ABSTRACT
ObjectivesNon-attendance of scheduled hospital appointments represents a major issue affecting service effectiveness, efficiency and quality of care costing the NHS over £1billion annually. This impact is even more detrimental at a time where the NHS is experiencing record high waiting times in the peri- COVID-19 pandemic era.Rather than a reactive model of discharging patients for nonattending their appointments, we propose a proactive model identifying patients at risk of not showing up and provide them with right support at the right time. This approach is especially important for vulnerable population including young people (YP) due to the complex interplay between developmental, socio-economic factors can impact significantly on their medical care.The increasing use of electronic health record systems (EHRS) and data availability creates opportunities to develop risk scores for specific patient populations.In this study, we aim to develop a machine learning approach to develop a complex, multi-dimensional predictive model to identify YP at risk of clinic nonattendance.MethodsUniversity College London Hospital (UCLH) switched to a new EHRS in April 2019 . We extracted data on outpatient Adolescent and Young Adult Rheumatology (AYAR) between 2019 -2022.Our primary outcome was nonattendance of a scheduled appointment.Our Predictor variables were defined after literature review, consultation with clinical and operational teams. We extracted data on 67 predictors of nonattendance. These variables are broadly divided into demographics (e.g, Age, Sex, ethnicity) and index of multiple deprivation (IMD) extracted from office of national statistics (ONS) database. We also included service utilisation history (e.g., previous history of clinic non-attendance.), appointment information (month, day, time, clinic codes), and patient engagement (e.g., active in MyChart [ online patient portal]).Using data from 11602 outpatient appointments in (AYAR) clinics at UCLH, we built and assessed the performance of a predictive model as to whether a YP would not attend a scheduled outpatient appointment. We used logistic regression analysis to fit and assess the Model built. We evaluated its fit based on discrimination and calibration.ResultsWe identified a total of 1517 clinic non-attendance out of total of 11602 (13.1%) appointment.Female/male ratio was 2.03 in non attendance group as compared to 2.33 in total clinic population.In terms of age group, 10% (606/5547) of clinics booked for YP aged 14–18 were not attended as compared to 15% (651/4282 ) in those aged [19–24].Feature engineering analysis revealed that the most significant factors were IMD followed by distance, previous history of Non-attendance, age group and appointment hour.ConclusionsAiming to identify YP at risk of Non-attendance, we used a step-by-step approach to build a model that can be applied using EHR and IMD data at the point of care. High proportion of YP nonattending their appointments were from deprived areas.Accurate stratification of non-attendance risk can provide us with unique opportunity for preventative interventions, supporting to most vulnerable YP and improve the use of resources within the wider system
ABSTRACT
Introduction: There is growing recognition of the impact of societal factors on health throughout a patient's lifespan. The COVID-19 pandemic has exposed the impact of racial disparity on health outcomes. Aims: We aimed to investigate the association between ethnicity and the multidisciplinary team (MDT) interventions for young people (YP) with complex care needs. Method: This retrospective, single-centre, cross-sectional study was conducted within the department of adolescent and young adult rheumatology at University College Hospital, London, between August 2019 and August 2021. We extracted demographic, clinical and laboratory data. The index of multiple deprivation was extracted from the Office for National Statistics database. R software was used for analysis. Results: We identified 310 YP referred to the MDT with a median age of 18 years (interquartile range 17-19). The female patient to male patient ratio was 2.4. Over a third of our cohort were from deprived areas. Comparison between Black, Asian and minority ethnic (BAME) and White ethnic groups revealed significant differences in terms of referral for pain optimisation (p=0.006), social support (p<0.00001), and adherence and non-clinic attendance (p=0.0004). Conclusion: Our findings reveal the importance of quality data for early identification and support of vulnerable YP, particularly those from BAME communities.
ABSTRACT
Infection with SARS-CoV-2 may trigger a delayed hyper-inflammatory illness in children called paediatric multisystem inflammatory syndrome temporally associated with COVID-19 (PIMS-TS). A similar syndrome is increasingly recognised in adults termed multisystem inflammatory syndrome in adults (MIS-A) and may present acutely to medical or surgical specialties with severe symptoms, such as acute abdominal pain or cardiogenic shock. No national guidelines exist in the UK for the management of MIS-A and there is limited evidence to guide treatment plans. We undertook a national Delphi process to elicit opinions from experts in hyperinflammation about the diagnosis and management of MIS-A with the dual aim of improving recognition and producing a management guideline. Colleagues in paediatrics successfully initiated a national consensus management document that facilitated regional multidisciplinary referral and follow-up pathways for children with PIMS-TS, and we propose a similar system be developed for adult patients across the UK. This would facilitate better recognition and treatment of MIS-A across the multiple specialties to which it may present as well as enable follow-up with specialty services post-discharge.