Preoperative Acute Care Spending Predicts Costs in Bundled Payment Programs

ABSTRACT

Objectives: Surgical alternative payment models (APMs), such as the Bundled Payments for Care Improvement (BPCI) initiative, aim to reduce health care costs by issuing lump-sum payments for surgical episodes. However, these programs have not achieved net savings. Because preoperative care may influence postoperative expenditures, incorporating this phase into cost-containment strategies may improve APM performance. This study aimed to (1) evaluate the association between preoperative and postoperative acute care costs for 4 BPCI-targeted surgical procedures—percutaneous coronary intervention, cardiac defibrillator placement, lower extremity joint replacement, and spinal surgery—and (2) identify clinical and nonclinical drivers of perioperative costs.

Study Design: We conducted a retrospective cohort study using 2018 Healthcare Cost and Utilization Project data from Florida, Maryland, New York, and Wisconsin. The sample included patients with both a preoperative and a postoperative acute care encounter within 90 days of surgery.

Methods: Multivariable ordered logistic regression models assessed the associations among pre-, intra-, and postoperative cost quartiles.

Results: Episode-related costs ranged from $12,808 (bottom quartile) to $70,286 (top quartile). Patients in the highest preoperative cost quartile were significantly more likely to remain in the highest operative and postoperative quartiles. Adjusted models showed that being in the highest preoperative cost quartile was associated with a 13.7–percentage point (95% CI, 12.2-15.2; P < .001) higher probability of being in the top postoperative cost quartile. Facility-level BPCI participation was associated with higher preoperative costs.

Conclusions: Preoperative spending is a strong predictor of postoperative expenditures. Accounting for this relationship may enhance the design and performance of future surgical APMs.

Am J Manag Care. 2026;32(5):e147-e158. https://doi.org/10.37765/ajmc.2026.89940

Takeaway Points

Current surgical bundled payment models, including the Bundled Payments for Care Improvement (BPCI) initiative, focus primarily on postoperative costs. Our findings demonstrate that preoperative acute care costs are strong predictors of postoperative spending but are not addressed by current policy.

• Preoperative costs are significantly associated with postoperative expenditures across common BPCI-targeted procedures.
• Ignoring preoperative care in bundled payments may limit opportunities for cost control and quality improvement.
• Incorporating risk adjustment or incentives for efficient preoperative care could improve alternative payment model performance.
• Policy makers should consider expanding episode definitions to better capture total perioperative value.

The US spends more on health care than any other developed country in the world, totaling $4.9 trillion in 2023.¹ To help slow year-over-year US health care spending, CMS has introduced several policy reforms that transition reimbursements from traditional fee-for-service to alternative payment models (APMs) that focus on value-based care.² Surgical APMs to date (eg, the Bundled Payments for Care Improvement [BPCI] initiative) have largely focused on payment bundles, whereby providers are compensated based on a risk-adjusted lump sum for surgery and the associated 90-day postoperative period.³

However, no surgical bundled payment demonstrations have achieved net cost savings after accounting for incentive payments, and in some cases, they have led to worse health care disparities by exacerbating racial/ethnic and socioeconomic differences in access to surgical care.^4,5 These lackluster results may be due to policies that overemphasize evaluation of the postoperative period and do not adequately reward differences in preoperative care,⁶ a time in the clinical course when there is an opportunity for earlier and more efficient patient engagement. Consequently, by refocusing surgical APMs on the preoperative period, policy makers may be able to more readily incentivize higher-value perioperative provider behavior and/or interventions.^7,8

Although significant research has been devoted toward understanding the relationship between postoperative and total episode-based costs,⁶ little has been done to analyze preoperative factors that may influence operative and postoperative costs. Findings from a previous study using symptomatic urolithiasis as a model condition for surgical episodes of care demonstrated that preoperative acute care costs are highly correlated with postoperative costs and, by proxy, total episode-related perioperative costs. Additionally, the same study identified several potentially mutable clinical (eg, comorbidity status) and nonclinical (eg, facility volume) factors that may drive costs.⁸

