Patient Engagement

Dr Chien-Tzung Chen, superintendent of Linkou Chang Gung Memorial Hospital in Taiwan, wants to establish a remote system to monitor patients at home using wearable devices.

Healthcare organizations are embracing modern digital tools and emerging technologies like AI to reimagine how they interact with patients, connect with communities and advance lifesaving missions. However, as many of these organizations are registered as 501(c)(3) nonprofits, they share the common ailments that afflict the nonprofit sector at large: growing demand and shrinking budgets. These complex, highly personal healthcare ecosystems need efficiency at scale, and digital engagement is proving to be a path to reaching more patients, enhancing operational productivity and improving patient satisfaction. Insights from Twilio’s 2024 State of Nonprofit Digital Engagement Report, which surveyed more than 1,400 nonprofit employees and 1,500 nonprofit end users, showed that healthcare nonprofits continue to adopt digital communication at unprecedented levels, with 7 in 10 organizations expecting patient engagement to be digitally driven as early as next year. This enthusiasm is tempered by ongoing perception gaps between providers and patients, especially around AI transparency and personalization. The insights from this report identified areas of strength and opportunity for healthcare sector leaders to consider as they head into 2025. Strength: AI is key to improving patient interaction It’s easy to understand why nearly all healthcare organizations (93%) believe AI will improve patient engagement, a sentiment with which the majority (59%) of patients agree. The top three reported ways AI is used in healthcare are analyzing data to better understand unique patient needs, transcribing patient visits/calls and automating responses to frequent patient questions. AI is empowering providers to accelerate response times, reduce missed appointments and improve patient satisfaction, which are all paramount to these organizations’ reputation and revenue. As healthcare organizations are challenged to do more with less, AI is increasingly viewed as an essential tool to maximize scarce time and resources. In fact, roughly half (51%) of these healthcare organizations have already integrated AI into their digital communications, outpacing similar adoption rates (47%) for private business-to-consumer brands. Opportunity: Trust through transparency is a work in progress For all its transformative potential, implementing AI carries risk, with healthcare organizations particularly concerned about the privacy and security of patient data. It’s difficult to think of another industry where customer data is as sensitive as personal health information, and patients rightfully have providers under a microscope over how their data is being handled. Every enterprise, whether public or private, for-profit or nonprofit, has reservations about data privacy and security regarding AI, so it’s no surprise that this also ranks as the top concern for AI implementation among healthcare nonprofits. Interestingly, most organizations (88%) believe they’re transparent with patients in how their data is used for AI, but only 1 in 4 patients agree. This stark disconnect presents a significant opportunity for providers that leverage AI to revamp policies and clarify how they disclose these policies with their audiences. Organizations should provide patients a clear view into the lifecycle of their data, from how it’s collected, shared and stored to where it’s used and how it’s protected. For example, Twilio introduced ‘AI Nutrition Facts,’ a way for the company to clearly communicate with its customers and their end users how their data is being used in a clear and familiar way. The adage “trust is hard-earned and easily lost” is particularly true in this untamed AI frontier, but transparency builds trust, and trust helps loyalty flourish. Strength: Availability of developer talent continues to...develop To scale digital engagement and integrate AI appropriately, healthcare organizations need skilled developers who can build, maintain and optimize these solutions. Nonprofits, however, cited the lack of technical expertise as one of their top concerns around AI implementation. In 2022, Twilio’s report found an alarming talent gap that persisted across the broader nonprofit sector, but its 2024 research shows that there have been significant strides to address this issue. Nearly 7 in 10 (67%) healthcare nonprofits now have 10-plus developers on staff, and 65% say they have sufficient or even surplus developer talent. And more help is on the way; 92% of surveyed organizations plan to hire at least one developer this year. Despite this optimism, 1 in 5 organizations still report a developer shortage and nearly all (96%) admit they’ll continue to need some outside developer support. For budget-conscious, talent-strapped providers, pro bono contributions from volunteer developers continue to be widely used to help ensure that they meet digital milestones and maintain systems. Opportunity: Engagement is no longer one size fits all Whether it’s engaging with their favorite retailer, coffee shop or healthcare provider, personalization is now table stakes, especially for younger digital natives like Millennials and Gen Z who demand tailored services and communication. With budget constraints, evolving patient preferences and deeply fragmented data ecosystems inhibiting accurate, holistic patient personas or “golden records,” healthcare has the lowest level of personalization of any nonprofit sector. Only about 6% of healthcare nonprofits say they always personalize communications, although 66% of patients say this is highly important. Patients are expecting more from their care experience than simply having their name added to a short message service (SMS) or email appointment reminder. With AI to help sift through and analyze swaths of patient data such as demographics, unique interests and previous visits, healthcare nonprofits can and should quickly personalize communication at scale. This can look like an SMS that recommends specific immunizations ahead of a patient’s tropical vacation, or an email with tips for low-sugar alternatives this holiday season for a patient with diabetes. Given that only 1 in 3 organizations rate their patient engagement as “excellent,” individualization can avoid generic messages and ensure that patients truly feel seen, valued and understood. A great example is Cleveland Clinic, which recently invested in a comprehensive system to redefine patient communication and care through more personalized interactions. The system, which integrated all aspects of provider-patient interactions from appointment reminders to satisfaction surveys, handles nearly two million monthly messages. This has streamlined the clinic’s communication processes, reduced missed appointments and helped the organization move toward its mission to provide the highest standards of patient care. Digital engagement is a journey, not a destination. By investing in technologies like AI, building tech talent, committing to a culture of transparency and prioritizing personalization, healthcare organizations can ensure that their digital communications hit the mark. Article written by Erin Reilly, Chief Social Impact Officer, Twilio

