Patient Outcome & Readmission Flow Analyzer

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How to read this report. Every figure is tagged by where it came from — hover any underlined number to see its source or formula. A full breakdown of every value is in the appendix.

This report contains 23 figures you provided, 8 calculated figures (8 of them independently re-checked), and 18 stated assumptions.

18 of the 49 figures in this report are our estimates, not your numbers. They are marked est. and carry an amber underline. To make this report entirely your own, give us: Admitted count, Applicable penalty, Avg los days, Discharged count, Ed throughput hrs, Hai rate baseline, Hai rate target, Hcahps baseline, Hcahps target, Hospital medicare base payments, Mortality oe ratio, Readmission rate baseline, and 4 more.

You entered this Calculated from your inputs Calculated, leans on an assumption Assumption (not from your data), marked est.

Executive Summary

FY2026 HRRP places 78.2% of assessed hospitals under active penalty — this hospital’s 13.0% readmission rate, 68%est. HCAHPS score, and 2.8%est. HAI rate collectively sustain a payment adjustment that, applied to a 0.44%est. mean penalty against Medicare base operating payments, puts an estimated $123,200 at risk. Dollar exposure is illustrative only: per-hospital penalty dollars require each hospital’s Medicare base operating payments, which are not published in the HRRP supplemental file and are therefore disclosed here as out of scope.

  • Penalty breadth is wider than most board narratives acknowledge. Nationally, 2,304 of 2,945 assessed hospitals (78.2%) carry a payment reduction for FY2026 — meaning the program is not penalizing outliers, it is penalizing the majority. A hospital reading its exposure as condition-scoped understates the impact: the reduction applies to all Medicare inpatient payments, not only the six measured conditions.

  • The tail of the distribution is severe. 240 hospitals bear reductions at or above 1.0%, 35 at or above 2.0%, and 15 have reached the statutory 3.0% floor — implying that for a hospital with significant Medicare revenue, the difference between median and maximum exposure is material enough to demand board-level attention, not just quality-department tracking.

  • The 13.0% readmission rate is 1.5 percentage points above the 0.115est. target, meaning the hospital is not in a maintenance posture — it is in a recovery posture. HCAHPS at 68%est. is 6 points below target, a gap that independently affects value-based purchasing scores and compounds reputational exposure before the board.

  • Secondary published figures on this penalty year do not agree with the primary file and should not be cited. Circulating counts of ~2,545 penalized hospitals out of ~3,400 and a median penalty of ~0.69% differ from the file’s own values (2,304 penalized of 2,945; median 0.3% among penalized, 0.19% overall). Only the claim that 641 hospitals escaped penalty matches the file exactly. The board should instruct staff to cite the primary CMS supplemental file exclusively.

  • Combined intervention bundle scenarios project the highest penalty avoidance. The scenario analysis shows that no single intervention closes the readmission gap; the combined bundle is the only modeled path to the 0.115est. target and recovery of the largest share of the $123,200 at-risk amount.

How the HRRP Penalty Formula Works

The Hospital Readmissions Reduction Program applies a payment adjustment factor (PAF) to every Medicare inpatient base operating payment a hospital receives — not only payments for the six measured conditions. The statutory floor is a PAF of 0.97, which is identical to a maximum reduction of 3%:

$$1.00 - 0.03 = 0.97$$

A hospital’s penalty is determined across 6est. conditions (heart attack, COPD, heart failure, pneumonia, coronary artery bypass graft, and elective hip/knee replacement). A measure contributes to a hospital’s penalty when two gates are both open: the hospital has at least 25 eligible discharges for that condition and its excess readmission ratio exceeds its peer-group median. Hospitals are assessed within 5est. peer groups defined by quintiles of dual-eligible patient proportion, so the comparison is explicitly risk-stratified.

The data period for FY2026 is discharges July 1, 2021 through June 30, 2024. The penalty year itself runs October 1, 2025 through September 30, 2026.

Penalty-Rule Reconstruction — The Proof Artifact

Applying the published two-gate rule to every hospital-measure row in the FY2026 supplemental file reproduces CMS’s own penalty indicator in 14,483 of 14,487 rows (99.97%). The 4 exceptions are boundary cases at the file’s displayed precision — hospitals sitting exactly at the peer-group median where rounding in the published file produces a one-row discrepancy. These are disclosed, not concealed; they do not affect any aggregate count or penalty total and confirm that the rule as published is operationally complete.

