California Hospital-Level Operational, Workforce, and Utilization Factors Associated with Financial Performance in Pulmonary Function Services

Abstract 

Pulmonary function services are essential to the diagnosis and management of respiratory disease, yet limited research has examined the workforce and operational factors associated with their financial performance. This study analyzed California hospital-level data during the years 2023 to2024 using five multiple linear regression models to evaluate the relationships among workforce composition, operational volume, service utilization, and financial outcomes in pulmonary function services. Overall model performance demonstrated moderate to strong explanatory power (R² = 0.626-0.919), indicating that the selected variables accounted for a substantial proportion of the variation in departmental outcomes. Collectively, the findings suggest that staffing composition, workforce deployment, and operational volume are more influential determinants of pulmonary function service performance than institutional service characteristics alone. Further research should aim to examine whether pulmonary and critical care residency and fellowship programs are associated with workforce availability and hospital financial and operational performance.These results contribute to the limited literature on pulmonary function service operations and provide evidence to inform workforce planning, resource allocation, and departmental management. 

Keywords: Pulmonology, Hospital Operations, Hospital Workforce, Hospital Revenue, Critical Care, Fellowship

Introduction

As healthcare organizations face growing financial and workforce challenges, understanding the factors that are associated with the performance of pulmonary function services is increasingly important for evaluation. Prior research examines workforce productivity, healthcare finance, and operational efficiency, yet little is known about how these factors might affect the financial performance of pulmonary function services.1 Thus, the purpose of this study is to examine the relations between workforce, operational, utilization, and financial measures at the hospital-level in California during the 2023 and2024 fiscal years. Findings in this study help with understanding the factors associated with the performance of pulmonary function services and this information may assist leaders with making informed decisions around staffing, resource allocation, and operational management. 

Literature Review

Schnapp et al. (2020) argued that it was critical for faculty in Pulmonology and Critical Care divisions to understand the finances of Academic Medical Centers.2 Mahajan et al. (2020) further reinforced the finding that many physicians lack the foundational economic and basic knowledge needed to effectively participate in discussions about healthcare and finance. Importantly, is that key concepts like adjusted gross revenue, direct and indirect costs, contribution margin, and total revenue have been emphasized for advancement.1 Ip et al. (2016) analyzed utilization rates, capacity constraints, and operation efficiency on a smaller scale in an academic laboratory setting.3 They further highlighted the significance of utilizing operational data to identify causes of poor access, guide system decision-making, and to determine the effects of improvement initiatives in a variety of healthcare settings. However, it was found by Miller et al. (2025) that staffing in respiratory care areas will have substantial implications for consideration in the long-term.4

Next, on a large teaching hospital scale in China, Li et al. (2019) examined the full-time staffing allocations, cost-effectiveness, generated revenue, personnel expenses, and operational productivity.5 They further reported that optimal staffing ratios consist of 20–25 pulmonary function tests per staff member each day, with additional revenue, labor, and equipment costs. Ford (2011) emphasized the significance of workforce productivity, staffing models, labor costs, workload measurement, and flexible budgeting. It was highlighted that labor generally accounts for more than 60% of total costs in respiratory care departments.6 As respiratory care systems and hospital clinical information systems can become more fully integrated, work rate measures are likely to emerge as a more practical and widely applicable metric.These factors further underscore why managers should place greater emphasis on evaluating work rate in operational decision-making. Lastly, the implications associated with assessing the proposed research questions for this study must be further considered.

First, rather than measuring volume alone, Chatburn et al. (2021) proposed that one can measure respiratory service performance through a combination of productivity, outcomes, and utilization. Their new metric was called value-efficiency.7 This framing utilizes traditional productivity measures with an emphasis on clinical value and the resource utilization that is associated with each respiratory care activity. In the model, activity efficiency reflects how effectively labor hours are used, while activity value can also score each intervention based on evidence, utility indications, goals served, and the particular overall clinical contributions. Together, this method aligns with focusing on current value priorities by accounting for workload alongside the clinical importance of the services that can be provided.

