The Cost of Last-Minute Cancellation

Abstract

Background:

The aim of this study was to determine if cancellations within 1 week of surgery are more costly and whether failure to obtain medical clearance is a common reason for nonelective cancellation.

Methods:

A retrospective review of 1,684 consecutive scheduled surgeries at 1 surgery center by 1 surgeon was performed. Demographics, including age, gender, and hand dominance; timing of surgical scheduling; and surgery type were recorded. Cancellations within 2 weeks of surgery were recorded including the date and reason, categorized as elective and nonelective, and whether the timeslot was filled or unfilled. Cancellations were analyzed based on number of days prior to surgery: 0 to 7 versus 8 to 14 days versus >14 days from surgery. Estimated revenue and Relative Value Unit (RVU) losses were calculated using values and the average revenue of shoulder surgery found in existing literature. Statistical analysis was used for comparisons between the groups using t-tests, analysis of variance, χ² test, and Fisher exact test.

Results:

There were 175 cancellations, 96 occurring within 2 weeks of the scheduled surgery. The average cancellation time was 5.9 ± 4.2 days before surgery. 43.8% (28/64) of cancellations 0 to 7 days before surgery were filled, a significantly lower number compared with those that occurred 8 to 14 days earlier with 93.4% (30/32) of timeslots filled following the cancellation (p = 0.00), and all cancelations >14 days before surgery were filled (79/79). The rate of elective and nonelective cancellations did not differ between the 0 to 7 and 8 to 14 days (p = 0.74). Nonelective cancellations related to lack of medical clearance contributed to 17.2% (11/64) of cancellations that occurred 0 to 7 days before surgery. Approximately $385,624 or 2,990 RVUs of revenue was lost due to unfilled timeslots.

Conclusions:

There was an inflection point observed 7 days before scheduled surgery, which marked a statistically significant decrease in the rate at which surgical cancellations were filled with alternative cases at available surgical timeslots. Lack of medical clearance also led to a substantial loss of revenue.

Level of Evidence:

Level III. See Instructions for Authors for a complete description of levels of evidence.

Introduction

Cancellations of elective surgical procedures are common and create financial and logistical burdens, such as locating alternative patients and securing last-minute insurance approvals. Cancellation rates vary widely (5%-30%) and are particularly high in elective orthopaedic surgery^1-5. Elective orthopaedic surgeries have particularly high last-minute cancellations rates. For example, in Finland, orthopaedic surgeries showed some of the highest cancellation rates among surgical specialties⁶. Similarly, orthopaedics had the highest cancellation rate (36.5%) of elective procedures when compared with other specialties in a large public tertiary hospital⁴.

There is a particular lack of research investigating high-volume, elective orthopaedic shoulder practices at a tertiary center. Timing and reasons for cancellations affecting ability to fill vacated slots are poorly understood. The purpose of this study was to evaluate elective surgical cancellation rates based on timing of cancellations, identify underlying causes, and estimate the associated financial implications. We hypothesized that cancellations would be more costly when cancelled within 1 week of the scheduled date compared with when cancelled beyond 1 week and that failure to obtain medical clearance would be a common reason for nonelective cancellation.

Materials and Methods

This was a retrospective review of consecutive scheduled surgeries over a 2-year period at 1 surgery center by 1 fellowship-trained orthopaedic shoulder and sports medicine surgeon (senior author A.L.). All scheduled elective surgeries from January 1, 2022, to December 31, 2023, were included. All scheduled surgeries were outpatient procedures at UPMC East, Pittsburgh, PA. Emergency and add-on cases were excluded. Patients were categorized into 3 groups: surgical patients (S.P.), non–last-minute cancellations (NLMC), and last-minute cancellations (LMC). The SP group consisted of surgeries that were performed as scheduled or were cancelled outside of the 2-week window. The NLMC group included cancellations that occurred between 8 and 14 days before surgery. The LMC group included cancellations that occurred between 0 and 7 days before the scheduled surgery date.

