Description: View the following report regarding Risk Scoring in Health Insurance: A Primer.

Description: Abstract from a paper that describes results from a hierarchical Bayesian statistical model which predicts the probability of Non-Acceptable Claim, an unnecessary hospital stay, using hospital discharge claim records as inputs.

Description: This report compares health care utilization and expenditures between individuals with Crohn’s disease who had used infliximab and those who had not. Claims data from the 1999-2003 Thomson Reuters MarketScan commercial claims and encounters databases and inpatient, outpatient, and prescription drug claims data of individuals older than 18 years of age with a diagnosis of Crohn’s were included.

Description: In this report, we explore the impact of clustering using the Medical Expenditure Panel Study (MEPS) data for children (aged 5 to 17) to build a bridge between oral health and overall health using dental and medical expenditures. We include social determinants of health as cluster input variables using the Partitioning around Medoids (PAM) method to identify children and their families who could potentially benefit from population health management related to oral health.

Description: In this report, we explore the impact of clustering using the Medical Expenditure Panel Study (MEPS) data for children (aged 5 to 17) to build a bridge between oral health and overall health using dental and medical expenditures. We include social determinants of health as cluster input variables using the Partitioning around Medoids (PAM) method to identify children and their families who could potentially benefit from population health management related to oral health.

Description: Research report that explores key factors associated with an insurer’s reputational risk and conditions that would generate incentives for insurers to behave in a way that causes loss of their positive reputation.

Description: In this report, we explore the impact of clustering using the Medical Expenditure Panel Study (MEPS) data for children (aged 5 to 17) to build a bridge between oral health and overall health using dental and medical expenditures. We include social determinants of health as cluster input variables using the Partitioning around Medoids (PAM) method to identify children and their families who could potentially benefit from population health management related to oral health.

Description: Data files for presentation.

Description: Supplemental Appendices for Using Social Determinants to Identify Profiles of U.S. Children with Low Dental Expenditures and High Medical Expenditures with Clusters

Description: Supplemental Appendices for Using Social Determinants to Identify Profiles of U.S. Children with Low Dental Expenditures and High Medical Expenditures with Clusters