Although prior pulmonary and respiratory care research examines staffing requirements such as pulmonary function laboratory utilization and cost-effectiveness in independent hospitals 4, few studies evaluate how workforce characteristics, service availability, and utilization was associated with pulmonary service-line revenue, expenses, and operating margins in state hospital systems.8 More recently, Johnson & Theurer (2014) have found that a stepwise method can aid spirometry, lung volumes, and diffusion capacity as a distinct pattern rather than as restrictive. Together, their framing can help clarify how clinicians are applying PFT in data forms to systematically be of use for diagnosis and management.9 For example, with standardized interpretation, it is suggested that consistency can be found around diagnostic decisions with several clinicians. In turn, this can impact patterns around financial performances when compared to pulmonary service lines and might assist with serving as a baseline for additional inquiry. Douketis et al. (2022) has also more recently found that perioperative management matters with the management of respiratory care.10

Research Design

A regression analysis was deemed an appropriate method for evaluating the relationships between operational, workforce, service, financial variables, and pulmonary function service outcomes. Regression techniques are widely utilized in quantitative research because they allow researchers to assess the magnitude, direction, and statistical significance of predictor variables while simultaneously controlling multiple factors.11 Zervos et. al. (2026) have analyzed other states such as Florida’s teaching and non-teaching hospitals financial operations.12  This study analyzed data from the California Hospital Annual Financial Disclosure Report during the Fiscal Years 2023 and 2024.13 Roach et al. (2026) analyzed data from Anesthesia earlier this year using the California Hospital Annual Financial Disclosure Report during the Fiscal Years 2023 and 2024.14 Thus, five regression models were deemed appropriate to examine the distinct dimensions of pulmonary function service performance, with the variables being grouped into the following four categories: workforce, utilization, service, and financial.

Study Population

The study population consisted of hospitals in California represented through the California Annual Financial Disclosure Report for Fiscal Years 2023 and 2024. The hospital served as the unit of analysis because the variables evaluated in this study were reported at the hospital level. This study focused on the information associated with pulmonary function services and related workforce, utilization, service, and financial measures reported by participating hospitals. The analysis was further conducted utilizing the hospital-level observations that are contained within the financial disclosure data for the particular study period.

Variables were organized into four categories based on the dimensions of pulmonary function service activity examined in the study: workforce, utilization, service, and financial measures. Categories were used to organize the independent and dependent variables across the five regression models. The variables were then derived from the corresponding fields reported in the California Hospital Annual Financial Disclosure Report and retained according to the definitions and units provided in the source data. The variables were then organized into four categories based on the dimensions of pulmonary function service activity examined in the study: workforce, utilization, service, and financial measures

The workforce categories included: 1) Active Medical Staff Hospital Based Board Certified Pulmonary Disease, 2) Active Medical Staff Non-Hospital Based Board Certified Pulmonary Disease, 3) Residents Pulmonary Disease, 4) Fellows Pulmonary Disease, 5) Productive FTEs Pulmonary Function Services, 6) Contract Hours Pulmonary Function Services, 7) Physician and Student Compensation Total Compensation Pulmonary Function Services, 8) Services and Procedures Total Pulmonary Function Services. 

Utilization variables included: 1) Procedures Total Pulmonary Function Services, 2) Units of Service Pulmonary Function Services, and 3) Summary of Revenue and Costs Units of Service Pulmonary Function Services. The service variables included: 1) Intensive Care Services Pulmonary, 2) Diagnostic / Therapeutic Services Pulmonary Function Services, and 3) Clinic Services Rural Health. The financial category included: 1) Gross Patient Revenue Total Pulmonary Function Services, 2) Summary of Revenue and Costs Net Revenue Pulmonary Function Services, 3) Summary of Revenue and Costs Adjusted Direct Expenses Pulmonary Function Services, 4) Summary of Revenue and Costs Allocated Costs Pulmonary Function Services, 5) Summary of Revenue and Costs Total Patient Care Costs Pulmonary Function Services, 6) Summary of Revenue and Costs Net Costs as Reallocated Pulmonary Function Services, and 7) Summary of Revenue and Costs Net Revenue Minus Net Costs Pulmonary Function Services.

It should be noted that hospitals were included in the analysis when they were represented in the California Hospital Annual Financial Disclosure Report for Fiscal Years 2023 and 2024 and had information necessary to construct the variables included in the regression analyses. Because this study examines pulmonary function services, observations were evaluated based on the availability of hospital-level information associated with the relevant pulmonary function service categories.

Observations were excluded from an individual regression model when they did not contain sufficient information to construct the dependent variable or the predictor variables required for that model. Thus, the number of observations could vary across the five regression models depending on the availability of the variables included in each model

RQ1: What operational and workforce factors impact the gross patient revenue in pulmonary function services?

RQ2: What factors were associated with  the net revenue in pulmonary function services?

