What are patient engagement and adherence AI applications? While the terms might sound new, the concepts are critical to achieving optimal health outcomes. Conventionally, health practitioners have struggled with ensuring optimal patient engagement and adherence to medical regimens.
As the term suggests, patient engagement involves taking/ considering a patient’s opinion when making medical decisions. The process is complex; healthcare professionals have struggled to ensure optimal patient engagement. Adherence refers to the patient’s commitment to adhering to a medical regimen. The absence of means for medical personnel to monitor a patient’s adherence to medication poses an even more significant challenge. Artificial intelligence (AI) proposes a solution.
The applications of AI in the healthcare industry are growing. It redefines every aspect, from patient data management to more intrusive procedures like medical surgery. In this case, they redefine how healthcare providers interact with their patients. This article explores various AI applications that make the change possible in patient engagement.
Healthcare AI Applications in Patient Engagement
Patient engagement involves establishing communication channels between healthcare providers and the patient. How the communication is implemented is crucial in determining how the patient recovers. The channels should encourage patients to make informed decisions while taking a proactive role in managing their health.
AI as a communication channel facilitates a more personalized, interactive, and accessible healthcare experience. AI as a communication channel enables a more personalized, interactive, and accessible healthcare experience. The process culminates in better patient engagement and health outcomes.
Areas in which AI is making a difference include
- Personalized Communication through Chatbots and Virtual Health Assistants.
Chatbots and health assistants delivered electronically, such as text messages, provide 24/7 support. Integrating AI makes the systems intelligent and capable of offering immediate responses and personalized medication management advice. AI uses Natural Language Processing (NLP) and machine learning (ML) to make the digital conversations as human as possible.
- Enhanced Patient Education via AI-driven Platforms
Patient education is another front of patient engagement. Imagine a scenario where you have to explain to a patient with amnesia how often to take medication. AI can provide customized learning experiences specific to every patient. The platform can monitor a patient’s health data or health parameters through devices and use the data to deliver personalized content. The content could take any format: video, article, interactive model, etc, each specific to a patient’s condition.
The AI platform could also create patterns according to the patient’s learning curve. The patterns could then be used to set up functions like reminders or tips; this way, they enhance understanding and motivate patients to take a proactive role in managing their health.
AI-Driven Solutions for Medication Adherence
Non-adherence to medication is a societal problem acknowledged by medical professionals worldwide. A significant threat arising from this negligence is anti-microbial resistance, where microbes evolve to evade the therapeutic properties of medication. Several reasons can result in non-adherence:
- Forgetfulness,
- Misunderstanding of prescription,
- Outright ignorance.
Whatever the reason, it is a problem that we must overcome. Other impacts of non-adherence include increased hospitalizations, high healthcare costs, and worsening health conditions.
AI proposes a solution. These include:
- Smart Medication Dispensers
Smart medication dispensers are equipment that manages and dispenses medication at a predetermined time. Dispensers equipped with AI algorithms go further by collating data from sensors and connectivity features to send real-time alerts to patients and caregivers, ensuring the patient takes medication at the right time and with the correct dosage. Similar equipment used in hospitals can have facial recognition to ensure the right patient gets the proper medication.
- Medication Adherence Apps Powered by AI
Medical adherence apps track medication schedules, send timely reminders, and provide feedback based on patient adherence patterns. The apps use machine learning algorithms to learn user behavior to create tailor-made reminders and tips. The app can also monitor patients’ vitals and send any red flags/ recovery patterns to medical personnel.
Predictive Analytics in Patient Care
Data analysis on patient records (with consent) can provide a rich dataset for AI algorithms. The data could be the patient’s recovery process, medical history, wearables data, or genetic profile. By learning from the dataset, the algorithm can then provide personalized insights of the patient, such as predictive analysis with the current regimen.
The algorithm could also create test scenarios with other medications to analyze whether they provide better outcomes. By anticipating health outcomes, medical personnel can optimize resources, intervene earlier, and improve the quality of care.
So, how do AI algorithms and models predict health deterioration and non-adherence risks?
AI algorithms analyze vast datasets of patient information through machine and deep learning techniques. The datasets could include:
- Patient demographics
- Clinical histories
- Treatment responses
- Social health determinants.
AI can report health deterioration by analyzing data from wearables or patient records when they identifies trends that may indicate the worsening of a condition.
AI can also identify patients struggling with regimen adherence by analyzing data from
- Past medication refill histories
- Patient engagement levels with healthcare systems,
- Behavioral health data.
The predictive abilities of AI can also make predictions by using previous patterns to identify patients who will likely miss their regimens based on their profiles. The reports can then facilitate the creation of targeted interventions by healthcare personnel.
Health insights are another aspect of the predictive prowess of AI algorithms. The insights are intelligent and not conventional, going beyond basic health information. The insights are tailored based on an individual’s specific health data and predicted risks.
For instance, the AI could advise a person with diabetes on the best time to take medication based on the food and previous patterns of their blood sugar levels. In this instance, the AI algorithm calculates the amount of sugar in the diet and guides medication adherence. Another example is an AI algorithm predicting when a patient may experience a heart attack; in this case, an early warning is crucial.
