Understanding the Impact of Conversational AI in Healthcare

Getting an appointment with a specialist can be challenging. It can be hard to know how to get started, and waiting for an opening can feel excruciating.

When it comes to healthcare, time is crucial, and personal touches along the way can make all the difference. A quarter of consumers said they can't get the care they need when they need it, according to a McKinsey survey. On the bright side, artificial intelligence (AI) is making healthcare more efficient and personalized. Today, conversational AI in healthcare helps patients and providers alike by sifting through information from patient records and research databases to provide summaries and touchpoints.

Conversational AI helps machines communicate in a way that sounds natural to humans. AI for customer service moved from basic chatbots providing canned answers to using decision trees and scripts to respond to patients' questions. Now, conversational AI includes advanced chatbots and virtual assistants trained on human dialog and large datasets, so it can go far beyond answering simple questions.

It's not to be confused with agentic AI, although the two technologies are related and can be quite complementary. Conversational AI in healthcare focuses on communicating with patients and providers, while agentic AI involves autonomously completing tasks and tackling complicated problems. They can work together to get jobs done while communicating with humans in a natural way. For instance, healthcare providers don't need to write code or learn a complicated computer program in order to take advantage of AI. They can use simple, natural-sounding prompts to ask tools to get the provider up to speed on a patient's medical history by gathering their medical records and describing their medical history in an easy-to-read summary.

• Machine learning (ML) enables machines to continuously improve by learning as they go. They are trained on initial datasets, but they include models and algorithms that allow them to look for patterns in the data that help refine their predictions. They become even more accurate and autonomous.
• Neural networks are inspired by the way the human brain works. They enable deep learning, a type of AI that uses layers of neural networks to do the kind of analysis that is more complicated and human-like, such as speech recognition. Neural networks are the underlying technology that makes l arge language models (LLMs) and generative AI tools possible.
• Natural language processing (NLP) uses computer science and linguistics to help computers understand, interpret, and generate language in the same ways as humans. It's crucial for allowing virtual assistants and tools like chatbots to respond to human queries in a way that sounds natural and trustworthy, rather than the robotic responses that early chatbots produced. It's related to speech recognition, a type of NLP that converts audio into text and analyzes and interprets the meaning so machines can understand humans' voice commands.

These underlying technologies come together to allow AI systems to process and interpret patient inputs to generate accurate responses. Here are four ways this can work for patient services:

1. Gathering patient medical history. A provider requests a summary of a patient. An AI assistant analyzes data from the customer relationship management system (CRM) and patient records, then produces relevant information, such as care plans, medications, clinical encounters, and referrals.
2. Providing a patient medication summary. After a prompt, the conversational AI tool pulls information from the CRM and medication-related entities to generate patient medication summaries, including dosage, frequency, and contraindications.
3. Generating an email for patients. AI tools pull data from the CRM and referral management entities, then create personalized emails such as appointment reminders and helpful care information.
4. Creating a simplified patient assessment. In this case, the tool uses CRM data to digitize healthcare assessments to transform industry-standard templates into actionable documents.

These examples illustrate how AI can help busy healthcare providers gather the information they need to make smart decisions and keep patients engaged. Advanced tools like conversational AI in healthcare can be deployed in different ways.

Cloud computing is extremely common for organizations in a wide variety of industries. This involves using a network of remote servers hosted on the internet to store, manage, and process data. Many organizations operate in the cloud because it provides flexibility and the ability to access the huge processing power that many AI tools require. However, cloud adoption has been slower in healthcare due to concerns about privacy, security, and compliance. HealthTech reports that 63% of healthcare IT leaders value the cloud for its reliability and recovery capabilities.

As an alternative to the cloud, healthcare organizations can set up their AI programs and other technologies with servers on-premises ("on-prem"). However, both options present challenges (protecting sensitive data and integration challenges, respectively). Hybrid computing, a blend of the two deployment methods, is possible as well and could provide the best of both worlds.

Conversational AI tools depend on the data they can access. Integrating these tools with electronic health records (EHRs), for example, can enable personalization based on the patient's medical history, such as allowing a chatbot to follow up regarding a patient's chronic disease or recent visits. It also helps clinicians by summarizing information from EHRs, such as pulling together a list of medications the patient is prescribed or putting together a pre-visit summary. It can also be helpful to integrate conversational AI tools with telehealth platforms. For instance, a chatbot could provide support before and after a telehealth appointment, as well as post-visit summaries.

According to a study published in the International Journal of Health Services, physicians spend 16.6% of their working hours on administrative tasks, which not only pulls them away from patients, but also lowers their career satisfaction.

It doesn't have to be this way.

• Taking on some of the workload: Artificial intelligence can reduce the pressure of administrative tasks by handling repetitive ones like generating reports and scheduling appointments.
• Keeping patients on track: Conversational AI automates and streamlines many of the tasks before and after appointments to inform care decisions and encourage patients to follow their care plans.
• Making healthcare more accessible: Without conversational AI, a patient who has a question may have to hold for a long time on the phone or wait until someone returns the message they sent through the portal during business hours. Conversational AI can provide support at all hours of the day, in any language, on a range of mobile devices and messaging platforms.

