Who Misses Appointments Made Online? Retrospective Analysis of the Outpatient Department of a General Hospital in Jinan, Shandong Province, China

Abstract

Purpose: Missed appointments in outpatient registration pose challenges for hospital administrators, especially in the context of China’s shortage of medical resources. Previous studies have identified factors that affect healthcare access via traditional appointment systems. Few studies, however, have specifically investigated Internet appointment systems. Therefore, this study explored the key factors related to missed appointments made on the Internet appointment system of a general hospital in Jinan, Shandong Province.

Methods: Online appointment data were collected from the outpatient department of a general hospital in Jinan from September 2017 to February 2018. Logistic regression was used to analyze the relative importance of eight variables: gender, age, interval between scheduling and appointment, day of the week, physician’s academic rank, appointment fee, previous missed appointments, and clinical department.

Results: A total of 48,777 online appointment records were collected, which included a 15% no-show rate. The key factors associated with no-shows included age, interval between scheduling and appointment, previous missed appointments, and clinical department. No significant relationships were found between no-shows and gender, day of the week, and appointment fee.

Conclusion: No-show rates were influenced by many factors. Based on this study’s findings, targeted measures can be taken to decrease no-show frequency and improve medical efficiency.

Introduction

A "no-show" appointment is one that a patient misses without notifying the clinic or hospital that he or she will not attend the appointment. Patient no-show rates can range from 3% to 80%, depending on the patient population and the type of clinic.^1,2 No-show appointments can undermine planning, reduce access for others, and decrease efficiency, resulting in an underutilization of resources, which thereby raises costs, increases wait times, and decreases satisfaction.^1,3,4

It is well known that "no-show" appointments are a serious problem in healthcare, especially in China, where medical resource tends to be scarce. For administrators in general hospitals, how to effectively utilize existing medical resources is a major challenge. As a major intervention mode in hospital resources, outpatient appointment systems significantly affect the efficiency of hospital services as well as patient experiences. Traditional outpatient registration methods—including manual window registration and telephone registration—have great defects in terms of timeliness and efficiency. With the application of information technology in the medical field—especially "Internet+medical" applications—online appointment systems have become a viable alternative that optimizes the appointment process. Online appointments greatly improve the efficiency of hospital services, reduce patient congestion, shorten wait times, and improve patient experiences.

Parmar et al found that online outpatient booking systems had a significantly better rate of attendance than traditional appointment methods, with a 17.8% no-show rate.⁵ Habibi et al found that the no-show rate decreased from 25% to 11% after the introduction of an online appointment scheduling system.⁶ Dusheiko et al also identified a reduction in no-shows with the use of online appointments.⁷ Yet, the problem of no-show appointments has yet to be sufficiently resolved, despite the fact that various interventions have been introduced to reduce no-show rates, such as message reminders and temporary account suspensions.^8–13 Since no-shows are influenced by a number of factors, simple reminders or penalties cannot fundamentally solve the problem. Thus, for hospital administrators, identifying the key factors affecting no-shows is an important problem to solve.

Regarding the factors affecting no-show appointments, previous studies have identified factors such as forgetfulness, age, interval between scheduling and appointment, feeling better or worse, transportation problems, insurance issues, and gender.^1,14–20 However, such studies mainly focused on traditional appointment systems while few have considered online appointment systems. The present study, therefore, aimed to evaluate the factors affecting missed appointments using online appointment data from a general hospital over a 6-month period. Furthermore, by analyzing no-show variables, we aimed to extract the potential influencing factors to find a measurement to reduce the rate of missed appointments.

Materials and Methods Data

The data contained the online appointment records of the outpatient department of a general hospital in Jinan, Shandong Province, China, from September 2017 to February 2018. Traditional appointment records were excluded. The raw data were provided by Shunneng Network Technology Limited Company, who built the online appointment system for the hospital. The data included gender, age, interval between scheduling and appointment, day of the week, physician's academic rank, appointment fee, and clinical department. Identifying information (eg, resident identity card, telephone number) was excluded; incomplete records were excluded as well. The data also contained the outcomes of appointments—namely, cancellation or attendance. The number of previous no-show appointments for the hospital was also collected for the period January 2017 to August 2017. This study was approved by the Academic Board of the School of Management, Science and Engineering, Shandong University of Finance and Economics (A-19-0089). At the same time, the Ethics Committee accordance with the principles stated in the Declaration of Helsinki approved the waiver of informed consent because of the following reasons. Firstly, we obtained the appointment records in this study from the data source in the Internet, and consent would be impossible or impracticable to obtain for such research. Secondly, the waiver of informed consent did not adversely affect the rights and health of the subjects. Thirdly, the privacy and data of the subjects are confidentiality. Finally, this study does not involve medical records that the subjects have explicitly refused before.

