Conversational AI for Healthcare: 10 use cases and real-world examples

If there’s one thing to be said about healthcare today, it’s that the healthcare system is buckling under the weight of increasing costs, staff shortages, and growing patient numbers. Against this challenging backdrop, the potential of conversational AI in healthcare is touted as a much-needed lifeline that offers a promising solution to healthcare’s toughest burdens.

However, as healthcare providers consider which conversational AI solution to bank on, it’s important to avoid the shiny object syndrome and invest in resilient tools. So, let’s see what conversational AI healthcare solutions are here to stay. 

What is conversational AI technology in healthcare?

Healthcare conversational AI relies on advanced natural language processing to interact with patients and other healthcare stakeholders in a natural way. The technology can manifest as text-based conversational chatbots, virtual assistants, or voice-enabled interfaces that act as co-pilots, automating various tasks.

Compared with traditional, rule-based chatbots, conversational AI interfaces offer a significant leap forward, providing a more natural, adaptable user experience.

Feature	Rule-based chatbots	Conversational AI interfaces
Task	Navigation-focused	Dialog-focused
Type of input data	Cannot directly process unstructured data	Can leverage unstructured data (e.g. purchasing and accounts payable data) to shape outputs
Language understanding	Pre-determined and scripted with limited understanding of context	Advanced natural language processing, understands nuances and context
Response generation	Predefined responses, limited flexibility	Dynamic response generation, can adapt to various queries
Adaptation	Limited learning capabilities, requires explicit training	Continuous learning, improves over time through machine learning
Personalization	Provides generic responses	Personalized responses based on user history and preferences
Complexity of interactions	Handles linear, predictable queries	Can handle complex, multi-turn conversations
Flexibility of deployment	Trained for a specific task	Generalizable nature, can be integrated into multiple healthcare settings

Proven benefits of healthcare conversational AI

Over 70% of leading healthcare companies are experimenting with or planning to scale generative AI — a core conversational AI enabler — across the enterprise. Let’s probe into the gains they can already reap by implementing conversational AI solutions.

Bringing patient self-service into the practice

Whether it’s due to high costs, inherent stigma, or shortage of healthcare professionals, 29% of the US population choose not to seek needed medical care. Patient-facing conversational AI agents and chatbots can remove the obstacles in the path to healthcare services and give patients the autonomy to manage health on their own terms.

Conversational AI systems interact directly with patients to perform tasks that span from mental health support to appointment scheduling and medication management. With conversational interfaces in tow, individuals can get the necessary support and direction, even when the care organization’s resources are spread thin.

Looking to automate Rx management, a US-based healthcare provider reached out to *instinctools. Our team developed a custom virtual medical assistant that handles repeat prescription refills from patients autonomously and proactively notifies patients when the refill is due. The result was an estimated 120% increase in patient satisfaction and slashed admin costs.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

Driving administrative cost-efficiency 

While evaluating the high-value areas lined up for gen AI disruption, 60% of healthcare leaders deem administrative efficiency to be one of them. From automated patient data extraction to medical record management, conversational agents can execute administrative tasks related to revenue cycle, reporting, and approval processes.

By automating these operations, healthcare organizations can potentially save up to 15% to 25% of total healthcare spending.

Making patients feel heard

Patients often waste hours on getting their issues resolved through IVRs and other systems. The lack of contextual understanding, long wait times, and inaccessible interfaces result in low first-call resolution percentages and leave patients feeling abandoned. 

Healthcare conversational AI can flip the script. By building on structured and unstructured patient data, past interactions, and real-time contextual cues, conversational AI interfaces can bring humanity back into the experience and share the workload with human agents.

One of our clients, a health insurance provider, implemented conversational AI to handle a growing volume of claims processing calls. By automating the initial intake, claim status updates, and document verification, our AI-powered solution helped the client decrease resolution time by 40%, increase deflection rate by 25%, and lower costs by 20%.

— Pavel Klapatsiuk, AI Lead Engineer, *instinctools

Enhancing health outcomes

Conversational AI in healthcare wears many hats — with each of them contributing to enhanced patient outcomes. Whether it’s through personalized medication reminders, symptom checking, or billing assistance, human-like AI interfaces can positively transform the way patients interact with existing healthcare systems.

More importantly, conversational AI healthcare solutions help clinicians fill the gaps in patient data — both directly and indirectly — by enabling proactive patient engagement and facilitating comprehensive data collection. Having more validated patient data on hand allows healthcare providers to make more informed decisions about diagnosis, treatment, and preventative measures.