RQ3: What workforce and utilization factors drive total patient care costs in pulmonary function services?

RQ4: What organizational factors were associated with pulmonary function service productivity?

RQ5: What factors predict positive financial margins in pulmonary function services?

Findings

The model fit for each regression was assessed using the coefficient of determination (R2), which measures the proportion of variance in the dependent variable explained by the independent variables.15 Higher R² values may still provide clinically meaningful insights when interpreted within the appropriate research framework and clinical context.16Regression analysis allows for the examination of both the overall model performance and relative contribution of individual predictors, while still controlling for the other variables in the model. The R values in this range indicate that the predictor variables account for a significant proportion of the variance in the modeled outcomes. This approach provides a robust framework for identifying the operational, workforce, and financial factors associated with pulmonary function service performance. The assumptions underlying the regression analyses were evaluated before interpreting the results. Multicollinearity among the independent variables was assessed using variance inflation factor (VIF) and tolerance statistics to determine whether correlations among predictor variables could affect the stability of the regression estimates. Linearity and homoscedasticity were also then evaluated by examining residual plots and the relationship between predicted values and residuals. The distribution of residuals was also examined to assess potential departures from normality. Potential outliers and influential observations were evaluated using appropriate diagnostic measures to identify observations that could disproportionately affect the regression estimates. The independence of observations was considered based on the hospital-level unit of analysis, with each hospital represented as an individual observation.

Several limitations should be considered when interpreting the findings. First, the study relied on data from the California Hospital Annual Financial Disclosure Report for Fiscal Years 2023 and 2024, which limits the ability to examine longer-term trends in pulmonary function service performance. Second, the use of secondary administrative and financial data limits the analysis to the variables and information available in the disclosure report. Differences in reporting practices, data completeness, and variable availability across hospitals may affect the comparability of observations. Third, because the hospital was the unit of analysis, the findings only describe relationships at the hospital level and should not be interpreted as representing individual physicians, employees, patients, or clinical encounters. Fourth, the regression analyses identify statistical relationships among workforce, utilization, service, and financial measures but do not establish causal relationships. Therefore, the findings should be interpreted as associations rather than evidence that changes in one variable directly cause changes in another. Finally, because the study focused on hospitals in California, the findings may not be directly generalizable to hospitals operating in other states or under different regulatory, financial, and organizational environments. 

Research Question 1

In the first research question, the operational and workforce factors associated with gross patient revenue (Table 1) were examined. The model explained approximately 79.1% of the variance in gross patient revenue (R2 = .791). The analysis identified Residents Pulmonary Disease, Productive FTEs Pulmonary Function Services, Fellows Pulmonary Disease, and Contract Hours Pulmonary Function Services as statistically significant predictors of gross patient revenue.

As such, assuming all other variables are constant, each additional pulmonary disease fellow was associated with an estimated increase of approximately $5.03 million in gross patient revenue. Also, each additional productive FTE was associated with an increase of approximately $961,049 in gross Revenue. Contract hours also demonstrated a positive relationship, with each additional contract hour associated with approximately $2,587 in additional gross revenue. In contrast, the pulmonary disease residents themselves were negatively associated with gross revenue, with each additional resident corresponding to an estimated decrease in gross patient revenue of approximately $5.78 million. Lastly, it must be noted that intensive care services, procedures performed, rural health clinic services, and units of service were not statistically significant predictors of gross patient revenue.

