Factors Influencing Compliance to Follow-up Visits in Orthopaedic Surgery

Abstract

Background:

Orthopaedic procedures require postoperative follow-up to maximize recovery. Missed appointments and noncompliance can result in complications and increased healthcare costs. This study investigates the relationship between patient postoperative visit attendance and the distance traveled to receive care.

Materials and Methods:

A retrospective review of all surgeries performed by a single orthopaedic surgeon in 2019 at level 1 trauma center in a midsized city serving a largely rural population was completed. We excluded patients who underwent another subsequent procedure. Distance to care and time traveled were determined by the patient's address and the clinic address using Google Maps Application Programming Interface. Other variables that may affect attendance at follow-up visits were also collected. Univariate and multivariate logistic regression was done with purposeful selection.

Results:

We identified 518 patients of whom 32 (6%) did not attend their first scheduled follow-up appointment. An additional 47 (10%) did not attend their second follow-up. In total, 79 patients (15%) did not attend one of their appointments. Younger age, male sex, Black or African American race, self-pay, Medicaid insurance, accident insurance, and increased distance were individual predictors of missing an appointment. In the final multivariate logistic regression model, male sex (OR 1.74), Black or African American race (OR 2.78), self-pay (OR 3.12), Medicaid (OR 3.05), and traveling more than 70 miles to clinic (OR 2.02) markedly predicted missing an appointment, while workers' compensation (OR 0.23) predicted attendance.

Discussion:

Several nonmodifiable patient factors predict patient noncompliance in attending orthopaedic postoperative visits. When patients are considered at high risk of being lost to follow-up, there may be an opportunity to implement interventions to improve follow-up rate and patient outcomes, minimize patient costs, and maximize profitability for the hospital.

Follow-up visits are a vital part of orthopaedic postoperative care to monitor patient progress and modify treatment plans to maximize patient recovery. Missed appointments and noncompliance are challenging topics that prevent the surgeon from adequately addressing postoperative complications and have been associated with poorer outcomes including worse function, greater pain, strain on the patient-physician relationship, higher costs, and delays in recognition of complications such as surgical site infection or fracture nonunion.^{1,2,3,4,5,6,7,8,9,10,11,12,13,14}

Previous studies have examined factors associated with patients with orthopaedic trauma missing follow-up appointments at urban level 1 trauma centers with conflicting results regarding distance.^1,2 Influence of distance on attendance to follow-up visits in a more rural setting has yet to be investigated. Orthopaedic care in nonurban environments is already limited, and populations trend toward higher poverty rates and lower general educational achievement, which have also been associated with poor outcomes.¹⁵

The purpose of this study was to investigate the relationship between orthopaedic patient attendance at postoperative follow-up visits and distance to care at a level 1 trauma center in a midsized city serving a largely rural population crossing several state lines. Of secondary interest are other variables that may affect patient compliance, such as the patient's age, demographics, insurance, pathology, mechanism of pathology, and surgical intervention.

Methods

We conducted a retrospective chart review of all adult patients who underwent surgery between January 2, 2019, and December 23, 2019, by a single fellowship-trained orthopaedic surgeon. All surgeries took place at a single level 1 trauma center. Patients who required several (≥ 2) procedures over the course of the year were excluded because they represent a unique demographic unrepresentative of the general population. Patients were also excluded if they did not have a follow-up appointment scheduled.

At our institution, patients are commonly scheduled for their first postoperative appointment at their preoperative history and physical appointment for elective procedures and at the time of discharge for patients with trauma. Patients who have a cellphone listed in the electronic medical record (EMR) are sent a reminder text message before their appointment and a prearrival survey. If the patient does not complete the prearrival survey, the patient receives a second reminder text message 24 hours before his or her appointment. If the patient does not have a cellphone number in the EMR or if they are a new patient, he or she will also get a letter in the mail listing the appointment date and time. After a patient no-shows an appointment, the nursing staff makes an attempt to call and reschedule the patient and a letter is mailed as an attempt to reschedule the patient if they do not answer the phone call.

