This predictive analytics based solution was developed for payer client to leverage existing claims database of 835, 837 forms to gain insights and deliver a proactive approach for cost optimization without affecting care quality. The solution makes use of pre-post event analysis, pareto analysis, RFM analysis and geographical profiling. Multidimensional analysis of claims data provided actionable insight to make informed decision regarding contract renegotiation, payment bundling and pricing. The solution Identified benchmark spend for each procedure and drug. Overpayments calculated based on the benchmark allowed quantification of the size of contract renegotiation opportunity/ potential savings.For a utilized claims database, significant variance in paid amounts of certain services across providers in the same region implied large opportunity for savings through renegotiation. This permitted client to implement pro-active approach on curtail the overspend and monitor and prioritize specific regions/ providers for cost optimization.
Input variables : Claims Data,Payment data,Patient Demographics
Output Variables : Benchmark spend for each procedure and drug, overpayments , opportunity savings
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : medictiv.citiustech.com
| Model Category | : | Commercial |
| Date Published | : | October, 2017 |
| Healthcare Domain | : | Payer |
| Code | : | Not available |
| Claims Management |