Do bed-day shortages mean we need to build more bed capacity? Maybe not

As patients nationwide increasingly opt to seek care at large urban academic health systems (AMCs) recognized for their availability of specialized care and a higher quality of care, AMCs in the nation’s largest cities are beginning to experience significant bed-day shortages. In response, they are proposing to build new bed-day capacity. But simply building more beds, while tempting, may not be the best strategy. Our recent research demonstrates that a better strategic focus might be on reducing quality-of-care failures that directly impact bed-day utilization.

Trend shows hospitalizations on the rise

After decades of declines, total U.S. hospitalizations are projected to grow by 11% between now and 2034.^a This rise in hospitalizations — combined with functional bed shortages due to labor constraints, hospital closures and an aging U.S. population —  could create a significant inpatient bed shortage in some geographic areas. Present inpatient bed-day capacity problems are already contributing to overcrowded hospital emergency departments (EDs) and observation units, resulting in patient walk-outs from the ED, ambulance diversions and delays in diagnosis, which in turn can lead to hospital admissions of sicker patients requiring a longer average length of stay (LOS).^b

To assess the extent of this challenge and whether expanding bed capacity is the most viable strategy, we analyzed the utilization of bed capacity in eight major U.S. cities with multiple AMCs: Baltimore, Boston, Chicago, Cleveland, Los Angeles, New York, Philadelphia and Washington.

The findings of this analysis not only have important implications for the AMCs in these eight cities, but also have significant implications for health systems in smaller markets nationwide related to the migration of patients away from smaller markets to larger markets, including the eight markets included in our study.

An earlier study, for example, found a general trend of patient migration to larger, more comprehensive hospitals.^c The study found that in North Carolina, for instance, there has been a migration of patients to the 15 largest urban and suburban hospitals, leaving the other 91 hospitals to struggle for patient volume. The research determined that the migration is motivated in part by patients’ desire for access to specialized and higher quality care.

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Warning signs from Boston

The study’s findings for Boston are particularly noteworthy. In Boston, the inpatient bed shortage is being called a “capacity disaster,”  with urgent calls for construction of additional new inpatient beds.^d Evaluating bed capacity requirements is difficult not only because Boston has multiple major AMCs but also because the patient population served is complex, with a high volume of patients being referred from locations outside of the city, which makes city population growth estimates less useful for bed capacity planning. This reality is corroborated by the Massachusetts Health Policy Commission, which has characterized the state’s healthcare market as being dominated by large AMCs that attract patients from across the state and beyond.”^e

Far from being isolated, Boston’s situation may offer a “canary in a coal mine” warning of impending significant bed capacity shortages in cities with major AMCs. Aggressive marketing by by AMCs is likely to further increase demand for inpatient care in cities with these hospitals. For example, NewYork-Presbyterian’s Emmy Award-winning “Stay Amazing” ad campaign encourages patients to be proactive about their healthcare by seeking care at NewYork-Presbyterian.^f

Challenges to building more beds, and alternatives

Regulators will inevitably question whether building more bed capacity is necessary or whether hospital bed capacity shortages can be addressed by making better use of existing bed capacity. Since 2000, hospital prices have increased at twice the rate of overall medical care and triple the rate of inflation.^g Building new hospital bed capacity is extremely costly and could exacerbate this cost spiral.

Moreover, proposals to expand bed capacity would likely encounter intense regulatory scrutiny. A goal of the study therefore was to illustrate the type of justification regulators might seek when evaluating expansion of bed-day capacity. For this purpose, the study performed the following analyses regarding the bed-day utilization of the major AMCs in the eight cities:

• Bed-day utilization patterns compared with national risk-adjusted LOS benchmarks
• Extent of excess bed-day utilization in each city
• Rate of negative outcomes that directly impact bed-day utilization — such as inpatient complications — compared with national risk-adjusted negative outcome rates
• A comparison across the eight cities of the bed-day capacity impact of excess bed-day utilization and excess negative outcomes

Study findings and implications

The following are the key findings of these analyses.

1 Expected bed-day utilization. Different types of patients were defined using the all-patient refined diagnosis-related groups (APR DRG) patient classification system^, which differentiates patient types according to diagnosis (e.g., a heart failure  patient undergoing coronary bypass surgery) based on four severity-of-illness levels (i.e., minor, moderate, major or extreme).^h For each city, the risk-adjusted expected bed-day utilization is the number of bed days that would occur if the city’s hospitals  had an average LOS for each type of patient (each APR DRG) equal to the corresponding national average LOS. (i.e., the product of the city’s number of patients in each APR DRG multiplied by the corresponding national average LOS and summed over all APR DRGs).ⁱ For example, Boston has an actual Medicare average length of stay 6.09 days but would have an average LOS of 5.22 days if its case mix of patients were treated at the national average LOS by APR DRG, meaning Boston has a 16.6 percent higher average LOS than expected. If the city’s actual number of bed days were to exceed the expected number of bed days, the difference represents the amount bed days in the city could potentially be reduced.

