No-Show Rates in a Urogynecology Clinic

IMPORTANCE

No-show appointments, or scheduled appointments that patients do not attend without giving notice of cancellation, are a prevalent problem in the outpatient setting.

OBJECTIVE

The objective of this study was to compare the proportion of patients by decades of life who "no-show" to their urogynecology appointments.

STUDY DESIGN

This retrospective cohort included women 20 years and older who did not show to their urogynecologic clinical encounters at an academic practice between January 1, 2022, and December 31, 2022. Demographics and visit history were recorded. The primary outcome was the proportion of patients by decade of age who were a "no-show" to their appointments. All decades were compared with women in their 70s, the decade with the most patients seen. Secondary outcomes included descriptive data of patients. Descriptive statistics and χ2 analyses were used.

RESULTS

The cohort of 450 no-show encounters (composed of 391 patients), out of 6729 encounters, demonstrated an overall no-show rate of 6.7%. Baseline demographics of "no-show" patients were 67.3% White and 27.4% Black. The odds of women in their 20s-50s who no-show was 2-3 times higher than women in their 70s (P < 0.01). The highest no-show rates occurred in 20s (12.6%) and 40s (11.8%). Forty-six patients missed multiple appointments. The odds of a Black patient having multiple no-shows was 3.15 times higher than the odds of a White patient.

CONCLUSIONS

No-show rates are low in this urogynecology practice. Younger women are more likely to no-show. This knowledge can facilitate potential double bookings necessary for urgent appointments and to maximize resource utilization.

Racial disparities in telehealth use during the coronavirus disease 2019 pandemic

OBJECTIVE

To investigate the impact of coronavirus disease 2019 on initial infertility consultations.

DESIGN

Retrospective cohort.

SETTING

Fertility practice in an academic medical center.

PATIENTS

Patients presenting for initial infertility consultation between January 2019 and June 2021 were randomly selected for prepandemic (n = 500) and pandemic (n = 500) cohorts.

EXPOSURE

Coronavirus disease 2019 pandemic.

MAIN OUTCOME MEASURES

The primary outcome was a change in the proportion of African American patients using telehealth after pandemic onset compared with all other patients. Secondary outcomes included presentation to an appointment vs. no-show or cancellation. Exploratory outcomes included appointment length and in vitro fertilization initiation.

RESULTS

The prepandemic cohort vs. the pandemic cohort had fewer patients with commercial insurance (64.4% vs. 72.80%) and more African American patients (33.0% vs. 27.0%), although the racial makeup did not differ significantly between the two cohorts. Rates of missed appointments did not differ between the cohorts, but the prepandemic cohort vs. the pandemic cohort was more likely to no-show (49.4% vs. 27.8%) and less likely to cancel (50.6% vs. 72.2%). African American patients, compared with all other patients, during the pandemic were less likely to use telehealth (57.0% vs. 66.8%). African American patients, compared with all other patients, were less likely to have commercial insurance (prepandemic: 41.2% vs. 75.8%; pandemic: 57.0% vs. 78.6%), present to their scheduled appointment (prepandemic: 52.7% vs. 73.7%; pandemic: 48.1% vs. 74.8%), and cancel vs. no-show (prepandemic: 30.8% vs. 68.2%, pandemic: 64.3% vs. 78.3%). On multivariable analysis, African American patients were less likely (odds ratio 0.37, 95% confidence interval 0.28-0.50) and telehealth users were more likely (odds ratio 1.54, 95% confidence interval 1.04-2.27) to present to their appointments vs. no-show or cancel when controlling for insurance type and timing relative to the onset of the pandemic.

CONCLUSION

Telehealth implementation during the coronavirus disease 2019 pandemic decreased the overall no-show rate, but this shift did not apply to African American patients. This analysis highlights disparities in insurance coverage, telehealth utilization, and presentation for an initial consultation in the African American population during the pandemic.

Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures

OBJECTIVE

Avoidable case cancellations within 24 h reduce operating room (OR) efficiency, add unnecessary costs, and may have physical and emotional consequences for patients and their families. We developed and validated a prediction tool that can be used to guide same day case cancellation reduction initiatives.

DESIGN

Retrospective hospital registry study.

SETTING

University-affiliated hospitals network (NY, USA).

PATIENTS

246,612 (1/2016-6/2021) and 58,662 (7/2021-6/2022) scheduled elective procedures were included in the development and validation cohort.

MEASUREMENTS

Case cancellation within 24 h was defined as cancelling a surgical procedure within 24 h of the scheduled date and time. Our candidate predictors were defined a priori and included patient-, procedural-, and appointment-related factors. We created a prediction tool using backward stepwise logistic regression to predict case cancellation within 24 h. The model was subsequently recalibrated and validated in a cohort of patients who were recently scheduled for surgery.