Demographics and Background Information

Demographics including age, gender, and hand dominance and type of scheduled surgery were recorded using chart review of the electronic medical record. Race was also collected as part of the demographic data set; however, it was not included in statistical analyses, as it did not pertain to the primary aims of this study. The type of surgery was categorized into 5 main groups based on the most common procedures performed: arthroplasties, rotator cuff repairs (open and arthroscopic), instability procedures (open and arthroscopic), miscellaneous shoulder, and other (i.e., knee). Procedures included in the “miscellaneous shoulder” category included arthroscopic procedures excluding rotator cuff repairs and instability procedures, such as arthroscopic acromioclavicular joint resection/reconstruction and superior capsular reconstruction, and nonarthroplasty open procedures, such as pectoralis major tendon repairs and distal tibial allograft reconstruction. Surgery type was analyzed to assess whether certain procedures were more prone to cancellation.

Cancellations and Reasons for Cancellations

Data on all cancellations were collected including the scheduling date, date of cancellation, and reason for cancellation for those that occurred within 2 weeks of the scheduled surgery date. Date of cancellation was also categorized by season, with Winter (December-February), Spring (March-May), Summer (June-August), and Autumn (September-November) defined accordingly. The rate of cancellations that were filled was compared between NLMCs and LMCs (Equations 1 and 2). To account for varying daily sample sizes, average fill rate (Equation 2) was used to better represent the likelihood of filling surgeries. Date of cancellation was used to calculate the number of days in advance that the cancellation occurred, and the scheduling date was used to calculate how far in advance the patient scheduled their respective surgical procedure.

Equation 1: Fill Rate

$$Fill\hspace{0.17em}rate=\frac{\left(\#Filled\hspace{0.17em}time\hspace{0.17em}slots\right)}{\left(\#Cancellations\right)}\times 100$$

Equation 2: Average Fill Rate

$${Average\hspace{0.17em}fill\hspace{0.17em}rate}_{\left(day\hspace{0.17em}0-7\right)}=\frac{{Fill\hspace{0.17em}rate}_{\left(day\hspace{0.17em}0\right)}+{Fill\hspace{0.17em}rate}_{\left(day\hspace{0.17em}1\right)}\dots +{Fill\hspace{0.17em}rate}_{\left(day\hspace{0.17em}7\right)}}{8}$$

Reasons for cancellation were categorized as elective or nonelective. Elective cancellations were those made by patient's personal choice (opting for conservative management, not being ready for surgery, etc.), while nonelective reasons included medical reasons, family emergencies, and insurance difficulties. Among nonelective cancellations, a subgroup analysis examined lack of preoperative clearance. Lack of preoperative clearance was analyzed based on whether it resulted from failure to see a physician or incomplete preoperative testing. Two authors assessed cancellation reasons; a third resolved disagreements.

Calculating Revenue and RVUs

Estimated revenue losses were calculated using a weighted average according to the surgery type (rotator cuff repair, arthroplasty, instability procedures) breakdown of the senior author's surgical patients using average revenue of shoulder surgery types found in existing literature (Equation 3). In total, the surgery type breakdown was as follows: 58.6% rotator cuff repairs, 10.2% instability procedures, 21.3% arthroplasty procedures, and 9.8% other procedures. Other procedures were accounted for using the average cost between rotator cuff repairs, instability procedures, and arthroplasty procedures. Using costs found in existing literature, our calculated average cost was $10,526.33 for rotator cuff repairs⁷, $21,947.67 for instability procedures⁸, $19,364 for arthroplasty procedures⁹, and $16,568 for other procedures. The final average weighted cost of shoulder surgery using the equation below was $10,148 ± $6,141.

