: From June 18 to March 9, 2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6603 (11%) were accepted across all departments, and 5399 (8.9%) visits were completed. Patients were seen a median (IQR) of 14 (4-33) days sooner for their appointments. In a multivariate logistic regression model with primary outcome Fast Pass offer acceptance, patients who were aged 65 years or older (vs 20-40 years; P=.005 odds ratio [OR] 0.86, 95% CI 0.78-0.96), other ethnicity (vs White; P<.001, OR 0.84, 95% CI 0.77-0.91), primarily Chinese speakers (P<.001; OR 0.62, 95% CI 0.49-0.79), and other language speakers (vs English speakers; P=.001; OR 0.71, 95% CI 0.57-0.87) were less likely to accept an offer. Fast Pass added 2576 patient service hours to the clinical schedule, with a median (IQR) of 251 (216-322) hours per month. The estimated value of physician fees from these visits scheduled through 9 months of Fast Pass scheduling in professional fees at our institution was US $3 million.
CONCLUSIONS: Self-scheduling tools that provide patients with an opportunity to schedule into cancelled or unfilled appointment slots have the potential to improve patient access and efficiently capture additional revenue from filling unfilled slots. The demographics of the patients accepting these offers suggest that such digital tools may exacerbate inequities in access.
OBJECTIVE: We evaluated an electronic health record (EHR)-based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on the ability to fill cancelled appointment slots, patient access to earlier appointments, and clinical revenue from visits that may otherwise have gone unscheduled.
METHODS: In this retrospective cohort study, we extracted Fast Pass appointment offers and scheduling data, including patient demographics, from the EHR between June 18, 2022, and March 9, 2023. We analyzed the outcomes of Fast Pass offers (accepted, declined, expired, and unavailable) and the outcomes of scheduled appointments resulting from accepted Fast Pass offers (completed, canceled, and no-show). We stratified outcomes based on appointment specialty. For each specialty, the patient service revenue from appointments filled by Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer.
RESULTS: From June 18 to March 9, 2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6603 (11%) were accepted across all departments, and 5399 (8.9%) visits were completed. Patients were seen a median (IQR) of 14 (4-33) days sooner for their appointments. In a multivariate logistic regression model with primary outcome Fast Pass offer acceptance, patients who were aged 65 years or older (vs 20-40 years; P=.005 odds ratio [OR] 0.86, 95% CI 0.78-0.96), other ethnicity (vs White; P<.001, OR 0.84, 95% CI 0.77-0.91), primarily Chinese speakers (P<.001; OR 0.62, 95% CI 0.49-0.79), and other language speakers (vs English speakers; P=.001; OR 0.71, 95% CI 0.57-0.87) were less likely to accept an offer. Fast Pass added 2576 patient service hours to the clinical schedule, with a median (IQR) of 251 (216-322) hours per month. The estimated value of physician fees from these visits scheduled through 9 months of Fast Pass scheduling in professional fees at our institution was US $3 million.
CONCLUSIONS: Self-scheduling tools that provide patients with an opportunity to schedule into cancelled or unfilled appointment slots have the potential to improve patient access and efficiently capture additional revenue from filling unfilled slots. The demographics of the patients accepting these offers suggest that such digital tools may exacerbate inequities in access.
