How can you use AI to engage patients?

In the industry, we need to get over the idea that AI is a utopia. It is just like anything else as good as the input we need to create it, and apply privacy standards like anything else it’s ridiculous.

AI chatbots and virtual assistants boost patient engagement by providing tailored assistance and responses. These tools are invaluable for managing healthcare routines and gathering patient insights. For instance, an AI assistant can guide patients through post-operative care, helping adherence to instructions and improving recovery. Effectively and empathically designed, these chatbots can extend the doctor's guidance beyond the clinic, contributing to better health outcomes.

AI can boost patient engagement by providing personalized health reminders, answering questions through chatbots, and offering tailored educational content. These tools help keep patients informed and proactive about their health. Implementing AI in your practice can make interactions smoother and more efficient.

AI chatbots powered by fine-tuned Large Language Models (LLMs) will revolutionize patient care. They can offer personalized interactions at a scale never seen before by analyzing a patient's communication style, personality type, and mental health status. This personalization enhances digital health interventions, especially for patients with mental health conditions. These AI tools provide 24/7 support, can monitor symptoms, and alert healthcare providers if a patient's condition worsens while catering to the changing needs of patients. The data collected can also provide valuable insights for healthcare providers. As AI technology advances, we're moving towards a future where healthcare is more personalized, accessible, and effective.

One of the best uses of AI to engage patients is by understanding the ideal outreach channel to maximize the likelihood of success. In many healthcare-related situations, it is key to outreach patients with mail, SMS, emails, etc. to nudge them to take an action (e.g. use a specific medication). AI can identify the optimal channel to reach out to them with, based on demographics, previous interactions, and more.

“Passively collected measures are powerful because nobody wants to fill in a survey,” said Andy Coravos, CEO of HumanFirst in an interview for my Forbes article. “Imagine a patient with a gastrointestinal condition running to the bathroom in the middle of the night. The person is unlikely to note the frequency of occurrence or intensity of these symptoms.” Sensors and wearables collect data passively, around the clock, removing the burden of patient reporting. Digital biomarkers and wearables are game changers since they can help gather rich data about patients, passively and remotely.

AI supercharges telehealth and remote monitoring, turning them into high-powered analytical platforms 📊. It analyzes real-time patient data during virtual consultations, offering tailored treatment plans and alerting healthcare providers to potential issues. Predictive analytics enable early, proactive interventions 🎯. Automated triage quickly sorts patient complaints by urgency, routing critical cases for immediate review 🚑. In remote monitoring, AI scans data from wearables, flagging any vital anomalies 📈. It helps manage healthcare resources efficiently 👩⚕️👨⚕️ and boosts data security by detecting unusual patterns. All in all, AI's deep data analysis drives timely medical interventions and elevates patient care.

In addition to monitoring, AI assistants offer the ability to serve patients with real-time recommendations based on health data. Real time recommendations are much more likely to be followed and remembered.

In the realm of wound care, remote monitoring and telehealth have shown immense potential. These technologies allow for regular monitoring of patient's wound healing progress without the need for frequent clinic visits, reducing the burden on both the patient and healthcare providers. Moreover, it enables early detection of complications, leading to timely intervention, improved patient outcomes, and potentially reducing overall healthcare costs.

Data acquired from such devices can provide an additional stream of information, that used in conjunction with traditional methods, can treat and identify conditions faster. Consideration must be made to how accurate the data is, whether it conflicts with the account of the individual and all possible interpretations of the data rather than the most common or obvious.

Personalised medecine will be one of the most promising area of AI applications. However, to be successful we need AI models with high level performance and high quality data.

Pharma firms leverage AI to improve customer experience through customer targeting and personalized engagement. It's commonly called 'Next Best Action,' and there are 3 common scenarios: - Patient support program (PSP) interactions between support specialists and patients - Non-personal campaigns for healthcare providers (HCPs) and patients - Personal sales force engagements with HCPs The objective in each case is to target customers most likely to respond, at an opportune time, with personally relevant messaging delivered via content formats and channels most likely to generate a response.

Excellent contribution , we need to focus on local versions of LLM models Basically, it’s a rather simple to put together a LandChain app that the person can speak that will be converted to text and immediately loaded into LLM , and the integrated with the larger medical model. frankly, the problem is due to social media and the immature accelerated adoption of technology before there are best practice that have been well thought out there is little attention to the actual human evaluation of generative content the quality is poor A model can perform the prompt and all we need to Patient to do isn’t private respond and we’ll have it

Tracking a patient's emotions using Emotion Recognition Software (ERS) over time can provide insight into how they feel about the treatment. Is it effective in reducing fearful communication? Is it effective in limiting angry thoughts? Are signals of sadness abating? Questionnaires and surveys along with in person treatment provide opportunities to collect data on their emotions. Sentiment analysis can provide mood, if you want "positive," "negative," "neutral," or "mixed" signals.

Incorporating clustering algorithms forming groups of patients similar to each sharing distinct characteristics presents a powerful method for engaging patients. By leveraging data analysis and predictive modeling, these algorithms can tailor care plans and interventions to match patients' needs, preferences, and goals. The use of AI-driven systems to analyze medical history, genetics, lifestyle, and behavior enables the delivery of optimal treatment options, dosages, or lifestyle adjustments. Such AI-driven personalized approaches not only enhance patient care but also provide valuable insights into trends in patient behaviors, enabling healthcare providers to continually refine and optimize their strategies for better patient outcomes.

Gamification is a powerful tool. AI boosted gamification even more. 1️⃣ Engagement Boost: Gamification adds game-like elements to treatment, improving engagement. Social media lets patients share achievements, further motivating them 🏆. 2️⃣ Community Effect: Social media provides peer-to-peer support 3️⃣ Educational Reach: Gamification tools and social media offer interactive educational content, enhancing patient understanding and compliance 📚. 4️⃣ Instant Gratification: Real-time feedback in gamification and social media likes serve as micro-rewards 🌟. 5️⃣ Data Insights: Analytics from both platforms offer valuable data on patient engagement and social support, useful for monitoring adherence 📈.

Gamification, powered by AI, offers an engaging way to enhance patient care. Incorporating game elements into health apps can boost adherence to health plans. Meanwhile, AI can personalize these elements based on user behavior patterns. Simultaneously, social media platforms, moderated and informed by AI, can create supportive patient communities. However, patient privacy is paramount, requiring robust data protection measures. It's also crucial to balance gamification so that it motivates without trivializing health journeys. Importantly, AI should enhance, not replace, human touch in healthcare, complementing the empathy and understanding provided by professionals.