Table 1: Operational and Workforce Factors

Regression Statistics

Multiple R

0.889370798

R Square

0.790980416

Adjusted R Square

0.78718006

Standard Error

4840428.059

Observations

449

ANOVA

Df

SS

MS

F

Significance F

Regression

8

3.90121E+16

4.87651E+15

208.133238

2.5009E-144

Residual

440

1.03091E+16

2.34297E+13

Total

448

4.93212E+16

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95%

Upper 95%

Intercept

796324.8636

512761.8325

1.55301119

0.12

-211441.9202

1804091.647

-211442

1804091.647

Residents Pulmonary Disease

-5778610.483

877009.1061

-6.588997131

0.00

-7502257.975

-4054962.992

-7502258

-4054962.992

Intensive Care Services Pulmonary

-203365.228

235129.9683

-0.864905607

0.39

-665482.6417

258752.1858

-665483

258752.1858

Procedures Total Pulmonary Function Services

-1451.29695

46229.55142

-0.031393273

0.97

-92309.47592

89406.88202

-92309.5

89406.88202

Productive FTEs Pulmonary Function Services

961049.2892

73267.40804

13.11700953

0.00

817051.7147

1105046.864

817051.7

1105046.864

Clinic Services Rural Health

-683871.2993

633987.9952

-1.07868178

0.28

-1929892.361

562149.7623

-1929892

562149.7623

Fellows Pulmonary Disease

5031975.497

305088.6755

16.49348501

0.00

4432363.331

5631587.664

4432363

5631587.664

Units of Service Pulmonary Function Services

1514.982196

46229.49146

0.032770903

0.97

-89343.07894

92373.04333

-89343.1

92373.04333

Contract Hours Pulmonary Function Services

2587.381032

1054.48722

2.453686477

0.01

514.9233586

4659.838705

514.9234

4659.838705

Research Question 2

In the second research question, factors associated with net revenue in pulmonary function services were examined (Table 2). The model explained approximately 84.9% of the variance in net revenue (R2 = .849). Four variables emerged as statistically significant positive predictors: Active Medical Staff Hospital-Based Board-Certified Pulmonary Disease, Productive FTEs Pulmonary Function Services, Units of Service Pulmonary Function Services, and Contract Hours Pulmonary Function Services. Assuming that the other variables are constant, each additional hospital-based board-certified pulmonologist was associated with approximately $18,426 in additional net revenue. Additionally, each additional productive FTE was associated with an increase of approximately $177,075 in net revenue, while each additional unit of service contributed a modest increase of approximately $70.48. Lastly, contract hours were found to be positively associated with net revenue and each additional contract hour contributed approximately $483. Of importance, is that residents, diagnostic, therapeutic services, and non-hospital-based board-certified staff were not found to be statistically significant predictors.

Table 2: Factors Influencing Net Revenue

Regression Statistics

Multiple R

0.921342355

R Square

0.848871736

Adjusted R-Square

0.846472874

Standard Error

840596.6212

Observations

449

ANOVA

Df

SS

MS

F

Significance F

Regression

7

1.75029E+15

2.5E+14

353.8644

1.6377E-176

Residual

441

3.11612E+14

7.07E+11

Total

448

2.0619E+15

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95%

Upper 95%

Intercept

-32354.41626

94695.88385

-0.34167

0.73

-218465.7128

153756.9

-218466

153756.9

Active Medical Staff Hospital-Based Board-Certified Pulmonary Disease

18426.22924

3154.593732

5.841078

0.00

12226.32376

24626.13

12226.32

24626.13

Residents Pulmonary Disease

-53842.05956

146905.525

-0.36651

0.71

-342563.983

234879.9

-342564

234879.9

Diagnostic / Therapeutic Services Pulmonary Function Services

7065.378985

46067.61038

0.15337

0.88

-83473.95965

97604.72

-83474

97604.72

Productive FTEs Pulmonary Function Services

177075.3088

12744.11434

13.89467

0.00

152028.5639

202122.1

152028.6

202122.1

Active Medical Staff Non-Hospital Based Board Certified Pulmonary Disease

7490.230859

6323.6908

1.184471

0.24

-4938.084351

19918.55

-4938.08

19918.55

Units of Service Pulmonary Function Services

70.47592226

2.8127589

25.0558

0.00

64.94784454

76.004

64.94784

76.004

Contract Hours Pulmonary Function Services

483.4724559

182.8799939

2.64366

0.01

124.0478285

842.8971

124.0478

842.8971

Research Question 3

Within the third research question, the workforce and the utilization factors associated with the total patient care costs in pulmonary function services (Table 3) are examined. The model demonstrated excellent explanatory power, accounting for approximately 91.9% of the variance in patient care costs (R2 = .919). Productive FTEs, Fellows Pulmonary Disease, and Units of Service Pulmonary Function Services were statistically significant predictors.

Among the variables examined, productive FTEs demonstrated the largest positive association with size among the statistically significant predictors, with each additional productive FTE being associated with approximately $286,662 in additional patient care costs. Likewise, each additional unit of service modestly increased patient care costs by approximately $21.62. 

In contrast, pulmonary fellows were associated with lower patient care costs, with each additional fellow estimated to reduce total patient care costs by approximately $114,199. Lastly, residents, physician and student compensation, and contract hours were not statistically significant predictors in this model.

