Gen AI Applications – The Patient View

Publication Date: 05/12/2023

5th December 2023 – Cranfield, UK – In the previous Insight, we defined what generative AI (gen AI) is and discussed the gen AI value chain and how the ecosystem is centred around big tech. To recap, gen AI is an AI built on a foundation model trained on a large corpus of unlabelled data. In contrast, traditional machine learning algorithms use labelled data, and are trained to perform a specific task.

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Figure 1: Generative AI Value Chain

The following series of Insights will focus on the Applications of gen AI solutions. This is a precursor to a more in-depth analysis in Signify Research’s Generative AI Market Intelligence Service.

As you may remember from the previous Insight, the care network concept was introduced with three stakeholders: the patient, provider and payer. The interconnection between them serves as the application areas for gen AI, summarised in the diagram below.

Figure 2: The Care Network

We can identify three distinct end-user groups within the care network with similar use cases. These end users are patients, clinicians, and institutions. The use case clusters around these end-user groups are presented in the graphic below.

Figure 3: End User Groups and Use Case Clusters

This Insight will delve into the use case clusters around the Patient. The following two Insights will cover the end-user groups centred around the Clinician and the Institution.

Engaging the patient

My recent report on patient engagement defines patient engagement as a comprehensive set of tools, applications, and health services that synergise patients’ knowledge, skills, capacity, and inclination to actively manage their health and well-being, guided by healthcare providers (HCPs).

The primary objective of patient engagement is to facilitate patient activation and foster positive patient behaviours. This collaborative approach is intended to empower patients to take an active role in their care while benefiting from professional guidance. Patient engagement encompasses various strategies, including patient-provider interactions and personalised health services.

Patient engagement can be present at any stage of the healthcare lifecycle. It can significantly enhance patient outcomes, from preventive measures and early diagnosis to ongoing treatment and post-care support.

Within the report, patient engagement is categorised into three distinct use cases, each elucidating different facets of patient interaction:

  1. Patient Involvement – pertains to patient-led actions. In this context, patients take action with the support of their HCP.
  2. Patient Activation & Monitoring – entails deliberate efforts undertaken by healthcare providers to encourage and motivate patients to participate actively in their healthcare journey.
  3. Billing & Payment – pertains to the financial aspect of healthcare. It involves the processes associated with patient interactions concerning the reimbursement and payment of healthcare services rendered.

Similarly, gen AI applications can be split along similar dimensions, as seen from the diagram of application areas within the care network from the Patient’s view:

Figure 4: End User Group – The Patient ViewThe following table lists specific patient engagement capabilities where gen AI can deliver impact. The list is not exhaustive but indicative.

Figure 5: Patient Engagement Capabilities

Many different capabilities fall under the term of patient engagement, and often vendors’ solutions overlap in these capabilities. For example, provider finder, triage, and appointment booking can all be provided by one vendor in the same solution. Hence, the column on the right above can be any combination of capabilities in the centre column. As technology evolves and new use cases and capabilities are developed, they will be added to the list.

This Insight will explain the patient engagement capabilities outlined in the table and how gen AI can be applied to a specific facet of patient engagement. At the end of each section, a few vendors are provided as examples in this field.

Patient Involvement

Provider Finder:

Provider finder utilises specialised software to search and locate suitable medical professionals or healthcare settings based on patient preferences and needs. Gen AI can access unstructured data to reduce search friction and help patients find the right provider more easily. It does this by facilitating dynamic and real-time filtering of the most suitable specialists based on criteria such as patient’s medical needs, geographical preferences, speciality, availability, patient ratings, and accepted insurance.

Digital Patient Record >> Personalised Health Information:

A comprehensive digital archive of a patient’s medical history, incorporating data from healthcare settings and self-documentation, can be integrated with gen AI, which analyses individual patient data to generate personalised health information. This information can include insights into specific conditions, treatment options, and lifestyle recommendations tailored to the patient’s unique health profile.

Conversational AI

It is a virtual health assistant driven by gen AI, a round-the-clock patient companion. The assistant can respond instantly to medication requests, treatment plans, and general health information queries. For example, gen AI can generate interventions for mental health support, such as mindfulness exercises, stress reduction techniques, and cognitive-behavioural therapy modules. These interventions can be personalised based on an individual’s mental health history and current needs, fostering better emotional well-being.

Patient Education:

Patient education provides information to enhance patients’ understanding of medical conditions, treatments, and healthy practices. This leads to improved health literacy, enabling patients to actively participate in discussions with HCPs, leading to more effective communication and shared decision-making. In the context of education, gen AI can be deployed in several ways:

  • Gen AI can be used for developing interactive educational content. This can include virtual reality experiences, animated videos, or interactive simulations that help patients better understand complex medical concepts, procedures, and the consequences of various lifestyle choices.
  • Gen AI can facilitate the creation of virtual patient communities where individuals with similar health conditions can connect, share experiences, and offer support. These online communities, powered by gen AI algorithms, can foster a sense of belonging, reduce isolation, and provide valuable insights into managing and coping with health challenges.
  • For diverse patient populations, language can be a barrier to effective communication. Gen AI can assist in real-time language translation, ensuring patients receive information in their preferred language and promoting inclusivity in healthcare communication.
  • Gen AI can simplify and translate medical jargon into easily comprehensible language, as many patients face challenges in understanding medical terminology and complex healthcare information.