Use of the data was approved by Shunneng Network Technology Limited Company. And these data were permitted to access after authorization. Data were compiled using Microsoft Excel 2010.

Statistical Methods

Descriptive statistical analysis was used to reveal the characteristics of different variables.

Univariate regression analysis was performed on all eight variables using SPSS 18.0 (SPSS Inc., Chicago, IL, USA). Multivariate logistic regression tests were conducted to illuminate potential correlations between the variables and missed appointments; p<0.05 was considered to be statistically significant.

Results Description of Data

A total of 48,777 appointment records in the online appointment system were collected, including 7378 missed appointments (15%); 465 incomplete records were deleted. Among the patients making appointments, 33,437 were female (68.55%), 12,569 had an appointment interval of 7–8 days (25.77%), and 38,366 had no record of a missed appointment before June 2017 (78.66%). Most patients (93.35%) used WeChat to make medical appointments while only 4.17% used the official hospital website. Among the no-show appointments, 4815 were female (65.27%), 4554 had an interval of 0–3 days (61.72%), and 4761 had no record of previous missed appointments (64.53%). Among the eight variables, higher no-show rates were observed among females (16.7%), below age 40 (14.52–19.38%), interval of 0–3 days (17.07%), Friday (16.47%), assistant professor, <10-yuan appointment fee (19.26%), other clinical departments (20.00%), and history of no-show appointments (20.62–43.54%). Table 1 and Figure 1 show the characteristics of the appointment records and no-show appointments.

Table 1 Characteristics of Missed Appointments Made on the Internet

Figure 1 The rate of missed appointment of different characteristics. (A) Age. (B) Day of week. (C) Previous number of missed appointment. (D) Clinic department.

Logistic Regression

Univariate analysis showed that eight of the eight variables were statistically significant (p<0.05) (appointment fee were not significant). Then, multivariate logistic regression indicated that five of those seven variables remained statistically significant (p<0.05) (gender, day of the week, and appointment fee were not significant). Table 2 shows the detailed results of the regression analyses. In the multivariate analysis, age was a key factor in missed appointments; the younger the patient, the greater the likelihood of a no-show. Patients with three or more previously missed appointments were more likely to be no-shows. Meanwhile, compared to patients with 0–6 days between scheduling and appointment, those with a 7- to 8-day wait were more likely to show up.

Table 2 Univariate and Multivariate Analyses of Missed Appointments

Discussion

No-show appointments reduce healthcare efficiency. Therefore, understanding the factors associated with missed appointments is an important matter for hospital administrators.^3,4 Previous studies have associated factors such as age, insurance type, scheduling–appointment interval, and prior missed appointments with no-show appointment rates. Yet, there have been conflicting results with regard to variables such as age, gender, scheduling–appointment interval, and clinical specialty.¹ Furthermore, most previous studies were conducted in developed countries, making their findings less applicable to China. Today, with the widespread application of Internet technologies, online appointments have become widely adopted. It is important, therefore, to investigate whether the factors affecting no-shows based on traditional appointment systems are the same as for Internet appointment systems.

In this study, the overall no-show appointment rate was 15%. Except for gender, appointment fee, and day of week, all other variables, as well as prior no-show history, influenced the occurrence of no-show appointments.

A prior no-show history has been widely recognized as the main influential factor in previous studies;^1,21–23 the same holds true for the present study. In our results, a patient with a record of no-show appointments had a sharply increased likelihood of not showing up. When previous no-show appointments increased from one to three or more, the patient was more likely to miss appointments (OR: 1.81 vs 5.02). This is consistent with Cronin et al (1.65 vs 3.62).¹⁸ The main reason could be that missing appointments becomes habitual for some patients, who take missing appointments for granted.

