Comparing human vs. machine-assisted analysis to develop a new approach for Big Qualitative Data Analysis
Authors: Martin Sam , Beecham Emma , Kursumovic Emira , Armstrong Richard A. , Cook Tim M. , Déom Noémie , Kane Andrew D. , Moniz Sophie , Soar Jasmeet , Vindrola-Padros Cecilia ,   collaborators
Published: July 2024

Background

Analysing large qualitative datasets can present significant challenges, including the time and resources required for manual analysis and the potential for missing nuanced insights. This paper aims to address these challenges by exploring the application of Big Qualitative (Big Qual) and artificial intelligence (AI) methods to efficiently analyse Big Qual data while retaining the depth and complexity of human understanding. The free-text responses from the Royal College of Anaesthetists’ 7th National Audit Project (NAP7) baseline survey on peri-operative cardiac arrest experiences serve as a case study to test and validate this approach.

Methodology/Principal Findings

Quantitative analysis segmented the data and identified keywords using AI methods. In-depth sentiment and thematic analysis combined natural language processing (NLP) and machine learning (ML) with human input - researchers assigned topic/theme labels and sentiments to responses, while discourse analysis explored sub-topics and thematic diversity. Human annotation refined the machine-generated sentiments, leading to an additional “ambiguous” category to capture nuanced, mixed responses. Comparative analysis was used to evaluate the concordance between human and machine-assisted sentiment labelling. While ML reduced analysis time significantly, human input was crucial for refining sentiment categories and capturing nuances.

Conclusions/Significance

The application of AI-assisted data analysis tools, combined with human expertise, offers a powerful approach to efficiently analyse large-scale qualitative datasets while preserving the nuance and complexity of the data. This study demonstrates the potential of this novel methodology to streamline the analysis process, reduce resource requirements, and generate meaningful insights from Big Qual data. The integration of NLP, ML, and human input allows for a more comprehensive understanding of the themes, sentiments, and experiences captured in free-text responses. This study underscores the importance of continued interdisciplinary collaboration among domain experts, data scientists, and AI specialists to optimise these methods, ensuring their reliability, validity, and ethical application in real-world contexts.

Author Summary

The use of Artificial intelligence (AI) in health research has grown over recent years. However, analysis of large qualitative datasets known as Big Qualitative Data, in public health using AI, is a relatively new area of research. Here, we use novel techniques of machine learning and natural language processing where computers learn how to handle and interpret human language, to analyse a large national survey. The Royal College of Anaesthetists’ 7th National Audit Project is a large UK-wide initiative examining peri- operative cardiac arrest. We use the free-text data from this survey to test and validate our novel methods and compare analysing the data by hand (human) vs. human-machine learning also known as ‘machine-assisted’ analysis. Using two AI tools to conduct the analysis we found that the machine- assisted analysis significantly reduced the time to analyse the dataset. Extra human input, however, was required to provide topic expertise and nuance to the analysis. The AI tools reduced the sentiment analysis to positive, negative or neutral, but the human input introduced a fourth ‘ambiguous’ category. The insights gained from this approach present ways that AI can help inform targeted interventions and quality improvement initiatives to enhance patient safety, in this case, in peri-operative cardiac arrest management.