By leveraging big data analytics tools, including artificial intelligence, machine learning, and natural language processing, organizations can allow patients to actively participate in their own care, leading to improved care delivery and health outcomes. These tools have been used to:
- increase adherence to treatment plans,
- enhance patient health literacy so that they take an active role in their own care, and
- increase clinical trial engagement to facilitate the development of novel treatments, more accurate diagnoses, and innovative cures for debilitating diseases.
Machine learning
At Beacon Health Options, a behavioral health management service provider, clinicians are using machine learning tools to extract actionable insights from patient data. With this information, the organization is working to enhance care coordination for patients with mental illness, as well as ensure patients stay engaged and adhere to their care plans. Their goal is to move from being a reactive model that solely looks at what has happened historically to being a much more predictive, proactive, and targeted service provider.Natural language processing
The surge in patient portal adoption has made it easier for patients to access their clinical data. However many still struggle to make sense of their medical information. Low health literacy rates, complicated by medical jargon, can lead to poor medication adherence, misinterpretation of lab results, and deficient disease management. Natural language processing (NLP) tools have emerged as a viable solution to this issue, allowing individuals to translate complex medical information and improve patient health literacy.
Artificial intelligence
A recent report from Deloitte found that virtual trials that utilize AI-driven tools such as smartphone apps and wearable devices support both passive and active data collection, and can significantly enhance patient enrollment and engagement in clinical research. Mayo Clinic and IBM Watson Health recently partnered to develop an AI-driven tool that accurately matched breast cancer patients with relevant clinical trials. After using the tool for 11 months, Mayo Clinic saw an 80 percent increase in trial enrollment.