Age is also potentially associated with increased no-show rates. In this study, age was found to be inversely proportional to the occurrence of no-shows. Patients aged 0–19 years had the highest no-show rate (19.38%) while those 60 and older had lower no-show rates, ranging from 10.05% to 12.43%. Logistic regression also indicated that age played an important role in no-show appointments—a finding that is similar to the results of most previous studies.^1,24,25 The primary reason could be that younger patients tend to miss appointments because of school, work, and family responsibilities. A secondary reason could be that younger people are more likely than older patients to make appointments online, which is consistent with Ganguli et al.²⁶

In most previous studies,^1,21,27,28 scheduling–appointment interval was a strong predictor of no-shows; the greater the amount of time, the greater the risk of no-show. Cronin et al, for example, found that missed appointment rates increased with a longer wait between scheduling and appointment because of an increased likelihood of forgetting, among other reasons.¹⁸ In our study, however, patients with a 7- to 8-day interval were less likely to miss appointments. One reason for this difference could have to do with interval length. In our study, the longest wait was 8 days; in Cronin et al and others^21,27,28; however, scheduling–appointment intervals could exceed 21 days. Cohen et al found lower no-show appointment rates with intervals of 1–7 days compared to intervals of 7–15 days.²⁹ One reason could be that patients were anxious to visit doctors because of health issues, but they had difficulty making appointments for the type of disease or a specific doctor and were therefore more likely to make appointments further in advance.

Clinical specialty was also found to be an important determinant of no-shows. Dantas et al found physiotherapy having the highest median missed appointment rate (57.3%).¹ Meanwhile, examination clinics and pediatrics had the lowest median no-show rates.¹ Different from Dantaset al,¹ ophthalmology department had higher no-show rates (17.01%) in our study while psychological consultation departments had the lowest (11.11%). One reason could be that ophthalmology is generally not related to serious, urgent health issues; thus, if a patient has other obligations, he or she might be more likely to miss an ophthalmology appointment.

Regarding gender, females were likely than males to make appointments online. However, logistic regression showed no relationship between gender and missed appointments. This finding aligns with Cronin et al¹⁸ but differs from other studies. George et al³⁰ and Shabbir et al³¹ found that females had a higher no-show frequency while Hamilton et al found that males missed more appointments.³² It could be that the number of patients in some previous studies was too low to obtain accurate results. Our study and Cronin et al included 48,794 and 47,348 patients, respectively. Meanwhile,George et al³⁰ and Hamilton et al³² included only 150 and 2078 patients, respectively.

Because of the existence of multicollinearity (r=0.92) between appointment fee and physician's academic rank, only appointment fee was used in the multivariate analysis. As for the appointment fee, we did not examine the significant differences between the four types of appointment fee after the multivariate logistic analysis (p=0.239). However, for physician's rank, assistant professors had higher no-show rates (19.26%) than well-known professors (15.92%) and professors (14.96%). There were significant differences among the four ranks in the univariate analysis (p<0.001), which aligns with Fiorillo et al.¹⁹

Similar to previous studies,^1,28,33 we did not observe a significant association between day of the week and no-show rates. We did, however, find that missed appointment rates peaked on Fridays (16.47%), which is consistent with some studies^1,34 but different from Fiorillo et al, who found that Monday was the peak no-show day.¹⁹ Further, while there were significant differences in the univariate analysis, none were observed in multivariate analysis.

This study has several limitations. First, we only selected data from a general hospital to explore the variables affecting no-show appointments. Differences in location, facility size, transportation convenience, and hospital reputation might all affect no-show appointments. Another limitation is that all of the data came from the online booking system, not from appointments made via traditional methods. Thus, we did not obtain relative patient information, including socioeconomic status, insurance coverage, forgetfulness, the unavailability of transportation, and inability to get time off from work/school. These factors have been previously confirmed as common reasons for missing appointments. However, along with technological development, online appointment systems are now widely used in different hospitals, and reminder messages are now sent via mobile phones while traditional phone reminders are less common. Thus, it is necessary to analyze the association between object data in online systems and missed appointments.

Recommendations

First, it is important to take measures to decrease no-show rates among patients with a history of missed appointments. We found a significantly positive association between no-shows and patients with a history of missed appointments, accounting for 21.3% of all no-show patients. For such patients, measures for decreasing no-show risk can include lengthening the interval between future appointments and previously missed appointments and sending more reminder messages via mobile phones.

Second, it is necessary to extend wait times or increase the number of appointments with long wait times to reduce no-shows for professors or well-known professors. We found that a 7- to 8-day interval had the lowest no-show rates compared to a 1- to 6-day interval. Thus, efficiency can be increased by extending the waiting intervals for certain clinical departments or for junior professors. At the same time, we also found an imbalance in online appointments, mainly involving the beginning and end of certain intervals, with few registrations in the middle. Thus, hospitals should take measures to change this imbalance by guiding patient registration via publishing messages online and arranging different numbers of online registrations at different stages.