MAIN RESULTS

8.6% and 8.7% scheduled procedures were cancelled within 24 h of the intended procedure in the development and validation cohort, respectively. The final weighted score contains 29 predictors. A cutoff value of 15 score points predicted a 10.3% case cancellation rate with a negative predictive value of 0.96, and a positive predictive value of 0.21. The prediction model showed good discrimination in the development and validation cohort with an area under the receiver operating characteristic curve (AUC) of 0.79 (95% confidence interval 0.79-0. 80) and an AUC of 0.73 (95% confidence interval 0.72-0.73), respectively.

CONCLUSIONS

We present a validated preoperative prediction tool for case cancellation within 24 h of surgery. We utilize the instrument in our institution to identify patients with high risk of case cancellation. We describe a process for recalibration such that other institutions can also use the score to guide same day case cancellation reduction initiatives.

The effect of a public transportation expansion on no-show appointments

OBJECTIVE

To test whether there were fewer missed medical appointments ("no-shows") for patients and clinics affected by a significant public transportation expansion.

STUDY SETTING

A new light rail line was opened in a major metropolitan area in June 2014. We obtained electronic health records data from an integrated health delivery system in the area with over three million appointments at 97 clinics between 2013 and 2016.

STUDY DESIGN

We used a difference-in-differences research design to compare whether no-show appointment rates differentially changed among patients and clinics located near versus far from the new light rail line after it opened. Models included fixed effects to account for underlying differences across clinics, patient zip codes, and time.

DATA EXTRACTION METHODS

We obtained data from an electronic health records system representing all appointments scheduled at 97 outpatient clinics in this system. We excluded same-day, urgent care, and canceled appointments.

PRINCIPAL FINDINGS

The probability of no-show visits differentially declined by 0.5 percentage points (95% confidence interval [CI]: -0.9 to -0.1), or 4.5% relative to baseline, for patients living near the new light rail compared to those living far from it, after the light rail opened. The effects were stronger among patients covered by Medicaid (-1.6 percentage points [95% CI: -2.4 to -0.8] or 9.5% relative to baseline).

CONCLUSIONS

Improvements to public transit may improve access to health care, especially for people with low incomes.

How scheduling systems with automated appointment reminders improve health clinic efficiency

Missed clinic appointments or no-shows burden health care systems through inefficient use of staff time and resources. Scheduling software with automatic appointment reminders shows promise to improve clinics' management through timely cancellations and re-scheduling, but at-scale evidence is missing. We study a nationwide text message appointment reminder program in Chile implemented at primary care clinics for patients with chronic disease. Using longitudinal clinic-level data, we find that the program did not change the number of visits by chronic patients eligible to receive the reminder but visits from other patients ineligible to receive reminders increased by 5.0% in the first year and 7.4% in the second. Clinics treating more chronic patients and those with a relatively younger patient population benefited more from the program. Scheduling systems with automatic appointment reminders were effective in increasing clinics' ability to care for more patients, likely due to timely cancellations and re-scheduling.

Overbooking for physical examination considering late cancellation and set-resource relationship

BACKGROUND

Late cancellations of physical examination has severe impact on the operations of a physical examination center since it is often too late to fill vacancy. A booking control policy that considers overbooking is then one natural solution. Unlike appointment scheduling problems for clinics and hospitals, in which treating a patient mostly requires only one type of resource, a physical examination set typically requires multiple types of resources. Traditional methods that do not consider set-resource relationship thus may be inapplicable.

METHODS

We formulate a stochastic mathematical programming model that maximizes the expected net reward, which is the examination revenue minus overage cost. A complete search algorithm and a greedy search algorithm are designed to search for optimal booking limits for all examination sets. To estimate the late cancellation probability for each individual consumer, we apply logistic regression to identify significant factors affecting the probability. After clustering is used to estimate individual probabilities, Monte Carlo simulation is conducted to generate probability distributions for the number of consumers without late cancellations. A discrete-event simulation is performance to evaluate the effectiveness of our proposed solution.

RESULTS

We collaborate with a leading physical examination center to collect real data to evaluate our proposed overbooking policies. We show that the proposed overbooking policy may significantly increase the expected net reward. Our simulation results also help us understand the impact of overbooking on the expected number of customers and expected overage. A sensitivity analysis is conducted to demonstrate that the benefit of overbooking is insensitive to the accuracy of cost estimation. A Pareto efficiency analysis gives practitioners suggestions regarding policy determination considering multiple performance indications.