Equation 3: Average Weighted Cost of Shoulder Surgery

$$Average\hspace{0.17em}Weighted\hspace{0.17em}Cost\hspace{0.17em}of\hspace{0.17em}Shoulder\hspace{0.17em}Surgery=\frac{\begin{array}{c}\left({Cost}_{Rotator\hspace{0.17em}Cuff\hspace{0.17em}Repair}\times {Rate}_{Rotator\hspace{0.17em}Cuff\hspace{0.17em}Repair}\right)\hspace{0.17em}\left({Cost}_{Instability\hspace{0.17em}Repair}\times {Rate}_{Instability\hspace{0.17em}Repairs}\right)\hspace{0.17em}\\ \left({Cost}_{Arthroplasty}\times {Rate}_{Arthroplasty}\right)\hspace{0.17em}\left({Average\hspace{0.17em}cost}_{Rotator\hspace{0.17em}Cuff,Instability,Arthroplasty}\times {Rate}_{Other}\right)\end{array}}{3}$$

In addition to revenue, Relative Value Unit (RVU) loss was calculated using the 2025 CMS Physician Fee Schedule. Facility Total RVUs were recorded per surgery type based on commonly used CPT code combinations. For example, an arthroscopic rotator cuff repair includes CPT codes for the rotator cuff repair (CPT 29827), biceps tenodesis (CPT 29828), subacromial decompression (CPT 29823), and extensive debridement (CPT 29853), totaling 83.69 RVUs. Average RVUs were assigned as follows: rotator cuff repairs—83.69 RVUs, instability procedures—81.86 RVUs, and arthroplasty—81.86 RVUs. Other procedures were assigned an average of these—75.94 RVUs.

Statistical Analysis

Comparisons between groups were analyzed with either one-way analysis of variance for continuous variables or either chi-square or Fisher exact tests for categorical variables. If a variable was significant on the initial test, post hoc pairwise comparisons were used for continuous variables and pairwise logistic regressions were used for categorical variables. A threshold value of p < 0.015 was deemed statistically significant.

Results

There was a total of 1,684 scheduled surgeries between January 1, 2022, and December 31, 2023, with an overall cancellation rate of 10.4% (175 cancellations). In total, 5.7% of all scheduled surgeries were cancelled within 2 weeks of the scheduled timeslot. Ninety-six cancellations occurred within 2 weeks of the scheduled surgery date (54.9%). The average cancellation time was 5.9 ± 4.2 days before surgery. Application of our inclusion and exclusion criteria left a total of 1,548 surgeries in the SP group, 32 in NLMC, and 64 in LMC.

Age, Gender, and Hand Dominance

The racial distribution of all patients included in this study was as follows: 92.6% White, 9.9% African American, 1.1% Asian, and 2.3% undisclosed. There were no differences between groups for age, gender, or correlation between surgical and dominant side (p > 0.05) (Table I).

TABLE I.

Demographics and Surgery Information

	SP	NLMC	LMC	p
Total	1,548	32	64	N/A
Age	55.1 ± 17.4	53.2 ± 14.6	55.8 ± 16.6	0.78
Sex (% male)	45.7	53.1	39.1	0.57
Surgery type (%)				0.45
Rotator cuff repair	58.5	50.0	67.2
Instability procedures	10.5	15.6	7.8
Arthroplasty procedures	21.3	18.8	18.8
Miscellaneous shoulder	7.5	15.6	6.3
Other (i.e., knee)	2.3	0	0
Time to scheduling (weeks)	5.1 ± 5.0	6.7 ± 4.4	6.6 ± 5.4	0.02
Dominant side vs. surgical side (% dominant = surgical)	57.9	46.9	57.8	0.46
Season of surgery (%)				0.009
Summer	24.1	29.7	6.25
Autumn	26.5	21.9	37.5
Winter	25.1	30.0	46.9
Spring	24.4	18.8	9.4

LMC = last-minute cancellation, and NLMC = non–last-minute cancellations. Bolded values indicate a statistically significant difference (p < 0.015).

Specific Type of Scheduled Procedure (Instability, Rotator Cuff, Arthroplasty, and Other)

A breakdown of scheduled surgery types is presented in Table I. The percentage of surgery types did not differ between SP, NLMC, and LMC groups (p = 0.449).

Timing of Scheduling and Cancellation

For SPs, the average time of scheduling was 5.0 ± 5.3 weeks before the surgery date, while the average time to schedule for NLMCs and LMCs was 6.7 ± 4.4 weeks and 6.6 ± 5.4 weeks, respectively (p = 0.02) (Table I).