Third, it is essential to reduce the waste of medical resources among certain clinical departments and for certain age groups. In our study, ophthalmology patients and those aged 0–39 had the highest no-show rates. Thus, measures should be taken to increase efficiency, including increasing the number of online appointments or manual window appointments.

Lastly, no-show rates could be decreased by strengthening publicity and education. In our study, most patients made appointments via WeChat. Therefore, publishing advertisements or reports on the hazards of no-show appointments via WeChat could help decrease no-show rates by educating the public.

Conclusion

With the development of information technology, online appointment systems have become the primary means of healthcare-resource allocation. Missed appointments waste resources. Thus, how to rationally arrange medical resources online, target specific patient groups, and decrease no-show frequency are urgent problems for modern hospitals.

Through logistic regression analysis based on data from an online appointment system, we highlighted the diverse factors affecting no-shows and revealed the predicative value of patient age, history of missed appointments, time interval, and clinical department. Thus, considering these factors, hospital administrators need to take effective measures to decrease missed appointments and improve the use of healthcare resources. Such measures can include optimizing operational processes, extending the intervals between missed and future appointments, strengthening publicity and education through WeChat, and sending reminder messages to patients with a history of missed appointments.

Ethics Approval and Consent to Participate

This study was approved by the Academic Board of the School of Management, Science and Engineering, Shandong University of Finance and Economics (A-19-0089).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no competing interests.

References

1. Dantas LF, Fleck JL, Cyrino Oliveira FL, et al. NO-shows in appointment scheduling-a systematic literature review. Health Policy (New York). 2018;122(4):412–421. doi:10.1016/j.healthpol.2018.02.002

2. Rust CT, nh GALLUPS, Clark WS, et al. Patient appointment failures in pediatric resident continuity clinics. Arch Pediatr Adolesc Med. 1995;149(6):693–695. doi:10.1001/archpedi.1995.02170190103018

3. Parikh A, Gupta K, Wilson AC, et al. The effectiveness of outpatient appointment reminder systems in reducing no-show rates. Am J Med. 2010;123(6):542–548. doi:10.1016/j.amjmed.2009.11.022

4. Campbell JD, Chez RA, Queen TBA, et al. The no-show rate in a high-risk obstetric clinic. J Womens Health Gend Based Med. 2000;9(8):891–895. doi:10.1089/152460900750020928

5. Parmar V, Large A, Madden C, et al. The online outpatient booking system choose and book’ improves attendance rates at an audiology clinic: a comparative audit. Inform Prim Care. 2009;17(3):183–186.

6. Habibi MRM, Mohammadabadi F, Tabesh H, et al. Effect of an online appointment scheduling system on evaluation metrics of outpatient scheduling system: a before-after multicenter study. J Med Syst. 2019;43(8):281. doi:10.1007/s10916-019-1383-5

7. Dusheiko M, Gravelle H. Choosing and booking-and attending? Impact of an electronic booking system on outpatient referrals and non-attendances. Health Econ. 2018;27(2):357–371. doi:10.1002/hec.3552

8. Peng Y, Erdem E, Shi J, et al. Large-scale assessment of missed opportunity risks in a complex hospital setting. Inform Health Soc Care. 2016;41:112–127.

9. Muthuraman K, Lawley M. A stochastic overbooking model for outpatient clinical scheduling with no-shows. IIE Trans. 2008;40(9):820–837. doi:10.1080/07408170802165823

10. Daggy J, Lawley M, Willis D, et al. Using no-show modeling to improve clinic performance. Health Informatics J. 2010;16(4):246–259. doi:10.1177/1460458210380521

11. Zeng B, Turkcan A, Lin J, et al. Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities. Ann Oper Res. 2010;178(1):121–144. doi:10.1007/s10479-009-0569-5

12. Samorani M, LaGanga LR. Outpatient appointment scheduling given individual day-dependent no-show predictions. Eur J Oper Res. 2015;240(1):245–257. doi:10.1016/j.ejor.2014.06.034

13. Kopach R, Delaurentis PC, Lawley M, et al. Effects of clinical characteristics on successful open access scheduling. Health Care Manag. Sci. 2007;10(2):111–124. doi:10.1007/s10729-007-9008-9

14. Menendez ME, Ring D. Factors associated with non-attendance at a hand surgery appointment. Hand. 2015;10(2):221–226. doi:10.1007/s11552-014-9685-z

15. Torres O, Rothberg MB, Garb J, et al. Risk factor model to predict a missed clinic appointment in an urban, academic, and underserved setting. Popul Health Manag. 2015;18(2):131–136. doi:10.1089/pop.2014.0047

16. Kempny A, Diller G, Dimopoulos K, et al. Determinants of outpatient clinic attendance amongst adults with congenital heart disease and outcome. Int J Cardiol. 2016;203:245–250. doi:10.1016/j.ijcard.2015.10.081

17. Kheirkhah P, Feng Q, Travis LM, et al. Prevalence, predictors and economic consequences of no-show. BMC Health Serv Res. 2016;16:1–6.