As shown in the exhibit below, there are 270,246 excess LOS bed days (above the risk-adjusted expected bed days). The percentage of total bed days that are excess bed days in the eight cities is 10.8%,  ranging from 19.5% for Washington to 1.2% for Chicago. The wide variation in the proportion bed days that are excess bed days suggests that in some cities there is an opportunity to significantly lower bed-day utilization.

Excess length of stay (LOS) bed days across the 8 U.S. cities

2 Negative outcomes that impact bed-day utilization. The study also considered the extent to which the excess bed days could be countered through process improvements that can reduce inpatient bed utilization. There are many such measures, including:

• Improved surgical scheduling, especially for elective surgeries
• Improved management and financial incentives for hospitalists
• Improved coordination with post-acute care facilities

Process improvement efforts also can be supplemented with efforts to avoid unnecessary hospital admissions and reduce negative outcomes from quality failures that directly and measurably affect bed-day utilization. Identifying negative outcomes that potentially could have been avoided can provide targeted and actionable information on interventions that can reduce excess bed days while also improving patient safety and experience.

The study highlighted four such potentially avoidable negative outcomes:

• Excess 30-day readmissions back to the same hospital
• Excess inpatient complications
• Underuse of outpatient surgery
• Excess medical admissions through the ED

Evaluations of each outcome were limited to patients for whom the negative outcome could reasonably be considered potentially avoidable. It is unlikely that bed-day utilization can be reduced for patients whose clinical circumstances are beyond a hospital’s control, such as readmission for an appendectomy following orthopedic surgery.

The study used available systems for identifying patients with a potentially avoidable inpatient complication  or readmission.^j Underuse of outpatient surgery was determined based on 27 categories of that were routinely being performed in both an inpatient and outpatient setting  Patients at high severity at admission or who had multiple distinct procedures were not consider candidates to have the procedure performed as an outpatient .^k Excessive medical (non-surgical) admissions from the ED were determined based on conditions such a upper respiratory infections for which outpatient care is deemed a viable option. Patients who were at high severity at admission or who had an LOS greater than three days were excluded.

3 Negative outcome risk-adjusted benchmark. A national benchmark rate for each negative outcome was calculated as the average rate of occurrence for each type of patient (each APR DRG) for the subset of patients at risk of a potentially avoidable negative outcome. Because major teaching hospitals generally have better quality of care than other hospitals, it is reasonable to expect that the  negative outcome rate should be lower in these hospitals than in the national average rate.^l Therefore,  hospitals were ranked based on actual versus expected negative outcomes performance. The subset of hospitals with the best performance that constituted 80% of all admissions were then used to set the benchmark rate for each negative outcome.  

For each negative outcome in each city, the risk-adjusted expected number of negative outcomes is the number of negative outcomes that would occur if the hospitals in the city had a negative outcome rate for each type of patient (each APR DRG) equal to the benchmark rate. (i.e., the product of the city’s number of patients at risk for the potentially avoidable negative outcome in each APR DRG multiplied by the corresponding benchmark negative outcome rate summed across all APR DRGs). If the actual number of negative outcomes exceeds the expected number of negative outcomes, the difference provides a measure of the reduction in number of negative outcomes that could be achieved.

4 Bed-day impact of negative outcomes. With each negative outcome, the number of bed days increases. A bed-day conversion factor is needed to convert differences in negative outcome volume within each APR DRG into bed days. For inpatient complications, the bed-day conversion factor is the average LOS difference between patients with and without a complication. The bed-day conversion factor for the other negative outcomes is the average LOS of patients with the negative outcome (e.g., ALOS of patients who were readmitted). Multiplying the difference between the actual and expected number of negative outcomes in a city by the corresponding bed-day conversion factor determines the bed-day impact of a negative outcome.

As shown in the exhibit below, 113,523 bed days are associated with an excess volume of negative outcomes (42.0% of the excess 270,245 LOS bed days).