Examples of vendors present in the space include:

  • SayHeart, a personal AI translator that turns medical data into simple, understandable health insights.
  • Several Natural Language Processing (NLP) solutions offer conversational help for mental health, including Woebot Health, Wysa, and Yuouper. However, all use natural language processing (NLP) and not Large Language Model (LLM) technology, meaning that these solutions are not generative if the presence of a foundation model defines gen AI.

Patient Activation & Monitoring

Patient Outreach & Communication:

Gen AI can communicate with patients to provide information, updates, or reminders. This includes payment and appointment reminders, messages to support care management and provision of access to educational content. For example, gen AI can craft personalised automated messages, ensuring a consistent and timely flow of information without putting too much strain on human resources.

Appointment Booking & Management:

Gen AI can be used in appointment management, including self-scheduling, patient-initiated follow-up (PIFU), smart waitlist and effective cancellation handling. AI-driven systems can streamline the appointment scheduling process. By understanding patient preferences and clinic availability, AI can assist in arranging appointments, minimising scheduling conflicts, and optimising healthcare resources. Furthermore, by leveraging historical data and predictive analytics, gen AI can estimate potential wait times for appointments with different HCPs. This information can aid users in selecting providers based on their availability and the urgency of the medical issue.


Triage refers to prioritising patient care by utilising clinical questions and chatbots to evaluate urgency and determine the necessary level of attention. Gen AI can assist in the initial assessment of patient symptoms. By analysing patient input through chat interfaces or online forms, the AI can generate preliminary insights into potential medical conditions, helping prioritise cases based on urgency. The AI can be trained to identify high-risk cases by considering symptom severity, medical history, and demographic information. This helps prioritise patients who may need immediate attention.

Patient Intake:

Gen AI can be used to electronically complete pre-visit registration, including providing consent and authorisations before a healthcare appointment. Gen AI can automate the collection of patient information by generating forms based on predefined templates. An AI-powered dynamic questionnaire can be adapted based on patient responses, gen AI can generate follow-up or skip irrelevant questions, ensuring the intake process is tailored to each patient’s circumstances. Gen AI can also analyse and compile a patient’s medical history from various sources, including electronic health records and self-reported information. This comprehensive view aids healthcare providers in making informed decisions.

Gen AI can assist in generating and explaining privacy and consent forms to patients. It ensures that patients are well-informed about using their data and facilitates the collection of necessary permissions clearly and understandably.

This reduces the need for manual data entry, minimises errors, and streamlines the intake process. Using NLP, generative AI can understand and process natural language input from patients. This facilitates a more conversational and user-friendly intake experience, allowing patients to express their symptoms and concerns in their own words.

Follow-up Questionnaires and Surveys:

Gen AI can be used to collect patient feedback, including Patient-Reported Outcomes (PROs), Patient-Reported Outcome Measures (PROMs), Patient-Reported Experience Measures (PREMs) and patient experience surveys to assess patients’ experiences, outcomes, and satisfaction. After the initial intake, gen AI can send follow-up messages, collect feedback, and engage with patients to ensure a continuous flow of communication. This can contribute to improved patient satisfaction and ongoing care coordination. Gen AI can also analyse patient interactions to gather feedback on healthcare services and educational materials. This feedback loop allows HCPs to continuously improve patient engagement strategies, ensuring the information provided is relevant, accessible, and impactful.

Health Tracking Tools:

Gen AI can create personalised medication adherence programmes, sending timely reminders and educational content about the importance of sticking to prescribed routines. This not only aids in treatment adherence but also educates patients on the significance of their medications. In addition, it can facilitate reminders to patients to go to follow-up appointments, take medications and answer their basic questions to check the patient’s recovery progress, medication adherence, and potential complications. This can help prevent readmissions and ensure ongoing care.

Examples of vendors present in the space include:

  • Although not using foundation models, Ada Health is one of the biggest examples of digital health solutions aiding in symptom assessment for triage, which soon may face competition from gen AI counterparts.

Billing & Payment

Prior Authorisation, Insurance Verification and Eligibility:

Gen AI can automate the checks of a patient’s insurance coverage and eligibility and streamline the insurance verification process. It can also analyse medical documentation, such as patient records and treatment plans, to generate comprehensive and accurate prior authorisation requests, which can be tracked in real-time. In addition, Gen AI can identify potential gaps in insurance coverage, alerting healthcare providers to possible reimbursement issues. By analysing historical data and patterns, gen AI can also predict the likelihood of prior authorisation approval. This information can assist HCPs in making informed decisions and planning for potential treatment delays.

