The rapid advancements in large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI. This paper presents an overview of the current landscape of LLMs in healthcare, specifically focusing on their applications in analyzing and generating conversations for improved patient engagement. We showcase the power of LLMs in handling unstructured conversational data through four case studies: (1) analyzing mental health discussions on Reddit, (2) developing a personalized chatbot for cognitive engagement in seniors, (3) summarizing medical conversation datasets, and (4) designing an AI-powered patient engagement system. These case studies demonstrate how LLMs can effectively extract insights and summarizations from unstructured dialogues and engage patients in guided, goal-oriented conversations. Leveraging LLMs for conversational analysis and generation opens new doors for many patient-centered outcomes research opportunities. However, integrating LLMs into healthcare raises important ethical considerations regarding data privacy, bias, transparency, and regulatory compliance. We discuss best practices and guidelines for the responsible development and deployment of LLMs in healthcare settings. Realizing the full potential of LLMs in digital health will require close collaboration between the AI and healthcare professionals communities to address technical challenges and ensure these powerful tools' safety, efficacy, and equity.

Patients who effectively manage their symptoms often demonstrate higher levels of engagement in conversations and interventions with healthcare practitioners. This engagement is multifaceted, encompassing cognitive and socio-affective dimensions. Consequently, it is crucial for AI systems to understand the engagement in natural conversations between patients and practitioners to better contribute toward patient care. In this paper, we present a novel dataset (MedNgage), which consists of patient-nurse conversations about cancer symptom management. We manually annotate the dataset with a novel framework of categories of patient engagement from two different angles, namely: i) socio-affective (3.1K spans), and ii) cognitive use of language (1.8K Figure 1: Our dataset contains patient-nurse conversations spans). Through statistical analysis of the annotated with engagement derived from socioaffective data that is annotated using our framework, and cognitive use of language. We hypothesize we show a positive correlation between patient that patients who have high engagement tend to symptom management outcomes and their engagement have better symptom control. in conversations.

Artificial Intelligence and Machine Learning is changing the way patients are treated in the global healthcare business. AI-ML is used not only in areas of precision medicine and disease detection, but also has the potential to enhance coordination of care via the automation of processes, personalisation of treatment regimens and optimisation of service delivery. AI in the healthcare industry is expected to grow at a CAGR of 43 per cent and cross $27 billion by 2025. Indian tech-enabled firms serving the US and European healthcare markets are leading from the front in using AI technologies such as Computer Vision and ML right from pathology to patient-care journey. Gaurav Jain, Senior VP-Clinical Support Solutions, IKS Health, said virtual clinical support enables patient engagement, reduces after-hours documentation for physicians, gives the clinical team more time for patient engagement and ensures safer and more efficient care.

Artificial Intelligence and Machine Learning is changing the way patients are treated in the global healthcare business. AI-ML is used not only in areas of precision medicine and disease detection, but also has the potential to enhance coordination of care via the automation of processes, personalisation of treatment regimens and optimisation of service delivery. AI in the healthcare industry is expected to grow at a CAGR of 43 per cent and cross $27 billion by 2025. Indian tech-enabled firms serving the US and European healthcare markets are leading from the front in using AI technologies such as Computer Vision and ML right from pathology to patient-care journey. Gaurav Jain, Senior VP-Clinical Support Solutions, IKS Health, said virtual clinical support enables patient engagement, reduces after-hours documentation for physicians, gives the clinical team more time for patient engagement and ensures safer and more efficient care.

For our HTN Now: Citizen Transformation event we were joined by Ben Panton, Senior Digital Partnership Manager for NHS Arden and GEM Commissioning Support Unit, for a discussion on developments in home diagnostics, with focus on the latest trial plans and patient reactions and perceptions around remote diagnostics and artificial intelligence. Ben began by establishing some background information on NHS Arden and GEM CSU, explaining that they work with over 90 organisations across health and care systems such as local authorities, ICBs, trusts and primary care services. "I'm particularly focused on the digital transformation side of things," he said. "I've been in post for around six months now, working with colleagues across our digital and IT teams. Our particular focus within digital transformation service redesign has been working with potential partners such as the ICBs, to really understand their digital priorities and challenges, and how digital solutions can potentially mitigate some of the challenges that are being faced by the NHS, social care, and local authorities."

Patient engagement is an important factor in any healthcare organization. By engaging patients, you can improve patient care and decrease costs. However, there are a few things to keep in mind when engaging patients. First, you need to make sure your patient engagement strategy is effective. Second, you need to create a patient engagement plan that is tailored specifically for your hospital or clinic. Finally, be sure to track and measure the effectiveness of your patient engagement efforts.

The use of artificial intelligence has increased across a variety of industries in recent years. AI is significantly impacting the healthcare industry, specifically in patient engagement. FREMONT, CA: It has become increasingly popular in recent years to utilize artificial intelligence (AI) in various industries. Patient engagement is one area where AI is beginning to impact the healthcare industry significantly. Engaging patients with online support and education, automating reminders and alerts, and predicting their health outcomes are all examples of how AI can be used.

Patient engagement is critical in healthcare as it keeps patient satisfaction scores high. Improved patient engagement helps to maximize outcomes and also boost the care experience. Memorial Health System, in Illinois, believes that in today's world it is a challenge to improve patient engagement levels and communication channels. Patients want to feel empowered and engaged throughout their healthcare journey. Many hospitals have also deployed Patient Engagement (EMR) software solutions to communicate with patients more efficiently.

Artificial Intelligence is proving its prominence in every industry out there and the healthcare industry is no different. From patient care to Administrative processes AI has huge potential in the healthcare industry. There are many research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks. We have seen robots performing surgeries or assisting doctors with more precision and flexibility. Algorithms are outperforming radiologists in detecting dangerous tumors and advising researchers on how to build cohorts for expensive clinical trials.

We talk about patient engagement a lot in healthcare today. But the reality is, we often don't just want a patient to engage with a piece of content or information; we want them to take action. The traditional CRM and marketing automation are great tools for the former – keeping patients and their healthcare providers connected. But it does little to move beyond a digital connection. Today's patients (or, consumers) expect more from all of their relationships, especially their healthcare providers.