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Applications of Big Data and Predictive Modeling
By Somesh Nigam, SVP, Chief Analytics & Data Officer, Blue Cross and Blue Shield of Louisiana
As data infrastructures mature, the shift to data analysis and innovation intensifies, allowing us to turn data into rich information that we can use consistently, predictively and prescriptively. Focusing on enabling analytics from a high-quality and well-governed single source of truth within a healthcare organization, the architecture provides emerging technologies to support areas such as predictive modeling, which uses data mining and probability to forecast future outcomes, and prescriptive modeling to advise possible preventive actions to take.
Each model is made up of a number of predictors, which are variables that are likely to influence future results. Once data has been collected for relevant predictors, a statistical model is formulated. The model may employ a simple linear equation or it may be a complex neural network, mapped out by sophisticated software.
One core enabling piece, though, is the talent—experts in open source and distributed computing. Pairing young millennial talent with seasoned experts applies rigor to look at outcomes and predictive algorithms. And, it creates a very intellectual, stimulating environment.
In terms of applications for artificial intelligence and machine learning, the outputs are exciting and designed to create operational measures with resulting process-based interventions. With emerging collaborative models of care through strong provider partnerships with Accountable Care Organizations (ACOs), payers cannot only share actionable data with network healthcare providers, but offer financial incentives to work together in coordinating care, driving improved health outcomes and quality.
One of the biggest targets is reducing hospitalizations and readmissions. Payers have opportunities for better care coordination, better outreach, better discharge and better education, and the result of success is both tremendous clinical value as well as economic value. Our predictive models can show a practice that out of the 200 diabetic patients being seen there, two seem particularly at-risk of being hospitalized. That allows the physicians to do outreach, conduct more intense interventions for those two patients and be in a better position to lower their risks of hospitalization—while still meeting optimal care measures for diabetes for the other patients who are at lower risk.
Earlier this year, Blue Cross and Blue Shield of Louisiana implemented a machine learning-based model to make member referrals to the Population Health Care Management division, by identifying members at high risk of hospitalization in the next six months. The model has had a very high level of accuracy compared to commercially available models. By having this ability to identify at-risk members in advance, case management and disease management nurses and other clinical staff can intervene sooner for health coaching, education and self-care support, which can potentially mitigate the clinical event, save lives and reduce costs.
We also have been looking at how payers can use data to drive effective coordination between customer service and clinical care teams, determining which specific interventions are effective for improving clinical outcomes and member experience.
As we evaluate the effectiveness of natural language processing and machine learning to predict whether a member contacting his or her health insurance carrier was likely to be dissatisfied, we’ve learned such predictive modeling tools have a place in the payer-provider-patient realm because they can decrease administrative and medical costs and increase member satisfaction and engagement.