The Mid-Size Independent Hospital Has a Cash Velocity Problem

What the Aggregate Number Hides

The Q1 2026 reference report established the headline finding: median Days in AR for independent PPS hospitals reached 92.7 days in FY2024, against an HFMA-cited well-managed benchmark of 35–50 days. The 75th percentile sat at 147.5 days.

Those aggregate figures describe the cohort at a population level. They do not describe what is happening inside it. When the same cohort is segmented by hospital size — measured by total beds reported on Worksheet S-3 — a different picture emerges. The mid-size independent hospital, in the 100–199 bed range, is operating with materially worse cash velocity than either the smallest or the largest hospitals in the cohort.

The Size Distribution

Of the 1,114 contact-level cohort assignments examined in the reference report, the dataset includes 879 unique hospitals after deduplication on CMS Certification Number. Of those, 683 hospitals reported total bed counts on Worksheet S-3 sufficient for size segmentation. The size distribution:

The sub-50 bed bucket is the largest segment by hospital count — independent PPS hospitals operating at small scale, with median net patient revenue near $30M. The mid-size buckets (50–99, 100–199) and the larger hospitals (200–299, 300+) represent a smaller share of the cohort but a disproportionate share of total revenue and bad debt exposure.

Days in AR by Bed Size: The Mid-Size Pattern

Three findings stand out.

1. The 100–199 bed bucket is the most distressed cash-velocity segment in the cohort. Median Days in AR of 113.4 days is more than 20 days above the cohort median of 92.7. The 75th percentile of 242.3 days indicates that one in four mid-size independent hospitals is carrying receivables beyond eight months. The four-year deterioration of +28.8 days is the largest absolute worsening of any size bucket — these hospitals are not stable at a poor level; they are moving worse, fast.

2. The 50–99 bed bucket is the only segment with a median below the cohort aggregate. Median DAR of 76.3 days, with a 6.7-day improvement over four years. This is a small subset (n=57) of independent PPS hospitals operating at the scale where revenue cycle infrastructure is large enough to be functional but small enough to be coherent. The 75th percentile of 217.3 days indicates significant within-bucket variation — the median tells one story; the worst-performing quartile is still in the same distressed range as the rest of the cohort.

3. The 300+ bed bucket has held steady, not improved. Median DAR of 88.7 days, essentially flat over the four-year window (−0.8 days). These are the largest independent PPS hospitals in the cohort — academic medical centers, large community systems, and high-volume independents with median net patient revenue near $780M. They have the scale and the staffing to maintain cash velocity; they have not improved it.

The pattern, taken together: cash velocity in the independent PPS cohort is not a uniform problem. It is concentrated in the 100–199 bed segment, where the operational scale exceeds the revenue cycle infrastructure, and where the four-year trajectory is straight deterioration.

PC Ratio by Bed Size: An Inverse Pattern

The size relationship inverts when the metric shifts from cash velocity to collection rate. Sub-50 bed independent PPS hospitals collect a median of 49.5 cents per gross charge dollar — more than twice the rate of the 300+ bed bucket at 22.6 cents.

The mechanism is well understood: chargemaster strategy. Larger hospitals — particularly academic medical centers and large community independents — operate aggressive chargemasters with high gross charges and substantial contractual adjustments. The PC ratio compresses as a result, but the dollars-per-encounter still rise with the gross charge level. Smaller hospitals with simpler service mixes maintain higher PC ratios because the charge-to-collection gap is narrower.

What this means for a CFO reading the data: PC ratio comparisons across size buckets are not apples-to-apples. The diagnostic value of PC ratio is within-bucket — comparing your 150-bed hospital against the 0.237 median for 100–199 bed independents — not against the aggregate cohort median or against larger or smaller comparators.

Operating Margin and Bad Debt by Bed Size

Median operating margin worsens with hospital size up through the 200–299 bed bucket (−15.3%) before improving at 300+ beds (−7.7%). The largest hospitals carry the highest absolute bad debt — $41M median — but their proportionally lower margin distress reflects their scale advantages: payer mix diversification, ancillary revenue, and access to capital.