Payment Estimates:

Gen AI can analyse historical billing data, insurance policies, and healthcare procedures to generate accurate patient cost estimates. This offers patients billing transparency by providing cost estimates before care. Using dynamic pricing models, gen AI can adapt to changes in healthcare pricing models, considering factors such as insurance coverage, negotiated rates, and specific medical procedures, ensuring that payment estimates reflect the latest pricing structures.

Gen AI can facilitate interactive discussions with patients about payment estimates, answering queries and providing additional information to help patients understand and plan for their financial responsibilities. In addition, Gen AI can generate personalised patient guidance regarding their insurance coverage, including information about co-pays, deductibles, and covered services. This helps manage patient expectations and improve financial transparency.

Payment Collection:

Gen AI can design personalised payment plans based on the patient’s financial situation, making it easier for individuals to manage and fulfil their financial obligations. This can include instalment options and tailored schedules. Gen AI could predict future payment behaviour by analysing payment history and patterns, allowing healthcare providers to anticipate potential challenges and tailor their collection strategies accordingly.

Examples of vendors present in the space include:

  • Health Harbour automates a claims request process by retrieving the necessary information from the insurance company.
  • Using an LLM, Basys translates unstructured texts in medical notes into structured information and automatically cross-checks against the policy requirement. This accelerates request processing time significantly.
  • Cedar recently partnered with Google to use the latter’s Vertex AI to build solutions that help patients understand and resolve their healthcare bills.

Health Assistant

Health Assistant refers to a gen AI-empowered resolution that combines several patient engagement capabilities under one solution. Examples include:

  • Gyant, which announced its front door software, launched with VHC Health in 2022. Gyant’s digital front door uses conversational AI to lead patients through their clinical journey to find doctors, triage symptoms, schedule appointments, get answers and care navigation, provider search and support for administrative needs, such as billing and insurance-related questions.
  • Hyro recently integrated GPT-4 into its offering to create adaptive and conversational assistants for various business needs, such as call centre automation, FAQ resolution, IT help desk, etc. My colleague, recently wrote an Insight on Hyro’s integration with patient engagement vendor Artera.


Gen AI’s role in patient engagement is a dynamic and evolving landscape, significantly enhancing the healthcare experience. In essence, the applications of gen AI in patient engagement are diverse, ranging from personalised information delivery to interactive educational tools. By leveraging these technologies, HCPs can inform patients and actively involve them in their healthcare journey, ultimately leading to more empowered and informed individuals.

The table below showcases select vendors competing in the patient engagement space. Although much technology is not built using foundational models, it is likely to change as more players are likely to offer wrappers on top of existing technology, such as Hyro, in its use of OpenAI.

Figure 6: Patient Engagement Capable VendorsThis Insight is a precursor to a more in-depth analysis of in Signify Research’s Generative AI Market Intelligence Service. Beyond just informative reports, the Service offers a robust product database. This database compares announced gen AI applications and simplifies the understanding of the most noteworthy use cases and competing vendors. It’s designed to empower decision-makers like you with valuable insights.

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Related Research 

Our new Generative AI Market Intelligence Service provides a rolling 12-month coverage of the fast-moving Generative AI market. It includes six market reports over this period examining:

  • Healthcare IT Vendor Opportunities and Strategies
  • Generative AI – Key Use Cases and Applications
  • Detailed Analysis of Generative AI Trends Observed and New Products Announced at Key Industry Events (e.g. HIMSS, RSNA)

About Vlad Kozynchenko

Vlad joined Signify Research in 2023 as Senior Market Analyst in the Digital Health team. He brings several years of experience in the consulting industry, having undertaken strategy, planning, and due diligence assignments for governments, operators, and service providers. Vlad holds an MSc degree with distinction in Business with Consulting from the University of Warwick.

About the Digital Health Team 

Signify Research’s Digital Health team provides market intelligence and detailed insights on numerous digital health markets. Our areas of coverage include electronic medical records, telehealth & virtual care, remote patient monitoring, high-acuity clinical information systems, patient engagement IT, health information exchanges, Generative AI and integrated care & value-based care IT. Our reports provide a data-centric and global outlook of each market with granular country-level insights. Our research process blends primary data collected from in-depth interviews with healthcare professionals and technology vendors, to provide a balanced and objective view of the market.

About Signify Research

Signify Research provides healthtech market intelligence powered by data that you can trust. We blend insights collected from in-depth interviews with technology vendors and healthcare professionals with sales data reported to us by leading vendors to provide a complete and balanced view of the market trends. Our coverage areas are Medical Imaging, Clinical Care, Digital Health, Diagnostic and Lifesciences and Healthcare IT.

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