Data collected included age, sex, patient-reported race, and ethnicity as recorded in the EMR, pathology, pathology mechanism, surgical intervention, insurance type, return visit dates, and if the patient attended the appointments. We collected patients' addresses at the time of surgery. Driving distance to care and driving time to care were calculated using Google Maps Application Programming Interface between each patient's address and the clinic address. Of note, the clinic, emergency center, surgical center, and hospital are all on the same campus.

All statistics were done using Stata (StataCorp. 2013; Stata Statistical Software: Release 13: StataCorp LP). Univariate logistic regression was done to obtain an odds ratio (OR) representing the effect of various preoperative factors on likelihood of patients missing one of their first two scheduled follow-up visits after orthopaedic surgery. Univariate logistic regression was also done for missing the first appointment and again for missing the second appointment. Multivariate logistic regression was then done to identify predictors of patients missing one of their first two scheduled follow-ups using purposeful selection with P-values <0.200.^16-18 P-values of <0.05 were considered significant.

Results

We identified 518 patients with demographics, which is summarized in Table 1. The average age was 48.7 ± 17.1 years (range 18.1 to 91.2). Our population was 55% men (n = 287). Our population was predominately White, non-Hispanic/Latino (n = 315, 61%) followed by White Hispanic or Latino (n = 172, 33%), Black/African American (n = 26, 5%), and Native American (n = 2) with three patients without race or ethnicity data. Most of the patients had either private insurance (n = 128, 25%), Medicare (n = 115, 22%), or Medicaid (n = 113, 22%). We additionally had 57 patients (11%) on workers' compensation (WC), 22 (4%) covered by accident insurance, 28 (5%) Veteran's Administration, and 55 (11%) self-pay. More than half of the pathologies were traumatic in nature/mechanism (n = 291, 56%). The average distance from care was 70 ± 132 miles (range 1.3 to 1,365). We had 196 (37%) of our patients living more than 50 miles and 140 (27%) living more than 100 miles from the clinic. The average time traveled to care was 69 ± 118 minutes (range 5 to 1,216).

Table 1.

Demographics by Attendance

Demographic	Total n = 518	Attended n = 439	Missed n = 79	P
Age (years)	48.7 ± 17.2	49.6 ± 17.0	43.6 ± 1.9	0.001
Male	287 (55%)	236 (54%)	51 (65%)	0.075
Race				0.008
White	484 (93%)	416 (95%)	68 (86%)
Black/African American	26 (5%)	16 (4%)	10 (13%)
Native American	2 (<1%)	2 (<1%)	0
No Response	6 (1%)	5 (1%)	1 (1%)
Ethnicity				0.461
Not Hispanic or Latino	333 (64%)	281 (64%)	52 (66%)
Hispanic or Latino	174 (34%)	150 (34%)	24 (30%)
No response	11 (2%)	8 (2%)	3 (4%)
Insurance				<0.001
Private	128 (25%)	115 (26%)	13 (16%)
Veteran's Administration (VA)	28 (5%)	26 (6%)	2 (3%)
Medicare	115 (22%)	105 (24%)	10 (13%)
Medicaid	113 (22%)	82 (19%)	31 (39%)
Accident coverage	22 (4%)	18 (4%)	4 (5%)
Workers' compensation (WC)	57 (11%)	55 (13%)	2 (3%)
Self-pay	55 (11%)	38 (9%)	17 (22%)
Mechanism of pathology
Trauma	291 (56%)	240 (55%)	51 (65%)	0.103
Distance (miles)	70 ± 132	61 ± 108	120 ± 217	<0.001
Within city limits	265 (51%)	230 (52%)	35 (44%)
<30	295 (57%)	258 (59%)	37 (47%)
30-49	27 (5%)	23 (5%)	4 (5%)
50-99	56 (11%)	52 (12%)	4 (5%)
100-150	82 (16%)	63 (15%)	18 (23%)
>150	58 (11%)	42 (10%)	16 (20%)
Time (minutes)	69 ± 118	61 ± 97	115 ± 194	<0.001

P-values in this table are based on the t-test and chi-squared. Bolded P-values are considered significant.

Thirty-two patients (6%) did not attend their first scheduled follow-up appointment. An additional 47 patients (10%) did not attend their second scheduled follow-up for a total of 79 patients (15%) who did not attend one of their appointments.

