Beyond Boundaries: The Promise Of Conversational AI In Healthcare

Whether your practice is an early adopter when it comes to healthcare technology or more cautious, it’s not too early to start thinking about the implications of AI and how it can improve patient communications and productivity. Conversational AI is becoming an increasingly important tool for healthcare organizations, and the use cases for this technology are ever expanding. For example, CSAT surveys (customer satisfaction surveys) are one of the most commonly used tools, across all industries, to measure how satisfied clients are with their interactions with a business. Generally, CSAT surveys are sent to clients or patients immediately after an interaction like a support call or a live chat conversation.

As conversational agents are often touted as having the potential to reduce the burden on health care resources, evaluations of the implications of the agents for improved health care provision and reduced resource demand also need to be assessed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback. It will be important for future studies of conversational agents to take care to properly structure and report their studies to improve the quality of the evidence. Without high-quality evidence, it is difficult to assess the current state of conversational agents in health care – what is working, and what needs to be improved to make them a more useful tool.

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Easy access to and the ability to keep track of patients’ conversations and data allows these agents to personalize the information and information delivery to an unprecedented degree. If the agent has access to the patient’s clinical and health services history and, once authorized, the system does not need to repeatedly request patients’ credentials as is the case with current consultations over the phone. This can save considerable time and conveys the idea to the patient of having a personal health coach literally “in their pocket”. Often anthropomorphic elements, such as a human-like avatar or natural language use, make interactions more humane and personal.

On top of it, many even struggle with the preparation of this data and setting up dialog flow to make the conversation flow seamlessly. This can be addressed by integrating with electronic medical records and other healthcare systems and adopting tools like dbt. An AI Assistant can answer common queries and FAQs related to a particular disease, health condition or epidemic.

Use Cases of Conversational AI for Healthcare Industry

Those pre-recorded voice commands invoke our custom Google Action (voice applications). This setup allows us to run experience sampling surveys, which provide subjective user assessments throughout the day [

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]. To gauge the users’ current context, the speaker asks about people’s availability, boredom level, mood, and current activities. Invoking the survey is done in regular intervals but, with the help of sensor data, surveys can be triggered by certain events as well, such as the presence of a person, when the user wakes up in the morning or before leaving their home. For patients living with chronic health conditions, specific types of mini-surveys and reminders can be implemented in voice applications and be deployed on our system to collect data about patients’ medical or mental conditions and support medication adherence.

Perceived ease of use or usefulness (27/30, 90%), the process of service delivery or performance (26/30, 87%), appropriateness (24/30, 80%), and satisfaction (26/31, 84%) were the outcomes that had the most support from the studies. Just over three-quarters (23/30, 77%) of the studies also reported positive or mixed evidence of effectiveness. During the screening process, studies of conversational agents that were not capable of interacting with human users via unconstrained NLP were excluded. These included conversational agents that only allowed users to select from predefined options or agents with prerecorded responses that did not adapt to subsequent user responses. The basis for this exclusion is that, without the capability of using NLP, computational methods and technologies are rudimentary and do not advance the aims of AI for autonomous computational agents. As many studies did not explicitly state whether the investigated agent was capable of NLP, a description in the paper of the conversational agent allowing free-text or free-speech input was used as an indicator for NLP, and these studies were included.

The sheer number of active cases may already be overwhelming for a regional hospital but monitoring active cases only may not be sufficient. For effective COVID tracing, the broader circle of people who have been in contact with active cases need to be monitored as well. Therefore, the number of people who require regular check-ins increases exponentially as the circle of contacts increases and this makes manual tracking by medical professionals (or other service providers) almost impossible. The COVID-19 pandemic has accelerated the digitization of healthcare services, making this technology more relevant than ever before.

• Similarly, there is a gap in the evidence regarding the health economics of these agents.
• First, are the conversational agents investigated effective at achieving their intended health-related outcomes, and does the effectiveness vary depending on the type of agent?
• The High-Impact Nature of Scenarios and Use CasesThe common use cases in finance, retail entertainment, or sales and marketing involve topics that are relatively harmless.
• These included conversational agents that only allowed users to select from predefined options or agents with prerecorded responses that did not adapt to subsequent user responses.

But in healthcare, where it is often a life or death matter, the stakes are much higher. A parent could be enquiring about the right treatment for her injured child or a user might be in need of urgent emergency care for a stroke. In such high-impact scenarios, chatbots may have to prioritize accuracy and knowledge over other traits like personality. Differences in Symptom Descriptions and Medical TerminologyThe healthcare industry is somewhat unique due to the vast medical terminology it uses. Specifically, there could be a big gap between the language of the user’s queries and the correct medical terms corresponding to those queries. Common queries around location and operating hours aside, users could ask about medical procedures, health screening, symptoms, and matching doctors and could even share their personal info.

Overall Evaluation of Conversational Agents

Organizations that can implement gen AI quickly are likely to be in the best position to see benefits, whether in the form of better efficiency or improved outcomes and experience. Back-office work and administrative functions, such as finance and staffing, provide the foundations on which a hospital system runs. But they often operate in silos, relying on manual inputs across fragmented systems that may not allow for easy data sharing or synthesis. conversational ai in healthcare Gen AI represents a meaningful new tool that can help unlock a piece of the unrealized $1 trillion of improvement potential present in the industry. Atomwise has partnered with pharmaceutical companies and research institutions, leveraging its AI technology to expedite their drug discovery efforts. These collaborations are not only speeding up the development of new drugs but are also helping in repurposing existing drugs for new therapeutic uses.