AI and Health Apps: Revolutionizing Healthcare on Mobile Platforms

Patients complain about hospital overcrowding, inaccurate diagnosis, long appointment waiting times, and overall low service quality. Technology, including artificial intelligence, can resolve all these issues. 

According to recent research by the World Economic Forum, a typical hospital produces 50 petabytes per year. At the same time, 97% of all global hospital data each year goes unused. Instead, this data can be the basis of effective AI insights and recommendations.

An AI-powered mobile app can be the right answer for many healthcare institutions. After reading this article, you will learn the main applications of artificial intelligence for mobile healthcare solutions and get step-by-step guidance on how to develop a mHealth solution. 

How mHealth Solutions are Enhancing Patient-Centric Healthcare

Take a look at the graph below. It illustrates the biggest healthcare system problems according to a survey of participants from 34 countries. What bugs patients down the most?

• Long waiting times and healthcare inaccessibility
• Medical staff shortages
• Expensive treatments
• Bureaucracy

What if we told you that each of these major issues can be resolved with a mobile app with AI functionality? It’s true — artificial intelligence technology allows healthcare organizations to make their services more accessible and promote a patient-centric approach.

Patient-centric healthcare is an alternative to a traditional healthcare approach. Instead of filling up the doctors’ and patients’ time with numerous paperwork hassles and bureaucratic procedures, patient-centric healthcare offers to put the patient's individual needs and preferences first and engage them more in the process.

Patient-centric healthcare can be achieved through introducing an AI-enhanced mobile app. How?

• Tailoring treatment plans based on each patient’s illness history, socioeconomic background, health condition, and more
• Enabling remote patient monitoring to watch chronic disease and recently discharged patients, thus reducing in-person visits and hospital readmission rates. AI can present patterns, detect abnormalities in a patient's vital signs, and send an alert directly to the clinic
• Empowering patients to take an active position in their health management by sending regular activity and medication reminders, plus giving insights into their lifestyle patterns and advice on how to manage their health conditions
• Offering AI-based health assessment and symptom checker with the possibility to get a professional medication consultation online right in the app to avoid commute and long waiting times at the hospitals, especially for minor issues

Deloitte highlights that until 2040, they expect the patient to be at the center of the health model. Interoperable data will promote closer collaboration among providers, patients, and insurers. Treatments will likely become more precise, less complex, less invasive, and cheaper. Let’s take a look at how to make this reality closer.

What are the Technologies Behind AI in Healthcare Apps

One important thing to understand about artificial intelligence is that when we say AI, we tend to imagine something like ChatGPT. In reality, AI is somewhat of an umbrella term that includes a variety of technology, such as

• Machine Learning (ML): Algorithms that allow computers to learn from and make predictions or decisions based on data. In healthcare, ML can be used for predictive analytics, such as identifying patients at risk of developing certain conditions, optimizing treatment plans, and improving diagnostic accuracy.
• Natural Language Processing (NLP): a set of techniques that enable computers to understand, interpret, and respond to human language. In healthcare apps, NLP is often used to extract meaningful information from clinical notes, automate documentation, enable patient interaction through chatbots, and assist in clinical decision support.
• Deep Learning (DL): A subset of machine learning involving neural networks with many layers, enabling computers to recognize patterns and make decisions with high accuracy. In healthcare, DL is used for image analysis (e.g., radiology, pathology), detecting anomalies in medical imaging, and even predicting disease progression from complex datasets.

Of course, these are not all the AI technologies existing out there. Still, these are the commonly used ones for healthcare apps. Let’s move on from theory to practice and see the main applications of AI in healthcare.

Top Applications of AI-based Mobile Apps in Healthcare

From automatic symptom checkers and patient chatbots to predictive analytics and remote patient monitoring, AI-based mobile apps can solve many patient requests and make healthcare more accessible for everyone.

Virtual health assistants

One of the most common patient-centric AI features is a virtual health assistant. How does it work? Usually, a virtual assistant is a chatbot inside of a mobile app. A patient can send messages to a bot as if talking with a doctor. 

The process typically involves the following steps:

• Symptom description: The patient describes their symptoms and everything that bothers them through the chatbot
• Clarifying questions: The bot asks clarifying questions about the symptoms, such as their duration, severity, and specific characteristics (e.g., "How long have you been experiencing this pain?" or "Is the pain sharp or dull?"). It also inquires about the patient's health history, including any chronic conditions, medications, or recent medical events.
• Preliminary analysis: Based on the information provided, the chatbot uses machine learning algorithms to analyze the data and identify potential conditions that match the symptoms. The bot can suggest several possible conditions, giving patients an idea of what might be wrong.
• Initial recommendations: The chatbot offers first-line treatment recommendations, such as resting, drinking water, taking a painkiller, or applying a cold compress. These recommendations are based on general medical knowledge and are intended to provide immediate relief.
• Disclaimer and referral: The chatbot clearly states that it is not a doctor and cannot provide a final diagnosis. If the symptoms suggest a more serious condition or if the patient needs further evaluation, the chatbot recommends seeing a doctor. The bot can even refer patients for an appointment with a healthcare provider, facilitating access to professional medical care.