This study sought to determine whether similar findings are present when evaluating 4 procedures targeted by model year 3 of the BPCI program (ie, lower extremity joint replacement [LEJR], percutaneous coronary intervention [PCI], cardiac defibrillator placement, and spine surgery other than fusion). Model year 3 of the BPCI program ran from October 2015 through September 2018 and included approximately 600 participating facilities in the retrospective risk-bearing phase. We hypothesize that preoperative events are strongly associated with both operative and postoperative outcomes/costs and therefore represent an important target for APMs seeking to optimize care. This study aimed to (1) estimate the association between preoperative and postoperative costs and (2) identify clinical and nonclinical drivers of episode-based costs associated with 4 surgical conditions explicitly targeted by model year 3 of the BPCI program.⁹ Ultimately, we sought to provide insights into pathways of surgical care more broadly, thereby shedding insight into how to improve ongoing surgical APM performance.

METHODS

This study was deemed exempt by the University of North Carolina at Chapel Hill institutional review board. We followed the Strengthening the Reporting of Observational Studies in Epidemiology guideline for cross-sectional studies.

Data and Study Population

We used 2018 Healthcare Cost and Utilization Project (HCUP) state-level data from Florida, Maryland, New York, and Wisconsin. HCUP represents the largest all-payer data source of longitudinal hospital care and includes billing charges, which were deflated to costs using hospital-specific HCUP cost-to-charge ratio files.¹⁰ These files account for county-level differences in cost of living via an area wage index factor. We selected individuals who underwent 1 of the following 4 surgeries: (1) major LEJR, (2) PCI, (3) cardiac defibrillator placement, or (4) spine surgery excluding spinal fusion. We focused on these surgeries because both inpatient and ambulatory surgical encounters triggered an episode of care in 2018 under model year 3 of BPCI.

Episodes were identified using CMS’ diagnosis related groups and Healthcare Common Procedure Coding System–based triggers for inpatient and outpatient episodes, respectively (eAppendix Table 1 [eAppendix available at ajmc.com]) (N = 270,944). Consistent with BPCI, surgical episodes started on day of discharge after an inpatient encounter or on the day of the outpatient procedure.¹¹ Because we wished to assess the association between preoperative and postoperative acute care cost categories, we further limited our sample to individuals with both a preoperative and a postoperative hospital-based acute care encounter within 90 days of index surgical encounter discharge (n = 23,860). Although this represents only approximately 10% of the addressable BPCI population, we felt that focusing our analysis on individuals with high-intensity preoperative health care resource utilization would provide valuable insights into potentially impactful cost-saving strategies, as evidenced by mounting literature demonstrating the significant cost savings achieved by avoiding high-intensity postdischarge care (eg, in skilled nursing facilities).¹¹ The 90-day time frame was chosen because BPCI currently defines a clinical episode as the 90-day period following an inpatient stay or ambulatory procedure.¹² We then used BPCI exclusion criteria to remove individuals who died during admission (n = 2028), had a diagnosis of end-stage renal disease (n = 2871), and/or underwent multiple procedures in the calendar year (n = 464).

To ensure that we accurately attributed preoperative hospital-based acute care encounters to facilities performing the surgery, we calculated the percentage of index surgical encounters that had at least 1 preoperative acute care encounter at the index facility and found that more than 90% of index surgical episodes had a preoperative acute care encounter at the same facility.

Flow of Acute Care Costs During a Surgical Episode

We calculated acute care costs accrued 90 days prior to surgery, during the index surgical encounter, and 90 days postoperatively. Cost data were then divided into quartiles within each perioperative phase to focus on relative cost flows,^13,14 which were displayed via an unadjusted Sankey diagram.¹⁵

Predictors of Preoperative and Postoperative Costs

We estimated multivariable ordered logit models on the pre- and postoperative quartiles and calculated average marginal effects of each covariate on the probability of being in each outcome quartile. We adjusted each model for patient demographics (age, sex, race), clinical factors (clinical comorbidities via the Elixhauser Comorbidity Index [ECI], index hospitalization length of stay, discharge disposition), and regional characteristics possibly affecting cost distributions (county-level income, county-level high school education, urban/rural status, state, payer at time of surgery, hospital-level APM participation, minority-serving hospital status, facility-level surgical volume) (eAppendix Table 2). We then calculated predicted marginal probabilities to quantify the likelihood of being in a specific cost quartile based on an individual’s cost quartile at an earlier phase of care. We also performed subgroup analyses for each type of surgical procedure. Analyses were conducted using Stata 16 (StataCorp LLC) and SAS 9.4 (SAS Institute Inc).