CONCLUSIONS

Our proposed overbooking policies may greatly enhance the overall performance of a physical examination center.

A Bibliometric Analysis on No-Show Research: Status, Hotspots, Trends and Outlook

No-show is a thorny issue within the social scope. It not only affects the sustainability of service system operation but also causes heavy irretrievable losses. To maintain and develop the sustainability of service, this paper adopts bibliometric technology to reflect the current status and future prospects about no-show research. And we strive to explore and summarize appointment scheduling methods for no-show problems. The bibliometric analysis was carried out from various aspects including research areas, countries/regions, institutions, journals, authors and author keywords based on papers harvested from Web of Science Core Collection database. The total 1197 papers show that the United States is in a leading position in this field, followed by England and Canada. University of London is the most productive institution with the highest total citations and H-Index. BMC Health Services Research ranks first as the most productive journal, followed by European Journal of Operational Research and Production and Operations Management. Through the analysis of hot articles, we can conclude that how to reduce the impact of no-shows on the sustainability of service systems has become the main research direction. In addition to appointment scheduling, other effective methods are also mentioned. Further study on these methods will be a major research direction in the future.

Using overbooking to manage no-shows in an Italian healthcare center

Background

In almost all healthcare systems, no-shows (scheduled appointments missed without any notice from patients) have a negative impact on waiting lists, costs and resource utilization, impairing the quality and quantity of cares that could be provided, as well as the revenues from the corresponding activity. Overbooking is a tool healthcare providers can resort to reduce the impact of no-shows.

Methods

We develop an overbooking algorithm, and we assess its effectiveness using two methods: an analysis of the data coming from a practical implementation in an healthcare center; a simulation experiment to check the robustness and the potential of the strategy under different conditions. The data of the study, which includes personal and administrative information of patients, together with their scheduled and attended examinations, was taken from the electronic database of a big outpatient center. The attention was focused on the Magnetic Resonance (MR) ward because it uses expensive equipment, its services need long execution times, and the center has actually used it to implement an overbooking strategy aimed at reducing the impact of no-shows. We propose a statistical model for the patient’s show/no-show behavior and we evaluate the ensuing overbooking procedure implemented in the MR ward. Finally, a simulation study investigates the effects of the overbooking strategy under different scenarios.

Results

The first contribution is a list of variables to identify the factors performing the best to predict no-shows. We classified the variables in three groups: “Patient’s intrinsic factors”, “Exogenous factors” and “Factors associated with the examination”. The second contribution is a predictive model of no-shows, which is estimated on context-specific data using the variables just discussed. Such a model represents a fundamental ingredient of the overbooking strategy we propose to reduce the negative effects of no-shows. The third contribution is the assessment of that strategy by means of a simulation study under different scenarios in terms of number of resources and no-show rates. The same overbooking strategy was also implemented in practice (giving the opportunity to consider it as a quasi-experiment) to reduce the negative impact caused by non attendance in the MR ward. Both the quasi-experiment and the simulation study demonstrated that the strategy improved the center’s productivity and reduced idle time of resources, although it increased slightly the patient’s waiting time and the staff’s overtime. This represents an evidence that overbooking can be suitable to improve the management of healthcare centers without adversely affecting their costs and the quality of cares offered.

Conclusions

We shown that a well designed overbooking procedure can improve the management of medical centers, in terms of a significant increase of revenue, while keeping patient’s waiting time and overtime under control. This was demonstrated by the results of a quasi-experiment (practical implementation of the strategy in the MR ward) and a simulation study (under different scenarios). Such positive results took advantage from a predictive model of no-show carefully designed around the medical center data.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-2979-z) contains supplementary material, which is available to authorized users.

No-shows in appointment scheduling - a systematic literature review

No-show appointments significantly impact the functioning of healthcare institutions, and much research has been performed to uncover and analyze the factors that influence no-show behavior. In spite of the growing body of literature on this issue, no synthesis of the state-of-the-art is presently available and no systematic literature review (SLR) exists that encompasses all medical specialties. This paper provides a SLR of no-shows in appointment scheduling in which the characteristics of existing studies are analyzed, results regarding which factors have a higher impact on missed appointment rates are synthetized, and comparisons with previous findings are performed. A total of 727 articles and review papers were retrieved from the Scopus database (which includes MEDLINE), 105 of which were selected for identification and analysis. The results indicate that the average no-show rate is of the order of 23%, being highest in the African continent (43.0%) and lowest in Oceania (13.2%). Our analysis also identified patient characteristics that were more frequently associated with no-show behavior: adults of younger age; lower socioeconomic status; place of residence is distant from the clinic; no private insurance. Furthermore, the most commonly reported significant determinants of no-show were high lead time and prior no-show history.