Surgery season differed between groups (p = 0.009). Surgeries performed in the Winter had a significantly higher odds of having a last-minute cancellation compared with both NLMCs (OR: 7.5, 95% CI: 1.5-37.4, p = 0.014) and SPs (OR: 7.2, 95% CI: 1.6-31.7, p = 0.009), with Summer serving as the reference season (Table I). The season in which surgeries were scheduled was not significantly different between the groups (p = 0.27).

Fill rate

All surgeries cancelled >2 weeks before surgery were filled (79/79). Among surgeries cancelled <2 weeks before surgery (n = 96), 60.4% (58/96) were filled. Fill rates were significantly lower for LMCs vs NLMCs (43.8% vs 93.4%, p = 0.00). Fill rate per day was 46.2% for 0 to 7 days and 83.7% for 8 to 14 days before surgery.

Reasons for Cancellation

Reasons for cancellation were assessed for all NLMCs and LMCs (n = 96) and were categorized as elective or nonelective cancellations (Table II). Across all cancellations within 2 weeks, 59.3% (n = 57) were due to nonelective reasons, while 40.6% (n = 39) were due to elective reasons. Lack of medical clearance (n = 15) accounted for 15.6% of all cancellations and 26.3% of nonelective cancellations. Of all patients who did not obtain medical clearance before surgery, 20.0% (n = 3) did not schedule preoperative testing with a primary care provider before their surgical date, 20.0% (n = 3) experienced insurance coverage difficulties, and 60% (n = 9) of these patients needed cardiac follow-up due to abnormal preoperative EKGs and did not have time before their scheduled date to complete necessary testing. Of these patients needing further cardiac clearance, 30% (n = 3) required further cardiac intervention, including cardiac catheterization and pacemaker placement.

TABLE II.

Cancellation Analysis

	N (Total)	N (NLMC)	N (LMC)
Total cancellations	96	32	64
Nonelective cancellations	57	16	41
Elective cancellations	39	16	23
Unfilled cancellations	38	2	36
Nonelective cancellations (medical clearance)	15	2	13
Nonelective unfilled cancellations	30	2	28
Elective unfilled cancellations	7	0	7
Nonelective (medical clearance) unfilled cancellations	8	0	8

LMC = last-minute cancellations, and NLMC = non–last-minute cancellations.

Within the NLMC group, 50.0% (n = 16) were due to nonelective reasons, 50.0% (n = 16) were due to elective reasons and 6% (n = 2) were due to a lack of medical clearance. Within the LMC group, 64.1% (n = 41) were due to nonelective reasons, 35.9% (n = 23) were due to elective reasons, and 21.9% (n = 14) were due to a lack of medical clearance. Types of cancellation reasons were not significantly different between the LMC and NLMC groups (p = 0.74).

The estimated revenue loss for all unfilled cancellations was $385,624 ± $233,396 (2,989.78 RVUs) (Tables III and IV). Revenue loss and RVU Loss associated with the reasons for cancellation are presented in Tables III and IV.

TABLE III.

Revenue Loss for Unfilled Cancellations

	Total Revenue Loss ($)	NLMC Revenue Loss ($)	LMC Revenue Loss ($)
Unfilled cancellations	385,624 ± 233,396	20,296 ± 12,284	365,238 ± 221,112
Nonelective unfilled cancellations	304,440 ± 184,260	20,296 ± 12,284	284,144 ± 171,976
Elective unfilled cancellations	71,036 ± 42,994	0	71,036 ± 42,994
Nonelective (medical clearance) unfilled cancellations	81,184 ± 49,136	0	81,184 ± 49,136

LMC = last-minute cancellations, and NLMC = non–last-minute cancellations.

TABLE IV.

Relative Value Unit (RVU) Loss for Unfilled Cancellations

	Total RVU Loss	NLMC RVU Loss	LMC RVU Loss
Unfilled cancellations	2,989.78	157.8	2,831.98
Nonelective unfilled cancellations	2,349.42	157.8	2,192.62
Elective unfilled cancellations	640.76	0	640.76
Nonelective (medical clearance) unfilled cancellations	848.61	0	848.61

LMC = last-minute cancellations, and NLMC = non–last-minute cancellations.