18. Cronin PR, DeCoste L, Kimball AB. A multivariate analysis of dermatology missed appointment predictors. JAMA Dermatol. 2013;149(12):1435–1437. doi:10.1001/jamadermatol.2013.5771

19. Filrillo CE, Hughes AL, I-Chen C, et al. Factors associated with patient no-show rates in an academic otolaryngology practice. Laryngoscope. 2018;128(3):626–631. doi:10.1002/lary.26816

20. Tsai WC, Lee WC, Chiang SC, et al. Factors of missed appointments at an academic medical center in Taiwan. J Chin Med Assoc. 2019;82(5):436–442. doi:10.1097/JCMA.0000000000000068

21. Giunta D, Briatore A, Baum A, et al. Factors associated with nonattendance at clinical medicine scheduled outpatient appointments in a university general hospital. Patient Prefer Adherence. 2013;7:1163–1170.

22. Norris JB, Kumar C, Chand S, et al. An empirical investigation into factors affecting patient cancellations and no-shows at outpatient clinics. Decis Support Syst. 2014;57:428–443. doi:10.1016/j.dss.2012.10.048

23. Lenzi H, BenA J, Stein AT. Development and validation of a patient no-show predictive model at a primary care setting in Southern Brazil. PLoS One. 2019;14(4):e0214869. doi:10.1371/journal.pone.0214869

24. Milne V, Kearns R, Harrison A. Patient age, ethnicity and waiting times deter-mine the likelihood of non-attendance at a first specialist rheumatology assessment. Int J Rheum Dis. 2014;17(1):19–25. doi:10.1111/1756-185X.12126

25. Nancarrow S, Bradbury J, Avila C. Factors associated with non-attendance in a general practice super clinic population in regional Australia: a retrospective cohort study. Australas Med J. 2014;7:323–333. doi:10.4066/AMJ.2014.2098

26. Ganguli I, Orav EJ, Lupo C, et al. Patient and visit characteristics associated with use of direct scheduling in primary care practices. JAMA Netw Open. 2020;3(8):e209637. doi:10.1001/jamanetworkopen.2020.9637

27. Guay MO, Tanzi S, Arregui MT, et al. Characteristics and outcomes of advanced cancer patients who miss out-patient supportive care consult appointments. Support Care Cancer. 2014;22(10):2869–2874. doi:10.1007/s00520-014-2254-8

28. Guzek LM, Fadel WF, Golomb MR. A pilot study of reasons and risk fac-tors for no-show in a pediatric neurology clinic. J Child Neurol. 2015;30(10):1295–1299. doi:10.1177/0883073814559098

29. Cohen AD, Kaplan DM, Shapiro J, et al. Health provider determinants of nonattendance in pediatric otolaryngology patients. Laryngoscope. 2005;115(10):1804–1808. doi:10.1097/01.mlg.0000175202.50499.63

30. George AC, Hoshing A, Joshi NV. A study of the reasons for irregular dental attendance in a private dental college in a rural setup. Indian J Dent Res. 2007;18(2):78–81. doi:10.4103/0970-9290.32425

31. Shabbir A, Alzahrani M, Khalid AA. Why do patients miss dental appointments in Eastern Province Military Hospitals, Kingdom of Saudi Arabia? Cureus. 2018;10(3):e2355.

32. Hamilton W, Round A, Sharp D. Patient, hospital, and general practitioner characteristics associated with non-attendance: a cohort study. Br J Gen Pract. 2002;52:317–319.

33. Pehr K. No show: incidence of nonattendance at a dermatology practice ina single universal payer model. J Cutan Med Surg. 2007;11(2):53–56. doi:10.2310/7750.2007.00005

34. Huang YL, Hanauer DA. Time dependent patient no-show predictive modelling development. Int J Health Care Qual Assur. 2016;29(4):475–488. doi:10.1108/IJHCQA-06-2015-0077