Analysis of personal health data, personalized proposals and recommendations

Business problem

People find it difficult to interpret their own health data, understand their risk factors and make informed lifestyle choices.
Healthcare providers often lack the resources to provide guidance and support to healthy people in addition to caring for the sick.

To create a health app that helps people to stay healthy by providing lifestyle, exercise, nutrition and stress management advice.
If the data indicates a possible disease, it will suggest primary or specialist care.

Data collection and integration: users enter various health data, such as their medical history, current symptoms, exercise habits, eating habits, sleep habits and wearable device data (e.g. heart rate, steps taken, etc.). The application can also integrate with electronic health records (EHRs) with the user's consent, and include relevant medical history and test results.
Data analysis and machine learning algorithms: the application uses machine learning algorithms to analyse the data collected. These algorithms are able to identify patterns, correlations and potential health risks based on the user's data and compare them with anonymised population data and medical research.
Create personalised health profiles: based on the analysis, the app creates personalised health profiles for each user. These profiles highlight areas of concern, potential risk factors and areas for improvement. For example, it can identify whether a user's high stress levels and poor sleep quality are contributing to their elevated blood pressure.
Making recommendations: the app generates personalised recommendations for each user to improve their health and well-being. These recommendations can include lifestyle changes, dietary modifications, exercise routines, stress management techniques and even medication reminders where appropriate.

Advanced data analytics and machine learning empower people to take control of their health based on personalised analytics and actionable recommendations.
The health app will reduce or eliminate the burden on healthcare providers. By giving health promotion advice to healthy people, it can delay the onset of diseases that need treatment or, if specialist intervention is needed, it can refer the patient to the right specialist in time.
In addition, international examples show that on average a quarter of the cost of medical treatment is not recovered. With this in mind, conservative estimates suggest that data-driven improvements in any health specialty can improve cost-effectiveness by 10%, which is significant at the individual, sectoral and national economy level.