Prediction of Hospital Outpatient Attendance in UK Hospitals: A Retrospective Study Applying Machine Learning to Routinely Collected Data for Patients of All Ages
Prediction of Hospital Outpatient Attendance in UK Hospitals: A Retrospective Study Applying Machine Learning to Routinely Collected Data for Patients of All Ages
复制标题
英国医院门诊就诊率的预测：一项回顾性研究，将机器学习应用于定期收集的所有年龄段患者的数据
DOI：
10.1101/2022.01.24.22269733
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Holdship J
中科院分区：
文献类型：
--
作者：
Holdship J
目的：患者门诊不出勤（DNA）是医疗服务提供者关注的主要问题。不出诊增加了等候名单，减少了获得护理的机会，并可能对没有出诊的病人有害。然而，降低DNA率的非针对性干预措施可能并不有效，因此我们的目标是建立一个模型，可以准确预测哪些预约会参加。方法在这项工作中，训练随机森林分类算法来预测是否会错过700万门诊预约。该模型应用于伦敦一家大型教学医院的所有年龄和所有专业的患者，包括涵盖COVID-19大流行的验证集。结果该模型在测试数据上的AUROC得分为0.76，准确率为73%。我们发现预约和预约之间的等待时间，病人过去的DNA行为，以及他们所在地区的剥夺程度是预测未来DNA的重要因素。我们的模型对医院门诊预约是否会被参加有很强的预测性。它在没有出现在培训数据中的患者和涵盖Covid-19大流行的不同时间段的预约上的表现表明，它在面对面和虚拟预约中都很好地推广了，可以用来针对那些可能错过预约的患者提供资源和干预。此外，它突出了剥夺对患者获得医疗保健的影响。结论我们的模型成功地预测了患者门诊预约的出勤率。本研究部分由STFC DiRAC创新奖学金资助，该奖学金资助了Jonathan Holdship和Harpreet Dhanoa的工作。奖励/资助编号不适用于此资助。本研究的优势和局限性我们建立了第一个能够准确预测大型多站点教学医院所有年龄和专科门诊就诊人数的模型。我们发现患者不出勤和剥夺之间存在很强的相关性，这表明随机森林算法可以提供洞察力和有用的预测。我们发现，与为特定门诊诊所建立的文献相比，广泛的目标人群导致的模型准确性略低
ObjectivesPatient non-attendance at outpatient appointments (DNA) is a major concern for healthcare providers. Non-attendances increase waiting lists, reduce access to care and may be detrimental not for the patient who did not attend. However, non-targeted interventions to reduce the DNA rate may not be effective and thus we aim to produce a model which can accurately predict which appointments will be attended.MethodsIn this work, a random forest classification algorithm was trained to predict whether an appointment will be missed using 7 million past outpatient appointments. The model was applied to patients of all ages and appointments with all specialties at a major London teaching hospital including a validation set covering the COVID-19 pandemic.ResultsThe model achieves an AUROC score of 0.76 and accuracy of 73% on test data. We find that the waiting period between booking an the appointment, the patient’s past DNA behaviour, and the levels of deprivation in their local area are important factors in predicting future DNAs.DiscussionOur model is strongly predictive of whether a hospital outpatient appointment will be attended. Its performance on both patients who did not appear in the training data and appointments from a different time period which covers the Covid-19 pandemic indicate it generalized well across both face to face and virtual appointments and could be used to target resources and intervention towards those patients who are likely to miss an appointment. Moreover, it highlights the impact of deprivation on patient access to healthcareConclusionOur model successfully predicts patient attendance at outpatient appointments.FundingThis study was partially funded by STFC DiRAC innovation fellowships which funded the work of Jonathan Holdship and Harpreet Dhanoa. An Award/Grant number is not applicable to this funding.Strentghs and limitations of this studyWe produce the first model which can accurately predict the attendance of all outpatient appoints for all ages and specialities at a large, multisite teaching hospital.We show a strong correlation between patient non-attendance and deprivation demonstrating that random forest algorithms can provide insight as well as useful predictionsWe find that the broad target population results in a slightly less accurate model than those from the literature that are built for specific outpatient clinics
影响因子：
1.2
作者：
Goffman, Rachel M.;Harris, Shannon L.;Vargas, Dominic L.
通讯作者：
Vargas, Dominic L.
影响因子：
2.9
作者：
Soong, John T. Y.;Kaubryte, Jurgita;Hopper, Adrian
通讯作者：
Hopper, Adrian