Carolinas HealthCare System: Consumer Analytics

Why has CHS invested in DA? There was an entry of consumer tech companies into the healthcare space and therefore CHS needed analytics capabilities from an industry player that the consumer would trust to integrate their healthcare data in the future. Therefore, they went for DA. DA had ongoing strategic priorities to predict health needs, continually enhance patient outcomes and drive transformative solutions to address community health issues. Da had successfully launched several pilots covering a variety of medical conditions, geographies, and functional capabilities. In order to renew the strategic road map to CHS’s growth, CHS partnered with DA to improve outcomes rather than increasing its size. Most of DA’s capacity was used to provide tools in order to support CHS-affiliated hospitals in delivering best-in-class healthcare to patients. DA developed analytical tools for evidence based population health management, personalized patient care and predictive modelling.

How successful has DA been so far? Why? It collected and handled vast amounts of data efficiently; it created a data governance structure; and it helped shift the organisation away from the anecdotal culture to evidence based one. After the DA was launched, the team received more than twice as many requests as it had capacity to accept. And therefore it created a priority setting process with predictive analytics. DA’s criteria for success was to improve outcomes rather than increasing size of CHS. DA was committed to building strong relationships with the nurses and physicians. The data collected through CHS network, collected at many points of care, was used and any further recommendations and tools were implemented by physicians. The communication strategy deployed by DA focussed on improving the quality of outcomes by engaging the patient to change his or her behaviour.

What are Dulin’s most important challenges going forward? Dulin was recruited to help execute on CHS’s vision of creating a unified, data-driven system. Along with his team he had to decide out of the existing pilots which of them could be extended for related issues without minimal redesigning. Having received large investments for DA infrastructure, the CHS leadership wanted to explore external business opportunities for DA where it could generate profits. Since internal DA services demand was quite high, external market capabilities could not be tested for DA. Ensuring the data from the predictive models improved the workflow, by engaging with the clinicians, was a major focus.

Which organizations are best-placed to provide integrated data management for individual patients? Companies like IBM Watson Health is meant to help physician, researchers, insurers and patients use big data, analytics and mobile technology to achieve better health outcomes, calling the business unit IBM’s “Moonshot” in healthcare. The University of California San Francisco and Intel are working together to create a deep learning analytics platform that will deliver clinical decision support and predictive analytics capabilities to its users. Microsoft has come up with a new initiative Healthcare NExT which will combine work from existing industry players and Microsoft’s research and AI units to help doctors to reduce data entry tasks, triage sick patients more efficiently and ease outpatient care. Royal Philips has developed ItelliSpace the latest edition of its comprehensive advanced visual analysis and quantification platform. The product helps radiologists, detect/diagnose and follow up on treatment of diseases while using new machine learning capabilities to support the physicians. Sentrein’s end to end remote patient intelligence solution leverages the revolution in wearable multimodal bio-sensors and machine learning to detect health deterioration in high risk patients earlier and with higher accuracy so lower cost interventions can be utilized before the patient becomes acute and requires hospitalization.