RESULTS

Descriptive Statistics

The study included 18,317 individuals (Table 1 [part A and part B]). Median (IQR) episode-related costs ranged from $12,808 ($8783-$15,527) for the bottom quartile to $70,287 ($57,682-$93,637) for the top quartile. Episode-related costs stratified by procedure are presented in tabular form via eAppendix Table 3. When comparing top vs bottom total cost quartiles, individuals in the top quartile experienced a greater median number of total acute care encounters (5 vs 3; P < .001) and longer median index hospitalization length of stay (5 vs 1 days; P < .001) and were more often discharged to a skilled nursing, long-term acute care, or rehabilitation facility (29.2% vs 9.7%; P < .001).

Flow of Acute Care Costs During a Surgical Episode

Sankey diagram. Of the 4580 individuals who began in the highest preoperative quartile, 1736 (37.9%) remained in the highest operative cost quartile, whereas only 806 (17.5%) moved to the lowest operative cost quartile. In the lowest preoperative cost quartile, 1440 (31.4%) individuals remained in the lowest operative cost quartile, and 721 (15.7%) moved to the highest operative cost quartile. Similar trends were observed regarding the flow of individuals from the operative to the postoperative phase (Figure 1).

Predicted marginal probabilities of subsequent cost quartiles. Individuals generally remained in the same cost quartile across all phases of perioperative care. Individuals in the lowest preoperative cost quartile were most likely to remain in the lowest operative and postoperative cost quartiles, with predicted probabilities of 52.1% and 50.4%, respectively (Figure 2). Similarly, most individuals in the highest preoperative cost quartile remained in the highest cost quartiles, with predicted probabilities of 46.7% and 53.3% of being in the highest operative and postoperative cost quartiles, respectively.

Predictors of Postoperative Cost

On average, being in the highest preoperative cost quartile was associated with a 13.7–percentage point (PP) higher (95% CI, 12.2-15.2; P < .001) probability of being in the top postoperative cost quartile and a 12.9-PP lower (95% CI, –14.3 to –11.5; P < .001) probability of being in the bottom postoperative cost quartile (Table 2 [part A, part B, and part C]). Similar associations were observed across all preoperative cost quartiles, although the magnitude of this association was smaller for the 2 middle preoperative cost quartiles. The positive association between pre- and postoperative cost held when stratifying our analysis by procedure type, apart from spine surgery, where no association was found (eAppendix Table 4).

Regarding the association between operative and postoperative cost, only the highest operative cost quartile was found to influence postoperative cost. On average, being in the highest operative cost quartile was associated with a 5.0-PP lower (95% CI, –7.09 to –2.85; P < .001) probability of being in the lowest postoperative cost quartile and a 5.2-PP higher (95% CI, 2.98-7.44; P < .001) probability of being in the top postoperative quartile relative to the lowest operative cost quartile.

Other factors associated with increased probability of being in the top quartile of postoperative cost included undergoing PCI (4.8 PP; 95% CI, 3.29-6.25; P < .001) or spine surgery (6.1 PP; 95% CI, 3.92-8.19; P < .001), being discharged to a short-term hospital (18.3 PP; 95% CI, 12.2-24.4; P < .001), and having a significant comorbidity burden (14.0 PP; 95% CI, 11.7-16.3; P < .001). Conversely, receiving care at a non–minority-serving hospital (2.5 PP; 95% CI, 0.727-4.25; P < .01) and female sex (2.9 PP; 95% CI, 1.97-3.91; P < .001) were associated with being in the lowest postoperative cost quartile.

