How AI is Changing the Game in Chronic Disease Care

As the world continues to grapple with the ever-increasing burden of chronic diseases such as diabetes, heart disease, and cancer, it becomes increasingly apparent that traditional methods of disease management and prevention are no longer sufficient.

According to the World Health Organization (WHO), chronic diseases account for almost 40 million deaths globally and are projected to increase to 57 million by 2020[1]. Furthermore, 80% of all deaths from chronic diseases occur in low- and middle-income countries[2].

The need for innovative solutions that address both the medical and lifestyle factors contributing to chronic disease development is paramount. The complexity of this problem requires a multi-disciplinary approach that takes into account the social, economic, and environmental determinants of health, making it a challenging issue to tackle.

‍

The Challenge

Managing chronic diseases such as diabetes, heart disease, and cancer poses a considerable challenge due to the requirement of ongoing medical treatment and management. This ongoing care can be a significant burden for patients and their families, as it often entails frequent visits to healthcare providers, regular medication consumption, and the adoption of lifestyle changes to manage the disease. Furthermore, lifestyle factors, such as tobacco use, unhealthy diet, and physical inactivity, not only contribute to the development of chronic diseases but also make them more difficult to manage. These changes can be challenging and may require support from healthcare providers, family, and community resources. Addressing the challenges associated with chronic disease management necessitates a holistic approach that addresses both medical treatment and lifestyle factors.

In addition to the burden on patients and families, the rising cost of chronic disease management in India is a major concern for healthcare systems and policymakers. The economic cost of chronic diseases in India, which was around $100 billion just five years ago, is expected to continue to rise in the coming years, further exacerbating these challenges.

‍

The Solution

Preventing the onset of chronic diseases such as diabetes, heart disease, and cancer is a crucial approach to addressing the challenges associated with their treatment and management. However, the overburdened and understaffed nature of healthcare systems makes it difficult to provide the necessary care and support to prevent these diseases from developing in the first place. To more effectively address these challenges, advanced tools and technologies are required to aid and better equip healthcare providers. Artificial Intelligence (AI) is one such tool that has the potential to revolutionize the management of chronic diseases.

Artificial Intelligence technology-based solutions can be leveraged to improve the prediction, diagnosis, and treatment of chronic diseases by providing healthcare professionals with more efficient and accurate tools. Utilizing AI in chronic disease management can also help to reduce costs and optimize resource utilization. It can assist healthcare professionals in making more informed decisions, identifying at-risk patients, and providing more personalized care.

AI-driven solutions have the potential to bridge the gap between the rising costs and the increasing burden of chronic diseases, making them more accessible and affordable for the population. By incorporating AI-driven solutions, healthcare systems can improve patient outcomes and create a more sustainable healthcare system.

There are several Artificial Intelligence technology-based solutions that are currently being used or have the potential to be used in chronic disease management and prevention:

1. Predictive modeling

Predictive modeling in chronic disease care can be used to identify individuals at high risk of developing a chronic disease and predict outcomes through analyzing patient data such as electronic health records. It can be helpful in chronic disease care by identifying individuals at high risk of developing a chronic disease, predicting outcomes, and guiding the treatment and preventive care decisions.

• Identifying high-risk patients: Machine learning algorithms can be trained on large amounts of data, such as patient EHRs, to identify patterns and predict outcomes.
• Predicting progression: Predictive modeling can be used to predict the progression of chronic diseases such as diabetes by analyzing data such as blood glucose levels and medication use.

