Chatbots in Healthcare: Improving Patient Engagement and Experience
Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review PMC
Although preliminary results do indicate positive effects in a number of application domains, reported findings are for the most part mixed. The timeline for the studies, illustrated in Figure 3, is not surprising given the huge upsurge of interest in chatbots from 2016 onward. Although health services generally have lagged behind other sectors in the uptake and use of chatbots, there has been greater interest in application domains such as mental health since 2016. This finding may reflect both the degree to which conversational technologies lend themselves to the kinds of interactive methodologies used in mental health and the necessity for greater scrutiny of the methods that are used by health practitioners in field.
Chatbots are made on AI technology and are programmed to access vast healthcare data to run diagnostics and check patients’ symptoms. It can provide reliable and up-to-date information to patients as notifications or stories. The healthcare industry incorporates chatbots in its ecosystem to streamline communication between patients and healthcare professionals, prevent unnecessary expenses and offer a smooth, around-the-clock helping station. While chatbots offer many benefits for healthcare providers and patients, several challenges must be addressed to implement them successfully.
Chatbot Keeps Your Patients Satisfied
The research suggests that psychotherapy chatbots can act as a supplemental tool as part of the broader psychotherapy process  across a broad range of psychotherapeutic methodologies and approaches (see Multimedia Appendix 2 for a summary of chatbot roles). For RCTs, the number of participants varied between 20 to 927, whereas user analytics studies considered data from between 129 and 36,070 users. Overall, the evidence found was positive, showing use of chatbots in healthcare some beneficial effect, or mixed, showing little or no effect. Most (21/32, 65%) of the included studies established that the chatbots were usable but with some differences in the user experience and that they can provide some positive support across the different health domains. Surprisingly, there is no obvious correlation between application domains, chatbot purpose, and mode of communication (see Multimedia Appendix 2 [6,8,9,16-18,20-45]).
Another startup called Infermedica offers an AI engine focused specifically on symptom analysis for triage. It can integrate into any patient-facing platform to automatically evaluate symptoms and intake information. This allows doctors to process prescription refills in batch or automate them in cases where doctor intervention is not necessary. Generative AI tools like ChatGPT reached mass adoption in record time, and reset the course of an entire industry. Gao asked for references to studies about the gene and ChatGPT offered three “very plausible” citations.
The future perspective of chatbots for healthcare
The level of conversation and rapport-building at this stage for the medical professional to convince the patient could well overwhelm the saving of time and effort at the initial stages. There are three primary use cases for the utilization of chatbot technology in healthcare – informative, conversational, and prescriptive. These chatbots vary in their conversational style, the depth of communication, and the type of solutions they provide. Furthermore, hospitals and private clinics use medical chat bots to triage and clerk patients even before they come into the consulting room. These bots ask relevant questions about the patients’ symptoms, with automated responses that aim to produce a sufficient medical history for the doctor.
The cognitive behavioral therapy–based chatbot SMAG, supporting users over the Facebook social network, resulted in a 10% higher cessation rate compared with control groups . Motivational interview–based chatbots have been proposed with promising results, where a significant number of patients showed an increase in their confidence and readiness to quit smoking after 1 week . No studies have been found to assess the effectiveness of chatbots for smoking cessation in terms of ethnic, racial, geographic, or socioeconomic status differences. Creating chatbots with prespecified answers is simple; however, the problem becomes more complex when answers are open.