Predictors of Preoperative Cost

Findings showed that, on average, a greater burden of comorbid conditions (ECI score ≥ 3 vs 0) was associated with a 17.5-PP higher (95% CI, 15.8-19.3; P < .001) probability of being in the highest preoperative cost quartile (Table 3 [part A and part B]). A similar, but smaller, increase in probability was seen for those with scores of 1 or 2 vs 0. The type of procedure was associated with the probability of being in the highest preoperative cost quartile. Compared with LEJR, patients receiving PCI had a 9.60-PP greater (95% CI, 8.21-11.0; P < .001) probability, and patients receiving a cardiac defibrillator implant had a 22.1-PP greater (95% CI, 20.1-24.2; P < .001) probability of being in the highest preoperative cost quartile. Other predictors associated with a greater probability of being in the highest quartile included age (0.138 PP; 95% CI, 0.092-0.184; P < .001), hospital participation in bundled payment APMs (5.20 PP; 95% CI, 2.60-7.80; P < .001), and hospital participation in both bundled payment and an accountable care organization (3.03 PP; 95% CI, 0.043-5.63; P < .05).

Meanwhile, several variables were associated with a lower likelihood of being in the highest preoperative cost quartile. Having private insurance was associated with a 2.68-PP lower (95% CI, –4.18 to –1.19; P < .001) average probability. Compared with White race/ethnicity, Black race was associated with a 2.61-PP lower (95% CI, –4.00 to –1.22; P < .001) and Hispanic ethnicity with a 2.91-PP lower (95% CI, –4.98 to –0.849; P < .01) average probability. On a facility level, hospitals serving a smaller percentage of minority patients had a 3.64-PP lower (95% CI, –5.91 to –1.38; P < .01) average probability. Other predictors associated with a lower likelihood of being in the highest quartile included female sex (–1.43 PP; 95% CI, –2.40 to –0.460; P < .01); living in a small metropolitan area (–3.33 PP; 95% CI, –5.10 to –1.55; P < .001), micropolitan area (–5.30 PP; 95% CI, –7.99 to –2.62; P < .001), or rural area (–7.04 PP; 95% CI, –10.0 to –4.05; P < .001); and living in Florida (–4.14 PP; 95% CI, –6.12 to –2.17; P < .001) or Maryland (–4.12 PP; 95% CI, –7.27 to –0.966; P < .05).

DISCUSSION

To our knowledge, this study is the first population-based cohort study to demonstrate an association between pre- and postoperative acute care expenditures among individuals undergoing 1 of 4 procedures targeted by BPCI model year 3. We identified several potentially mutable patient and facility-level factors that appear to influence both pre- and postoperative costs. Our findings hold significant clinical and policy implications in the setting of evolving US value-based reforms seeking to tie surgical reimbursements to provider and facility-level perioperative performance/outcomes.

We found that higher preoperative acute care costs were associated with higher postoperative and, by proxy, total-episode costs, regardless of the preoperative cost quartile in which individuals began their perioperative journey. Although we also observed an association between operative and postoperative costs, it applied only to the highest operative cost quartile, and its magnitude was dwarfed by that between pre- and postoperative cost quartiles. These findings are notable given that current and future surgical APMs exclusively focus on the operative and postoperative periods when determining episode-based target pricing.¹⁶ More specifically, our findings suggest that developing policies that promote care coordination and engagement between generalists and specialists (eg, perioperative care clinics, multidisciplinary perioperative e-consults) early in the perioperative pathway will pay dividends with regard to surgical spending and align well with the CMS Innovation Center’s goal of supporting patient-centered specialty care.¹⁷

In addition to preoperative cost, we identified patient and facility-level factors associated with higher postoperative cost. Consistent with prior literature,¹⁸ discharge disposition influenced postoperative costs, with individuals who avoided discharge to an acute care facility generally experiencing lower postoperative costs. With regard to clinical factors, increasing comorbidity status at the time of surgery was associated with a significantly higher probability of being in a higher postoperative cost quartile. Collectively, these findings suggest that optimizing patients’ comorbidity status prior to surgery may lead to perioperative cost savings, with additional cost savings being realized through preoperative care strategies that increase the probability of postoperative discharge to home. For example, Leeds and colleagues found that nonsurgeon optimization of a major comorbidity prior to elective colectomy decreased the odds of a major postoperative complication by 31%, resulting in $14,724 in episode-specific cost savings.¹⁹ However, although the notion of “prehabilitation” or comorbidity optimization is appealing, practical implementation is often limited by timing—many surgeries (eg, PCI, cardiac defibrillator placement) cannot be postponed for months. Nonetheless, studies have shown that even short-term interventions (eg, anemia correction, smoking cessation) can reduce postoperative complications and downstream costs.^20,21

Given the importance of preoperative costs in determining overall cost outcomes, we also sought to identify drivers of preoperative costs. We found that age and greater comorbidity burden were associated with a higher probability of being in the highest 2 preoperative cost quartiles. Age is known to be a strong risk factor for greater perioperative expenditures,^1,22,23 and other studies have shown that having more comorbid conditions often results in higher overall health care costs.^24-26 Consequently, our finding of higher preoperative expenditures in this cohort may be appropriate and even necessary to ensure safe outcomes.