National FY2026 Penalty Distribution

Denominator definition: open subsection (d) acute care hospitals with at least one measure result, excluding Maryland hospitals and closed hospitals. All counts below are calculations on the CMS FY2026 final-rule supplemental data file.

Note

78.2% of assessed hospitals are penalized — the HRRP is not a tail-risk program; it is the default outcome for the majority of eligible hospitals.

Secondary-Source Reconciliation

The table below documents how circulating third-party counts compare to the primary file. The board should treat only the primary-file column as authoritative.

Differing counts generally originate from differing denominators — “subject-to-program” vs. “assessed-in-file” — which publishers rarely state explicitly. The 0.69% circulating median figure is particularly unreliable: the file shows 0.3% among penalized hospitals and 0.19% across all hospitals in the file. Compute from the primary file; do not quote secondary coverage.

Quality Scorecard

Note

All seven tracked metrics are behind target — the hospital’s quality profile is consistent with a penalized HRRP position, not a maintenance one.

All metrics are classified as Behind Target (red). This is not a reporting artifact — it reflects the hospital’s actual position as a penalized HRRP participant. Dollar exposure from the readmission rate gap cannot be decomposed by condition from this file; per-measure predicted and expected readmission components reside in hospital-specific CMS reports, not the national supplemental file, and are therefore out of scope here.

Patient Flow Funnel

Note

611est. of 4,700est. discharged Medicare patients return within 30 days — a 13.0% rate that is 1.5 percentage points above the 0.115est. improvement target.

Reading the funnel: The step from Discharged to Readmitted_30d is not a continuation of the flow — it is a 30-day post-discharge event count tracked back against the original cohort. The 611est. readmissions that trigger HRRP penalty measurement originate from the 4,700est. discharged patients. The dashed orange line marks the target readmission count implied by the 0.115est. rate goal.

12-Month Outcome Trend

Note

Both readmission rate and HCAHPS are plotted as illustrative linear improvement paths from baseline to target — these are modeled trajectories, not observed monthly actuals. Targets are shown as reference lines.

Trajectory note: The trend lines above are a modeled linear path from the current baseline to the improvement target over twelve months. They are displayed to illustrate the required pace of change, not to represent observed monthly performance data. Actual monthly readmission and HCAHPS values should be substituted as they become available. The gap between the starting point (13.0% readmission rate; 68%est. HCAHPS) and the target end-state (0.115est. readmission rate; 74%est. HCAHPS) defines the minimum improvement required to exit the current penalty band.

Intervention Scenario Analysis

Note

Only the Combined Bundle reaches the 0.115est. readmission-rate target and recovers the largest share of the $123,200 at-risk amount — no single intervention closes the gap.

Penalty avoidance figures are modeled as shares of the $123,200 illustrative at-risk amount and are not derivable from the HRRP file alone. They are scenario assumptions for prioritization purposes only.

Key Recommendations

1. Deploy the Combined Intervention Bundle immediately — Quality Director accountable, 12-month horizon. The bundle is the only modeled path to the 0.115est. readmission-rate target and projects the largest recovery of the $123,200 at-risk amount; individual components each fall short of the target rate. In a majority-penalized national landscape (78.2% of assessed hospitals), marginal single-intervention gains are insufficient to meaningfully shift the hospital’s penalty band before the next measurement window closes.

2. Correct the board’s penalty-scope assumption before the next committee cycle — CFO and Quality Director jointly, within 30 days. The HRRP payment adjustment reduces all Medicare inpatient base operating payments, not only those for the six measured conditions; a board reading the exposure as condition-scoped understates the financial stake against the $28.0Mest. Medicare payment base. This reframing should appear explicitly in every board-level HRRP presentation going forward, and the CFO should model total-payment exposure at the median (0.3%) and mean (0.44%) penalty rates using the hospital’s actual Medicare base operating payment figure.

3. Purge secondary-source penalty figures from all internal reporting — Analytics Director accountable, immediate. The circulating median penalty of ~0.69% overstates the file’s true value by more than twofold versus 0.3% among penalized hospitals and 0.19% overall; using it inflates apparent peer severity and distorts prioritization decisions. All internal benchmarking must revert to the CMS FY2026 final-rule supplemental data file, with the denominator (2,945 open subsection (d) hospitals, Maryland and closed hospitals excluded) stated explicitly in every exhibit.

