Predictive Analytics for Denial Management
The model analyzes factors that cause denials, predict claim denials and reimbursed/ denied amounts and uses Logistic and linear regression analysis and ensemble model (random forest) on claims payment and patient demographic data. The claim denial model showed an ROC-AUC of 0.94, Accuracy of 89%, Sensitivity: 78% and Specificity: 94%
Input variables : Claim payment (835) data, Patient demographic data
Output Variables : Factors that caused denials, predict claim denials and reimbursed/ denied amounts
Metrics to Monitor
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Statistical
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:
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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
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Business
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:
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Claims Processed |
$ Saved |
Denial Rate |
Top Denial Reasons |
Denials by CPT |
Denial by DRG |
Denial by Payer
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Infrastructure
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:
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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
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Visit Model :
medictiv.citiustech.com