A Novel Neurosurgery Referral Course: Feasibility, Validation, and Inferences for Patient Care

Pradini-Santos, Laura; Craven, Claudia; Usher, Inga; Mullins, Sophie; D’Antona, Linda; Al-Ahmad, Selma; Abuhusain, Hazem J.; Sayal, Parag

doi:10.1016/j.jsurg.2020.04.017

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…3 C). Educational interventions which aim to improve recognition and initial management of neurosurgical presentations would be particularly useful in improving referral quality 47 and could be directed at high-volume referral centers (Fig. 5 B), where a greater impact is likely to be made.…”

Section: Discussionmentioning

confidence: 99%

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Pandit

Jalal

Toma

et al. 2022

Sci Rep

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Characterizing acute service demand is critical for neurosurgery and other emergency-dominant specialties in order to dynamically distribute resources and ensure timely access to treatment. This is especially important in the post-Covid 19 pandemic period, when healthcare centers are grappling with a record backlog of pending surgical procedures and rising acute referral numbers. Healthcare dashboards are well-placed to analyze this data, making key information about service and clinical outcomes available to staff in an easy-to-understand format. However, they typically provide insights based on inference rather than prediction, limiting their operational utility. We retrospectively analyzed and prospectively forecasted acute neurosurgical referrals, based on 10,033 referrals made to a large volume tertiary neurosciences center in London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021 through the use of a novel AI-enabled predictive dashboard. As anticipated, weekly referral volumes significantly increased during this period, largely owing to an increase in spinal referrals (p < 0.05). Applying validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point, with Prophet demonstrating the best test and computational performance. Using a mixed-methods approach, we determined that a dashboard approach was usable, feasible, and acceptable among key stakeholders.

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Section: Discussionmentioning

confidence: 99%

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Pandit

Jalal

Toma

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Other studies of neurosurgical referral networks have also recognised the attachment or completion of relevant imaging studies as a recurring issue [32]. Clear clinical guidelines for referrers and referrer education programmes are among the proposed mitigation strategies [33,34]. Our centre has implemented an annual event within our referral network directed at emergency department physicians, with an audit of the impact on referral quality ongoing [33,34].…”

Section: Findings In the Context Of Existing Literaturementioning

confidence: 99%

“…Clear clinical guidelines for referrers and referrer education programmes are among the proposed mitigation strategies [33,34]. Our centre has implemented an annual event within our referral network directed at emergency department physicians, with an audit of the impact on referral quality ongoing [33,34].…”

Section: Findings In the Context Of Existing Literaturementioning

confidence: 99%

Historical and Future Trends in Emergency Pituitary Referrals: A Machine Learning Analysis

Pandit

Khan

Hanrahan

et al. 2022

Preprint

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Purpose Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. Methods A data-driven analysis was performed using all available electronic neurosurgical referrals (2006-2022) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm and Prophet. Mean absolute, and percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. Results 462 of 36224 emergency referrals were included (referring centres=48; mean patient age=56.7-years, female:male=0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM and Prophet algorithms across scoring metrics, with standard accuracy being achieved for 6-monthly and yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p<0.001). Conclusion This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimating future demand and highlighting areas for service improvement.

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Historical and future trends in emergency pituitary referrals: a machine learning analysis

et al. 2022

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Purpose Acute pituitary referrals to neurosurgical services frequently necessitate emergency care. Yet, a detailed characterisation of pituitary emergency referral patterns, including how they may change prospectively is lacking. This study aims to evaluate historical and current pituitary referral patterns and utilise state-of-the-art machine learning tools to predict future service use. Methods A data-driven analysis was performed using all available electronic neurosurgical referrals (2014–2021) to the busiest U.K. pituitary centre. Pituitary referrals were characterised and volumes were predicted using an auto-regressive moving average model with a preceding seasonal and trend decomposition using Loess step (STL-ARIMA), compared against a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) algorithm, Prophet and two standard baseline forecasting models. Median absolute, and median percentage error scoring metrics with cross-validation were employed to evaluate algorithm performance. Results 462 of 36,224 emergency referrals were included (referring centres = 48; mean patient age = 56.7 years, female:male = 0.49:0.51). Emergency medicine and endocrinology accounted for the majority of referrals (67%). The most common presentations were headache (47%) and visual field deficits (32%). Lesions mainly comprised tumours or haemorrhage (85%) and involved the pituitary gland or fossa (70%). The STL-ARIMA pipeline outperformed CNN-LSTM, Prophet and baseline algorithms across scoring metrics, with standard accuracy being achieved for yearly predictions. Referral volumes significantly increased from the start of data collection with future projected increases (p < 0.001) and did not significantly reduce during the COVID-19 pandemic. Conclusion This work is the first to employ large-scale data and machine learning to describe and predict acute pituitary referral volumes, estimate future service demands, explore the impact of system stressors (e.g. COVID pandemic), and highlight areas for service improvement.

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A Novel Neurosurgery Referral Course: Feasibility, Validation, and Inferences for Patient Care

Cited by 3 publications

References 11 publications

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Historical and Future Trends in Emergency Pituitary Referrals: A Machine Learning Analysis

Historical and future trends in emergency pituitary referrals: a machine learning analysis

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