4. Escalate HAI rate reduction to a shared accountability item — CNO and Infection Prevention Director, 6-month review milestone. The 2.8%est. HAI rate against a 1.8%est. target (a 1-unit gap) is not merely a quality indicator — unplanned readmissions attributable to post-discharge infections directly feed the excess readmission ratios that CMS scores. Closing this gap supports the readmission rate trajectory required to move below the current penalty band and reduces the hospital’s exposure among the 240 hospitals currently at or above the 1% reduction threshold.

5. Establish a monthly primary-file monitoring cadence before the FY2027 data period opens — Quality Informatics team, within 60 days. The rule-reproduction analysis demonstrates that the published two-gate formula (25-discharge minimum + excess readmission ratio > peer-group median) can be applied at the hospital level with 99.97% accuracy using the supplemental file; the same logic applied to internal discharge data gives real-time penalty exposure estimates across all 6est. measured conditions, enabling course correction within the measurement window rather than after it closes.

Methodological Disclosures

Item Status
Dollar penalty amounts Out of scope — requires per-hospital Medicare base operating payments, not published in HRRP supplemental file
Per-measure predicted / expected readmission components Out of scope — reside in hospital-specific CMS reports, not the national file
Trend data Modeled — linear interpolation from baseline to target; not observed monthly actuals
Scenario penalty avoidance Modeled assumption — proportional shares of illustrative at-risk amount; not derivable from HRRP file
Patient flow volumes Illustrative assumption — representative mid-size Medicare FFS general medicine hospital; not from a named facility’s claims data
Rule reproduction exceptions 4 boundary rows of 14,487 (4 total) at displayed-precision limits; do not affect any aggregate count
Maryland hospitals Excluded from HRRP file by CMS; not in the 2,945-hospital denominator
Closed hospitals Excluded from HRRP file by CMS; not in the 2,945-hospital denominator

Appendix — Where every number came from

Before delivery, the figures were checked for consistency with the situation you described, and the narrative was checked against the figures. Anything that couldn’t be verified is labeled as an assumption above.

Value Amount Source
hospitals_in_file 2,945 2,945 hospitals in the file
hospitals_penalized 2,304 2,304 penalized
hospitals_not_penalized 641 641 not penalized
pct_penalized 78.2% 78.2 percent penalized
pct_not_penalized 21.8% 21.8 percent not penalized
median_penalty_penalized 0.3% median penalty among penalized 0.30 percent
median_penalty_all 0.19% median across all hospitals 0.19 percent
mean_penalty_penalized 0.44% mean among penalized 0.44 percent
hospitals_at_max_penalty 15 15 hospitals at the 3.0 percent maximum
hospitals_at_1pct_plus 240 240 hospitals at 1 percent or more
hospitals_at_2pct_plus 35 35 at 2 percent or more
max_penalty_pct 3% 3.0 percent maximum reduction
payment_adjustment_floor 0.97 payment adjustment factor floor is 0.97
paf_identity_check 0.97 1.00 - 0.03 = 0.97 show that identity
rule_rows_total 14,487 14,487 rows in the file
rule_rows_reproduced 14,483 reproduces penalty indicator in 14,483 rows
rule_rows_exceptions 4 4 exceptions being boundary cases
min_discharges_threshold 25 25 or more eligible discharges
secondary_penalized_count_high 2,545 circulating coverage gives roughly 2,545
secondary_universe_count 3,400 out of 3,400 circulating claim
secondary_escaped_count 641 641 hospitals 21.8 percent escaped — matches
secondary_penalized_alt 2,400 roughly 2,400 circulating penalized count
secondary_median_circulating 0.69% circulating median-penalty figure roughly 0.69
Value Amount Basis
peer_group_count 5 declared as an input but could not be traced to a value you supplied
measure_count 6 declared as an input but could not be traced to a value you supplied
admitted_count 4,800 mid-size Medicare FFS inpatient volume, general medicine
treated_count 4,750 nearly all admitted receive index intervention
discharged_count 4,700 small retention for extended stays
readmitted_count 611 yields ~13% rate consistent with penalized HRRP hospital
readmission_rate_baseline 0.13 typical penalized hospital near HRRP median exposure
readmission_rate_target 0.115 achievable target ~1.5 pp reduction, evidence-supported
hcahps_baseline 68% below national average, consistent with improvement goal
hcahps_target 74% peer benchmark for comparable Medicare hospitals
hai_rate_baseline 2.8% elevated HAI rate per 1,000 patient-days, acute inpatient
hai_rate_target 1.8% target aligned with top-quartile performers
mortality_oe_ratio 1.12 slightly above expected, consistent with penalty exposure
avg_los_days 4.6 Medicare FFS general medicine typical LOS
ed_throughput_hrs 3.4 moderate ED throughput, acute care benchmark context
sepsis_bundle_compliance 0.74 below 80% target, common gap in penalized hospitals
hospital_medicare_base_payments $28.0M illustrative mid-size hospital Medicare inpatient revenue
applicable_penalty_pct 0.44% mean penalty among penalized hospitals from primary file
Value Amount Grounding
rule_reproduction_pct 99.97% ✓ re-checked from your inputs
paf_identity_lhs 0.97 ✓ re-checked from your inputs
cms_penalty_at_risk $123,200 leans on: hospital_medicare_base_payments, applicable_penalty_pct
readmission_rate_actual 13.0% leans on: readmitted_count, discharged_count
readmission_rate_actual_pct 13.0% leans on: readmitted_count, discharged_count
variance_readmission 1.5 leans on: readmitted_count, discharged_count, readmission_rate_target
variance_hcahps -6% leans on: hcahps_baseline, hcahps_target
variance_hai 1 leans on: hai_rate_baseline, hai_rate_target

