Predictive Factors for Patients’ Failure to Show for Initial Outpatient Physical Therapist Evaluation

Abstract

Objective

The objective of this study was to examine primary factors that may predict patients’ failure to show at initial physical therapist evaluation in an orthopedic and sports outpatient setting.

Methods

A retrospective analysis of patients’ demographic data for physical therapist evaluations between January 2013 and April 2015 was performed. A binary logistic regression model was used to evaluate the odds of a no-show at evaluation. Demographic variables of age, employment status, days waited for the appointment, payer source, and distance traveled to the clinic were analyzed. Independent variables were considered significant if the 95% CIs of the odds ratios (ORs) did not include 1.0.

Results

A total of 6971 patients were included in the final analysis, with 10% (n = 698) of the scheduled patients having a no-show event for their initial evaluation. The following factors increased the odds of patients having a no-show event: days to appointment (OR = 1.058; 95% CI = 1.042–1.074), unemployment status (OR = 1.96; 95% CI = 1.41–2.73), unknown employment status (OR = 3.22; 95% CI = 1.12–8.69), Medicaid insurance (OR = 4.87; 95% CI = 3.43–6.93), Medicare insurance (OR = 2.22; 95% CI = 1.10–4.49), unknown payer source (OR = 262.84; 95% CI = 188.72–366.08), and distance traveled 8 or more kilometers (OR = 1.31; 95% CI = 1.01–1.70). Female sex (OR = 0.73; 95% CI = 0.57–0.95) and age 40 years or older (OR = 0.44; 95% CI = 0.33–0.60) decreased the odds of a no-show event.

Conclusions

Results from this study indicate there may be some demographic factors that are predictive of patients failing to attend their first physical therapist visit.

Impact

Understanding the predictive factors and identifying potential opportunities for improvements in scheduling processes might help decrease the number of patients failing to show for their initial physical therapy appointment, with the ultimate goal of positively influencing patient outcomes.

Attendance, Employment, Health Insurance, Medicaid, Medicare, No-Show, Patient Characteristics, Time to Appointment, Workers’ Compensation

Introduction

Within the US health care system, timely access to medical care can be a challenge both for patients and providers.¹ Overall, wait times for new patient appointments with physicians in the United States have increased on average by 30% to 24 days since 2014 (in large metropolitan markets) and 33% to 32 days (in midsized markets).¹ More striking, the average wait time to see a family physician is 29 days in large metropolitan markets and 56 days in midsize markets.¹ Wait times may be even longer to see a specialist considering most require a referral after seeing another provider.² Delayed care is associated with worse outcomes and increased socioeconomic costs.³ Despite the high need of care for certain patient populations, patient no-show rates at medical appointments are as high as 30% nationwide, which costs the health care system an estimated $150 billion per year.⁴ While economic impacts of these missed appointments are high, patient no-shows can also interfere with other patients’ medical access and outcomes, clinical productivity and efficiency, and overall patient satisfaction.⁵ The effects of no-shows and cancellations have been well documented for various health care systems, primary care physicians, and medical specialties.^6–13 However, there is a paucity of evidence of the impact of patients not showing and appointment cancellations for outpatient physical therapist evaluations in the United States.

Early physical therapist management enhances patient outcomes and reduces overall health care costs.^14–16 Also, attendance for postoperative procedures like anterior cruciate ligament repairs has been associated with improved knee function.¹⁷ However, a systematic review found that physical therapist visits (evaluations/follow-ups) had the highest median no-show rate (57.3%) compared to other specialties.¹⁸ Investigations from Nigeria,¹⁹ the United Kingdom,^20,21 New Zealand,^22,23 and South Africa^22,23 suggest that nonattendance of physical therapist sessions was influenced by cost of care, distance to clinic, and patient progress. However, similar data in the United States are lacking. The unique health system organization and socioeconomic factors in the United States necessitate investigation of potential barriers and facilitators to patients attending physical therapy appointments in the United States. Examination of archival data from hospitals in Florida demonstrated that one-fifth of physical therapy appointments were missed, resulting in a loss of approximately 2 million dollars in revenue.²⁴ Thus, better understanding the impact of missed physical therapy appointments in the United States could lead to changes that improve patient outcomes and decrease the fiscal burden of no-shows for outpatient physical therapy clinics.^14,17,24

The purpose of this study was to examine patient failure to show for initial evaluations at an outpatient orthopedic and sports medicine physical therapy clinic within an academic medical center. We sought to identify primary factors that predicted the odds of patient attendance. Physical therapist evaluation appointments were chosen for this study because of their effects on future appointment availability, the high institutional cost, and the increased time requirement of the clinician.

