There are many potential use cases for big data analytics (‘BDA’) in health care. BDA can be used to: help researchers find causes of, and treatments for diseases; actively monitor patients so clinicians are alerted to the potential for an adverse event before it occurs; and personalize care so precious resources associated with a treatment are not administered to a patient who cannot benefit from the intervention.
Now that we have our data analytics engine built in CARETILES, we’re starting to gain new insights derived from big data analytics that will serve to advance personalized care plans, improve patient outcomes and avoid unnecessary costs. Here are some innovative ideas and solutions we envision in harnessing BDA:
- Clinical decision support – BDA technologies that sift through large amounts of data, understand, categorize and learn from it, and then predict outcomes or recommend alternative treatments to clinicians and patients at the point of care.
- Personalized care – Predictive data mining or analytic solutions that can leverage personalized care in real time to highlight best practice treatments to patients. These solutions may offer early detection and diagnosis before a patient develops disease or behavioral or mental health issue.
- Public and population health – BDA solutions that can mine web-based and social media data to identify triggers and help predict behaviors or outbreaks based on consumers’ search, social content and query activity.
- Clinical operations – BDA can support initiatives such as wait-time management, where it can mine large amounts of historical and unstructured data, look for patterns and model various scenarios to predict events that may affect wait times before they actually happen.
- Policy, financial and administrative – BDA can support decision makers by integrating and analyzing data related to key performance indicators.
Image 1: IBM