Figures 1–3 present univariate logistic regression for missing the first, second, or either postoperative follow-up appointment, respectively. The significance of age, ethnicity, insurance, traumatic mechanism, and distance did not change based on first, second, or either follow-up appointment. Male sex was only notable for missing the second follow-up appointment. Being Black or African American race was only notable for missing the second or either follow-up appointment. Both patients of Native American heritage attended their postoperative visits.

Figure 1.

Graph showing univariate logistic regression of each variable for missing the first appointment. Bolded odds ratios are considered significant with a P-value <0.05.

Figure 3.

Graph showing univariate logistic regression of each variable for missing either appointment. Bolded odds ratios are considered significant with a P-value <0.05. Confidence intervals and P-values are given in Table 2. All variables with a P-value <0.200 were included in the initial multivariate model, and then, purposeful selection was used to define the final model shown in Figure 7.

Figure 2.

Graph showing univariate logistic regression of each variable for missing the second appointment. Bolded odds ratios are considered significant with a P-value <0.05.

Each minute of travel time and each mile of distance from care increased the risk of missing one of the first two postoperative visits. An increase in 10 minutes of travel time led to an OR 1.03 (P = 0.001). In other words, every 10 minutes was a 3% increase in the odds of missing one of the appointments. An increase in 10 miles of distance led to an OR 1.02 (P = 0.001). In other words, every 10 miles was a 2% increase in the odds of missing one of the appointments. A failure curve of missing one of the first two appointments by distance from clinic was constructed, which is shown in Figure 4. The risk of missing an appointment became significant at 50 miles with an OR 1.65 (P = 0.042) but continued to increase at 70 miles (OR 1.92, P = 0.008), 100 miles (OR 2.37, P = 0.001), and 150 miles (OR 2.40, P = 0.007).

Figure 4.

Graph showing the failure curve of missing appointment by distance.

Figures 5–7 present multivariate logistic regression using purposeful selection with P-values <0.200 for missing the first, second, or either postoperative follow-up appointment, respectively. The significance of age, ethnicity, traumatic mechanism, and distance did not change based on first, second, or either follow-up appointment. Male sex and Black or African American race were only notable for missing the second or either follow-up appointment. Self-pay and Medicaid insurance were notable for missing the first, second, and either postoperative follow-ups, while WC was only notable for the second or either follow-up.

Figure 5.

Graph showing the final multivariate logistic regression model using purposeful selection for missing first appointment. Bolded odds ratios are considered significant with a P-value <0.05. Reference variables are as follows: † private insurance and ‡ distance <70 mi.

Figure 7.

Graph showing the final multivariate logistic regression model using purposeful selection. Bolded odds ratios are considered significant with a P-value <0.05. Reference variables are as follows: α female, β White, † private insurance, and ‡ distance <70 mi. Confidence intervals and P-values are given in Table 2.

Figure 6.

Graph showing the final multivariate logistic regression model using purposeful selection for missing second appointment. Bolded odds ratios are considered significant with a P-value <0.05. Reference variables are as follows: α female, β White, † private insurance, and ‡ distance <70 mi.

Comparison of the univariate and multivariate values for the first, second, and either follow-up visit is presented in Tables 2–4, respectively.

Table 2.

Univariate and Multivariate Logistic Regression for Missing First Appointment

Variable	OR* (CI)	P	OR	P
Age	0.97 (0.95-0.99)	0.008
Male	1.58 (0.75-3.35)	0.225
Race		0.081
White	Ref
Black/African American	3.08 (0.99-9.56)	0.052
Native American	Perfect attendance
Ethnicity		0.995
Not Hispanic or Latino	Ref
Hispanic or Latino	1.00 (0.47-2.13)	0.995
Insurance		0.003
Private	Ref		Ref
Self-pay	7.09 (1.80-27.87)	0.005	6.33 (1.60-25.10)	0.009
Veteran's Administration	1.54 (0.15-15.41)	0.712	1.57 (0.16-15.76)	0.701
Workers' compensation	0.74 (0.08-7.31)	0.800	0.67 (0.07-6.62)	0.731
Accident coverage	1.98 (0.20-19.99)	0.561	1.56 (0.15-15.98)	0.708
Medicare	1.50 (0.33-6.86)	0.600	1.55 (0.34-7.09)	0.573
Medicaid	5.89 (1.65-21.08)	0.006	6.08 (1.69-21.85)	0.006
Mechanism of pathology
Trauma	1.53 (0.72-3.24)	0.261
Distance (per 10 miles)	1.02 (1.01-1.04)	0.003
>70 miles	2.06 (1.01-4.22)	0.049	2.10 (1.01-4.44)	0.042

