Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care PMC

chatbot healthcare use cases

The primary role of healthcare chatbots is to streamline communication between patients and healthcare providers. They serve as round-the-clock digital assistants, capable of handling a wide array of tasks – from answering common health queries and scheduling appointments to reminding patients about medication and providing tailored health advice. This constant availability not only enhances patient engagement but also significantly reduces the workload on healthcare professionals. By automating responses to repetitive questions and routine administrative tasks, healthcare chatbots free up valuable time for healthcare staff, allowing them to focus more on critical care and patient interaction. One of the consequences can be the shift from operator to supervisor, that is, expert work becomes more about monitoring and surveillance than before (Zerilli et al. 2019). Thus, instead of only re-organising work, we are talking about systemic change (e.g. Simondon 2017), that is, change that pervades all parts of a system, taking into account the interrelationships and interdependencies among these parts.

Some patients may also find healthcare professionals to be intimidating to talk to or have difficulty coming into the clinic in person. For these patients, chatbots can provide a non-threatening and convenient way to access a healthcare service. Healthcare chatbots can help healthcare providers respond quickly to customer inquiries, improving customer service and patient satisfaction. From scheduling appointments to collecting patient information, chatbots can help streamline the process of providing care and services—something that’s especially valuable during healthcare surges. But healthcare chatbots have been on the scene for a long time, and the healthcare industry is projected to see a significant increase in market share within the artificial intelligence sector in the next decade. Survivors of cancer, particularly those who underwent treatment during childhood, are more susceptible to adverse health risks and medical complications.

Which algorithm is used for healthcare chatbot?

Privacy threats may break the trust that is essential to the therapeutic physician–patient relationship and inhibit open communication of relevant clinical information for proper diagnosis and treatment [96]. Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes.

chatbot healthcare use cases

However, these kinds of quantitative methods omitted the complex social, ethical and political issues that chatbots bring with them to health care. In the last decade, medical ethicists have attempted to outline principles and frameworks for the ethical deployment of emerging technologies, especially AI, in health care (Beil et al. 2019; Mittelstadt 2019; Rigby 2019). As conversational agents have gained popularity during the COVID-19 pandemic, medical experts have been required to respond more quickly to the legal and ethical aspects of chatbots. In these ethical discussions, chatbot healthcare use cases technology use is frequently ignored, technically automated mechanical functions are prioritised over human initiatives, or tools are treated as neutral partners in facilitating human cognitive efforts. So far, there has been scant discussion on how digitalisation, including chatbots, transform medical practices, especially in the context of human capabilities in exercising practical wisdom (Bontemps-Hommen et al. 2019). Chatbots streamline patient data collection by gathering essential information like medical history, current symptoms, and personal health data.

Customer service chatbot use cases

They then generate an answer using language that the user is most likely to understand, allowing users to have a smooth, natural-sounding interaction with the bot. Regulatory standards have been developed to accommodate for rapid modifications and ensure the safety and effectiveness of AI technology, including chatbots. With the growing number of AI algorithms approved by the Food and Drug Administration, they opened public consultations for setting performance targets, monitoring performance, and reviewing when performance strays from preset parameters [102]. The American Medical Association has also adopted the Augmented Intelligence in Health Care policy for the appropriate integration of AI into health care by emphasizing the design approach and enhancement of human intelligence [109].

  • This partnership involved strategic roadmapping, prioritizing use cases, conversation design services, bot tuning, and Conversational AI consulting.
  • If you are interested in knowing how chatbots work, read our articles on What are Chatbot, How to make chatbot and natural language processing.
  • Knowledge domain classification is based on accessible knowledge or the data used to train the chatbot.
  • Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals.
  • Their ability to automate repetitive tasks, offer timely support, and provide targeted recommendations makes them valuable assets in optimizing sales strategies and achieving higher customer satisfaction and conversions.

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