Temporal Characteristics of Missed Clinic Appointments in an Academic Center in West Texas

Missed clinic appointments are a nationwide issue in the field of healthcare. Appointments are categorized as a missed appointment when the patient fails to attend a scheduled appointment and does not notify the clinic. These missed clinic appointments have negative implications for patients, providers, and clinics. Patients at high-risk for missing appointments were more likely to have incomplete preventative screening for cancer, poorly controlled chronic diseases, and increased use of acute care centers when compared with patients with a low-risk for missing appointments.(1) Missing appointments can have poor outcomes for individuals with chronic conditions. For example, missed primary care appointments increased the risk of hospital admissions for diabetic patients.(2)

Physicians and clinics also face negative consequences when patients miss an appointment. Missed appointments result in decreased continuity of care, opportunities for resident education, and clinical productivity.(3) In addition, each missed appointment results in a loss to clinic revenue, estimated in one study as a loss of roughly $210 per missed appointment.(4) The money lost from patients missing appointments could have been used to improve patient access and care.(5)

Missed appointments have been linked to a variety of factors. Patients contacted after a missed appointment reported issues with transportation, forgetfulness, and limited time off from work as reasons for missing their appointment.(6) Additional issues that prevent patients from making appointments included having Medicaid insurance, distance from clinic, and the amount of time since the appointment was scheduled.(7) Other contributing factors were found to be requiring the use of a medical interpreter, frequent emergency center visits, less continuity with the clinic, and a lower proportion of visits with the provider.(8)

The Texas Tech University Health Sciences Center (TTUHSC) Internal Medicine outpatient clinic in Lubbock, Texas, has a high rate of missed appointments. West Texas and the Panhandle, except for a handful of counties, is considered both rural and medically underserved according to the Department of Health and Human Services.(9) Although a number of studies have explored the demographic predictors of missed clinic appointments, few studies have explored the temporal characteristics of missed appointments, particularly with a focus on time of day or times around holiday breaks. One study found a correlation between the rates of missed appointments and the days close to holidays.(10) Individual clinics must explore how demographic and regional variation may impact patterns of missed appointments. Although the TTUHSC Internal Medicine clinic may not be representative of other academic Internal Medicine clinics across the United States, the model for examining patterns in missed appointments is applicable across diverse clinics. This retrospective study aims to better understand the temporal characteristics of missed clinic appointments in hopes that scheduling changes or other interventions may be identified as possible ways to reduce the prevalence of missed appointments.

Methods

Data selection included 197,632 scheduled outpatient appointments for individuals who missed at least one appointment between 2011 and 2014 in the Internal Medicine clinic at TTUHSC in Lubbock. Appointments canceled by the patient or the clinic were not included in analyses, because the former indicates initiative by the patient to cancel or reschedule, whereas the latter is out of the patient’s control. A transact-SQL query was used to pull appointment and patient characteristics from the institutional database. The final sample includes 136,531 kept or missed appointments, linked to 17,978 individuals. As noted, 61,101 appointments were excluded because they were canceled by the patient or the clinic. The TTUHSC Institutional Review Board classified the project as quality improvement and declined to review it. Nevertheless, safeguards were taken to comply with HIPAA policies, analyzing the data in de-identified form on password protected computers and servers.

The unit of analysis was an appointment that was missed or kept. The data extraction parameters included only appointments of individuals with at least one missed appointment, meaning that no comparison population of appointments of individuals with no missed appointments was available. About 14.3% (n=2572) of patients in the sample had a single missed appointment, and no other kept or missed appointments, during the time period. Although basic patient demographics are provided for descriptive purposes, race, gender, and insurance status are excluded from the appointment-level analysis to prevent bias resulting from individuals with many appointments in the database. For example, most individuals have multiple kept and missed appointments, meaning that the two groups are not mutually exclusive, such that demographics of missed vs. kept appointments are not a meaningful comparison. Statistical analysis was performed using Stata 15.1 MP (StataCorp. 2017. Stata Statistical Software: College Station, TX: StataCorp LP). Wald tests were used to compare means by time of day or month, respectively, with statistical significance set at p <.05. It should be noted that data for this study is a population of appointments for a four-year span, rather than a random sample.

For the purposes of this analysis, holidays are defined as weekdays where the clinic was closed. These holidays are similar to most United States federal holidays, although the clinic is open for some federal and religious holidays, which provides for analysis of missed appointment rates on those days as well. These non-shared holidays when the clinic is open include President’s Day, Good Friday, Columbus Day, and Veterans Day. Given that very few of the scheduled appointments fell between noon and 1 PM, the focal comparison for this study is appointments scheduled from 8 AM to noon and 1 PM to 5 PM.

Results

Table 1 shows the outcomes of all scheduled appointments during the selected time frame. Approximately 26.8% of all appointments over the four-year span were missed, totaling more than 36,600 instances where patients did not show up and did not let the clinic know of their absence in time to cancel the appointment. Time from scheduling of the appointment to the appointment date itself is also shown in Table 1, with missed appointments having been scheduled about 16 days earlier than kept appointments. Although scheduling is a relevant data point in understanding missed appointments, this variable is not included in later analyses. The nature of the data did not provide adequate information to distinguish between appointments scheduled for urgent need versus those scheduled on a recurring three- or six-month basis; consequently, the variable had minimal descriptive power.

