Healthcare costs are currently on the rise, partially due to increasing cases of non-communicative and infectious diseases. As a result, insurance companies are spending more on patient healthcare costs. Machine learning uses predictive analysis through collected data to diagnose patients on an individual level. Current machine learning models help for quicker diagnosis of such conditions as heart disease or intestinal disorders.
Establishing Early Predictions
One of the benefits of adopting the machine learning 1 concept is utilizing a patient’s medical history to determine future health risks. For instance, someone with a pre-existing condition may be able to receive preventative care before they need it. Patients will then be able to save on healthcare expenses and frequent hospital visits. Electronic health records, wearable technology, and mobile apps are all methods of pushing the digital healthcare agenda.
Several entities in the medical field are testing new methods for comprehensive patient care, such as the Humanwide project. The Humanwide project is Stanford Medicine’s new pilot program. Patients in the pilot program participate in genetic assessments, wear mobile monitoring devices, and have access to a certified health coach to implement sustainable health goals. The program has already identified areas for preventative healthcare for its patients 2.
Understanding the Challenges
The logical data hub for digital health data would be through cloud storage. However, some unanswered questions remain to be addressed. Will patients be able to trust that their data is secure? How will the metrics interact with the legacy electronic systems the healthcare industry has in place now? Does the data populate in real-time? Real-time results may not always be an option.
Machine learning has shown favorable results for personalized, preventative healthcare for patients. Projects like Humanwide are moving the needle forward in serving specific needs through a tailored, collaborative approach. Now, the next step is ensuring that machine learning and digital advancements are a viable long-term solution for healthcare.
1 Predicting Hospitalization: Machine Learning Models on the Rise by Jayshree Pandya
2 Stanford Medicine pilot program uses data-driven, integrated team approach to predict, prevent disease by Stanford Medicine