Nonclinical factors were also associated with preoperative cost. For example, having private insurance was associated with a decreased likelihood of being in the highest 2 preoperative cost quartiles. Findings from a recent study demonstrated that individuals with private insurance were more likely than those with public insurance to report not having a regular physician and not regularly taking medications due to financial barriers.²⁷ Therefore, private insurance may be associated with lower preoperative costs due to these individuals being more cost-sensitive than publicly insured individuals, which in turn leads to both shorter and less frequent cost-intensive preoperative acute care encounters.

Our finding that hospitals serving fewer minority patients were less likely to be in the highest preoperative cost quartiles is consistent with the literature demonstrating that hospitals serving lower percentages of historically marginalized patient populations generally receive smaller reconciliation payments under BPCI. This observation is thought to be due to higher-resourced facilities facing lower target prices, which leaves less room to cut spending compared with higher target prices.²⁸ Collectively, these findings suggest that any hypothetical policy tying bonus payments to preoperative acute care spend should account for unintended consequences posed by dramatic differences in facility-specific target pricing (eg, punishing high-performing facilities).

Altogether, our study has important implications for evolving US value-based surgical reforms. Surgical APMs have effectively anointed postoperative costs and outcomes as the gold standard by which surgical reforms should be evaluated. However, to date, studies of BPCI have shown mixed success in containing costs.^28-31 Our findings suggest that improved surgical APM performance can be realized by refocusing these programs to evaluate and subsequently improve preoperative cost containment. Our identification of several potentially mutable factors associated with higher preoperative costs represents a logical target for future iterations of surgical APMs. More specifically, leveraging our findings to develop perioperative care interventions that identify surgical patients at high risk of experiencing inferior perioperative outcomes early in the care cycle may allow surgical APMs to realize significant cost savings and improved equity in terms of surgical access and outcomes.

Limitations

Our study is not without limitations. Our data stem from a longitudinal hospital database that does not capture cost information related to ambulatory care. If a patient with multiple comorbidities works with their primary care provider to optimize their health before surgery, our study will not reflect costs associated with those primary care visits, thereby underestimating the effect of preoperative engagement or care coordination efforts. Additionally, we evaluated preoperative, operative, and postoperative costs for 4 specific surgical procedures in the BPCI initiative in 4 states in 2018. As such, our findings have limited generalizability for individuals receiving these surgical procedures in the rest of the US. Finally, our study speaks to the relationship of certain predictors to preoperative and postoperative costs but is limited in elucidating the mechanisms that influence costs. Future research should investigate the association between preoperative and postoperative costs in the US across multiple years, accounting for ambulatory perioperative clinical encounters and how they affect preoperative and total episode costs.

CONCLUSIONS

Performance on current surgical APMs—including BPCI Advanced—is primarily evaluated by postoperative outcomes/costs. Our findings suggest that those designing surgical APMs should consider evaluating the preoperative period and rewarding high-value preoperative care to achieve perioperative cost savings. Our study also identified clinical and nonclinical drivers of preoperative cost that, if effectively targeted by providers and policy makers alike, could lead to cost savings and improved patient outcomes.

Author Affiliations: Department of Urology, University of North Carolina Medical Center (DFF), Chapel Hill, NC; School of Medicine, University of North Carolina at Chapel Hill (NLO, AES, JAK), Chapel Hill, NC; Department of Biostatistics, UNC Gillings School of Global Public Health (LL), Chapel Hill, NC; Departments of Urology and Population Health Sciences and Duke Clinical Research Institute, Duke University School of Medicine (CDS), Durham, NC; Center for Health Information and Research, College of Health Solutions, Arizona State University (MED), Phoenix, AZ.