Methods

Study Design and Setting

A retrospective analysis was performed on demographic data of patients who were scheduled for a physical therapist evaluation at an outpatient orthopedic and sports medicine physical therapy clinic within a large academic medical center between January 2013 and April 2015. The study was approved by the institutional review board.

The clinic where the data were obtained provides physical therapist services to a large, Midwestern metropolitan area with available public transportation. Referral for physical therapist evaluation was required by the institution. During the study period there were 3 front desk staff, 19 physical therapists, and 2 physical therapist assistants. Ten of the clinicians were female. The clinicians’ years of experience ranged from 1 to 18 years. The clinic most frequently receives referrals for nonoperative knee, shoulder, and spine pathologies and common sports medicine surgeries for shoulders (eg, rotator cuff/labral repairs, biceps tenodesis, arthroscopy), hips (eg, arthroscopies), knee (eg, cruciate ligament/quadriceps tendon/meniscal repairs, arthroscopy), and ankles (eg, Bronström procedures). The clinic primarily evaluates patients between ages 18 and 60 years and schedules evaluations for 60 minutes. The host city of the clinic, using the most up-to-date population estimates in 2019, had a population of 898,552, a population density of 3624 individuals per square mile, and a median household income of $51,612.²⁵ Fifty-one percent of the city’s population is female, and 60.2% of the population is between ages 18 and 65 years.²⁵ During the study period there was a stable political climate within the organization and metropolitan region.^26,27 Further, there were no shifts in staffing or community health, and weather parameters were typical for the geographic region in question.^28,29

Data Collection

Patient and clinical data were requested from the academic medical center’s information warehouse. Data obtained included attendance status (eg, completion, no-show, canceled) of a scheduled physical therapist evaluation, sex (male/female), age, time to appointment (days), employment status, insurance payer information, and distance of home address from clinic. The demographic data are routinely captured at patient registration. This resulted in 11,159 new patient physical therapist evaluations during the study period. Patient addresses were also obtained and the distance traveled to the clinic was measured “as the crow flies” using ArcGIS software (Esri, Redlands, California, USA).

Data Validation, Categorization, and Analysis

The data provided by the information warehouse were deidentified and follow-up appointments were removed from the data set, leaving only new patient evaluations for analysis. A total of 140 patients were then randomly selected from the data set. These 140 patients were cross-referenced in the electronic medical record and checked for accuracy with the data provided by the information warehouse. Data validation included ensuring whether the appointment was completed, when it was scheduled, and if the completion date in the electronic medical record matched the completion date provided from the information warehouse. All 140 appointments checked were accurate with the electronic medical record.

The flow diagram in the Figure illustrates the inclusion and exclusion of the obtained data. Two patients were excluded from the data set secondary to their attendance status being labeled as “left without being seen.” Next, all patients whose addresses were listed as out of the state of Ohio were excluded because patients who were college students may have listed out of state home addresses, which may have further confounded the data. Data were then categorized into “completed evaluation,” “canceled evaluation,” or “failure to show evaluation.” For the purposes of this project, the canceled evaluations were excluded because we were concerned only with data for patients who did not show versus those who completed their scheduled physical therapist evaluation. Further, canceled initial evaluations represent a different phenomenon compared to failure to show. Cancellations require contact with the clinic, which offers the opportunity for rescheduling and service recovery, whereas failing to show for an appointment does not offer this opportunity. Finally, cancellations may have different factors associated with their occurrence, such as scheduling someplace else earlier, perception of medical profession/provider, and negative health behavior exhibited by the patient.^30–32

Figure

Flowchart of scheduled physical therapy initial evaluations. Total data points collected in the retrospective analysis was 11,159. Two patients were removed from the data set secondary to being labeled as “left without being seen.” Next, patients whose primary address was out of state or there was no address were removed. These were excluded because of decreased reliability of the addresses reported. All canceled initial physical therapist evaluations were removed from the data set, leaving 6971 patient evaluations. Finally, patients for whom the mapping software was unable to determine the distance from the home address were removed, leaving 6595 patient evaluations. These were then separated into no-show appointments (N = 698) and completed appointments (N = 6273) and subsequently analyzed.