From code to cure: 10 applications of conversational AI in healthcare

While the storm is gathering in the healthcare sector, opportunities abound for private payers, hospitals, and labs to drive conversational AI innovation and usher in a brighter future. Let’s have a look at how conversational artificial intelligence can shake the healthcare status quo for the better.

1. Appointment scheduling

Conversational AI can not only make care easier to find but also easier to schedule. Available 24/7, AI appointment setters and schedulers align patients’ needs with provider-specific data to bring forth a speedier search and scheduling experience.

Along with scheduling appointments, conversational AI interfaces can:

• Offer a self-reschedule path to patients and alternative time slots.
• Update patients on the time and location of the upcoming appointment.
• Automatically serve canceled appointments to other patients on the waitlist.
• Sync online appointments, digital forms, insurance verification, payments, and patient interactions.

We made the strategic decision to invest in a conversational AI interface to reduce no-shows and keep calendars full without headaches. The solution allowed us to reduce missed appointments by 34 percent and streamline the process of pointing patients to the right care, at the right place and time.

— Head of Patient Services, plastic surgery & dermatology clinic, Los Angeles

2. Medical triaging 

In the US, primary care doctors deal with an average of 53 patient calls per day — and not all of those calls require immediate medical attention. Alleviating this burden is conversational AI that can streamline patient triage by assessing patient symptoms and determining the level of care they need. An AI chatbot can even defeat doctors at diagnosing illnesses — provided it’s properly prompted.

By integrating conversational AI into the triaging process, care providers can create autonomous patient entry points that:

• Gather symptoms and identify potential diagnoses.
• Provide patients with the most clinically appropriate care based on the symptoms.
• Automate the referral process, including scheduling appointments and coordinating with other healthcare providers.
• Integrate with internal systems, providing triaging nurses with access to relevant patient data.
• Shift to an accelerated lane for assistance if the patient needs urgent help and/or requests it.

3. Clinical decision support

To give the right clinical recommendation, doctors have to factor in and analyze patient context, clinical guidelines, and research literature. This time-consuming process can take hours upon hours, holding back timely interventions and leading to inappropriate treatments, if any piece of the puzzle is missed. 

No wonder, 76% of doctors reported using general-purpose LLMs in clinical decision-making. While the safety of this very method is dubious, custom healthcare-specific conversational AI solutions can amplify the doctor’s expertise and intuition by delivering real-time, evidence-based insights at the point of care.

For example, AI-powered interfaces can aid doctors in making dosing decisions based on individual patients’ profiles, identify high-risk patients, and determine personalized treatment plans, based on factors such as age, comorbidities, and drug allergies.

Aiming to address the clinical evidence challenge, Atropos Health released ChatRWD, a specialized medical language model that combines chat-to-database capability and AI agents. The model reduces the time needed for high-quality publication-grade real-world evidence from months to 5.23 minutes.

4. Remote patient monitoring

Traditionally, remote patient monitoring is considered a challenging care delivery mode due to logistical hurdles and the amount of data generated. Multimodal conversational agents can minimize the complexity of RPM and aid in monitoring a patient’s health status beyond healthcare settings. 

With the human-in-the-loop, such agents can conduct on-demand automated screening interviews over the phone or web browser and deliver explicit insights into the patient’s progress, risk factors, and treatment adherence — invaluable data for effective chronic disease management.

Along with assisted interviews, conversational AI can pitch in to support the following RPM activities:

• Automated check-ins — conversational agents can check up on a patient’s medication adherence, symptoms, and well-being.
• Wearable device data collection — AI-powered systems can team up with RPM devices to vacuum and analyze data on vital signs, activity levels, and sleep patterns.
• Personalized health coaching — conversational AI interfaces can deliver clear, actionable advice tailored to the patient’s specific health conditions, reducing the need for emergency room visits.
• Early intervention — by analyzing wearable devices, sensors, and patient-reported health data, agents can spot early signs of potential issues and notify care teams of such.
• Telehealth stunts — smart agents can support patients in between remote consultations and assist doctors during telehealth sessions by jotting down patient interactions, summarizing key points, and updating EHRs.

A healthcare conversational chatbot discusses a patient’s blood sugar levels, diet, exercise, and fatigue concerns.

5. Post-visit patient support and engagement

Lots of patients leave doctor’s offices without understanding how to care for themselves once they get home and what comes next. Disjointed care pathways add to the information divide, making it challenging for patients to navigate further care.