Source of Funding: Dr Friedlander would like to acknowledge funding support from an American Urological Association 2021-2023 Research Scholars Grant and Agency for Health Care Research and Quality K08 Career Development Award (1K08HS029562-01A1).

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DFF, CDS); acquisition of data (DFF); analysis and interpretation of data (DFF, NLO, LL, AES, JAK, CDS, MED); drafting of the manuscript (DFF, NLO, AES, JAK); critical revision of the manuscript for important intellectual content (DFF, NLO, LL, AES, JAK, CDS, MED); statistical analysis (DFF, LL, JAK); provision of patients or study materials (DFF); obtaining funding (DFF); administrative, technical, or logistic support (DFF, MED); and supervision (DFF, CDS).

Address Correspondence to: David F. Friedlander, MD, MPH, Department of Urology, University of North Carolina Medical Center, 170 Manning Dr, Houpt Bldg CB #7235, Chapel Hill, NC 27599. Email: dave_friedlander@med.unc.edu.

REFERENCES

1. NHE fact sheet. CMS. Accessed November 25, 2024. https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data/nhe-fact-sheet

2. Chee TT, Ryan AM, Wasfy JH, Borden WB. Current state of value-based purchasing programs. Circulation. 2016;133(22):2197-2205. doi:10.1161/CIRCULATIONAHA.115.010268

3. Schwartzman DA, Sheetz KH, Fendrick AM. Refining the recipe for alternative payment models for surgical care—importance of patient mix and venue match. JAMA Netw Open. 2021;4(9):e2128258. doi:10.1001/jamanetworkopen.2021.28258

4. Thirukumaran CP, Kim Y, Cai X, et al. Association of the comprehensive care for joint replacement model with disparities in the use of total hip and total knee replacement. JAMA Netw Open. 2021;4(5):e2111858. doi:10.1001/jamanetworkopen.2021.11858

5. Marrufo G, Bradley A, Somers J. Bundled Payments for Care Improvement Advanced (BPCI Advanced) Model Request for Application – Model Year 7 (2024). CMS. 2022. Accessed October 5, 2024. https://www.cms.gov/priorities/innovation/media/document/bpcia-my7-rfa

6. Finkelstein A, Ji Y, Mahoney N, Skinner J. Mandatory Medicare bundled payment program for lower extremity joint replacement and discharge to institutional postacute care: interim analysis of the first year of a 5-year randomized trial. JAMA. 2018;320(9):892-900. doi:10.1001/jama.2018.12346

7. Friedlander DF, Krimphove MJ, Cole AP, et al. Where is the value in ambulatory versus inpatient surgery? Ann Surg. 2021;273(5):909-916. doi:10.1097/SLA.0000000000003578

8. Oh NL, Frisbie J, Li L, et al. Preoperative care intensity and cost for renal colic: implications for surgical value-based reforms. Urol Pract. 2025;12(1):104-113. doi:10.1097/UPJ.0000000000000725

9. Bundled Payments for Care Improvement (BPCI) initiative: general information. CMS. Accessed January 10, 2025. https://www.cms.gov/priorities/innovation/innovation-models/bundled-payments

10. Healthcare Cost and Utilization Project. Agency for Healthcare Research and Quality. Accessed November 22, 2024. https://hcup-us.ahrq.gov/

11. Rolnick JA, Liao JM, Emanuel EJ, et al. Spending and quality after three years of Medicare’s bundled payments for medical conditions: quasi-experimental difference-in-differences study. BMJ. 2020;369:m1780. doi:10.1136/bmj.m1780

12. BPCI Advanced. CMS. Accessed November 22, 2024. https://www.cms.gov/priorities/innovation/innovation-models/bpci-advanced

13. Gebregziabher M, Lynch CP, Mueller M, et al. Using quantile regression to investigate racial disparities in medication non-adherence. BMC Med Res Methodol. 2011;11:88. doi:10.1186/1471-2288-11-88

14. Olsen MA, Tian F, Wallace AE, et al. Use of quantile regression to determine the impact on total health care costs of surgical site infections following common ambulatory procedures. Ann Surg. 2017;265(2):331-339. doi:10.1097/SLA.0000000000001590