OR* (CI) = Univariate Odds Ratios found in Figures 1-3, OR = Multivariate Odds Ratios found in Figures 5-7.

Bolded P-values are considered significant.

Table 4.

Univariate and Multivariate Logistic Regression for Missing Either Appointment

Variable	OR* (CI)	P	OR	P
Age	0.98 (0.96-0.99)	0.004
Male	1.57 (0.95-2.58)	0.073	1.74 (1.01-3.01)	0.047
Race		0.003
White	Ref		Ref
Black/African American	3.82 (1.67-8.77)	0.002	2.78 (1.12-6.91)	0.027
Native American	Perfect attendance		Perfect attendance
Ethnicity				0.461
Not Hispanic or Latino	Ref
Hispanic or Latino	0.86 (0.51-1.46)	0.585
Insurance		<0.001
Private	Ref		Ref
Veteran's Administration	0.68 (0.14-3.20)	0.626	0.60 (0.13-2.89)	0.528
Medicare	0.84 (0.35-2.00)	0.698	0.90 (0.37-2.16)	0.806
Medicaid	3.34 (1.65-6.78)	0.001	3.05 (1.46-6.37)	0.003
Accident coverage	1.97 (0.58-6.70)	0.041	1.38 (0.40-4.85)	0.608
Workers' compensation	0.32 (0.07-1.48)	0.144	0.23 (0.05-0.96)	0.045
Self-pay	3.96 (1.76-8.90)	0.001	3.12 (1.35-7.21)	0.008
Mechanism of pathology
Trauma	1.51 (0.92-2.48)	0.100
Distance (per 10 miles)	1.02 (1.01-1.04)	0.001
>70 miles	1.92 (1.18-3.12)	0.008	2.02 (1.19-3.42)	0.009

OR* (CI) = Univariate Odds Ratios found in Figures 1-3, OR = Multivariate Odds Ratios found in Figures 5-7.

Bolded P-values are considered significant.

Table 3.

Univariate and Multivariate Logistic Regression for Missing Second Appointment

Variable	OR* (CI)	P	OR	P
Age	0.98 (0.96-0.99)	0.001
Male	1.70 (1.01-2.84)	0.041	1.84 (1.05-3.25)	0.035
Race		0.007
White	Ref		Ref
Black/African American	3.47 (1.49-8.13)	0.004	2.54 (1.01-6.40)	0.048
Native American	Perfect attendance		Perfect attendance
Ethnicity
Not Hispanic or Latino	Ref
Hispanic or Latino	0.90 (0.53-1.54)	0.713
Insurance		<0.001
Private	Ref		Ref
Self-pay	3.96 (1.76-8.90)	0.001	3.10 (1.34-7.14)	0.008
Veteran's Administration	0.68 (0.14-3.20)	0.626	0.59 (0.12-2.84)	0.512
Workers' compensation	0.32 (0.07-1.47)	0.800	0.24 (0.05-1.10)	0.049
Accident coverage	1.97 (0.58-6.70)	0.280	1.38 (0.40-4.84)	0.612
Medicare	0.66 (0.26-1.66)	0.378	0.70 (0.28-1.77)	0.451
Medicaid	2.91 (1.43-5.96)	0.003	2.66 (1.26-5.61)	0.010
Mechanism of pathology
Trauma	1.53 (0.91-2.55)	0.100
Distance (per 10 miles)	1.02 (1.01-1.04)	0.001
>70 miles	1.95 (1.18-3.21)	0.009	1.99 (1.16-3.41)	0.012

OR* (CI) = Univariate Odds Ratios found in Figures 1-3, OR = Multivariate Odds Ratios found in Figures 5-7.