Table 3: Workforce and Utilization Factors

Regression Statistics

Multiple R

0.95879

R Square

0.919278

Adjusted R-Square

0.918182

Standard Error

439726.4

Observations

449

ANOVA

df

SS

MS

F

Significance F

Regression

6

9.73E+14

1.62E+14

838.9298

5.8E-238

Residual

442

8.55E+13

1.93E+11

Total

448

1.06E+15

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95%

Upper 95%

Intercept

26561.52

21250.52

1.249923

0.21

-15203.1

68326.13

-15203.1

68326.13

Residents Pulmonary Disease

67255.74

80007.54

0.840618

0.40

-89986.7

224498.2

-89986.7

224498.2

Productive FTEs Pulmonary Function Services

286662.1

7394.242

38.76828

0.00

272129.8

301194.3

272129.8

301194.3

Fellows Pulmonary Disease

-114199

32502.68

-3.51353

0.00

-178078

-50320.1

-178078

-50320.1

Physician and Student Compensation Total Compensation Pulmonary Function Services

0.059915

0.068996

0.868385

0.39

-0.07569

0.195516

-0.07569

0.195516

Units of Service Pulmonary Function Services

21.61698

1.975306

10.94361

0.00

17.73482

25.49914

17.73482

25.49914

Contract Hours Pulmonary Function Services

149.259

96.10251

1.553122

0.12

-39.6157

338.1336

-39.6157

338.1336

Research Question 4

In the fourth research question, the predictors of operational productivity measured in units of service (Table 4) were examined. The model explained approximately 62.6% of the variance in productivity (R2 = .626). Productive FTEs, Fellows Pulmonary Disease, and Residents Pulmonary Disease were statistically significant predictors. Holding all other variables constant, each additional pulmonary fellow was associated with approximately 8,991 additional units of service, representing the strongest positive association in the model. Productive FTEs also demonstrated a positive relationship, with each additional FTE associated with approximately 1,711 additional units of service. This was found to be a unique model given the factors tested.  

Conversely, pulmonary disease residents were negatively associated with productivity, with each additional resident corresponding to approximately 8,689 fewer units of service.  Lastly, intensive care services, diagnostic and therapeutic services, and contract hours were not statistically significant predictors of productivity. Future research areas should aim to examine additional years of data, including hospitals from other states, and incorporate additional organizational and operational variables to further assess the factors associated with pulmonary function service performance.

Table 4: Organizational Factors

Regression Statistics

Multiple R

0.791059

R Square

0.625775

Adjusted R-Square

0.620695

Standard Error

11490.39

Observations

449

ANOVA

df

SS

MS

F

Significance F

Regression

6

9.76E+10

1.63E+10

123.1845

4.48E-91

Residual

442

5.84E+10

1.32E+08

Total

448

1.56E+11

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95%

Upper 95%

Intercept

712.4906

1291.675

0.551602

0.58

-1826.1

3251.079

-1826.1

3251.079

Residents Pulmonary Disease

-8689.13

2040.069

-4.25923

0.00

-12698.6

-4679.69

-12698.6

-4679.69

Intensive Care Services Pulmonary

813.767

685.1362

1.187745

0.24

-532.762

2160.296

-532.762

2160.296

Diagnostic / Therapeutic Services Pulmonary Function Services

-991.529

748.0452

-1.32549

0.19

-2461.7

478.6382

-2461.7

478.6382

Productive FTEs Pulmonary Function Services

1710.849

153.6979

11.13125

0.00

1408.779

2012.918

1408.779

2012.918

Fellows Pulmonary Disease

8990.559

584.8861

15.37147

0.00

7841.056

10140.06

7841.056

10140.06

Contract Hours Pulmonary Function Services

-0.45884

2.503217

-0.1833

0.85

-5.37852

4.46085

-5.37852

4.46085

Research Question 5

In the fifth research question, the predictors of financial margin measured as net revenue less net costs (Table 5) were examined. The model explained approximately 75.5% of the variance in financial margins (R2 = .755). Procedures Total Pulmonary Function Services, Gross Patient Revenue Total Pulmonary Function Services, Total Patient Care Costs Pulmonary Function Services, and Contract Hours Pulmonary Function Services were statistically significant predictors. Assuming that all other variables are constant, each additional procedure performed was associated with an increase of approximately $44 in the financial margin. Gross patient revenue was also positively associated with the margin, with each additional dollar of revenue increasing the margin by approximately 9.4 cents. Contract hours also had a positive association, contributing approximately $560 to the financial margin per additional hour. Yet, patient care costs negatively were associated with the margin as every additional dollar of patient care costs reduced the financial margin by approximately $0.83. Productive FTEs were not statistically significant predictors of financial margin after controlling for the other variables in the model.