摘要：
背景：在许多大型医疗中心，患者面临漫长的预约等待时间和难以获得护理。最后一分钟取消和病人没有出现在临床医生的时间表中，加剧了因难以获得护理而造成的延误。门诊预约的供应与患者需求之间的不匹配导致卫生系统采用了许多工具和策略，以最大程度地减少预约未出现率，并填补患者取消预约留下的空缺。
目的：我们评估了一种基于电子健康记录（EHR）的自我调度工具，FastPass,在一个大型学术医疗中心，以了解该工具对填补取消的预约空位的能力的影响，患者获得较早的预约，以及可能没有计划的就诊的临床收入。
方法：在这项回顾性队列研究中，我们提取了FastPass约会优惠和日程安排数据，包括病人的人口统计,从2022年6月18日至2023年3月9日之间的EHR。我们分析了FastPass优惠的结果(接受，被拒绝,已过期,并且不可用)以及接受的FastPass优惠导致的预定约会的结果(已完成，取消,并且没有出现）。我们根据预约专业对结果进行分层。对于每个专业，FastPass填写的预约患者服务收入是使用填写的就诊时段计算的，任命的付款人组合，以及按付款人划分的缴款保证金。
结果：从6月18日至2023年3月9日，总共向患者发送了60,660份FastPass优惠，可预约21,978份。在这些提议中，6603(11%)被所有部门接受，完成5399次(8.9%)访视。患者的预约时间较早的中位数（IQR）为14（4-33）天。在具有主要结果的多元逻辑回归模型中，FastPass提供了接受，65岁或以上的患者（vs20-40岁；P=0.005比值比[OR]0.86，95％CI0.78-0.96），其他种族（与白人；P<.001，OR0.84，95%CI0.77-0.91），主要讲中文的人(P<.001；OR0.62，95%CI0.49-0.79)，和其他语言使用者（与英语使用者相比；P=.001；OR0.71，95％CI0.57-0.87）接受要约的可能性较小。FastPass在临床时间表中增加了2576个患者服务小时，中位数（IQR）为每月251（216-322）小时。从这些访问计划到9个月的FastPass计划在我们机构的专业费用中，医生费用的估计价值为300万美元。
结论：为患者提供安排取消或未填补的预约时段的机会的自我安排工具有可能改善患者的访问权限，并有效地从填补未填补的时段中获得额外收入。接受这些提议的患者的人口统计学表明，这种数字工具可能会加剧访问方面的不平等。
目的：我们评估了一种基于电子健康记录（EHR）的自我调度工具，FastPass,在一个大型学术医疗中心，以了解该工具对填补取消的预约空位的能力的影响，患者获得较早的预约，以及可能没有计划的就诊的临床收入。
方法：在这项回顾性队列研究中，我们提取了FastPass约会优惠和日程安排数据，包括病人的人口统计,从2022年6月18日至2023年3月9日之间的EHR。我们分析了FastPass优惠的结果(接受，被拒绝,已过期,并且不可用)以及接受的FastPass优惠导致的预定约会的结果(已完成，取消,并且没有出现）。我们根据预约专业对结果进行分层。对于每个专业，FastPass填写的预约患者服务收入是使用填写的就诊时段计算的，任命的付款人组合，以及按付款人划分的缴款保证金。
结果：从6月18日至2023年3月9日，总共向患者发送了60,660份FastPass优惠，可预约21,978份。在这些提议中，6603(11%)被所有部门接受，完成5399次(8.9%)访视。患者的预约时间较早的中位数（IQR）为14（4-33）天。在具有主要结果的多元逻辑回归模型中，FastPass提供了接受，65岁或以上的患者（vs20-40岁；P=0.005比值比[OR]0.86，95％CI0.78-0.96），其他种族（与白人；P<.001，OR0.84，95%CI0.77-0.91），主要讲中文的人(P<.001；OR0.62，95%CI0.49-0.79)，和其他语言使用者（与英语使用者相比；P=.001；OR0.71，95％CI0.57-0.87）接受要约的可能性较小。FastPass在临床时间表中增加了2576个患者服务小时，中位数（IQR）为每月251（216-322）小时。从这些访问计划到9个月的FastPass计划在我们机构的专业费用中，医生费用的估计价值为300万美元。
结论：为患者提供安排取消或未填补的预约时段的机会的自我安排工具有可能改善患者的访问权限，并有效地从填补未填补的时段中获得额外收入。接受这些提议的患者的人口统计学表明，这种数字工具可能会加剧访问方面的不平等。
