AI will play a significant role in transforming patient and member engagement. It offers tremendous opportunities to improve outcomes, reduce costs, enhance experiences, and develop data as a strategic asset. At this point, AI is not just a differentiator but a necessity for emerging companies building tools that allow consumers to better interact with their healthcare.
Below are some key areas where AI supports patient and member engagement throughout the healthcare system.
Automated Engagement Processes and Operations: AI has enormous potential to automate time-consuming and labor-intensive medical tasks and processes. Instead of replacing all the human touches, AI can assist and free up humans for more essential tasks. For instance, AI can gather, extract, and analyze patient data to identify, segment, and prioritize patients requiring immediate attention and proper engagement.
Data Management & Insight: Health data is often siloed, unstructured, and inaccessible. Over 80% of clinical and social-economic data elements are in unstructured data sources. The manual curation and structuring of health data are time and cost prohibitive. NLP, especially LLM-powered NLP, allows AI systems to extract valuable insights from unstructured data like medical notes, research papers, psychographics, social determinants, and patient feedback, leading to more informed decision-making.
Consumer Relationship & Personalization: Like NLP, AI can analyze patient data to predict potential health issues and stratify and prioritize at-risk individuals. This proactive approach allows healthcare providers to intervene early and prevent or manage high-risk conditions effectively. Predictive behavior models can be used to effectively personalize and automate appointment and other engagement reminders for patients.
Virtual Assistants: AI-powered virtual assistants can interact with patients, answer their questions, and provide guidance on health issues. These assistants can be available 24/7, reducing the need for immediate medical attention for minor concerns and easing the burden on healthcare facilities.
Personalized Care: AI can analyze patient data, including medical history, preferences, and lifestyle, to provide personalized treatment plans and health recommendations. This tailored approach can improve patient adherence and overall health outcomes.
Remote Monitoring: AI can enable remote monitoring of patients, collecting data on vital signs, symptoms, and medication adherence. This information helps healthcare professionals track patient progress and make timely adjustments to treatment plans.
Here are some high-level areas we look for when considering an investment in a digital health company implementing AI.
Data as a Strategic Asset: Emerging companies should prioritize gathering and generating differentiated health datasets. Prioritizing data generation and developing unique data as a differentiator is essential.
Focus on a Specific Niche: Instead of tackling every aspect of patient engagement, specialize in a particular area, such as mental health support or chronic disease management.
Outcome Measurement: Implement mechanisms to measure the impact and effectiveness of the AI solutions on patient outcomes and engagement, providing evidence of their value.
Seamless Integration: Ensure your AI solution seamlessly integrates with existing healthcare systems and workflows, making adoption easier for healthcare providers and patients.
Compliance and Security: Prioritize data privacy, security, and compliance with healthcare regulations to build trust with healthcare organizations and patients.
Proven ROI: Back your AI-driven solutions with proven ROI for your healthcare customers.
By focusing on differentiation and delivering tangible value, an emerging AI company in healthcare can carve out a unique and successful position in the patient and member engagement space. More than ever, Healthcare providers and payers are basing their buying decisions on the ability to generate real impact around cost reduction, increased revenue, patient experience, and decreased risk. Providing repeatable proof of these outcomes should be at the core of any digital health company building with AI.
Venture Partner, Empactful Capital
Founder, President and CISO of DeepThink Health