Table 2 describes the population of patients with a missed appointment. The population is 52.4% female and consists predominantly of non-Hispanic white (55.2%) and Hispanic (20.2%) patients. Medicare (41.8%) is the largest insurance provider for the patient population, followed by private insurance (28.1%) and Medicaid (13.3%).

Table 3 presents differences by day of the week and time of day. Missed appointments were significantly more common after 1 PM (28.6%) compared with before noon (25.0%). Missed appointment rates for Monday through Thursday ranged from 25.7% to 27.0%, rising to 29.8% on Fridays. Although all days of the week exhibited significant differences in missed appointment rates between morning and afternoon, Figure 1 highlights the fact that Tuesday (30.7%) and Friday (31.8%) afternoons presented particularly high rates of missed appointments.

Figure 1. Percent missed appointments by day of the week and time of day.

Missed appointments on days after holidays also were particularly high, at 31.0%. The missed appointment rate on the day before a holiday was significantly lower (28.5%) compared with the days following holidays. Rates for days before holidays dropped further with Fridays excluded (26.6%). Missed afternoon appointments on days after holidays also were particularly high (32.9%) and were significantly higher than the percentages of missed morning appointments on those days (28.9%). Finally, appointments on common federal or religious holidays when the clinic was open, e.g. Good Friday, exhibited a missed appointment rate of 27.1%, which is similar to non-holiday rates.

Table 4 shows monthly characteristics of missed appointments. Missed appointments were least prevalent in March (24.8%) and most prevalent in December (29.9%). Timing by week of the month was examined by comparing missed appointments during the first seven days of the month with the last seven days of the month. Missed appointment rates were 27.6% during the first seven days of the month, and 25.9% during the last seven days of the month. The difference of 1.6% was statistically significant.

Discussion

Few studies have examined the temporal data of missed appointments. The results of our study demonstrate that definitive temporal patterns exist around missed appointments. Missed appointments were more common after 1 PM, with significantly higher missed appointments on Tuesday and Friday afternoons. Days following holidays had a significant increase in missed appointments, especially the appointments after 1 PM on these days. Missed appointments were more common during the last week of the month compared with the first week. The results of this study demonstrate that missed appointment rates are affected by the time of the appointment, day of the week, month of the year, and holidays.

A number of studies have examined demographic and social factors that result in missed appointment rates for patients, but few have examined the role of temporal characteristics in understanding missed appointment rates. Using both the temporal data from this study and previous data on demographic factors could help clinics better structure appointment scheduling and clinic hours. Other factors that recently have been linked to missed appointments include the lead time (time between scheduling and the appointment), whether the patient has had prior missed appointments, whether the patient owns a cell phone, the patient’s tobacco use, and the number of days since the patient’s last appointment.(11)

One solution to combat high missed appointment rates on Friday could be to make Friday afternoons open for walk-in patients instead of scheduled visits.

Days following holidays exhibited the highest missed appointment rates for our outpatient clinic. Therefore, adding additional reminders on days at risk for higher missed appointment rates could be beneficial. A recent study found that using two automated reminders instead of one was effective at reducing missed appointments without reducing visit satisfaction among patients.(12) Additionally, previous studies have found that utilizing Short Messaging Service, otherwise known as text messages, improved patient satisfaction and reduced missed appointment rates.(13) Modest overbooking during periods with high rates of missed appointments could potentially be used to keep consistent patient flow. However, overbooking could lead to longer wait times, which has been shown to be a significant predictor for patient attendance in clinic.(14) An additional consideration in altering clinic structure is how patient satisfaction will be affected due to Medicare reimbursement being contingent on patient satisfaction. A recent systematic review of patient satisfaction studies found there was a variety of determinants of patient satisfaction, but due to there being no globally accepted formulation of patient satisfaction or measurement system, there was no succinct conclusion on factors affecting satisfaction.(15) Patient satisfaction should be included as a metric in future studies of missed appointments and subsequent interventions.

Friday afternoons were shown to have significantly higher missed appointment rates compared with other weekday afternoons. One solution to combat high missed appointment rates on Friday could be to make Friday afternoons open for walk-in patients instead of scheduled visits. Furthermore, previous research has found that patients who utilize patient portals had reduced missed appointment rates and fewer total office visits.(16) Increasing the use of the patient portal system also could help lower missed appointment rates.

Limitations

The population analyzed for this study is not a random sample, but, rather, is the full population of kept or missed appointments over a four-year time frame. We do not propose that the findings are necessarily generalizable to any other clinics. However, we believe that the systematic analysis of aggregate data is a useful exercise for clinics to undertake to allow for the identification of temporal patterns in each clinic’s respective patient populations. This information is a good starting point for administrators seeking to reduce inefficiencies in scheduling, staffing, and patient flow. However, billing data have limitations in identifying motivating causes for patient behavior in regard to keeping appointments. Future studies should evaluate reasons for patient missed appointments from the patient’s perspective, in conjunction with analysis of the socioeconomic, demographic, and temporal characteristics of the patient and appointment.

Conclusion

Based on the multifactorial issues contributing to missed appointments, it is unlikely that a single intervention is a “fix all” to the problem. However, many clinics have a wealth of data at their disposal that can be used to better understand the patterns in missed appointments. Solutions are likely to be dependent on region, clinic, and patient population, but the use of aggregate retrospective data to examine temporal patterns can be a valuable tool to help clinics maximize efficiency and better use resources.

Acknowledgements: The authors acknowledge use of resources at the Clinical Research Institute, TTUHSC. We thank Shalene Vick and Cari Crooks at TTUHSC for their assistance with data acquisition.

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