Advanced conversational AI systems can bridge this informational divide and enhance patient engagement post-visit and after discharge by:

• Integrating visit notes and discharge summaries with insurance coverage information to generate clear action plans for patients.
• Outlining care summaries for referrals and consolidating healthcare data such as medical records, lab results, and clinical notes.
• Extracting key information from specialist notes for primary-care physician teams.
• Estimating out-of-the-pocket costs for patients, including deductibles, copayments, and coinsurance.
• Walking the patient through insurance coverage and billing process.

Kaiser Permanente reported that its AI-powered patient messaging system resolved 32% of patient messages with no manual intervention, freeing up physicians’ time and timely attending to patient queries. 

6. Medication management

Only about 50% of patients stick to their prescribed medication regimen, while the other 25% are unsure about their post-prescription next steps. Polypharmacy patients have it the hardest: they have to keep a mental note of multiple medications, dosages, and timing. 

Virtual assistants equipped with conversational AI capabilities can ease the medication management burden for all sides of care: 

• They can serve as a personalized medication encyclopedia that breaks down information about prescriptions, including dosages, frequency, and potential side effects. 
• Conversational AI solutions can also send refill reminders, cross-reference medications, and pull patient medical data right from EHRs.
• They can help pharmacists reconcile medication lists to avoid medication errors.
• For doctors, such interfaces can provide evidence-based recommendations for medication prescribing, dosage adjustments, and treatment plans.

7. Reimbursement

In healthcare, reimbursement is a field full of speed bumps, with denied claims, complex coding, and inefficient billing processes being chief among them. No wonder this activity lends itself well to conversational AI and its unrivaled automation superpowers.

The technology can take over the following reimbursement tasks:

• Prioritizing claims for payer follow-up and generating automated responses, using physician’s notes.
• Automating the process of submitting claims to insurance providers and tracking their status.
• Verifying codes to improve coding accuracy.
• Identifying potential appeal opportunities by validating payer contracts.
• Monitoring payments from insurance providers and updating on any delays.
• Providing guidance on bills, insurance coverage, and payment options to patients.

8. Clinical operations

Today, doctors have to spend twice as much time on computers as they do with patients. Post-visit notes, patient forms, and other paperwork drain healthcare professionals and leave them with little time on their hands. Much of this paperwork is identical, and therefore redundant.

Clerical tasks are another strong suit for conversational AI in healthcare that can:

• Churn out post-visit summaries, care summaries for referrals, standardized consent forms, utilization reports, and rate comparisons.
• Create and organize clinical notes, EMR updates, dictations, and messages.
• Outline workflow materials and schedules for processes.
• Develop training materials and personalized learning plans for clinicians.
• Create educational content on disease diagnosis and treatment.

Conversational solutions can also work alongside a clinician during a patient visit to transcribe the clinician’s dictation into a structured note and auto-populate notes with EHR data. 

9. Clinical trials

With decentralized clinical trials sloping upwards and traditional clinical research grappling with patient maintenance, there’s much on the plate for AI-driven conversational agents. 

Conversational AI can address many shortcomings of both conventional clinical trial execution and decentralized clinical trials:

• Screening candidates based on eligibility criteria.
• Handling incoming clinical trial data, marrying it with images and lab results, and adding missing data points.
• Interacting with patients throughout the trial period to offer guidance on medication and prevent drop-outs.
• Identifying the right combination of drugs for an indication or the right patients.
• Fetching relevant data from clinical trial reports to prepare documentation for the FDA.

10. Back-office work and administrative functions

Finance, staffing, legal activities, and other picks and shovels of healthcare keep a hospital system running. However, the majority of healthcare operations in the industry are siloed and rely on manual inputs that lead to errors, gaps, and discrepancies.

Stepping up to the plate, conversational AI can shoulder the burden of repetitive tasks and introduce the following improvements across the board:

• Automating the onboarding process, enabling self-serve HR functions, and streamlining feedback collection.
• Optimizing staff schedules based on availability, skills, and workload.
• Automating invoice processing, payment tracking, and account reconciliation.
• Validating contracts for compliance with legal and regulatory requirements.
• Updating on evolving compliance regulations and regulatory changes.

Activate holistic healthcare conversational AI for your organization in 5 steps

Bringing conversational AI to healthcare can alleviate a slew of pressure points, provided HCPs deploy the right tech, operational, and talent resources to develop a robust conversational AI strategy.

