Elder Research automated fraud detection for a national Workers’ Compensation Insurance Program to optimize investigations and forensic analysis
Elder Research developed predictive models to rank patients based on their propensity to pay medical bills and identify actions to encourage payment.
Elder Research provided machine learning models using implanted medical sensor data to identify abnormal activity that are predictive of the disease.
Elder Research was tasked to evaluate the effectiveness of second opinions to encourage federal employees on long term disability to go back to work.
Elder Research developed predictive models for Michael J. Fox Foundation to score tests and biomarkers to find those valuable in predicting Parkinson’s disease.
Elder Research developed a predictive algorithm to define asthma and identify predictive biomarkers that are a molecular signature for asthma.
Elder Research developed a predictive model that identified 20-30% more patients with a high likelihood of becoming a donor to a health foundation.
John Elder was a keynote speaker at Predict 2017 in Dublin, Ireland, October 2017. In this video Dr. Elder discusses the extent of misleading discoveries in data, especially in medical research, and how data science can help validate these discoveries.
Department of Labor OIG wanted to detect fraud in Office of Workers’ Compensation Program data. The goals were to highlight abnormalities in the claims data that could be used for future audits and to create visualization tools to allow auditors to easily explore potentially fraudulent claims.
The goal for this project was to identify and quantify fraud, waste, and abuse indicators for a Medicare and Medicaid Dental Insurance client so that they could rank potentially fraudulent providers and target them for appropriate interventions.
Elder Research developed and deployed a custom solution to identify and prioritize questionable contracts and healthcare claims for fraud investigation.
Elder Research developed a risk scoring model to optimize management of long-term care claims and identify those most likely to benefit from outreach.
Elder Research developed a provider risk scoring model for a major dental insurance provider that enabled targeted intervention with low quality providers and reduced per patient cost by nearly 25 percent.
Elder Research combined state-of-the-art text analytics with traditional statistical techniques to create a solution for ranking disability claims for approval. The results were more accurate and consistent than any single doctor’s decision and allowed 20% of the claims to be approved immediately.
Pharmacia & Upjohn invited Elder Research to examine the data for a new drug and determine the drug’s viability. The research discovered a real effect, and these decisive results were communicated to decision makers using a novel visualization technique.