Missing Data is Informative
Over 30% of metrics are missing. In healthcare PE diligence, sellers who don't provide denial/AR data often have worse-than-average performance. Bayesian estimates below are shrunk toward peer priors — treat as conservative baselines, not forecasts.
Missing: Denial Rate, Clean Claim Rate, Net Collection Rate, First Pass Resolution, Appeals Overturn Rate, Days In Ar
Bayesian-Calibrated KPI Estimates
Each metric combines peer-group prior (from 44-bed hospital benchmarks) with observed data. Shrinkage shows the weight given to prior vs observed: 0% = all data, 100% = all prior. Green bar = data weight, amber = prior weight.
| Metric | Prior | Observed | Posterior | 90% CI | Shrinkage | Data vs Prior | Quality |
|---|---|---|---|---|---|---|---|
| Denial Rate | 10.0% | — | 10.0% | [1.1%, 18.9%] | 100% | Prior Only | |
| Clean Claim Rate | 89.0% | — | 89.0% | [81.0%, 97.0%] | 100% | Prior Only | |
| Net Collection Rate | 94.0% | — | 94.0% | [87.5%, 100.0%] | 100% | Prior Only | |
| First Pass Resolution | 72.0% | — | 72.0% | [57.5%, 86.5%] | 100% | Prior Only | |
| Appeals Overturn Rate | 35.0% | — | 35.0% | [17.9%, 52.1%] | 100% | Prior Only | |
| Days In Ar | 52.0 | — | 52.0 | [39.2, 64.8] | 100% | Prior Only | |
| Cost To Collect | 3.2% | — | 3.2% | [2.4%, 4.0%] | 100% | Prior Only | |
| Dnfb Days | 7.0 | — | 7.0 | [5.3, 8.7] | 100% | Prior Only | |
| Charge Lag Days | 4.0 | — | 4.0 | [3.0, 5.0] | 100% | Prior Only |
Methodology: Hierarchical Bayesian Partial Pooling
Rate metrics (denial rate, collection rate, clean claim rate) use Beta-Binomial conjugate updating. Continuous metrics (AR days, cost to collect) use Gamma/Normal approximation. Priors are stratified by hospital type (large/medium/small/rural). With zero observations, the posterior equals the prior. As n increases, the posterior converges to the observed value and the credible interval narrows. This prevents unstable estimates from thin data while letting real evidence dominate.