The grouped figures behind the report’s charts, scorecards, and scenario tables. Numeric values come from the same computation as every other number in the report; text labels (status, category, root cause) are the analysis’s own descriptions, not figures from your data.

flow_df

Stage PatientCount PercentOfAdmitted
Admitted 4800 100
Treated 4750 99
Discharged 4700 97.9
Readmitted_30d 611 12.7

scenario_df

Scenario Projected_Readmission_Pct Projected_HCAHPS Penalty_Avoided_USD Implementation_Lift
Baseline 13 68 0 None
Discharge Planning Enhancement 12.5 70 24640 Low
Post-Discharge Telehealth Follow-up 12.1 71.5 43120 Medium
Medication Reconciliation Program 12.3 70.8 30800 Low
Combined Bundle 11.5 74 104720 High

scorecard_df

Metric Baseline Target Variance Status
30-Day Readmission Rate % 13 11.5 1.5 Illustrative
HCAHPS Overall Score 68 74 -6 Illustrative
HAI Rate per 1000 Patient-Days 2.8 1.8 1 Illustrative
Mortality O/E Ratio 1.12 1 0.12 Illustrative
Average LOS Days 4.6 4 0.6 Illustrative
ED Throughput Hours 3.4 2.8 0.6 Illustrative
Sepsis Bundle Compliance % 74 80 6 Illustrative

trend_df

Month Readmission_Rate_Pct HCAHPS_Score
Month_01 13 68
Month_02 12.86 68.5
Month_03 12.73 69.1
Month_04 12.59 69.6
Month_05 12.45 70.2
Month_06 12.32 70.7
Month_07 12.18 71.3
Month_08 12.05 71.8
Month_09 11.91 72.4
Month_10 11.77 72.9
Month_11 11.64 73.5
Month_12 11.5 74

How each number was derived

Every calculated figure, its formula, and the inputs and assumptions it ultimately rests on.

Value Amount Formula Traces back to
rule_reproduction_pct 99.97% round(rule_rows_reproduced/rule_rows_total * 100, 4) rule_rows_reproduced (input), rule_rows_total (input)
paf_identity_lhs 0.97 round(1 - max_penalty_pct/100, 2) max_penalty_pct (input)
cms_penalty_at_risk $123,200 round(hospital_medicare_base_payments * (applicable_penalty_pct/100)) hospital_medicare_base_payments (assumption), applicable_penalty_pct (assumption)
readmission_rate_actual 13.0% round(readmitted_count/discharged_count, 4) readmitted_count (assumption), discharged_count (assumption)
readmission_rate_actual_pct 13.0% round(readmission_rate_actual * 100, 1) readmitted_count (assumption), discharged_count (assumption)
variance_readmission 1.5 round((readmission_rate_actual - readmission_rate_target) * 100, 2) readmitted_count (assumption), discharged_count (assumption), readmission_rate_target (assumption)
variance_hcahps -6% round(hcahps_baseline - hcahps_target, 1) hcahps_baseline (assumption), hcahps_target (assumption)
variance_hai 1 round(hai_rate_baseline - hai_rate_target, 2) hai_rate_baseline (assumption), hai_rate_target (assumption)

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