ChatGPT vs. Speech Recognition: Boosting Efficiency in Neurosurgical Documentation

Abstract

As clinical documentation in neurosurgery continues to heighten demands, accurate and efficient methods for producing reports are essential. This paper explores using a novel artificial intelligence tool to streamline the composition of neurosurgical discharge summaries and operative reports using Chat Generative Pre-trained Transformer or ChatGPT. We compared the time taken and factual accuracy of reports prepared by ChatGPT to those prepared using the standard speech recognition software, SpeaKING, for three neurosurgical conditions: chronic subdural hematoma, spinal decompression for stenosis, and craniotomy for tumor resection.

The mean time to discharge summaries and operative reports for all conditions studied were reduced, with statistical significance at p <0.001, on ChatGPT. While reports generated by ChatGPT were highly accurate for chronic subdural hematoma and spinal decompression, the factual correctness for craniotomy was a bit low, therefore requiring further refinement of complex surgical documents.

With ChatGPT, overall the time-saving benefits are significant but need better improvement in getting more detailed accuracy on complex procedures. Improving AI-based applications in medical writing also presents promising opportunities but raises some interesting questions of ethics and practicality that are to be a focus of further study.

Introduction

Currently, such a rapidly changing medical documentation world has a real need for speed and accuracy in high-stakes fields like neurosurgery. While there is an ever-growing demand for rapid and accurate clinical documentation, these kinds of tasks have thus come to be explored with advanced technologies, increasingly by the healthcare professionals who might use them. Traditionally, speech recognition software such as SpeaKING is often used to produce discharge summaries and operative reports. With the advent of large-scale language models, such as Chat GPT, comes a new road to automating medical writing.

OpenAI's ChatGPT is an NLP application that has astounded everybody with its remarkable capabilities to generate human-like text across a huge cross-section of domains. Despite the primary thrust of ChatGPT having been on conversational applications, the tool is quickly turning out to be one of the most impressive tools for clinical documentation. Given its ability to process and generate complex, context-specific content, ChatGPT could revolutionize medical writing by relieving the time burden of healthcare professionals while maintaining, or even improving, the accuracy and quality of reports.

The purpose of this study is to assess the utility of using ChatGPT for neurosurgical documentation by comparing its performance with the speech recognition software SpeaKING that is currently in use. We assess the potential time-saving benefit of ChatGPT on writing discharge summaries and operative reports for three of the most common conditions encountered in neurosurgery: chronic subdural hematoma, spinal decompression for stenosis, and craniotomy for tumor resection. We also verified the facts of the reports that ChatGPT generated and discussed the inferences it could make for clinical use in the future.

The Importance of Efficient Medical Documentation

The Burden of Clinical Documentation

Clinical documentation is a critical foundation to ensure quality care to patients, clear communication between various healthcare teams and to document the claims for billing and reimbursements. However, in the case of specialties like neurosurgery, detailed reporting often means recording an exhaustive listing of cases, which occupies a considerable amount of time. Many of the neurosurgeons could have used such an amount of time for their patients, research, or continuing education.

Speech recognition software, including products like SpeaKING, were developed to speed up documentation because they translates words into text. It does not save as much time, though because post-edited and proofread to correct errors. How effective speech recognition software is may vary with users because speech recognition's accuracy changes with noise and varying influences like accent and the specificity of medical terminology.

The Emergence of AI-Driven Solutions

As the levels of AI technology continue to advance, there is growing interest in developing an application of AI so that the process of medical documentation can be further optimized. And for chatGPT, this is something very special indeed-it not only transcribes but is also capable of generating coherent and structured content out of contextual understanding. Possessing a vast pool of data processing capabilities and learning from a great variety of inputs in text format, ChatGPT can address many of the shortcomings of traditional speech recognition software.

This means that the introduction of ChatGPT into clinical workflows will allow healthcare professionals to create discharge summaries and operative reports in significantly less time with significantly more accuracy, largely 'cognitively unloaded' from the burden of manual documentation. However, they must be treated just like any other AI-driven tool, on issues of factual correctness, ethical considerations, and proper use case scenarios.