Bolded P-values are considered significant.

The final multivariate logistic regression model for missing either appointment consisted of male sex (OR 1.74, P = 0.047), Black or African American race (OR 2.78, P = 0.027), self-pay (OR 3.12, P = 0.008), WC (OR 0.23, P = 0.045), Medicaid (OR 3.05, P = 0.003), and traveling more than 70 miles to clinic (OR 2.02, P = 0.009).

Discussion

Patient attendance at follow-up appointments can be a major obstacle in improving patient outcomes and sustainability of health care. The recovery timeline varies from patient to patient, and adjustments to postoperative visits are made accordingly. Orthopaedic postoperative appointments often require suture removal, changes in wound management, review of pain management, changes in mobility or weight-bearing status, adjustments to physical therapy protocols, and addressing of patient concerns.¹⁹ Missing appointments can result in detrimental effects on patient outcomes and need for additional visits or procedures.^5,6,20 Existing literature approximates that 27 to 33% of orthopaedic patients miss their first postoperative visit and that 70% of patients will miss at least one appointment in the first 6 months.^1,2,8,13 By comparison, our study found that 6% of patients did not attend their first scheduled follow-up appointment, an additional 10% missed their second follow-up appointment, and a total of 15% did not attend one of their appointments. To address this, many clinics will overbook appointments to retain productivity in the setting of missed visits; however, there are increased opportunity costs to patients through prolonged queues in clinic and decreased patient satisfaction, which may further exacerbate future no-show rates.^21,22

Distance and Travel Time

Distance from care is thought to have a negative effect on patient care and follow-up across many healthcare specialties.^20,23,24 Despite this, few studies have cited distance as a risk factor for orthopaedic surgery patients in not attending their follow-up appointments.^1,9,25 The literature surrounding this issue is mixed, although logically one would expect distance to propose a barrier to care.^1,2,7-9,11 In a study of orthopaedic trauma surgery follow-up by Whiting et al,¹ distance was only notable when traveling greater than 100 miles to the clinic. Yuenyongviwat et al⁹ found similar results with a cutoff of 100 kilometers resulting in missed follow-up appointments after total knee arthroplasty. Our study found that both distance and time traveled subsequently increased the risk of missing one of the first two postoperative visits. Every 10 minutes of travel time led to a 3% increase in the odds of missing one of the appointments. Travel time has yet to be addressed in the literature, but it is so closely intertwined with travel distance that it may be irrelevant. The risk of missing an appointment became notable at 50 miles but continued to increase at 70 miles, 100 miles, and 150 miles. By contrast, despite their level 1 trauma center serving patients with trauma who lived an average of 100 ± 64 miles away from the hospital, Zelle et al² found that distance was not a risk factor for missed follow-up. This was supported in shoulder arthroplasty literature.⁷ Our population lived an average of 70 miles from the hospital; however, our standard deviation is much higher at 132 miles indicating a larger portion of patients living further away, which may account for the difference in significance. We propose that given the notable drop off in attendance at 70 miles in our population, a good cutoff for clinics to use would be the average distance from clinic within their own population.

It has been proposed that individuals living close to clinics have easier access to care and therefore may only attend routine postoperative visits if they experienced a problem.⁹ The significance of distance in our study may be better explained by the nonurban population we serve. Individuals living in more rural communities may lack driver's licenses or reliable transportation.¹⁵ Arranging follow-up may represent an exacerbation of opportunity costs for these individuals including travel time, costs, and income loss due to missing work.^7,22,26 An estimated 45% of total joint arthroplasty patients preferred not to visit clinics in person for these reasons.²⁶ It is worth noting that our metro population has a median household income approximately $15,000 below the median household income in the general US population.²⁷

Insurance

We found that patients with Medicaid insurance or no insurance (or patients who elected to pay out-of-pocket) were over three times more likely to miss their follow-up appointments. Some Medicaid and self-pay patients are likely unaware that follow-up care after their procedure is included in the 90-day global payment fee that is billed before surgery at our institution and most others.²⁸ Meanwhile, Medicare and private insurance patients likely know they met their deductible with the surgery and assume that postoperative care is either included, covered by insurance, or they are unconcerned about the ability to afford it.