Identify the right use case

A successful conversational AI project starts with prioritizing potential use cases based on six key areas, including its impact, function, measurability, permission space, time to market, and extensibility. After identifying promising automation areas, organizations should design AI solutions to implement high-value use cases and determine any functional and technical gaps.

Tackle the 70 percent problem of data readiness

Data wrangling makes up 70% of all AI development efforts. Although healthcare has an edge over other industries in terms of data volume, most of this data is buried across fragmented systems in varying formats. Along with consolidating clinical and patient data, organizations might also need other data points to develop conversational AI solutions, such as PGHD, retail purchases, and wearable data.

Specific use cases such as medication management and clinical decision support also require healthcare organizations to tap into literature and knowledge bases, pharmacy data, and clinical trial data.

Address risks and biases

If mishandled, conversational AI can exacerbate existing data risks in healthcare — as well as usher in new ones, such as its inclination to hallucinate. For example, if the training data skews towards certain patient populations, then the output of the conversational AI solution is likely to be biased, providing patients with inaccurate and potentially harmful insights. 

So, before making headway with the technology, make sure to outline risk and legal frameworks that will govern the use of conversational AI and account for its risks in organizations. 

Plan integrations

If your conversational AI solution needs to interface with other healthcare systems (and it probably does), you need to account for additional layers around it to integrate the solution with EHRs, CDSS, telehealth, and other platforms. Here, you need to identify the integration points, design integration architecture, and determine what types of connectors your solution needs.

Test and iterate

Instead of going all in and scaling your conversational AI solutions to adjacent use cases — test, evaluate, and refine the performance of your initial AI model. Make sure the output of the model is accurate, aligned with the healthcare domains, and performs well across multiple dimensions. If necessary, you can iterate to fine-tune the model performance and revisit your data management strategy.

Challenges of putting conversational AI to work in healthcare

Conversational AI might be one of the most potent technologies to address the gaps in healthcare, but it’s not the easiest to adopt. For example, a mere 10% of patient interactions with healthcare conversational AI turn out to be successful and self-served. The following barriers might be to blame.

Data management

Healthcare notably has a data problem: its data is unstructured, sprinkled across siloed systems, and stored in varying formats. Moreover, many healthcare organizations lack the data maturity muscle, falling behind in data completeness, availability, and governance frameworks. For conversational AI, this data slump is not an option as it demands sufficient data for effective learning and prediction.

To maximize the use of internal data, healthcare organizations must invest in a comprehensive data management strategy, including data standardization, data security, governance, and integration. 

Regulatory compliance

The healthcare sector is a regulation-heavy industry with strict AI compliance standards. To demonstrate commitment to PHI and PII security, your conversational AI solution must comply with HIPAA, GDPR, CCPA, and other applicable regulations. The majority of these regulations require your solutions to integrate specific data security measures, such as data minimization, data encryption at rest and in transit, and other mechanisms.

Technical limitations

Over 73% of healthcare providers still rely on legacy information systems and architectures, making AI scale-ups a tough nut to crack. Complex integrations, data migration challenges, and even staff adoption reluctance — all stem from the tech stone age in healthcare. To break out of the tech rut and effectively leverage any type of artificial intelligence, healthcare leaders require an AI-ready tech infrastructure that includes centralized data repositories, cloud computing set-ups, and data controls and guardrails.

Ethical considerations

When it comes to something as high-stakes as conversational AI in healthcare, consumer trust hangs in the balance. Not all patients are enthusiastic about trading clinician advice for AI wisdom — and you need to address that if you plan to dabble in the technology. To address the skepticism, you can engage clinicians as change agents to demonstrate the credibility and clinical utility of AI.

To warm up customers to the solution, your organization should also be explicit about how it uses conversational AI to assist doctors and what patient data it feeds on. The human-in-the-loop approach is essential in such critical areas as healthcare to mitigate the risks associated with AI and build trust with patients.

Conversational AI in healthcare, a new pill for the future

With the repetitive task burden and the imperative for value-based care, the healthcare industry could benefit from conversational AI implementation. The latter, thanks to its unmatched automation potential and human-like interactions, can revolutionize healthcare delivery, boost operational efficiency, and put patients where they belong — at the center of care.

Around 59% of healthcare leaders are already partnering with third-party vendors to develop customized solutions. Those who succeed with scaling their conversational AI solutions past proof of concepts and to other use cases stand to gain early benefits that turn into long-term, flexible value.

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