Abstract

Healthcare is the industry in which customers want to deal with humans. No machines, they want to connect with real people in their most vulnerable time. In this context, women are more likely to be the center of the patient journey: from taking their loved ones to a doctor’s appointment to being the primary care of kids with chronic diseases (such as asthma).

The burden of care is still under women dealing with unpaid work. However, it’s not any better for men. In 2019, Cleveland Clinic conducted a survey which found that 72% of respondents preferred doing household chores like cleaning the bathroom to going to the doctor; 65% said they avoid going to the doctor for as long as possible; 20% admitted they are not always honest with their doctors about their health. On average, men die younger than women in the United States. American women had a life expectancy of 79 years in 2021, compared to 73 for men (CDC, 2022).

The goal of this research is to explore the gender differences in a patient journey by applying a corpus linguistic approach to create and annotate a dataset about chronic disease in Portuguese and English manually using social media data from Facebook, Instagram, YouTube and Twitter. Then, I’m applying text analysis methods to describe the dataset. Lastly, comparing the classification results of Generative AI to the traditional machine learning text analysis.

This analysis also wondered between the benefits and detriments of performing with such analysis. Despite the investment of language model resources, it’s valuable to use AI to uncover gender inequalities. The final goal is open discussion about how to take the burden from women, and also empowering men to feel comfortable about their own health. It’s also open space to discuss new methods exploring different gender classifications.

Goal: This proposal is intended to describe a study of how corpus linguistic and text analysis methods can be used to support research on language and communication within the context of healthcare using social media data about chronic disease in English and Brazilian Portuguese.

The specific goals evolve:

1. Performing literature review based on previous studies and benchmark datasets in the healthcare field – process finished.
2. Creating a dataset with social media posts from 2020 to 2023 in social networks such as Twitter, YouTube, Facebook, and local media channels. The dataset is composed of around 7k posts and specifies the patient’s gender, type of treatment/ medication, number of likes and comments – process already finished: dataset here.
3. Categorizing the corpus according to gender and the patient journey framework, from initial symptoms to diagnosis, treatment, and follow-up care – process already finished: dataset here.
4. Documenting the dataset and creating a codebook explaining the categories and the criteria for the categorization process – process already finished: code book and the the connection of the ontologies.
5. Applying categorization based on GPT3 results – in progress
6. Comparing the results with the manual classification with GPT3 results

Literature review

Several linguistic analyses and corpus analysis studies have investigated the patient journey in healthcare, exploring different aspects of communication between patients and healthcare providers, patient experience, and clinical outcomes. One area of research has focused on the use of language by healthcare providers to diagnose and treat patients. For example, a study by Roter and Hall found that physicians used a directive communication style, using commands and suggestions, more often than a collaborative communication style when interacting with patients. This style can create a power imbalance between the physician and patient, potentially leading to dissatisfaction or miscommunication. 

Another area of research has investigated patient experience and satisfaction. A corpus analysis study by Gavin Brookes,  and Paul Baker examined patient comments to identify factors influencing patient satisfaction with healthcare services during cancer treatments. They found that factors such as communication, empathy, and professionalism were key drivers of patient satisfaction.

Finally, several studies have investigated the use of language in electronic health records (EHRs) to improve patient care and outcomes. A corpus analysis study by Xi Yang and colleagues examined the use of EHRs and found that natural language processing techniques could effectively identify relevant patient information from unstructured clinical notes.

Overall, the literature on linguistic analyses and corpus analysis studies on healthcare patient journey suggests that communication and language play a critical role in patient care and outcomes. Effective communication between patients and healthcare providers, as well as clear and concise language in patient education materials and EHRs, can lead to improved patient satisfaction, empowerment, and self-management.

Method overview

• Data collection: collecting data based on keywords on social media;
• Coding data: using qualitative coding and annotation
• Data analysis: performing linguistics and statistical analysis 

References

“Doctors Talking with Patients—Patients Talking with Doctors: Improving Communication in Medical Visits.” Clinical and Experimental Optometry, 78(2), pp. 79–80

Yang, X., Chen, A., PourNejatian, N. et al. A large language model for electronic health records. npj Digit. Med. 5, 194 (2022). https://doi.org/10.1038/s41746-022-00742-2

Peterson KJ, Liu H. The Sublanguage of Clinical Problem Lists: A Corpus Analysis. AMIA Annu Symp Proc. 2018 Dec 5;2018:1451-1460. PMID: 30815190; PMCID: PMC6371258.

Adolphs, S, Brown, B., Carter, R., Crawford, C. and Sahota, O. (2004) ‘Applying Corpus
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Adolphs, S., Atkins, S., Harvey, K. (forthcoming). ‘Caught between professional requirements and interpersonal needs: vague language in healthcare contexts’. In J. Cutting (ed.) Vague Language Explored Basingstoke: Palgrave

Skelton, J.R., Wearn, A. M., and F.D.R. Hobbs (2002) ‘‘I’ and ‘ we’: a concordancing analysis of doctors and patients use first person pronouns in primary care consultations ’, Family Practice, 19, 5: 484-488

Biber, D. and Conrad, S. (2004) ‘Corpus-Based Comparisons of Registers’, in C. Coffin, A.
Hewings, and K. O’Halloran (eds) Applying English Grammar: Functional and Corpus
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