Maksymilian A Brzezicki1, Nicholas E Bridger2, Matthew D Kobetić3, Maciej Ostrowski4, Waldemar Grabowski5, Simran S Gill6, Sandra Neumann7. 1. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK. Electronic address: maksymilian.brzezicki@ndcn.ox.ac.uk. 2. Faculty of Health Sciences, University of Bristol, UK. Electronic address: nb12345@my.bristol.ac.uk. 3. Faculty of Health Sciences, University of Bristol, UK. Electronic address: mkobetic@neurologicalsociety.org. 4. Medical University of Lodz, Lodz, Poland. Electronic address: exegacek@gmail.com. 5. Institute of Physics, University of Zielona Gora, Zielona Gora, Poland. Electronic address: wgrabowski@gmail.com. 6. St. George's, University of London Medical School, London, UK. Electronic address: m1500843@sgul.ac.uk. 7. Department of Physiology and Pharmacology, Clinical Research and Imaging Centre, University of Bristol, UK. Electronic address: sandra.neumann@bristol.ac.uk.
Abstract
OBJECTIVES: 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. RESULTS: 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. CONCLUSION: 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.
OBJECTIVES: 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. RESULTS: 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. CONCLUSION: 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.
Authors: Ștefan Busnatu; Adelina-Gabriela Niculescu; Alexandra Bolocan; George E D Petrescu; Dan Nicolae Păduraru; Iulian Năstasă; Mircea Lupușoru; Marius Geantă; Octavian Andronic; Alexandru Mihai Grumezescu; Henrique Martins Journal: J Clin Med Date: 2022-04-18 Impact factor: 4.964