A dynamic approach for outpatient scheduling

AIMS

Patient no-show is a recurrent problem in medical centers and, in conjunction with cancellation of appointments, often results in loss of productivity and excessive patient time to appointment. The purpose of this study was to develop a dynamic procedure for scheduling patients within an outpatient clinic where patients are expected to have multiple appointments, such as physical therapy, occupational therapy, primary care, and dentistry.

METHODS

This retrospective study involved the year 2014 de-identified patient records from an outpatient clinic affiliated with a large university hospital. A number of patient characteristics, appointment data, and historical attendance records were examined to determine whether they significantly impacted patients who missed scheduled appointments (no-shows). Patient attendance behaviors over multiple appointments were examined to determine whether their no-show and cancellation patterns differed from one appointment to the next. Decision tree analysis was applied to those predictors that significantly correlated with patient attendance behavior to assess the likelihood of a patient no-show. A sample dynamic appointment scheduling procedure that utilized different overbooking strategies for different appointment numbers was then developed. Computer simulation was used to assess the effectiveness of the dynamic procedure versus two other methods consisting of randomly assigned and uniformly assigned appointments.

RESULTS

The dynamic scheduling procedure resulted in increased scheduling efficiency through overbooking but with less than 5% risk of appointment conflicts (i.e. two patients showing at the same time), equating to approximately 0.16 conflicts per clinician per day. It also increased clinic utilization by about 6.7%. It consistently outperformed the other two methods with respect to the percentage of appointment conflicts.

LIMITATIONS

The study is limited with respect to potential clinician cost increase resulting from possible appointment conflicts. A second limitation is that patients experiencing appointment conflicts might not wait for treatment, resulting in potential loss of revenue. A third limitation is that the model does not take into account patient satisfaction, nor the ethics of overbooking patients.

CONCLUSIONS

A dynamic appointment scheduling procedure was developed using actual patient characteristics. The procedure resulted in creation of more efficient appointment schedules thereby increasing the clinic utilization.

Getting patients in the door: medical appointment reminder preferences

PURPOSE

Between 23% and 34% of outpatient appointments are missed annually. Patients who frequently miss medical appointments have poorer health outcomes and are less likely to use preventive health care services. Missed appointments result in unnecessary costs and organizational inefficiencies. Appointment reminders may help reduce missed appointments; particular types may be more effective than other types. We used a survey with a discrete choice experiment (DCE) to learn why individuals miss appointments and to assess appointment reminder preferences.

METHODS

We enrolled a national sample of adults from an online survey panel to complete demographic and appointment habit questions as well as a 16-task DCE designed in Sawtooth Software's Discover tool. We assessed preferences for four reminder attributes - initial reminder type, arrival of initial reminder, reminder content, and number of reminders. We derived utilities and importance scores.

RESULTS

We surveyed 251 adults nationally, with a mean age of 43 (range 18-83) years: 51% female, 84% White, and 8% African American. Twenty-three percent of individuals missed one or more appointments in the past 12 months. Two primary reasons given for missing an appointment include transportation problems (28%) and forgetfulness (26%). Participants indicated the initial reminder type (21%) was the most important attribute, followed by the number of reminders (10%). Overall, individuals indicated a preference for a single reminder, arriving via email, phone call, or text message, delivered less than 2 weeks prior to an appointment. Preferences for reminder content were less clear.

CONCLUSION

The number of missed appointments and reasons for missing appointments are consistent with prior research. Patient-centered appointment reminders may improve appointment attendance by addressing some of the reasons individuals report missing appointments and by meeting patients' needs. Future research is necessary to determine if preferred reminders used in practice will result in improved appointment attendance in clinical settings.

Factors associated with non-attendance at a hand surgery appointment

BACKGROUND

Patients who miss scheduled appointments without notifying office staff--"no-shows"--disrupt practice workflow and decrease access for others, resulting in misuse of resources and lost revenue. The primary purpose of this study was to identify factors associated with no-shows in a hospital-based outpatient hand office. Secondarily, we studied factors associated with cancelations.

METHODS

Of the 14,793 new adult patient appointments to our outpatient hand surgery office scheduled between January 2011 and December 2013, 880 (5.9 %) were no-shows and 2715 (18 %) were cancelations. Data on patient demographics and timing of the visit were collected to construct a multinomial logistic regression model of determinants of appointment no-shows and cancelations.