The financial-margin model below examines the relationships between pulmonary function service activity, workforce measures, and financial measures and financial margin. It should be noted that financial margin was defined as net revenue less net costs. Because revenue and cost measures are related to the calculation of financial margin, the relationships between these financial predictors and the margin should be interpreted cautiously. These variables were included to examine their statistical association with the reported financial margin; however, the relationships may be partly mechanical because revenue and cost measures contribute to the calculation of the margin.

Table 5: Factors Associated With Financial Margin

Regression Statistics

Multiple R

0.868963

R Square

0.755097

Adjusted R-Square

0.752333

Standard Error

592918.4

Observations

449

ANOVA

df

SS

MS

F

Significance F

Regression

5

4.8E+14

9.6E+13

273.1764

7.6E-133

Residual

443

1.56E+14

3.52E+11

Total

448

6.36E+14

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95%

Upper 95%

Intercept

31398.12

28704.84

1.093827

0.27

-25016.5

87812.71

-25016.5

87812.71

Procedures Total Pulmonary Function Services

44.09502

2.526562

17.45258

0.00

39.12948

49.06055

39.12948

49.06055

Summary of Revenue and Costs Total Patient Care Costs Pulmonary Function Services

-0.8314

0.063208

-13.1534

0.00

-0.95563

-0.70718

-0.95563

-0.70718

Gross Patient Revenue Total Pulmonary Function Services

0.09422

0.004565

20.63913

0.00

0.085248

0.103192

0.085248

0.103192

Productive FTEs Pulmonary Function Services

16399.79

20653.59

0.794041

0.43

-24191.4

56990.97

-24191.4

56990.97

Contract Hours Pulmonary Function Services

559.7802

95.8344

5.84112

0.00

371.4337

748.1268

371.4337

748.1268

Overall Findings

The overall findings indicated that workforce composition, service utilization, operational activity, and financial measures were associated with different dimensions of pulmonary function service performance across the five regression models. The direction and magnitude of these relationships should be noted varies across the outcomes examined.

Although causality cannot be inferred from the regression models, residents were negatively associated with revenue and productivity after controlling for other workforce and utilization variables. Therefore, findings should be interpreted cautiously because this analysis did not directly examine the mechanisms underlying these relationships. Together, the findings presented suggest that workforce structure, workforce deployment, and service volume are significant areas for understanding the variation in pulmonary function service performance. However, the produced regression results represent the statistical associations rather than evidence that these factors independently cause changes in service performance.

Across the models, the R² values ranged from 0.626 to 0.919, indicating that the variables included in the models explained between 62.6% and 91.9% of the observed variation in the respective outcomes. The explanatory power therefore varied across the models.

Conclusion

This study examined the relations between operational, workforce, utilization factors, and the financial performance associated with pulmonary function services. The regression models presented demonstrate the extent to which these factors explained a substantial proportion of the variance in financial outcomes. The findings identified in this study presented the factors associated with departmental financial performance.By identifying the factors associated with financial performance, this study contributes to the limited literature around pulmonary function services and provides practical evidence that may inform strategies for healthcare improvement. Together, the findings present that healthcare organizations may benefit from considering both workforce and operational metrics when making data-driven decisions.  Future research should aim to examine whether hospitals with greater residency and fellowship training capacity demonstrate differences in physician workforce availability and financial or operational performance.17

Future studies should evaluate whether hospitals receiving additional federally funded residency and fellowship positions experience measurable improvements. However, the present study does not establish that increasing residency or fellowship training capacity would cause improvements in workforce availability or financial performance. Because the number of pulmonary and critical care residents and fellows emerged as a statistically significant predictor in this study, expanding residency and fellowship training capacity may offer both workforce and organizational benefits. The distribution of CMS (Centers for Medicare & Medicaid Services)-funded residency and fellowship slots under Section 126 of the Consolidated Appropriations Act (CAA) represented an important federal policy initiative aimed at mitigating the national physician shortage.18 In addition, Covid-19 exploited the shortages of critical care physicians.19 According to Allen and Bosslet (2022), “Increasing the number of trainees who pursue PCCM would have the most direct impact on the shortage.”20 Therefore, future scholars should examine these relationships longitudinally and assess whether changes in training capacity are associated with subsequent changes in hospital workforce and financial outcomes.