Regardless of the specific orthopaedic population assessed, not having commercial insurance has been found to be a risk factor for follow-up attrition.^1,2,19,29,30 Casp and colleagues³⁰ found that 73% of their patients who were lost to follow-up over a 6-month period did not have commercial insurance. Ten Berg et al found that patients unlikely to return are ‘unmarried, unemployed, and underinsured,’ stating that these are measures of socioeconomic status and limited education.¹⁹

Our current study further found that patients with WC had a markedly lower risk of attrition. This unique patient group represents individuals who must demonstrate visit attendance to receive the full benefits of coverage. This result has also been seen in a previous study of upper extremity injuries done by Rosenbaum et al²⁵ who demonstrated that patients with WC had a lower loss to follow-up than private insurance in the same group.

Demographics

Our results had notable differences when compared with existing early postoperative follow-up studies done on urban populations. Whiting et al.¹ did not find age, sex, or race to affect follow-up rates. Yet in our study, being Black or of African American descent increased the risk of missing appointments. Similar to the observations made by Canseco et al,¹³ demographic factors such as race may be serving as a proxy for lower socioeconomic status and level of education. However, these are challenging to assess on presentation and even more so in rural groups.^1,15 It is worth noting that the discussion of minority populations demonstrating higher no-show rates in orthopaedic surgery is complex. A systematic review on the topic found that indigenous populations showed higher attrition rates rather than African Americans and called for additional investigation into this issue.²⁹ By contrast, our study found that Native Americans were more likely to attend appointments; however, due to the small sample of Native American patients who met the inclusion criteria, we are not powered to make any conclusions about such.

Our study supports the current literature published by Zelle et al² that being male increases the likelihood of a missed appointment. It has been documented that male patients are less likely to use all healthcare services than their female counterparts.²⁹ Western perceptions of masculinity may influence a belief of self-resilience when faced with adversity.²⁹ Men may also be the primary bread winner with the inability to miss work; however, occupational status itself has been found to not be a notable contributor to missing appointments in the trauma literature.

Our study aligned with multiple reports that younger age is a risk factor for loss to follow-up in orthopaedic populations^9,31; although when taken into consideration in the multivariate model, age became insignificant indicating that other factors affected by age (such as insurance) may play a larger role. It has been conjectured that younger patients have less available time to attend visits or that they make up a large uninsured population—a group proven with being less likely to follow-up.^9,29

Strategies for Improvement

Missing postoperative follow-up appointments has medical-legal consequences for physicians in the setting of complications. Physicians are held to a standard of making adequate attempts to contact a patient regarding follow-up. Longitudinal studies have shown that regardless of the orthopaedic procedure, no-show rates increase with each subsequent appointment.^7,9 Many strategies have been proposed or proven to have effects on tackling the issue of follow-up in orthopaedic surgery. Phone call reminders placed 3 to 5 days after hospital discharge have been shown to improve follow-up rates, regardless of whether calls go to voicemail or an actual conversation is achieved.⁸ Text message reminders sent 1 week before visits produced similar results with Rohman et al¹² reporting a 12% reduction in no shows after introduction of the service and an economic benefit of nearly £20,000 (∼$25,000 USD).

Educating patients preoperatively on the importance of postoperative follow-up appointments could go a long way in preventing missed appointments. A single procedure is often billed under a 90-day global fee which covers the procedure itself and the follow-up visits during that time frame.^1,25 It is necessary to ensure that patients understand that all fees regarding postoperative care are included in their global payment. Barring facility fees, the only cost incurred may be for interval imaging which we do not routinely obtain until at least 6 weeks postoperatively unless there is a problem. Whiting et al¹ suggested educating patients about the inclusion of follow-up visits in their global fee to alleviate assumptions of financial barriers to attendance. However, additional investigation into whether this strategy creates a notable difference is warranted. In addition, it is vital that patients recognize the significance of these postoperative appointments and prioritize them accordingly.