RESULTS

Factors independently associated with no-shows included younger age, Hispanic or black race, unmarried status (single or divorced), appointment on a Monday or Tuesday, and residence near the office. Factors associated with cancelations were female sex, unmarried status (widowed or divorced), winter season, and appointment on a weekday other than Friday.

CONCLUSIONS

Non-attendees are more likely to be younger, unmarried, non-white, to have their appointments at the start of the week, and to live near the office. Knowledge of these factors might prove useful for implementation of tailored quality improvement initiatives to reduce non-attendance and maximize productivity in the hand surgery office setting.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prognostic IV.

Appointment reminder systems and patient preferences: Patient technology usage and familiarity with other service providers as predictive variables

This study had two aims: to measure patient preferences for medical appointment reminder systems and to assess the predictive value of patient usage and familiarity with other service providers contacting them on responsiveness to appointment reminder systems. We used a cross-sectional design wherein patients' at an urban, primary-care clinic ranked various reminder systems and indicated their usage of technology and familiarity with other service providers contacting them over text messages and e-mails. We assessed the impact of patient usage of text messages and e-mails and patient familiarity with other service providers contacting them over text messages and e-mails on effectiveness of and responsiveness to appointment reminder systems. We found that patient usage of text messages or e-mails and familiarity with other service providers contacting them are the best predictors of perceived effectiveness and responsiveness to text message and e-mail reminders. When these variables are accounted for, age and other demographic variables do not predict responsiveness to reminder systems.

Hereditary angioedema: a clinical review for the otolaryngologist

Hereditary angioedema (HAE) is a relatively rare genetic disorder that is usually characterized by either low levels of C1 esterase inhibitor (C1-INH) or the presence of dysfunctional C1-INH. It can present with relatively mild and self-limiting symptoms, but it is also potentially fatal; the most common cause of death is asphyxiation secondary to edema of the upper airway. The diagnosis of HAE, especially in the emergency situation, is not straightforward. HAE must be distinguished from several other types of angioedema that require different management approaches. Management approaches include trigger avoidance and pharmacologic therapy; the latter has traditionally involved the administration of attenuated androgens and antifibrinolytics. Recently, a new class of agent-C1-INH-has been introduced in the United States. This article provides an update on the pathophysiology, clinical picture, diagnosis, prophylaxis, and acute treatment of HAE. We must keep HAE in mind as a possible diagnosis whenever we are faced with a case of unexplained angioedema if we are to take advantage of the effective and more specific therapies that are becoming available.

A probabilistic model for predicting the probability of no-show in hospital appointments

The number of no-shows has a significant impact on the revenue, cost and resource utilization for almost all healthcare systems. In this study we develop a hybrid probabilistic model based on logistic regression and empirical Bayesian inference to predict the probability of no-shows in real time using both general patient social and demographic information and individual clinical appointments attendance records. The model also considers the effect of appointment date and clinic type. The effectiveness of the proposed approach is validated based on a patient dataset from a VA medical center. Such an accurate prediction model can be used to enable a precise selective overbooking strategy to reduce the negative effect of no-shows and to fill appointment slots while maintaining short wait times.

Education research: a new system for reducing patient nonattendance in residents' clinic

BACKGROUND

Patient nonattendance in neurology and other subspecialty clinics is closely linked to longer waiting times for appointments. We developed a new scheduling system for residents' clinic that reduced average waiting times from >4 months to < or =3 weeks. The purpose of this study was to compare nonattendance for clinics scheduled using the new model (termed "rapid access") vs those scheduled using the traditional system.

METHODS

In the rapid access system, nonestablished (new) patients are scheduled on a first-come, first-served basis for appointments that must occur within 2 weeks of their telephone request. Nonattendance for new patient appointments (cancellations plus no-shows) was compared for patients scheduled under the traditional vs the rapid access scheduling systems. Nonattendance was compared for periods of 6, 12, and 18 months following change in scheduling system using the chi2 test and logistic regression.

RESULTS

Compared to the traditional scheduling system, the rapid access system was associated with a 50% reduction in nonattendance over 18 months (64% [812/1,261 scheduled visits] vs 31% [326/1,059 scheduled visits], p < 0.0001). In logistic regression models, appointment waiting time was a major factor in the relation between rapid access scheduling and nonattendance. Demographics, diagnoses, and likelihood of scheduling follow-up visits were similar between the 2 systems.

CONCLUSIONS

A new scheduling system that minimizes waiting times for new patient appointments has been effective in substantially reducing nonattendance in our neurology residents' clinic. This rapid access system should be considered for implementation and will likely enhance the outpatient educational experience for trainees in neurology.