Healthcare and life sciences continue to acquire vast amounts of data, but the real value of that data has been largely untapped.
A recent piece on healthcare by management consulting firm Oliver Wyman reports, "In healthcare, data is the currency that will enable ‘right place, right time’ behavioral changes at an individual level." The healthcare industry is sitting on huge amounts of data about individuals, but understanding their behavior, gaining insights, and predicting outcomes will require identifying patterns across unrelated and dissimilar data sets.
Oliver Wyman observes that healthcare companies have an opportunity to do more with data by casting a wider data net. And gathering that data will mean drawing on much more than what is stored in the traditional medical and health data sources, which are often siloed and transaction-oriented. People are exposed to a wide variety of environmental, motivational, and personal factors that ultimately impact each person's health, according to their own genetics, personality, and lifestyle. Oliver Wyman proposes that the healthcare industry needs to make better use of a broad set of parameters, including:
- Social media activities
- Demographics, vitals, and family situation
- Genomic profile
- Purchasing behavior and habits
- Behavioral profile
- Financial spend and credit score
- Biometrics and activities
- Prescriptions and adherence to them
- Home, auto, and life insurance
- Recent life events and triggers
- Preferred providers and coverage history
- Clinical and claims history
Combining traditional healthcare data with this sort of information, and then using a knowledge graph to capture multiple relationships between different entities and data points, will provide a deeper understanding of the individual. The knowledge graph’s flexibility and potential diversity of data can reveal and lead to specific medical regimens that fit the behavior and motivations of the person. The outcome will mean a healthier person as well as society.
Oliver Wyman suggests that in the future, health knowledge graphs “will incorporate a combination of traditional clinical and claims data, new health monitoring (such as activity and biometrics), behavioral observations (such as social media, geolocation, and financial activity), and more. As a result, available data will become broader (within and beyond health), more granular, and real-time.”
That future is now with Katana Graph's Intelligence Platform. Katana Graph implements Graph AI using techniques such as graph neural networks (GNN), a powerful approach for finding new patterns in diverse datasets. Katana also provides easy-to-use and scalable packages for generating knowledge graph embeddings to use in applications such as node classification, link prediction, and recommendation systems.
Whether you’re using structured data, unstructured data, or both, Katana Graph can greatly improve the insights and opportunities uncovered from your organization’s data. Click to schedule a meeting.
