Predictive Disease Analytics Market Trends

Growth Drivers

• Adoption of AI and ML: The integration of artificial intelligence and machine learning in healthcare enables more accurate predictive models, helping in disease risk assessment, personalized treatment plans, and outcome forecasting. This boosts the predictive disease analytics market growth significantly. In February 2023, the Apollo Hospitals Group which is one of the largest healthcare providers in Asia announced the launch of the Apollo Clinical Intelligence Engine. It is a support tool for clinical decision-making, developed using the latest techniques in AI and ML.
• Demand for cost-effective healthcare solutions: Predictive analytics helps reduce healthcare costs by preventing hospital readmissions and optimizing resource allocation, making it appealing to healthcare providers and payers. According to the Diabetes Atlas summary, estimated diabetes cases are predicted to rise up to 643 million by 2030 and 783 million by 2045. 47% of these cases may remain undiagnosed due to a wide number of reasons. The predictive disease analytics market is projected to play a vital role in identifying such cases for the prevention of further complications.

Challenges

• Data privacy and security concerns: The handling of sensitive patient data raises concerns about data breaches and misuse. Companies are required to comply with the Health Insurance Portability and Accountability Act and General Data Protection Regulation. This leads to difficulty in managing and sharing data across platforms for wide usage. This may potentially slow down the adoption and expansion of the predictive disease analytics market during the forecast period.
• Limited access to quality data: Predictive models rely on large amounts of accurate and diverse data, but many healthcare providers face challenges in accessing, standardizing, and integrating this data from various sources, affecting model performance. Additionally, data from diverse populations, needed to build comprehensive and unbiased predictive models, is often lacking, which limits the accuracy and generalizability of these models. This further limits the predictive disease analytics market expansion.