Methods

Study Design

This study was conducted at a major university hospital, where we compared the time taken and accuracy of neurosurgical discharge summaries and operative reports generated using two different methods: SpeaKING (speech recognition software) and ChatGPT. We focused on three neurosurgical conditions commonly treated at the hospital:

• Chronic subdural hematoma (SDH)

• Spinal decompression for stenosis

• Craniotomy for tumor resection

For each condition, multiple discharge summaries and operative reports were generated using both SpeaKING and ChatGPT. The times taken for the completion of each report were recorded, and statistical analysis was conducted to determine whether there were significant differences between the two methods.

Factual Correctness Evaluation

Apart from determining the time efficiency of the reports, the factual accuracy of the reports was also determined. In this regard, every report produced was scrutinized by a panel of neurosurgeons to determine its accuracy against the original patient data. The panel scored the reports based on how well they detailed the patient's information, the surgical procedures performed, and the outcomes. Differences such as some errors in nomenclature, omission, or factual inaccuracies were noted and compared between the reports produced by SpeaKING and ChatGPT.

Results

Time Efficiency

The results showed a statistically significant reduction in the time taken to generate neurosurgical discharge summaries and operative reports using ChatGPT as compared to SpeaKING:

Chronic Subdural Hematoma (SDH): ChatGPT significantly reduced the time required to complete discharge summaries and operative reports compared to SpeaKING (p < 0.001).

Spinal Decompression for Stenosis: Similar time savings were observed for reports related to spinal decompression (p < 0.001).

Craniotomy for Tumor Resection: The time reduction was also statistically significant for craniotomy reports (p < 0.001).

These findings suggest that ChatGPT offers considerable time-saving advantages in clinical documentation across multiple neurosurgical conditions.

Factual Correctness

While the time efficiency of ChatGPT was impressive, the accuracy of the reports varied depending on the neurosurgical condition:

Chronic Subdural Hematoma and Spinal Decompression: Reports generated by ChatGPT for cSDH and spinal decompression exhibited a high degree of factual correctness, with minimal discrepancies noted by the reviewing neurosurgeons.

Craniotomy for Tumor Resection: In contrast, the factual accuracy of the craniotomy reports was slightly lower, with reviewers identifying several instances of missing or incorrect details (p = 0.002). These errors are primarily related to the description of surgical techniques and post-operative care.

Discussion

Implications for Clinical Documentation

The findings of this study demonstrate that ChatGPT can significantly reduce the time burden associated with neurosurgical documentation, making it a valuable tool for busy healthcare professionals. By automating the generation of discharge summaries and operative reports, ChatGPT allows neurosurgeons to focus more on patient care and less on administrative tasks.

However, while ChatGPT excels in time efficiency, the accuracy of its output—particularly for complex procedures like craniotomy—requires further improvement. This highlights the need for continued refinement of AI-driven models to ensure that they produce not only timely but also clinically reliable documentation.

Ethical Considerations and Future Directions

As AI tools like ChatGPT become more integrated into clinical practice, ethical considerations must be addressed. Issues such as the accountability for errors in AI-generated documentation, the protection of patient privacy, and the potential for AI to exacerbate healthcare disparities are critical topics that require careful deliberation.

Furthermore, future research should explore how ChatGPT can be tailored to specific medical specialties, ensuring that it can handle the unique complexities of each field. Collaboration between AI developers and healthcare professionals will be essential in fine-tuning these tools to meet the needs of modern medicine.

Conclusion

In summary, the application of ChatGPT promises a lot in terms of improving the efficiency of neurosurgical documentation, especially concerning the generation of discharge summaries and operative reports. While it cuts the time for documentation significantly compared to traditional speech recognition software, its factual accuracy needs to be improved for more complex cases. As the technology continues to evolve, AI will likely take an expanded role in medical documentation and provide opportunities for the optimization of clinical workflows and the quality of care. This requires a research agenda and discussion among the members of the medical community as regards the ethical and practical challenges posed by AI-generated writing in medical contexts.

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