15. Naqvi A. Stata graphs: Sankey diagram. Medium. February 3, 2023. Accessed November 22, 2024. https://medium.com/the-stata-guide/stata-graphs-sankey-diagram-ecddd112aca1

16. TEAM (Transforming Episode Accountability Model). CMS. Accessed November 25, 2024. https://www.cms.gov/priorities/innovation/innovation-models/team-model

17. Fogler S, O’Connell M, Quinton J, Ritter C, Waldersen B, Rawal P. Pathways for specialty care coordination and integration in population-based models. CMS blog. June 17, 2022. Accessed September 24, 2024. https://www.cms.gov/blog/pathways-specialty-care-coordination-and-integration-population-based-models

18. Navathe AS, Troxel AB, Liao JM, et al. Cost of joint replacement using bundled payment models. JAMA Intern Med. 2017;177(2):214-222. doi:10.1001/jamainternmed.2016.8263

19. Leeds IL, Canner JK, Gani F, et al. Increased healthcare utilization for medical comorbidities prior to surgery improves postoperative outcomes. Ann Surg. 2020;271(1):114-121. doi:10.1097/SLA.0000000000002851

20. Miles LF, Richards T. Hematinic and iron optimization in peri-operative anemia and iron deficiency. Curr Anesthesiol Rep. 2022;12(1):65-77. doi:10.1007/s40140-021-00503-z

21. Mills E, Eyawo O, Lockhart I, Kelly S, Wu P, Ebbert JO. Smoking cessation reduces postoperative complications: a systematic review and meta-analysis. Am J Med. 2011;124(2):144-154.e8. doi:10.1016/j.amjmed.2010.09.013

22. Alemayehu B, Warner KE. The lifetime distribution of health care costs. Health Serv Res. 2004;39(3):627-642. doi:10.1111/j.1475-6773.2004.00248.x

23. De Nardi M, French E, Jones JB, McCauley J. Medical spending of the US elderly. Fisc Stud. 2016;37(3-4):717-747. doi:10.1111/j.1475-5890.2016.12106

24. Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med. 2002;162(20):2269-2276. doi:10.1001/archinte.162.20.2269

25. Tran PB, Kazibwe J, Nikolaidis GF, Linnosmaa I, Rijken M, van Olmen J. Costs of multimorbidity: a systematic review and meta-analyses. BMC Med. 2022;20(1):234. doi:10.1186/s12916-022-02427-9

26. Fiasconaro M, Wilson LA, Poeran J, et al. Cost of care for patients with pre-existing comorbidities undergoing total joint arthroplasty: a retrospective cohort study evaluating disease-specific perioperative care. J Arthroplasty. 2019;34(12):2846-2854.e2. doi:10.1016/j.arth.2019.07.011

27. Wray CM, Khare M, Keyhani S. Access to care, cost of care, and satisfaction with care among adults with private and public health insurance in the US. JAMA Netw Open. 2021;4(6):e2110275. doi:10.1001/jamanetworkopen.2021.10275

28. Shashikumar SA, Gulseren B, Berlin NL, Hollingsworth JM, Joynt Maddox KE, Ryan AM. Association of hospital participation in Bundled Payments for Care Improvement Advanced with Medicare spending and hospital incentive payments. JAMA. 2022;328(16):1616-1623. doi:10.1001/jama.2022.18529

29. Chopra Z, Gulseren B, Chhabra KR, Dimick JB, Ryan AM. Bundled Payments for Care Improvement efficacy across 3 common operations. Ann Surg. 2023;277(1):e16-e23. doi:10.1097/SLA.0000000000004869

30. Shashikumar SA, Zheng J, Orav EJ, Epstein AM, Joynt Maddox KE. Changes in cardiovascular spending, care utilization, and clinical outcomes associated with participation in Bundled Payments for Care Improvement - Advanced. Circulation. 2023;148(14):1074-1083. doi:10.1161/CIRCULATIONAHA.123.065109

31. Navathe AS, Emanuel EJ, Venkataramani AS, et al. Spending and quality after three years of Medicare’s voluntary bundled payment for joint replacement surgery. Health Aff (Millwood). 2020;39(1):58-66. doi:10.1377/hlthaff.2019.00466