The 100–199 bed bucket sits in the middle on margin but is the most distressed on cash velocity (Exhibit 1). The interaction of moderate margin distress with severe cash velocity distress is what defines this segment’s specific operational risk: the hospital does not have a margin large enough to absorb the working capital strain of 113-day AR.

Why the 100–199 Bed Pattern Exists

The mid-size independent PPS hospital sits at a structural inflection point in the revenue cycle:

Operationally complex enough to require enterprise revenue cycle infrastructure. Volume, payer mix complexity, and service line diversity at 100–199 beds typically require denial management workflows, prior authorization staffing, contract management, and AR follow-up at a sophistication level closer to a large system than a small community hospital.

Not large enough to staff that infrastructure independently. A 150-bed independent typically does not have the revenue base to fully fund a senior revenue cycle leadership team, dedicated denial management infrastructure, and contract analytics capability that a 500-bed system can. The functions exist; they are typically under-resourced.

Without system support to backfill the gap. A 150-bed hospital inside a multi-hospital system shares revenue cycle infrastructure with sister facilities. A 150-bed independent does not. The combination of operational complexity, finite revenue base, and no shared services backfill is what produces the 113-day median DAR and the 28-day four-year deterioration.

This is the structural pattern. Whether it applies to a specific 150-bed independent hospital depends on the hospital’s actual revenue cycle staffing, technology, and contract management capability — but the cohort-level pattern points to a clear segment risk.

What This Means for CFOs

For CFOs of independent PPS hospitals in the 100–199 bed range, three diagnostic questions:

1. How does your Days in AR compare to the 113.4-day median for your size bucket — not to the 92.7-day cohort aggregate? A CFO benchmarking against the cohort median is comparing against a population that includes 405 sub-50 bed hospitals operating at a different revenue cycle scale. The relevant comparator is within size bucket.

2. What has your Days in AR done over the last four years? The 28.8-day cohort deterioration in the 100–199 bed bucket is severe. A hospital that has held flat or improved over the same window has identified something the rest of its size segment has not. A hospital that mirrors the cohort trajectory is sliding with the segment.

3. Is your revenue cycle infrastructure sized to your operational complexity, or to your revenue base? This is the structural question. The 100–199 bed independent that staffs revenue cycle to its revenue base will systematically under-resource the function relative to the operational complexity it carries. The hospitals in this segment that improved against the trend almost certainly did so by treating revenue cycle infrastructure as a fixed cost of operating at their complexity level — not as a variable cost of their revenue scale.

Methodology

Data source: CMS Healthcare Cost Report Information System (HCRIS), FY2021–FY2024 public filings. Bed counts derived from Worksheet S-3 total bed reporting. Days in AR, PC ratio, operating margin, and bad debt definitions follow the reference Q1 2026 report.

Population: 879 unique independent PPS hospitals deduplicated by CMS Certification Number from the 1,114 contact-level cohort assignments in the reference report. Of those, 683 hospitals reported total bed counts on Worksheet S-3 in at least one of FY2021–FY2024 and are included in this analysis. 196 hospitals were excluded because of missing or invalid bed count data — exclusion is not random with respect to hospital size; the missing data is concentrated in smaller and more recently filed cost reports.

Bed-size buckets: Sub-50, 50–99, 100–199, 200–299, 300+. Buckets selected to align with how independent hospital CFOs typically describe their organizations and how revenue cycle infrastructure scales.

Limitations: This is an analysis of contemporary financial performance, not of causal mechanisms. The structural reasons offered for the 100–199 bed pattern are drawn from cost-report consulting literature and field experience, not from a causal model. A hospital-specific assessment requires reading that hospital’s cost report, payer mix, denial data, and revenue cycle staffing structure.

Reference report: Revenue Cycle Distress at Independent U.S. Hospitals, Q1 2026 (Synergize AI). All cohort definitions and aggregate benchmarks are consistent with that report.