The variable of “time to appointment” was the numbers of days from scheduling the initial physical therapist evaluation to the actual date of the evaluation. Examination of these data revealed 2 patients with negative “time to appointment.” Further investigation demonstrated that these patients had same-day appointments but their entry into the system was not performed until after their initial evaluation. Thus, their “time to appointment” was listed as 0 for the analysis. Pertinent data such as employment type, payer source, sex, age, and distance traveled to the clinic were categorized. Employment type was categorized into “employed,” “retired,” “not employed,” unknown employment,” or “student.” In the initial building of the logistic regression model, categories of part time and self-employment were not significant predictors and had small numbers. Thus, they were pooled together with full-time employment to create the category of “employed.” Payer source was categorized into “private,” “Medicaid,” “Medicare,” “Veterans Administration,” “workers’ compensation,” “unknown,” and “self-pay.” Sex was categorized as “male” or “female.” Age groups were dichotomized by the median age (age 39 years) and then rounded up to the next decade of life (“over 40” and “under 40”). Distance to the clinic was categorized as “less than 8 kilometers” and “greater than 8 kilometers.” A total of 11,159 new physical therapist evaluations were scheduled between January 2013 and April 2015 at the clinic site, with 6595 patients included in the final analysis.

Descriptive and frequency statistics were performed to characterize the sample. A binary logistic regression was performed to evaluate the predictive relationships between the identified variables and no-show versus attendance for initial physical therapist evaluation. For the final model, the independent variables were entered into the model together using the “enter” method. All assumptions of logistic regression were met, including no evidence of multicollinearity (based on tolerance [0.906–0.994] and variance inflation factor [1.006–1.007] statistics) among the independent variables, and the independent continuous variable (days to appointment) was linearly related to the log odds. Independent variables were considered statistically significant if the 95% Cis of the odds ratios (ORs) did not include 1.0. Statistical analysis was performed with IBM SPSS Statistics version 26 (SPSS Inc, Chicago, Illinois, USA).

Results

The no-show rate in the final data set was 9.6% (n = 636) (Figure). For the entire sample, approximately 52% were female, 52% were younger than 40 years, 63% were employed, 70% had private insurance, and 49% lived within 8 kilometers of the clinic. The average age of the entire sample was 40 (SD = 16.9) years, distance to the clinic was 20.6 (SD = 41.8) kilometers, and time to appointment was 7.3 (SD = 7.1) days. Table 1 presents the demographic characteristics by “completed visit” or “failure to show visit.”

Table 1

Demographic Characteristics of Sample^a

	Completed Visit (N = 5959)	No-Show Visit (N = 636)
Male, No. (%)	2847 (48)	327 (51)
Age < 40 y, No. (%)	3027 (51)	410 (64)
Distance < 8 kilometers, No. (%)	2985 (50)	271 (43)
Time to appointment, mean (SD), d	6.89 (6.55)	10.88 (10.47)
Employment, No. (%)
Employed	3838 (64)	340 (53)
Retired	523 (9)	19 (3)
Not employed	686 (12)	179 (28)
Unknown	33 (1)	26 (4)
Student	879 (15)	72 (11)
Payer, No. (%)
Private	4605 (77)	92 (14)
Medicaid	660 (11)	74 (12)
Medicare	489 (8)	12 (2)
VA	22 (0.4)	1 (0.2)
BWC	92 (2)	4 (1)
Unknown	83 (1)	452 (71)
Self	8 (0.1)	1 (0.2)

^aBWC = Bureau of Workers’ Compensation; VA = Veterans Affairs.

Results of the logistic regression (Tab. 2) demonstrated that the following factors increased the odds of patients failing to show for their initial physical therapist evaluation: days to appointment (OR = 1.058; 95% CI = 1.042–1.074), unemployment status (OR = 1.96; 95% CI = 1.41–2.73), unknown employment status (OR = 3.22; 95% CI = 1.12–8.69), Medicaid insurance (OR = 4.87; 95% CI = 3.43–6.93), Medicare insurance (OR = 2.22; 95% CI = 1.10–4.49), unknown payer source (OR = 262.84; 95% CI = 188.72–366.08), and distance traveled 8 or more kilometers (OR = 1.31; 95% CI = 1.01–1.70). On the other hand, the following factors decreased the odds of patients failing to show for their initial physical therapist evaluation (ie, they were more likely to attend their appointment): female sex (OR = 0.73; 95% CI = 0.57–0.95) and age 40 years or older (OR = 0.44; 95% CI = 0.33–0.60). Thus, being male or younger than 40 years increased the odds of patients failing to show at their initial physical therapist evaluation.