Several technologies have matured in recent years, especially LLMs. Recent advances in generative AI have opened more possibilities for all kinds of advancements because AI can access and process data that was previously underutilized because it was unstructured. It works because NLP acts as the translator between patients and computers.

When a patient asks a question, NLP breaks down the question into a language that computers understand. Then the software application uses an LLM to compare the patient's question to training data, and algorithms predict the user's intent behind the question. It helps computers understand that question. Then conversational AI gives them an answer in a language that humans can understand and trust. What's better, like machine learning, conversational AI improves over time. As it gathers more information, it learns and adjusts accordingly.

AI tools can improve the overall patient experience through more efficient, personalized care coordination, such as automated scheduling, reminders, and follow-ups. It uses predictive intelligent appointment management to streamline scheduling and remind patients, increasing the chances they'll show up for their appointments.

As healthcare expands beyond the doctor's office, AI can help with continuous, proactive care coordination. AI tools can help with remote monitoring, for instance, by instantly flagging any patient health status changes or notifying care providers when patient health indicators drop.

Conversational AI is particularly helpful in streamlining patient support, and there are already many use cases today:

• Answering common questions
• Navigating care options
• Triaging more complex concerns to clinical staff when needed
• Interpreting patient language, providing multilingual assistance, and tailoring responses based on medical history or prior interactions

By automating routine tasks and surfacing key insights, conversational AI helps reduce response times, improve patient satisfaction, and free up healthcare teams to focus on direct care. A 2023 study by a group of researchers in California concluded that "implementing AI in daily clinical practice could potentiate the cognitive ability of physicians and decrease the workload through various automated technologies such as image recognition, speech recognition, and voice recognition due to its unmatchable speed and indefatigable nature when compared to clinicians."

AI is being used in mental health support and digital counseling. Using AI chatbots and virtual assistants, patients can get support and health information around the clock. Mental healthcare professionals can use AI to support their practice, for example, by integrating AI tools into apps to remind patients when it's time to take their medication, track side effects, and keep track of how well medications are working. Patients can also use AI-driven apps to monitor mood fluctuations and offer insights into potential triggers so they can identify patterns and make informed decisions about self-care and when to seek help.

AI promotes health literacy, helping patients better understand their conditions and treatments. Patients can use generative and conversational AI tools to better understand the latest medical research, as those tools can quickly summarize lengthy scientific publications and help translate medical jargon into more accessible language.

While conversational AI offers many opportunities for improving patient care, there are concerns to pay attention to.

• Privacy and data security: Using any data-sharing tech means taking HIPAA compliance and protecting patient information seriously. Adding multiple layers of security and following best practices for healthcare IT is essential.
• Accuracy: AI is still a relatively young technology, and AI models are not always complete. They are only as accurate as the data they consume. Even with excellent data, they can hallucinate and generate false information. When adopting AI applications, it's important to ensure they are based on a dataset that accurately represents the population it will be serving.
• Transparency: The ubiquitousness of AI can cause confusion when patients aren’t sure if they’re interacting with a computer or a human. Healthcare providers will need to ensure patients are aware of how AI is being used in their care. Be sure to prioritize consent and autonomy in AI-driven healthcare decisions.

Conversational AI is transforming healthcare by helping medical providers streamline administrative tasks, communicate more easily, and gather real-time insights. Technology like NLP, machine learning, and neural networks can lead to more personalized and accessible care. With thoughtful implementation and human oversight, conversational AI holds substantial benefits that can promote a patient-centered healthcare system. Look for a trusted provider to find an AI healthcare solution that works for your organization.

Presenting virtual conferences and educational events on digital platforms has made it much easier for healthcare professionals to stay current on industry advancements. These interactions save time and help physicians learn about new options without the time and expense of travel. Digital platforms can also streamline patient relationship management, helping providers improve patient care and experiences.

AI is transforming the healthcare industry, from pharma software to patient engagement strategies. AI-powered platforms, for example, can handle routine questions instantly, and AI-enabled self-service portals suggest content based on a doctor's specialty and interests so they can quickly find what they need.

Healthcare professionals want value-added conversations that address real challenges, not sales pitches. Life sciences software solutions like Salesforce Life Sciences Cloud connect your engagement data with clinical information so your team can create strategies that resonate with specific groups.

Optimizing HCP engagement with data-driven insights in a single platform enables powerful analytics to predict individual needs and identify next steps. Look for a platform that can track details like provider specialties, patient referrals, content performance, and more. A good fit will bring data sources together to deliver calculated insights and build predictive models that drive decision-making.

Medical advancements can save lives if healthcare providers know about them and use them to improve patient outcomes. Looking ahead, we can expect even more sophisticated AI-powered systems that anticipate and respond to HCP needs before communication barriers arise. In the future, we could see engagement platforms that automatically deliver the content each provider needs exactly when they need it based on specialty, patient population, and past interaction patterns.

Some organizations are already moving toward a model where data from multiple sources like clinical encounters, research interests, prescribing patterns, and digital behavior can be used to create provider profiles. This information will enable personalized interactions that feel valuable rather than intrusive, respecting the precious time constraints HCPs face daily.

In this environment, HCP engagement should view each provider interaction as an opportunity to become a trusted partner. By embracing omnichannel approaches, leveraging software and technology tools, and prioritizing personalized interactions, healthcare and life sciences companies can build meaningful relationships that benefit everyone.