Discussion

The main finding of this study was that the overall cancellation rate was 10.4% over a 2-year period at a high-volume practice of an orthopaedic shoulder surgeon, with 54.9% of cancellations occurring within 2 weeks of the scheduled surgery date. A longer time from cancellation to scheduled surgery date was significantly associated with the ability to fill the cancelled time slot. Nonelective cancellations were more common than elective cancellations, with the most common reason being lack of medical clearance due to the need for additional cardiac testing. Surgeries scheduled in winter showed higher cancellation rates. These findings may be due to multiple factors such as seasonal illnesses, inclement weather, and holiday-related scheduling conflicts. Further multivariate analysis is needed to identify the specific causes of seasonal variation in cancellation rates.

Cancellations within 7 days were harder to fill (LMC fill rate: 45% vs NLMC: 85%), supporting our hypothesis. These unfilled slots led to an estimated $385,000 (2,990 RVU) in lost revenue—$300,000 from LMCs and approximately $81,000 due to lack of medical clearance. Although based on estimates and literature averages, this analysis emphasizes the broad financial impact of last-minute cancellations across practices and hospital systems, aiming to quantify the frustration surgeons face with elective cancellations.

Our cancellation rate was similar to a multi-center study reporting 9.7% for elective procedures in urban hospitals¹⁰. When comparing types of elective orthopaedic surgeries, a recent study showed that procedures like total knee arthroplasty (TKA) and total hip arthroplasty (THA) have lower cancellation rates than anatomic TSA, RSA, and total ankle arthroplasty¹¹. Several studies focused on cancellations made on the day of surgery. One study reported that the most common reason for same-day cancellation among elective and emergency procedures during a 1-year period was the patient not being medically fit for surgery, followed by a lack of beds¹². A 2019 study found that history of heart failure, chronic kidney disease, and low socioeconomic status was associated with cancellations made within 24 hours of surgery.¹³

Unfilled time slots resulted in an estimated $400,000 loss over 2 years. Given musculoskeletal surgeries represent approximately 33% of hospital revenue, this loss is significant¹⁴. Specifically, the profit margins for RSA and TSA are 28% lower than TKA or THA, indicating that cancellations of these high margin procedures can lead to substantial revenue loss¹¹. Financial impact is likely underestimated since staffing and facility costs were excluded.

This study provides a foundation to reduce last-minute surgical cancellations, especially those that are harder to fill that result in frustration and revenue loss. Next steps include designing policies to reduce cancellations, improving timely medical clearance, and assessing patient outcome impacts. For example, Leite et al. found that online education and standardized nurse-led anesthesia interviews cut preventable cancellations from 34.3% to 20%¹⁵. Because of the time and cost burden, streamlining preoperative clearance would improve cost effectiveness and surgical efficiency. In addition, since 35% of cancellations were elective, practices might consider financial penalties for nonmedical, last-minute cancellations, as seen in other professions.

Limitations include reliance on estimated RVU/dollar values and potentially inflated fill rates due to high practice volume. In addition, all patients were at a single institution under the care of a single surgeon with a high-volume practice, which likely contributed to both the frequency of cancellations and the ability to fill vacated slots. Surgeons with lower case volumes may face greater challenges filling these openings, making the impact of cancellations even more pronounced. Another potential weakness that could benefit from further analysis would be the financial impact of patients with multiple cancellations.

Although focused on a single surgeon practice, these findings reflect a broader issue across surgical specialties and may inform efforts to reduce costly cancellations.

Conclusion

This study demonstrates that cancellations within 7 days of surgery are significantly harder to fill and more costly for the hospital system compared with non–last-minute cancellations. Lack of medical clearance was a leading cause of cancellation, representing 13.5% of all cancellations and 17.2% of last-minute cancellations. Unfilled cancellations resulted in approximately $400,000 in lost revenue for a single provider, highlighting the broader financial impact on hospital systems and surgical teams.