Table 2

Odds of Patients Failing to Show for Their Initial Physical Therapist Examination Appointment^a

	OR	95% CI
Female sex	0.734	0.567–0.951
Time to appointment, d	1.058	1.042–1.074
Age > 40 y	0.444	0.331–0.596
Employment status
Employed (ref)
Retired	0.650	0.317–1.322
Not employed	1.962	1.411–2.729
Student	0.744	0.488–1.133
Unknown	3.224	1.197–8.688
Payer
Private insurance (ref)
Medicaid	4.873	3.430–6.925
Medicare	2.221	1.098–4.490
VA	2.355	0.312–17.782
BWC	2.463	0.868–6.988
Unknown	262.843	188.719–366.083
Self	5.715	0.697–46.859
Distance |$\ge$|8 kilometers	1.310	1.012–1.679

^aBWC = Bureau of Workers’ Compensation; OR = odds ratio; ref = reference category; VA = Veterans Affairs.

Discussion

The purpose of this study was to examine factors associated with patients failing to show up for physical therapist initial evaluations at an outpatient orthopedic and sports medicine physical therapy clinic at an academic medical center. The factors that increased the odds of a patient failing to show for his or her initial physical therapist evaluation included increased time to appointment (days), unemployment, unknown employment, Medicaid insurance, unknown insurance, and having to travel greater than 8 kilometers to the clinic. Factors that decreased the odds of failing to show (or increased the probability of attending) included being female and older than 40 years. Our results add to the previous literature examining no-show/nonattendance behavior for physical therapist sessions in other countries.^{19–23,33,34} For example, in an outpatient physical therapy clinic associated with a teaching hospital in Nigeria, the top 3 factors for nonattendance of any physical therapist visit were cost of care, distance required to travel to the clinic, and the patient’s progress with the physical therapist’s plan of care.¹⁹ In England, female patients and patients with referrals from consultants were more likely to attend physical therapy appointments compared to male patients and patients with general practitioner referrals.³³ Other studies have found similar negative effects of missing physical therapy appointments within the United Kingdom,^20,21 New Zealand,^22,23 and South Africa.³⁴

Factors Predicting Failure to Show

Time to appointment (days) was a significant predictor of no-show appointments. For each day a patient had to wait for an initial physical therapy appointment, the odds of attending decreased by 5.8%. However, the OR was very close to 1 (1.058), so these results should be interpreted with caution. Similarly, a systematic review that found a high lead time to the appointment was a significant factor for failure-to-show behavior.¹⁸ Other specialties have reported similar results, with appointments scheduled out more than 37 days at a cancer genetic service facility likely to result in the patient not attending the appointment.³⁵ Although the average time to wait for a physical therapy appointment in the United States is unknown, data from physicians indicate a 30% increase (24.1 days) in average new-patient wait times since 2014.¹ This is further exacerbated by the fact that physician appointment wait times in midsize metropolitan markets are approximately 33% longer compared to large metropolitan markets (56.3 days vs 29 days, respectively). This has a compounding effect if patients are being referred to physical therapists from physicians and have to wait longer still for their initial physical therapy appointment. This backlog in care may limit their outcomes and increase their financial burden.^14,17,24

Cost of care has been shown to be a factor related to nonattendance of physical therapy appointments in Nigeria,^19,36 and socioeconomic status has been frequently associated with failure to show up at appointments.¹⁸ Although we did not examine cost of care in our study because we did not have access to those data, we did examine related factors such as employment status and payer source. Both of these factors demonstrated a clear impact on whether patients attended their initial physical therapist evaluation. For example, our analysis showed that patients who reported an unemployed work status had failure-to-show events almost 2 times more often than those who reported employment. This may represent an effect of socioeconomic status because those who are unemployed may have differing financial priorities. Further, when the employment status was unknown, these patients had a failure-to-show event more than 3 times more often. Unknown employment status may be an indicator of the physical therapy front desk not collecting this information when scheduling the appointment, patients not wishing to divulge this information, and/or it could be a representation of socioeconomic status. However, because socioeconomic status and cost of care data were not available, this is merely conjecture and requires further examination.