Proactive educational handouts have been shown to enhance the results of procedures and improve patient attendance rates to follow-up appointments.³² These proactive treatment plans for patients can be difficult to monitor due to the nature of some recommendations.³³ Lack of adherence to physician instructions can have a notable effect on the cost of health care in the range of $5271 to $52,341 per person each year.³⁴ Mitigating these extra costs is crucial for improving the sustainability of medical practices including orthopaedic surgery.

In December 2019, the rise of the COVID-19 pandemic gave rise to social distancing policies.^35,36 This led to widespread adoption of telemedicine visits with 63% of orthopaedic clinics offering this service.³⁶ With the ease of social restrictions in-person appointments returned, but telehealth utilization was noted to still be higher than prepandemic levels.^37,38 Widespread adoption into daily mainstream practice has been slow partly due to its view as a temporary service or because it is more difficult to connect with patients.^37,38

Implementation of telehealth follow-up appointments may increase patient commitment to care, but this may not be feasible in all clinics.²² Alternatively, telemedicine visits could be offered exclusively to patients who are at a higher risk of missing a postoperative appointment.²¹ Telehealth follow-up appointments would be suitable for addressing pain, wound healing, and mobility concerns. However, this would not address suture removal as is expected in the early postoperative period or any new necessary imaging if there was a recent complication like a fall. Suture removal concerns can be addressed by using dissolvable or buried sutures or follow-up at a different clinic. In cases where distance is the largest influence to missing an appointment (>100 miles), a possible intervention would be to decentralize care and promote follow-up with local orthopaedic or primary care clinics rather than the primary surgeon's office with or without direct communication back to the surgeon.^1,39 Providing the option for telehealth or alternative locations for follow-up appointments for patients living long distances from the clinic could alleviate their concerns of transportation costs and lost wages.

There may be an opportunity for development of an algorithm or score preoperatively that would determine the likelihood of noncompliance based on demographics, insurance, and distance from clinic. Patients considered to be high risk for missing a postoperative appointment could then undergo several of the interventions above including preoperative counselling and offers for alternative follow-up options or alternative wound closures. Eliminating noncompliance to follow-up visits through implementation of resources such as telehealth could ultimately result in a more successful recovery, thus improving patient outcomes, minimizing patient costs, and maximizing profitability for the hospital.⁴⁰

Limitations

This study had several limitations. It is retrospective in nature without any intervention, given that the problem and solutions are not well-defined. We assumed that patients were still at the address recorded at the time of surgery for their follow-up appointments; however, patients may stay elsewhere during their recuperation such as an inpatient rehabilitation center or with family. We did not control for travel time during peak hours; however, there is minimal traffic in our city even during peak hours and even less so in surrounding towns; therefore, we think that this is not relevant in our population. This study assumes that missing an appointment was the fault of the patient; however, we acknowledge that some errors may have been introduced in scheduling or miscommunication. We did not investigate whether our clinic successfully contacted these patients about their scheduled follow-up, missed follow-up, or attempted to reschedule these patients. In addition, we used a single institution and faculty member to obtain our data. Furthermore, our population was a mix of elective and patients with trauma. We excluded those with multiple procedures scheduled under the assumption that these patients do not translate to the general population. We did not record complications, and therefore, the clinical relevance of missing an appointment is not defined in this study. We included all surgeries completed over the course of 2019, after which COVID-19 was introduced into the United States at the start of January 2020. Due to social distancing restrictions enacted at this time, lower than expected rates of follow-up may have been observed.

Conclusion

Attending postoperative follow-up appointments after orthopaedic surgery is a vital component of the recovery process to ensure that the patient is healing appropriately, displaying an absence of infection, experiencing an appropriate level of pain, and gaining functional improvements. Our study demonstrated that increasing distance from clinic, self-payors, patients on Medicaid, patients of Black or African American race, and men were more likely to miss early postoperative clinic appointments after orthopaedic surgery, while being on WC and Native American predicted attendance to such appointments. When patients are considered at high risk of being lost to follow-up, there may be an opportunity to implement interventions to improve follow-up rates, patient outcomes, and patient satisfaction; minimize patient costs; and maximize profitability for the hospital. We think that properly educating patients on the significance of postoperative appointments and providing telehealth options will greatly improve compliance to follow-up visits.