According to Statista, the artificial intelligence (AI) healthcare market, valued at 241.8 billion U.S. dollars in 2023, is predicted to grow to 740 billion by 2030, accounting for a compound annual growth rate of 17.3%. AI is expected to impact the entire healthcare system, with many use cases foreseen in patient-facing healthcare apps, which form part of the movement towards personalized, patient-centric healthcare.
Health technology companies are looking for ways in which to implement AI to improve the efficiency with which they build products, whilst examining new types of features and interfaces that could be made available to end users. In addition, AI is being explored to support data computation and analysis, via the implementation of machine learning.
At the same time, healthcare industry regulators are trying to adapt and create guidance to ensure that AI development occurs with data security, and patient safety, at the forefront.
So what are the types of use cases we are seeing now, and expect to see more of, in relation to AI implementation in patient-facing apps?
Personalized Mental Health Support with AI Chatbots
One application of AI in health apps is the use of chatbots to augment psychological interventions, such as Cognitive Behavioral Therapy (CBT). These can be implemented in different ways, including providing pre-defined statements approved by human therapists. This guarded implementation allows AI-based personalization, but does not allow total “freedom” for the AI bot to respond and treat in any way it wants, safeguarding treatment.
These types of chatbots aim to make mental health support more accessible and affordable, filling critical gaps in service provision. For individuals dealing with issues such as sleep problems or chronic pain-related distress, AI chatbots could provide valuable evidence-based training and support, offering augmentation to traditional in-person therapeutic approaches.
AI-Enhanced Medication Management
Non-adherence to medication regimens remains a significant and costly challenge in healthcare. AI is helping make progrsss in addressing this issue through smartphone applications equipped with advanced features. In a study by Labovitz et al. (2023), an AI smartphone app used a neural network computer vision algorithm to monitor medication adherence in stroke patients. The results were impressive, with 100% adherence observed in the intervention group compared to 50% in the control group. The AI app not only provided reminders but also employed personalized dosing recommendations, showcasing its potential as a valuable tool in improving medication adherence.
AI interventions in medication management extend beyond reminders, empowering patients through personalized support. Machine learning and data analytics can be harnessed to cross-check patient and prescription data, identify incorrect doses, drug interactions, allergies, and contraindications. Predictive analytics further contribute by forecasting medication-related problems, allowing for targeted pharmacist interventions and resource allocation to high-risk patients.
Clinical Decision Support and Population Health Management
AI can play a pivotal role in supporting clinical decisions, particularly in the realms of population health management, treatment planning, and diagnostics. Machine learning facilitates predictive analysis based on data points, offering insights that can guide healthcare providers in making informed decisions. Predictive analytics, powered by AI, can identify patients at higher risk of medication-related problems, enabling targeted interventions and personalized care.
AI-enabled monitoring systems continuously analyze patient data from various sources, detecting abnormal trends and signs of deterioration in real-time. These systems, coupled with remote patient monitoring, enhance patient safety by facilitating early detection of critical events and enabling timely intervention.
Improving Patient-Provider Communication
A recent study found that 83% of patients report poor communication as the worst part of their healthcare treatment experience, demonstrating a need for better and clearer communication between patients and providers. AI technologies like natural language processing, predictive analytics and speech recognition could help healthcare providers have more effective interactions with patients. AI could, for instance, deliver more specific information about a patient’s treatment options, allowing the healthcare provider to have more meaningful conversations with the patient for shared decision-making.
Conclusion
We are only in the early stages of the development of AI and its implementation in healthcare, with a lot of potential new applications in patient-facing apps to come. The integration of AI holds a lot of promise for transforming the way in which treatment is accessed and delivered to patients. As technological advancements continue, AI's ability to personalize interventions, improve medication adherence, and support clinical decisions underscores its potential as a valuable ally in enhancing patient care.