Regarding payer source, according to the Current Population Survey Annual Social Economic Supplement (CPS ASEC) and the American Community Survey (ACS), private health insurance in the United States was more prevalent than public health insurance options (67.3% vs 34.4%, respectively).³⁷ For this reason and the fact that private insurance was most prevalent in our sample (70%), we used private insurance as the reference for the logistic regression when categorizing and binning the payer sources. Our analysis indicated that the odds of patients failing to show for their initial physical therapist evaluations were higher for patients with Medicaid insurance, Medicare insurance, and having an unknown payer source. Previous literature demonstrates that not having private insurance is associated with higher no-show rates than other insurance types.¹⁸ More specifically, the OR suggested that patients with Medicaid and Medicare had a failure-to-show rate approximately 5 and 2 times more often, respectively, than those with private insurance. Further, our analysis suggests that patients whose payer source was unknown had a failure to show event approximately 263 times more often than those with private insurance. Further investigation into these subgroups revealed that those who had an unknown payer source were patients who were often seeking first-time medical intervention from the university health system. However, when examining the logistic regression results, the OR and Wald statistic for the “unknown” payer source were quite high (OR = 262.843; Wald = 1086.433). This is likely because “unknown” payer source was almost exclusive to those patients who failed to show. Future research should examine this category in greater detail to further elucidate its potential impact. In comparison, Williams and Pepper²⁴ categorized patient attendance data into 3 payment methods (“workers’ compensation,” “private insurance,” and “other”). In their sample, the majority of patients had private insurance (78%), which included private insurance and Medicare.²⁴ The results of this study were not specific to the initial evaluation; however, they found those with private insurance who missed fewer than 3 physical therapy appointments had a decrease in odds of missing an appointment.²⁴ Interestingly, those with private insurance who lived in the same zip code as the clinic had a greater odds of missing an appointment.²⁴

Finally, distance traveled from the clinic was a significant predictor of no-show appointments. The OR suggested that patients living more than 8 kilometers from the clinic site had a no-show rate for their physical therapist evaluation that was 31% greater than those living within 8 kilometers from the clinic site. This could be due to the increased time required to drive to the clinic with increased distance and the likelihood the patient may encounter a barrier to attending such as traffic, transportation options, or being unable to overcome running late. However, the OR was very close to 1 (1.012), so these results should be interpreted with caution. Yet, our results are consistent with the literature that travel distance to the clinic is a major factor in missed physical therapy appointments,^19,36 and that patient access and adherence may improve with clinics located closer to home.^22,23 No-show rates due to distance from patient residence to the clinic is a barrier for multiple disciplines.¹⁸ Medical centers may use this information to strategically place physical therapy clinics in certain communities or provided explicit options for transportation support during the scheduling process. Also, if a patient lives more than 8 kilometers from the clinic, the registration staff or physical therapist offering patients a clinic closer to home may improve the odds of the patients attending their physical therapist evaluation. Having services closer to where people live or work may improve attendance. However, this may depend on other factors that we did not examine, such as patient conditions, established history with providers, or previous experiences. This requires additional investigation.

Factors Predicting Patient Attendance

Results from our study indicate that female patients have lower odds (OR = 0.73) of a no-show event or conversely attend their initial physical therapist evaluation more often. This is consistent with findings by Armitstead³³ demonstrating that female patients were more likely to attend physical therapy appointments than male patients. Mbada et al,¹⁹ who conducted a study in Nigeria, showed that female patients were more likely to not attend their physical therapy appointments. However, this study examined physical therapist sessions following patients’ initial physical therapist evaluation. Our results are specific to the physical therapist evaluation and not follow-up physical therapy appointments, which could also account for these differences. Further, these differences may depend on the population sampled, the community of interest, and other factors considered. Thus, further examining the effects of sex in other physical therapy settings is important to better understand its potential influence on attending physical therapist evaluations and follow-up sessions.

Finally, our analysis suggests that individuals older than 40 years completed their initial appointments more often than those who were younger than 40. Anecdotally, factors that may influence this result may be life roles related to work hours and childcare needs. Further, patients older than 40 years may have a better availability throughout the work week due to more convenient hours. However, both of these postulations require further examination to better elucidate their potential influence on patient attendance. There is a lack of literature linking physical therapy appointment no-shows to age.^19,24 Most studies investigating the effects of age on missed appointments use too large an age range to make specific conclusions, (< 18 years, 18–65 years, > 65 years).^19,24 For our study, we ultimately categorized patients as being older or younger than 40 years to include in our regression model. This was decided based on the age distribution in our sample. Further examination of the influence of age and no-show behavior could be valuable.

