Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Faculty of Health Sciences, University of Bristol, UK.
Clin Neurol Neurosurg. 2020 May;192:105732. doi: 10.1016/j.clineuro.2020.105732. Epub 2020 Feb 10.
Neurosurgical audits are an important part of improving the safety, efficiency and quality of care but require considerable resources, time, and funding. To that end, the advent of the Artificial Intelligence-based algorithms offered a novel, more economically viable solution. The aim of the study was to evaluate whether the algorithm can indeed outperform humans in that task.
PATIENTS & METHODS: Forty-six human students were invited to inspect the clinical notes of 45 medical outliers on a neurosurgical ward. The aim of the task was to produce a report containing a quantitative analysis of the scale of the problem (e.g. time to discharge) and a qualitative list of suggestions on how to improve the patient flow, quality of care, and healthcare costs. The Artificial Intelligence-based Frideswide algorithm (FwA) was used to analyse the same dataset.
The FwA produced 44 recommendations whilst human students reported an average of 3.89. The mean time to deliver the final report was 5.80 s for the FwA and 10.21 days for humans. The mean relative error for factual inaccuracy for humans was 14.75 % for total waiting times and 81.06 % for times between investigations. The report produced by the FwA was entirely factually correct. 13 out of 46 students submitted an unfinished audit, 3 out of 46 made an overdue submission. Thematic analysis revealed numerous internal contradictions of the recommendations given by human students.
The AI-based algorithm can produce significantly more recommendations in shorter time. The audits conducted by the AI are more factually accurate (0 % error rate) and logically consistent (no thematic contradictions). This study shows that the algorithm can produce reliable neurosurgical audits for a fraction of the resources required to conduct it by human means.
神经外科审核是提高安全性、效率和护理质量的重要组成部分，但需要大量的资源、时间和资金。为此，人工智能算法的出现提供了一种新颖且更经济可行的解决方案。本研究的目的是评估该算法是否真的可以在该任务中优于人类。
邀请 46 名人类学生检查神经外科病房 45 例医疗异常值的临床记录。任务的目的是生成一份报告，其中包含对问题规模（例如出院时间）的定量分析，以及如何改善患者流程、护理质量和医疗成本的定性建议列表。使用基于人工智能的 Frideswide 算法（FwA）分析相同的数据集。
FwA 生成了 44 条建议，而人类学生平均报告了 3.89 条。提交最终报告的平均时间为 FwA 为 5.80 秒，人类为 10.21 天。人类在总等待时间方面的错误率为 14.75%，在检查之间的时间方面的错误率为 81.06%。FwA 生成的报告在事实方面完全正确。46 名学生中有 13 名提交了未完成的审核，3 名提交了逾期提交。主题分析揭示了人类学生给出的建议中存在许多内部矛盾。
基于人工智能的算法可以在更短的时间内生成更多的建议。人工智能进行的审核在事实方面更准确（错误率为 0%）且逻辑上更一致（无主题矛盾）。本研究表明，该算法可以以人类所需资源的一小部分生成可靠的神经外科审核。
