Abstract
The study sought to examine the task-technology fit of ChatGPT for healthcare services. The research model was based on an integration of the task-technology fit (TTF) model and the technology acceptance model 2 (TAM2). The study used a quantitative research design. Data were collected from 265 health workers who have used ChatGPT in the performance of their work. Data analysis was done using structural equation modelling. Six of the seven hypotheses were accepted. The results show significant positive relationships between task characteristics and task-technology fit, task-technology fit and ChatGPT usage, ChatGPT usage and performance impacts, social influence and perceived usefulness, perceived usefulness and usage intention, as well as usage intention and ChatGPT usage. There was an insignificant positive relationship between task-technology fit and performance impacts.
TopIntroduction
Modern civilization has over the years encountered distressing life events such as dengue fever, avian flu, Ebola virus, and quite recently, the CoVID-19 pandemic. People encounter circumstances every day that could harm their health. According to Abdullah et al. (2023), stressful life events and health are related, and stressors can increase the likelihood of developing serious diseases. Aside from that, the population is aging, and more people have several comorbid conditions (Fan et al., 2021). Such situations highlight the need for developing sophisticated services, tools, equipment, and general high-quality clinical/hospital information systems. Maintenance of the health and wellbeing of the population is of utmost importance (Ivanovic et al., 2023). Enhancements to health-related quality of life (QoL) factors are significant and well acknowledged in treatments and follow-ups for people who have survived major diseases. The quality of life (QoL) of cancer patients is seriously disrupted during the active oncological treatment period or during follow-up care, in addition to the usual health issues such as anxiety, sleep disorders, mental impairment, pain, appetite loss, psychological difficulties, sexual dysfunction, et cetera (Fan et al., 2021).
Artificial intelligence (AI) has enormous and varied implications for the provision of health services. AI has the power to completely transform healthcare by improving the efficacy, accessibility, and efficiency of medical services. Conversational agents have been developed to support a range of health-related activities, such as behaviour modification, treatment support, health monitoring, training, triage, and screening assistance (Abdullah et al., 2023). This is due to the increased demand for healthcare services and the increasing capabilities of AI. Furthermore, the application of AI in healthcare is growing, providing chances to enhance the provision of medical services (Javaid et al., 2023)
Additionally, because AI may supplement or replace the limited processing power of human experts in primary health care, it has the potential to increase the efficacy and efficiency of the delivery of health care services (Javaid et al., 2023). The delivery of health services may change as a result of the advancement of AI mobile communication technology, which can offer a more practical means of attending to the various health needs of locals (Ivanovic et al., 2023). Additionally, the automation of digital health systems and the provision of medical care remotely through tele-interactions, tele-diagnosis, tele-assessment, and tele-monitoring are two more ways that AI technologies have the potential to revolutionize the delivery of healthcare (Javaid et al., 2023).
Conversational AI is facilitating mental health assessments and has been linked to better recovery rates, which is transforming the way mental health care is provided (Nadarzynski et al., 2019). Furthermore, AI techniques may be utilized to lessen the strain on public health, especially in situations like the COVID-19 pandemic (Liu et al., 2021).
The usage of eHealth solutions, which make use of the internet and related technologies to enhance and distribute health services and information, especially in rural regions, thereby expanding the reach of health services, is another way that artificial intelligence (AI) is affecting the delivery of healthcare services (Abdullah et al., 2023). Moreover, the integration of artificial intelligence (AI) into public health education programs highlights the various ways that AI technology might enhance health care delivery.
Key Terms in this Chapter
Natural Language Processing (NLP): The ability of computers to understand and process human language (Galassi et al., 2020 AU101: The in-text citation "Galassi et al., 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Khurana et al., 2023 AU102: The in-text citation "Khurana et al., 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Meera & Geerthik, 2022 AU103: The in-text citation "Meera & Geerthik, 2022" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Conversational Agents: Computer programs or systems, like ChatGPT, designed to engage in natural language conversations with users. In health services, conversational agents can be employed to interact with patients, answer queries, provide information, and assist in various healthcare-related tasks (Allouch et al., 2021 AU95: The in-text citation "Allouch et al., 2021" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Ethical AI: The responsible and fair development and deployment of artificial intelligence technologies (Mittelstadt, 2019 AU96: The in-text citation "Mittelstadt, 2019" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). In the context of ChatGPT and health services, ethical considerations include privacy protection, unbiased decision-making, and transparency in the use of AI in healthcare interactions.
Machine Learning (ML): A type of artificial intelligence that learns from data without being explicitly programmed (Binkhonain & Zhao, 2019 AU98: The in-text citation "Binkhonain & Zhao, 2019" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Bunod et al., 2022 AU99: The in-text citation "Bunod et al., 2022" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Mahesh, 2020 AU100: The in-text citation "Mahesh, 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Telehealth: The use of technology, such as video calls, chat platforms, and remote monitoring, to provide healthcare services and information remotely (Gajarawala & Pelkowski, 2021 AU104: The in-text citation "Gajarawala & Pelkowski, 2021" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). ChatGPT can potentially contribute to telehealth by serving as a conversational interface in virtual healthcare interactions.
Health Information Privacy: Involves safeguarding individuals' personal and health-related data (Hodge, 2003 AU97: The in-text citation "Hodge, 2003" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Zhang et al., 2018 ). In the context of using ChatGPT in health services, maintaining privacy is crucial to ensure the confidentiality and security of sensitive health information shared during conversations.
User Experience (UX): Encompasses the overall experience a person has when interacting with a system or service (Alomari et al., 2020 AU105: The in-text citation "Alomari et al., 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Luther & Tiberius, 2020 AU106: The in-text citation "Luther & Tiberius, 2020" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ). In the context of ChatGPT and health services, UX involves evaluating the ease of use, effectiveness, and satisfaction of users engaging with the model for healthcare-related communication.
ChatGPT: A large language model chatbot developed by OpenAI, trained on a massive dataset of text and code. Its key characteristics include generative, large language model, dialogue format, and machine learning based. It is can generate text, translate languages, write different kinds of creative content, and answers questions in an informative way (Wu et al., 2023; Herbold et al., 2023 AU94: The in-text citation "Herbold et al., 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Artificial Intelligence (AI): The intelligence demonstrated by machines, contrasted with the natural intelligence displayed by humans (Hwang & Kim, 2021 AU91: The in-text citation "Hwang & Kim, 2021" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Klimova et al., 2023 AU92: The in-text citation "Klimova et al., 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Samala et al., 2022 AU93: The in-text citation "Samala et al., 2022" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Health Services: The actions taken to maintain and improve the health of individuals and communities. They can be provided by a variety of professionals, including doctors, nurses, therapists, and pharmacists. Health services can be preventative, diagnostic, therapeutic, or rehabilitative.