Observational Study · Inverse-Probability Weighting

The Follow-Up Gap After Discharge

Using causal inference methods on observational data to estimate the impact of missed post-discharge follow-up on 30-day hospital readmissions — without randomizing patients to worse care.

Lakeview Regional Health System | Pseudonym
De-identified Outcomes Research
Carole Bonner, PhD | Lead Investigator
STATA · N = 100,000 discharges
−30.8 pp
Effect on Readmission
~7,000
Avoidable Readmissions/Yr
$48.6M
Projected Cost Avoidance
~30:1
Program ROI

Readmissions at 18.4% — bottom quartile nationally

Lakeview's 30-day all-cause readmission rate put the five-hospital network at risk of $2.8M/year in HRRP Medicare penalties. The CMO suspected gaps in post-discharge follow-up were a major driver, but the proposed transition-of-care program would cost $1.6M annually.

The question: is the causal impact of missed follow-up large enough to justify the investment?

An A/B test was not ethical — you cannot randomize patients to receive no follow-up. The team needed observational causal inference methods.

The patients who missed follow-up were sicker

% Missing Follow-Up by Patient Segment Low acuity 5.3% High acuity 60.4% Commercial ins. 12.9% Medicaid / Unins. 41.3% Sicker and underinsured patients were far more likely to miss follow-up AND to be readmitted.

Naïve → Regression → IPW

Estimated Effect of Missed Follow-Up on Readmission 0 pp (no effect) True: −30 pp Naïve No controls +1.5 pp ✗ OLS With controls −27.8 pp IPW + Reg Doubly robust −30.8 pp ✓

IPW reweights the sample so patients who received follow-up "look like" those who missed it. Combined with regression, the estimate is doubly robust — consistent if either model is correct.

Readmission rates stratified by acuity and follow-up

30-Day Readmission Rate (%) by Acuity × Follow-Up Status 60% 45% 30% 15% 0% 53.3% 37.9% High Acuity 25.5% 4.3% Low Acuity Received follow-up Missed follow-up

Within comparable acuity groups, follow-up was clearly protective. The naïve overall comparison masked this because high-acuity patients dominated the "missed" group.

From causal estimate to investment case

ComponentEstimate
Discharges missing follow-up/year~22,800
Excess readmissions attributed to gap~7,000/year
Cost per readmission$12,400
Total attributable cost$86.8M/year
Reach assumption (conservative)70%
Effectiveness assumption80%
Projected cost avoidance$48.6M/year

Program cost: $1.6M/year. Even if the true effect were half the estimate, the program would pay for itself many times over. Break-even requires only a 1.0 pp readmission reduction.

Phased rollout with built-in evaluation

Phase 1 (Months 1–3): Pilot targeting Medicaid/uninsured patients with HF and COPD — the highest-return segment. Follow-up contact rate rose from 39% → 78%. Early readmission data: 28.1% → 19.3%.

Phase 2 (Months 4–12): System-wide rollout across all five hospitals, 7-day TOC nurse coverage. Annualized program cost: $1.6M.

Subgroup findings: Medicaid patients showed a larger effect (−34.1 pp vs. −25.6 pp for commercial). HRRP-targeted conditions (HF, COPD) showed effects of −36.2 pp and −33.8 pp — concentrating the financial return where CMS penalties are highest.

Follow-up studies planned: Interrupted time series (pre-post impact), dose-response analysis (does a second contact add value?), and formal cost-effectiveness with QALYs for payer negotiations.

Fund the transition-of-care program. Phase in over 12 months.

Sensitivity analysis showed an unmeasured confounder would need to be 8× stronger than acuity + insurance combined to reduce the estimate below break-even. Three independent methods (OLS, IPW, doubly-robust) converged on a 26–31 pp effect. The CFO approved the investment in a single meeting.

~30:1
Projected ROI