The virtual health assistant operates using a large language model (LLM) API. The chatbot is built on an LLM, such as GPT-4, which can understand and generate human-like text based on the input it receives. Developers can take a basic LLM model and train it additionally on medical data, including medical texts, patient interactions, and symptom descriptions. This specialized training enhances the chatbot’s ability to understand and respond accurately to medical inquiries. 

The chatbot uses natural language processing (NLP) techniques to interpret the patient’s language, extract relevant information, and understand the context of the symptoms described. Machine learning algorithms then analyze the patient’s input to match symptoms with possible conditions and suggest appropriate preliminary actions. The virtual health assistant is integrated into the mobile app via an API, enabling seamless interaction between the user interface and the backend AI model.

This feature provides patients with medical assistance for minor issues without the need to drive to the hospital or wait in long lines. Visiting a doctor in person for smaller medical problems is not always accessible for every patient, whether due to mobility issues, transportation difficulties, or time constraints. Having an AI assistant is a great help in making healthcare more available and convenient.

By offering immediate advice and preliminary care, virtual health assistants help patients manage minor health concerns efficiently while ensuring that they seek professional medical attention when necessary. This improves the overall accessibility and availability of healthcare services, making it easier for patients to receive timely assistance.

Remote patient monitoring

Remote patient monitoring (RPM) uses technology to track patients' health outside of hospitals. It works by analyzing data from wearable devices like fitness trackers and glucose monitors to keep tabs on vital signs continuously. AI algorithms check this data regularly and alert both patients and doctors if anything seems off.

This system gives patients real-time updates on their health, allowing them to stay involved in managing their conditions. They can monitor trends in blood pressure, glucose levels, and heart rate, making it easier to make informed decisions and get help when needed.

RPM also makes life easier for patients by reducing the need for frequent clinic visits. Instead of going in person, they can have check-ins based on their wearable device data. This not only saves time but also helps hospitals manage patient care more efficiently.

For example, MD Revolution's RPM platform, RevUp, has shown to cut hospital readmissions by 50%, easing the strain on healthcare systems. Studies also suggest significant cost savings, potentially saving up to $15,000 per patient in the US healthcare system by preventing unnecessary hospital stays.

In addition, research on telemonitoring for high-risk patients after discharge found that it reduced readmissions or deaths within 30 days significantly compared to standard care. Emergency room visits also decreased, showing the effectiveness of remote monitoring in improving patient outcomes.

Health data predictive analytics

Predictive analytics in health data utilizes advanced technologies like machine learning and AI to analyze patient information and predict health outcomes. By examining historical and current data, these tools can forecast disease progression, identify individuals at high risk for chronic conditions such as cardiac diseases, and stratify patient populations based on their health risks.

These predictive models use data such as medical history, demographics, and lifestyle factors to pinpoint patients who are more likely to develop specific health issues. 

For example, a recent study proves that predictive models can effectively identify patients at risk of developing chronic diseases such as cardiac conditions and diabetes. By analyzing comprehensive datasets, including medical history, demographics, and lifestyle factors, these models enable targeted interventions that mitigate risks and improve health outcomes.

Additionally, predictive analytics plays a crucial role in predicting hospital readmissions. Studies indicate that by analyzing patient data, including social determinants of health, predictive models can accurately identify individuals at higher risk of readmission. A recent study concludes that routinely collecting medical and administrational patient data can identify patients at high risk of readmission.

Disease and lifestyle management

Many healthcare apps already feature comprehensive tracking capabilities, allowing users to monitor various aspects of their health and daily routines. Users can track their water intake, medication schedule, physical activities such as walking or exercise, blood sugar levels, blood pressure, mental health states, and any other parameters they wish to monitor. This data collection helps users maintain a detailed record of their health, promoting awareness and encouraging healthier habits.

AI enhances these tracking features by analyzing the collected data to establish patterns and correlations. For instance, the AI can identify potential triggers for migraines by examining the user's daily activities, dietary habits, sleep patterns, and environmental factors. Based on this analysis, the AI can suggest lifestyle adjustments to minimize migraine occurrences, such as changes in diet, sleep schedule, or stress management techniques.

Additionally, AI can provide personalized health recommendations tailored to the individual user's data. For example, if a user consistently records high blood pressure readings, the AI can suggest specific dietary changes, exercise routines, or stress-reduction techniques to help manage their condition. Similarly, suppose a user is tracking their mental health. In that case, the AI can offer insights into how certain activities or routines affect their mood and suggest mindfulness practices, relaxation techniques, or professional resources for support.

Moreover, AI can facilitate better medication adherence by sending timely reminders and offering information on the importance of each medication.

Perpetio has developed an app that uses object recognition technology to scan medications and provide detailed information about them. This feature helps users remember to take their medications and ensures they understand each medication's purpose and proper usage. 

Personalized treatment plans

AI can create personalized treatment plans by analyzing a wide range of user data such as age, chronic and past illnesses, lifestyle habits, dietary specifics, and current medications. By examining this information, AI helps doctors tailor treatment plans and lifestyle recommendations to fit each patient's unique needs.