The interventions create an environment where patients are more proactive in caring for their health. It also eases the burden on the healthcare professionals.
Telehealth and Remote Monitoring through AI
Telehealth is a form of providing health services digitally. It includes teleconsultations, remote patient monitoring (RPM), and mobile health applications. Telehealth is growing globally due to its ability to bridge geographical accessibility gaps and reduce the stress on health facilities.
AI-enhanced telehealth offers healthcare providers the ability to provide personalized and proactive care. AI contributes in several ways:
- Predictive Health Analytics: AI algorithms enable timely interventions to signs of health deterioration or worsening chronic conditions. They analyze data from wearable devices or the patients’ monitoring files to identify patterns from other patient records resulting in deteriorating conditions.
- Automated Patient Triage: An AI-powered triage can help optimize a hospital’s resources by identifying patients requiring immediate care or specialized treatment. The triage ensures the patients receive the proper care at the right time.
- Enhanced Patient Engagement and Adherence: As discussed, telehealth apps can provide tailored health education and also send reminders for medication, appointments, and health check-ups.
Real-world Examples of RPM and Telehealth Enhanced by AI
Here are three practical examples of how AI-enhanced telehealth and RPM transform healthcare:
- AI-enhanced Wearables for Cardiac Monitoring: Android wearables are great examples of wearables that help users monitor their vitals. When integrated with AI, the devices can generate patterns alerting users when anything changes. The subtle alerts can circumvent serious complications.
- Virtual Health Assistants for Chronic Disease Management: Chronic illnesses require close monitoring, a significant challenge for many patients. Virtual Health Assistants help patients by bridging the geographical gap of making regular facilities into health facilities. The devices can analyze self-reported data, advise the best course of care, and raise appropriate flags when the patient has to visit the doctor.
- Automated Image Analysis in Tele-Dermatology: AI algorithms analyze vast datasets of skin lesions and use machine learning to provide dermatology insights. The platforms. Tele-dermatology aids in preliminary assessment and identifies high-concern lesions. The platforms speed up diagnosis by identifying the lesions.
Behavioral Modification and AI
People’s behavior and psychological patterns significantly affect patient engagement and adherence. Health professionals must understand patients’ motivations and beliefs about health and treatment. By understanding their patient’s psychological patterns, healthcare professionals can create strategies to change destructive behaviors into healthy ones.
Gamification, Incentives, and AI-driven Behavioral Modification Techniques
Gamification and incentives make adopting healthy behaviors engaging and rewarding. Such techniques can integrate AI to amplify their effectiveness based on individual behavior and psychological patterns.
- Gamification: AI can analyze user data gaming elements such as points, levels, and rewards to tailor challenges to each individual’s profile. The platform could propose challenges based on treatment plans, physical activity, or health conditions. The proposal would result in an engaging and achievable game experience.
- Incentives: AI could tailor incentives based on a personal medication regimen or correct quizzes related to their conditions. Such a program would ensure that patients were in the loop of their current states and stayed motivated to maintain healthier lifestyles. Incentives could be discounts on health services or even financial incentives.
Cognitive Behavioral Therapy (CBT) Applications Powered by AI
Cognitive Behavioral Therapy (CBT) is a form of psychotherapy that helps patients understand and change patterns of thought and behavior contributing to their health issues. AI has the potential to make CBT more accessible and personalized:
- Automated CBT Applications: AI-powered apps deliver CBT techniques to users, helping them manage conditions like anxiety, depression, or stress, which can impact adherence and engagement. These apps use AI to personalize therapy sessions based on user feedback and progress, adapting the therapy content to suit the user’s needs.
- Virtual CBT Therapists: Some platforms leverage AI to create virtual therapists that guide users through CBT exercises via text or voice interaction. These virtual therapists can analyze user responses to refine their approach and provide real-time cognitive and emotional support, making mental health support more accessible to those who might not have the resources for traditional therapy.
AI-driven behavioral modification techniques, including gamification, incentives, and CBT applications, represent a significant leap forward in addressing the psychological barriers to patient engagement and adherence. By making behavioral modification more personalized, engaging, and accessible, AI helps patients adopt healthier behaviors and adhere to treatment plans, ultimately leading to improved health outcomes. These innovative approaches highlight the transformative potential of AI in enhancing patient care and support beyond traditional healthcare settings.
Conclusion
Artificial Intelligence (AI) has many applications within the healthcare industry. For patients, it promises better health outcomes when used with proper models. AI applications such as intelligent medication dispensers, AI-driven behavior modification techniques, and predictive analytics are at the forefront of this revolution. However, AI still has concerns, such as ethical considerations, data privacy, and the accuracy of the model’s interpretations.
To achieve AI’s full potential, a collaborative front led by AI developers, healthcare professionals, and patients. Through continued innovation and ethical practices, AI has the potential to not only augment healthcare delivery but fundamentally change it, making it more accessible, engaging, and effective for all.