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A project in China that is developing an autonomous and self-evolving virtual healthcare setting is targeted to go public next year. This is confirmed to Healthcare IT News by Yang Liu, a professor at Tsinghua University's Department of Computer Science and Technology and co-research head of the Agent Hospital project. The virtual hospital concept, developed by researchers at the university's Institute for AI Industry Research (AIR), simulates the real-world cycle of the hospital treatment process, from disease onset to follow-up. The institute claims the concept as the first of its kind globally. Findings from this research were first published in May in arXiv, Cornell University's open-access online research paper repository. WHY IT MATTERS All virtual actors in Agent Hospital, including patients, nurses, and doctors, are generated via a large language model (LLM). These AI characters will represent real people once the system goes live in public by the first half of 2025. A public pilot, to be conducted by AIR's spinoff startup Tairex, will begin sometime in the first quarter, said Prof Yang.  For the virtual hospital concept, researchers proposed a design method called MedAgent-Zero, which enables AI doctors to continuously learn and improve and become accurate in performing clinical tasks by interacting with patients, reviewing medical literature, and accumulating experience from handling both successful and unsuccessful cases.  Their research findings showed that through this novel method, AI doctors achieved 88%, 95.6%, and 77.6% accuracy in examining, diagnosing, and treating patients, respectively.   "The doctor agent is able to complete the diagnosis and treatment of tens of thousands of patients within a few days, which would typically take at least two years for a human doctor," the researchers also noted. Meanwhile, an AI doctor also showed up to 93% accuracy in answering a subset of the MedQA dataset – mostly based on the competitive United States Medical Licensing Examination, covering questions on major respiratory diseases.  As part of the concept's development, researchers plan to expand its range of disease coverage and extension in more medical departments. The virtual platform currently features 42 AI doctors in 21 medical departments, including emergency, respiratory, and cardiology.  They also plan to incorporate more features, including medical position promotions, changes in disease distribution with time, and historical patient medical records.  There is also a plan to optimise the selection and implementation of the base LLM. OpenAI's ChatGPT model versions 3.5 and 4 are currently utilised in their research. "We will use the latest and most advanced LLM," Prof Yang said.  THE LARGER TREND Other research initiatives in China have also developed medical LLMs for clinical decision support. A project at the Tongji University School of Medicine built a model called MedGo, which was trained using 6,000 medical textbooks and has since been integrated and utilised at the affiliated Shanghai East Hospital. An AI-focused institute under the Chinese Academy of Sciences – one of China's national research centres – introduced early this year the CARES Copilot chatbot based on Meta's Llama 2 LLM, which assists doctors in making medical diagnoses and treatments.