Limitations and Future Research

The results of this study have limitations worth considering. The data were obtained from an outpatient orthopedic and sports physical therapy clinic within a large academic health system in a large, Midwestern metropolitan area. This may limit the generalizability of the results to other settings and environments that might include different patient population types, transportation availability, or location accessibility.³⁶ Our analysis was also limited by the accuracy and consistency of information entered into the electronic medical record by the front office and scheduling staff. Errors and/or omission by those entering the data has the potential to influence results. Although the electronic medical records offer a great deal of information, variance in the methods of data entry exist and could result in skewed data. The referring diagnosis was not included in this data set. Examining the referring diagnosis in future projects may offer additional insight regarding factors that may affect patients’ failure to show up for their initial physical therapist evaluation. Further, we excluded those who canceled their initial physical therapist evaluation, and we did not examine other treatment/follow-up visits because we were interested in factors that may affect attendance at the initiation of physical therapist care. As these may represent potentially very different scenarios, examining these groups or conditions in the future may elicit additional factors that may influence attendance that are unknown and/or have yet to be characterized. In addition, cut points for some of the variables selected for our final model may have influenced the results obtained. In addition, determining if the failure to show appointment was ultimately rescheduled or permanently lost was not able to be reliably performed. Understanding if a patient ultimately rescheduled his or her appointment may affect the estimated impact of the lost appointment. Further, the sample analyzed for this project was fairly large. This may lead to an overpowered study and potentially identify variables that otherwise would not be significant. Thus, examining the intended variables should be within this context. Finally, we did not examine potential interactions in our analysis. The primary factors identified from this analysis will allow the generation of hypothesis-driven analysis for potential interactions in future studies.

Opportunities for future research may include examining the effects of interventions focused on mitigating factors that were found to increase the odds of a no-show event. More specifically, unknown payer source was identified in our sample to be one of the strongest predictors of patients failing to show for their initial physical therapy appointment. Administrative review of a percentage of these patients charts identified many of these patients as “new” to the health system. Examination of whether targeted interventions such as notifications and reminders (eg, text messages, phone calls) or creating a “welcome” program for new patients and informing them about the care provided for their concern may influence their odds of attendance and should be examined. There is overwhelming evidence that a reminder system can reduce no-show appointments in multiple settings; however, more research is needed to understand its effects on underserved populations and the behavior of missed or canceled appointments.³⁸ Further, examining other potential factors such as referral sources and patient conditions/diagnosis may also elucidate additional factors that may influence attendance and prioritization of scheduling.³⁶ Finally, we chose not to include ethnic or racial background in the data. The impact of ethnic or racial background would be better explored in future research using a qualitative design whereby culture and context could be studied within a more sensitive environment.

Conclusion

Results from this study indicate there may be some demographic factors that are predictive of patients failing to show for their first physical therapist visit. Understanding these factors and identifying other potential opportunities for improvements in scheduling processes may help decrease patients failing to attend their initial physical therapy appointment with the ultimate goal of positively influencing patient outcomes.

Author Contributions

Concept/idea/research design: M. Briggs, C. Ulses, L. VanEtten, C. Mansfield, A. Ganim, C. Quatman-Yates

Writing: M. Briggs, L. VanEtten, C. Mansfield, C. Quatman-Yates

Data collection: C. Ulses, L. VanEtten, A. Ganim

Data analysis: M. Briggs, C. Ulses, L. VanEtten, B.N. Hand, C. Quatman-Yates

Project management: M. Briggs, L. VanEtten, C. Mansfield

Providing facilities/equipment: M. Briggs

Providing institutional liaisons: M. Briggs

Consultation (including review of manuscript before submitting): L. VanEtten, C. Mansfield, A. Ganim, B.N. Hand, C. Quatman-Yates

Acknowledgments

The authors thank Nate Craig, PhD, Management Science at The Ohio State University Fisher College of Business, for initial guidance on conducting this project.

Funding

There is no funding to report for this study.

Ethics Approval

This study was approved by The Ohio State University Institutional Review Board.

Disclosures

The authors completed the ICMJE Form for Disclosure of Potential Conflicts of Interest and report no conflicts of interest.