Bed-day impact of excess negative outcomes across the 8 cities

City	Negative outcome bed-day impact	Percentage of excess length-of-stay bed days
Washington	5,615	14.1%
Baltimore	8,559	16.3%
Manhattan	30,068	39.5%
Boston	30,158	59.7%
Cleveland	7,886	49.9%
Philadelphia	11,378	60.4%
Los Angeles	8,685	66.4%
Chicago	11,174	329.4%
Total	113,523	42.0%

The 113,523 bed-day impact comprises:

• 41.1% from readmissions
• 22.2% from inpatient complications
• 19.1% from underuse of outpatient surgery
• 17.6% from excess medical admissions from the ED

In all eight cities there was a net excess number of bed days associated with the four negative outcomes. Chicago is the one city where the bed-day impact of the negative outcomes shown above (11,174) exceeded the excess LOS bed days (3,390, shown in the first table), with the former being 329.4% greater than the city’s excess LOS bed days.

The estimated negative outcome bed-day impact of 113,523 bed days is conservative because it only   includes bed days associated with a higher-than-expected number of negative outcomes. Assuming an 85% occupancy rate, the elimination of 113,523 negative outcome bed days in the eight cities is equivalent to increasing hospital bed capacity by 366 beds.^m Targeted interventions that address potentially avoidable negative outcomes will not only reduce excess bed-day utilization but also simultaneously improve patient safety and experience.

Conclusion and takeaways for healthcare executives

Although the eight cities with multiple AMCs had excess LOS bed-day utilization, there is substantial variation across cities in the magnitude of the excess LOS bed days. Moreover, that variation is in part related to differences in rates of potentially avoidable negative outcomes among the AMCs. This suggests that, in lieu of building more beds, the organizations might be better served by focusing on improving bed-day utilization and reducing avoidable negative outcomes.

To begin to develop strategies for responding to the trends outlined here, hospital and health system executives should take the following steps:

• Evaluate bed day and negative outcome performance compared with national standards across the patient population.
• Where there are excess bed days, use negative outcome performance to initiate targeted interventions that can lower bed-day utilization.
• In smaller hospitals, if there is a decline in bed days due to patient migration to larger markets, consider strategies to maintain patient volume such as greater use of telehealth, rapid transport options like helicopters and rotating specialists.

Footnotes

a. Leuchter, R., et al., ”Projected U.S. hospital bed shortage and associated excess mortality: 2024-2034,” Abstract 128 published at SHM Converge 2024, Journal of Hospital Medicine, Aug. 10, 2025.
b. Pines, J., “ER waits for hospital beds are deadly. Many hospitals aren’t fixing the problem.” Forbes, March 13, 2024; and Salway, R., “Emergency Department (ED) overcrowding: Evidence-based answers to frequently asked questions,” Revista Médica Clínica Las Condes, ScienceDirect, March-April 2017.
c. Ullrich, C.G., “Patient migration trends impacting hospitals, physicians, communities, and the state medical facilities plan,” presentation, North Carolina State Health Coordinating Council, March 4, 2020.
d. McGrath, C., and Niezgoda, A., “Amid capacity crisis, Massachusetts General Hospital asks state for more beds to ease waits,” NBC Boston 10, Jan. 19, 2024.
e. Health Policy Commission, Commonwealth of Massachusetts, Community hospitals at a crossroads: Finding from an examination of the Massachusetts Health Care System, March 2016.
f. “NewYork-Presbyterian’s ‘Stay Amazing’ commercial wins an Emmy Award,” NewYork Presbyterian Health Matters, Oct. 10, 2022.
g. “How hospitals inflate America’s giant health-care bill,” The Economist, March 20, 2025.
h. Averill, R.F., et al., “A closer look at All-Patient Refined DRGs,” Journal of AHIMA; and 3M Health Information Systems, 3M all patient refined diagnosis related groups: Methodology overview, October 2024.
i. For the analysis, the study used CMS data from the 2019 Medicare Standard Analytic Files (Limited Data Set) as a basis for identifying the number of bed days (see the sidebar “How the study cities were identified for analysis” below).
j. Hughes, J.S., et al., “Identifying potentially preventable complications using a present on admission indicator,” Health Care Financing Review, Spring 2006; and Goldfield, N.I., et al., “Identifying potentially preventable readmissions,” Health Care Financing Review, Fall 2008.
k. Averill, R.F., et al., The shift to outpatient surgery: Geographic variation and site-neutral payments, 3M Clinical and Economic Research, September 2021; see below for a list of these 27 procedure categories.
l. Burke, L.G., et al., “Association between teaching status and mortality in US hospitals,” JAMA, May 23/30, 2017.
m. National Guideline Centre (UK), “Bed Occupancy,” Chapter 39, Emergency and acute medical care in over 16s: service delivery and organisation, NICE Guideline, No. 94, March 2018.

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