For example, AI can examine a patient’s history of chronic conditions and past illnesses to get a clear picture of their overall health. Combining this with current lifestyle data allows AI to predict potential health risks and suggest the most effective treatment strategies. If a patient needs to be more active, the AI can recommend exercises suitable for their health condition and physical ability.

Dietary advice is another area where AI can help. For a person with diabetes, AI can suggest meal plans that keep blood sugar levels stable while also considering their food preferences and any dietary restrictions. This personalized approach ensures that dietary changes are both effective and manageable.

AI also improves medication management. By reviewing the patient’s current medications and medical history, AI can help doctors spot potential drug interactions and side effects, making the treatment plan safer and more effective. AI can also suggest alternative medications if necessary.

Recent studies support the effectiveness of AI in personalizing treatment plans. One study used patients’ gene expression data to train a machine learning model, successfully predicting the response to chemotherapy with an accuracy of over 80% across multiple drugs. In another study, researchers successfully predicted responses to different classes of antidepressants using electronic health records (EHR) of over 17,000 patients. These approaches highlight the potential of AI to enhance personalized treatment plans based on comprehensive patient data.

How to Develop an AI-enhanced mHealth App

When developing an AI-enhanced mHealth app, it's essential to understand the difference between a white-label solution and a custom-made app.

A white-label solution is more cost-effective but not tailored to the specific needs of your healthcare organization. We recommend developing a custom-made Minimum Viable Product (MVP) because it strikes a balance between being cost-effective and meeting your specific needs, offering a more personalized solution that aligns with your goals.

Here are the key steps to develop an AI-enhanced mHealth app:

Defining the scope and objectives of the project

This step involves clearly outlining what the AI-enhanced mHealth app aims to achieve and what problems it solves for users. For example, you might want the app to help patients manage chronic diseases, track medication adherence, or provide personalized health recommendations.

Choosing the right technology stack for development

The choice of the appropriate technologies and programming languages for building the app depends on the target platform (iOS or Android) and the app’s target functionality. For instance, you might choose native iOS or Android development and integrate APIs from AI companies like OpenAI for machine learning capabilities. Evaluate whether to purchase pre-trained models, modify existing APIs, or create custom AI models based on your project requirements.

Designing the user interface and experience

A highly important step in building an AI-based mHealth app is to design a user-friendly interface that is intuitive and visually appealing. The design should prioritize simplicity and intuitive navigation to enhance usability, especially considering the diverse needs of healthcare app users. Ensure the design aligns with Apple’s and Google’s design practices and standards.

Integrating customization options and settings

Offering users customization options can enhance their experience and satisfaction. For instance, users might want to customize notifications, choose themes, or personalize health-tracking features. Allow users to select which health parameters they want to track and how they receive feedback.

Testing the app for functionality, performance, and usability

Thoroughly test the app to ensure it functions as intended, performs well, and provides a seamless user experience. Testing should include functionality checks, performance evaluations, and user experience assessments to identify and address any bugs or issues before launch.

How Much Does it Cost to Create an AI-based Healthcare Application?

Determining the cost to build an AI-based healthcare application, particularly for an MVP (Minimum Viable Product), typically ranges between $40,000 to $1-0,000. However, the exact figure can vary significantly based on factors such as the app's complexity, desired features, technology stack, and development team's location and experience.

For instance, partnering with an experienced outsourcing team in Ukraine can deliver a high-quality product with cutting-edge technologies at a competitive rate, typically ranging from $35 to $50 per hour, depending on expertise and project requirements.

Investing in a custom-made MVP allows you to balance cost and functionality, ensuring that your app meets the specific needs of your healthcare organization while remaining within budget.

Main Considerations for AI Use in Healthcare Apps

Creating digital products for the healthcare industry requires extra consideration and attention from the development team. The medical area is highly regulated by several standards, and the introduction of AI for medical advice should not harm users.

HIPAA compatibility

Ensuring compliance with the Health Insurance Portability and Accountability Act (HIPAA) is crucial for protecting sensitive patient data. This involves implementing robust security measures, such as encryption and secure user authentication, to safeguard patient information.

Interoperability

Interoperability allows healthcare systems to work seamlessly together, improving patient care and data analysis. AI healthcare apps should integrate with electronic health records (EHRs) and other healthcare data sources, using standards like HL7 and FHIR to facilitate secure and efficient data exchange.

Responsible use of medical data

AI healthcare apps must handle user data responsibly, with clear privacy policies and user consent. Apps should include disclaimers about AI limitations, cautioning against self-treatment and encouraging users to consult healthcare professionals for accurate diagnoses and treatments.

Consider Perpetio Your Trusted Partner

Imagine a healthcare system where long waiting times, inaccessibility, staff shortages, expensive treatments, and bureaucracy are resolved with the help of AI. An AI-enhanced mobile app can tailor treatment plans based on individual patient data, enable remote monitoring, empower patients with regular reminders and insights, and offer AI-based health assessments.

At Perpetio, we're excited to help healthcare organizations leverage AI to create innovative, patient-centric solutions. Let's work together to make healthcare more accessible, efficient, and effective.