2. Early detection

Early detection in chronic disease care refers to identifying the presence of a disease or the risk of developing an infection at an early stage, before the onset of symptoms, or before the disease has progressed significantly. Early detection can be beneficial in several ways:

• Improving outcomes: Early detection can lead to more effective treatment and management of chronic diseases, which can improve outcomes for patients. For example, if a patient with diabetes is diagnosed early on, they can take steps to control their blood sugar levels and prevent the development of complications.
• Reducing costs: Early detection can reduce the costs associated with chronic diseases, as treatments and management strategies are more effective when they are initiated early on.
• Increasing access to care: Early detection can increase access to care for patients, as they are more likely to be diagnosed and treated before the disease progresses and becomes more difficult to manage.
• Reducing the burden on healthcare systems: Early detection can also help to reduce the burden on healthcare systems, as patients with chronic diseases that are diagnosed early on are less likely to require hospitalization or other intensive care.
• Improving the quality of life: Early detection can improve the quality of life for patients, as they can take steps to manage their disease and prevent complications before they occur.

3. Personalized treatment

Personalized treatment or I would prefer to call it precision medicine, is an approach to healthcare that takes into account individual differences in genes, environment, and lifestyle. In the context of chronic disease care, personalized treatment can be helpful in several ways:

• Tailoring treatment to individual needs: Personalized treatment can improve treatment outcomes by taking into account individual differences in genes, environment, and lifestyle.
• Reducing side effects: Personalized treatment can reduce side effects by avoiding treatments that are unlikely to be effective or that may be harmful to the patient.
• Optimizing patient outcomes and reducing costs: Personalized treatment can improve patient outcomes by providing the right treatment for the right patient at the right time, thus reducing the risk of disease progression or complications, and reducing healthcare costs by avoiding unnecessary treatments.

4 Remote monitoring

Patient monitoring is the ongoing measurement and tracking of a patient’s health status in order to detect changes and respond accordingly. Supplementing patient monitoring with Artificial Intelligence can enhance the benefits of patient monitoring in chronic disease care by providing real-time data analysis, personalized treatment plans, and early warning signs.

• Identifying changes in health status: AI algorithms can analyze patient data in real-time and generate alerts for abnormal readings or trends, which can help identify changes in a patient’s health status and allow for early interventions.
• Monitoring treatment effectiveness: AI can process large amounts of data and identify patterns that would be difficult for a human to discern, which can be used to adjust treatment plans as needed. This can help to determine the effectiveness of a treatment plan by tracking changes in a patient’s health status over time.
• Improving communication between healthcare providers, patient outcomes, and engagement: AI can provide healthcare providers with real-time data analysis and personalized treatment plans, which can improve communication between healthcare providers and make treatment decisions more informed. Additionally, AI can provide early warning signs and personalized feedback and advice to the patients to better understand their health status and take action to improve it.

‍

The Conclusion

AI is rapidly changing the paradigm of chronic disease care by leveraging its ability to process large amounts of data, identify patterns, and make predictions. Its potential to reduce mortality rates, alleviate the economic burden on healthcare systems, and improve the overall quality of life for individuals affected by chronic diseases, has been widely acknowledged by healthcare providers, policymakers, and community organizations.

From predictive modeling and patient monitoring to clinical decision support, AI is providing new and innovative ways to manage and treat chronic diseases. By utilizing sophisticated algorithms such as machine learning, deep learning, and natural language processing, AI is able to extract valuable insights from complex data sets. This can aid in the development of actionable strategies for disease management and prevention by healthcare providers, policymakers, and community organizations.

As AI technology continues to evolve and become more sophisticated, it has the potential to revolutionize chronic disease care, leading to better outcomes and a higher quality of life for patients. With its ability to provide real-time data analysis, personalized treatment plans, and early warning signs, AI is poised to change the game in chronic disease care, making it more efficient, effective, and personalized.

— Author: Gaurav Lohkna

P.S.: For a more detailed and technical analysis of AI-based solutions in Chronic Kidney Disease management (one of 13 major chronic diseases), kindly await the publication of my upcoming study/article. It provides an in-depth examination of cutting-edge technologies, algorithms, and methodologies in chronic care.

References
[1] World Health Organization (WHO). Noncommunicable diseases: Mortality https://www.who.int/data/gho/data/themes/topics/topic-details/GHO/ncd-mortality
[2] World Health Organization (WHO). Global health observatory data. https://www.who.int/gho/en/

‍