Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach
| Autor: | Joshua Symons, Daniel Ray, Ara Darzi, Hutan Ashrafian, Sion Philpott-Morgan, Dixa B. Thakrar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Cardiovascular Procedures
Cancer Treatment Vascular Surgery computer.software_genre Geographical locations Machine Learning Cohort Studies Outpatients Health care Medicine and Health Sciences Medicine Outpatient clinic health care economics and organizations Likelihood Functions education.field_of_study General Medicine Health Services Hospitals humanities Europe Surgical Oncology Oncology England Research Article Cohort study Clinical Oncology Computer and Information Sciences Patients Population education MEDLINE Surgical and Invasive Medical Procedures Machine learning Appointments and Schedules Artificial Intelligence Intervention (counseling) Humans European Union business.industry Targeted interventions Models Theoretical National health service United Kingdom Health Care Ophthalmology Health Care Facilities Artificial intelligence People and places Clinical Medicine business Delivery of Health Care computer |
| Zdroj: | PLoS Medicine, Vol 18, Iss 10, p e1003783 (2021) PLoS Medicine |
| ISSN: | 1549-1676 1549-1277 |
| Popis: | Background Unkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of unkept outpatient clinic appointments across hospital trusts in the England. In addition, we aimed to examine the predictors of unkept outpatient clinic appointments across specialties at Imperial College Healthcare NHS Trust (ICHT). Our final aim was to train machine learning models to determine the effectiveness of a potential intervention in reducing unkept appointments. Methods and findings UK Hospital Episode Statistics outpatient data from 2016 to 2018 were used for this study. Machine learning models were trained to determine predictors of unkept appointments and their relative importance. These models were gradient boosting machines. In 2017–2018 there were approximately 85 million outpatient appointments, with an unkept appointment rate of 5.7%. Within ICHT, there were almost 1 million appointments, with an unkept appointment rate of 11.2%. Hepatology had the highest rate of unkept appointments (17%), and medical oncology had the lowest (6%). The most important predictors of unkept appointments included the recency (25%) and frequency (13%) of previous unkept appointments and age at appointment (10%). A sensitivity of 0.287 was calculated overall for specialties with at least 10,000 appointments in 2016–2017 (after data cleaning). This suggests that 28.7% of patients who do miss their appointment would be successfully targeted if the top 10% least likely to attend received an intervention. As a result, an intervention targeting the top 10% of likely non-attenders, in the full population of patients, would be able to capture 28.7% of unkept appointments if successful. Study limitations include that some unkept appointments may have been missed from the analysis because recording of unkept appointments is not mandatory in England. Furthermore, results here are based on a single trust in England, hence may not be generalisable to other locations. Conclusions Unkept appointments remain an ongoing concern for healthcare systems internationally. Using machine learning, we can identify those most likely to miss their appointment and implement more targeted interventions to reduce unkept appointment rates. Sion Phillpott-Morgan and co-workers study occurrence and possible predictors of unkept outpatient appointments in the UK. Author summary Why was the study done? Unkept appointments cost the National Heath Service £1 billion annually. What did the researchers do and find? We determined rates of unkept outpatient clinic appointments and examined the predictors of unkept appointments across specialties. We found that patients who had previously missed their appointment were most likely to miss another appointment. What do these findings mean? Based on our results, we have identified a cohort of patients for whom targeted interventions can be implemented, to reduce the number of unkept appointments in order to improve patient care and reduce costs to healthcare providers. |
| Databáze: | OpenAIRE |
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