As we’ve examined previously here at the APEX of Innovation, the healthcare industry is becoming increasingly data-driven. Whether it’s insights from wearables or mobile apps that help manage chronic conditions, digital innovations have permanently changed the healthcare landscape. And as these technologies mature, we can expect the sector to continue to transform.
With that in mind, below are five emerging digital health trends poised to deliver significant industry benefits:
AI can help organizations address complex challenges related to population health management, such as declines in patient satisfaction, readmission rates, and rising care costs. However, one of the most interesting applications of the technology is at the consumer level. For example, AI can help patients better understand their symptoms and treatments by making personalized recommendations drawing on the patient’s unique biological data.
Wearable apps like Google Fit and Apple HealthKit are also helping push consumer AI further into healthcare, giving patients a detailed picture of their health and potentially helping them better understand their conditions.
Big Health Data
The popularity of wearables is also contributing to the rise of big health data, which can offer numerous insights to improve patient care. These include predicting the onset of chronic conditions in patients predisposed to them and creating more efficient and effective clinical pathways to improve hospital operations.
The cloud enables healthcare providers to make more informed, real-time decisions. In addition, organizations are also starting to use cloud-based technologies for better information management. Cloud networks make it possible for organizations to deploy better telehealth solutions like remote medical monitoring and mobile health services. As these technologies mature, they will be an increasingly attractive option for patients who want to receive care without leaving their homes.
Drug Discovery with Machine Learning
Expect machine learning to become increasingly applied to drug discovery, drug development, and pharmaceutical industry processes. For example, companies may use ML to predict patient responses to a certain drug and identify which patient populations will most benefit from specific treatments.
Personalized Genetic Testing
Genetic testing enables providers to analyze a patient’s DNA to create a model predicting how they are likely to respond to certain drugs or treatments. This then helps drug developers create personalized treatment plans with the potential to improve the lives of patients and save the industry millions of dollars associated with ineffective drug therapies.