No-Show PatientsNCT06114602
Impact of Email Reminders on No-Show Rates for Appointments in an Urology Department
An observational study of No-Show Patients and Reminder Systems, run by Hospital Italiano de Buenos Aires at 1 site in Argentina.
Trial summary
In this observational study, the investigators will analyze all patients who have scheduled appointments in the Urology Department from twelve months before the start date of the e-mail reminder dispatch (01/02/2023) to twelve months after (01/01/2022 to 31/12/2023). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- No-Show Patients, Reminder Systems
- What is being tested?
- E-Mail Reminder
- Type of treatment
- Lifestyle or therapy programme
- How many people
- 1,892 participants
- Where
- 1 siteArgentina
- Study dates
- 1 Jan 2022to 30 Mar 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The target population of the study corresponds to scheduled outpatient appointments at the urology department. The accessible population are the appointments requested for care in the Urology Deparment of HIBA, with an assigned date between January 1, 2022 and December 31, 2023 inclusive (24 months in total).
Who may be able to join
- We will include all patients over 18 years who have been assigned appointments or overappointments, scheduled or with note, in the Urology Department of HIBA between January 1, 2022 and December 31, 2023.
Who cannot join
- Teleconsultation appointments and face-to-face consultations made through spontaneous overappointments or spontaneous demand will be excluded. The latter two cases are not considered to be appointments assigned to the patient prior to the consultation, but are assigned at the same time the patient presents his or her request. The electronic information system has no way of knowing in advance that this patient is going to be seen and therefore has no way of sending a reminder.
Trial locations and contacts
1 site in 1 country.
Argentina1 site, 1 recruiting
- Recruiting
Hospital Italiano de Buenos
CABA
Diego Santillan, MD, +5491162504033, diego.santillan@hospitalitalitano.org.ar
About this study
Official title: Impact Analysis of Email Reminders on No-Show Rates in Scheduled Ambulatory Appointments in the Urology Department of Hospital Italiano de Buenos Aires
In this observational study, the investigators will analyze all patients who have scheduled appointments in the Urology Department from twelve months before the start date of the e-mail reminder dispatch (01/02/2023) to twelve months after (01/01/2022 to 31/12/2023). The investigators will divide them into two groups based on whether they have received the reminder or not. The investigators are going to compare the rate of no-show rates in both groups and then obtain the relative risk of the association between appointment reminders and no-show rates.
Read the detailed description
JUSTIFICATION
The Outpatient Department and the Medical Informatics Department of the Hospital Italiano de Buenos Aires (HIBA) designed a joint strategy based on e-mail reminders for scheduled outpatient appointments. Patients who have an appointment (either face-to-face or teleconsultation) scheduled with a doctor at the Hospital receive an e-mail at the time of their request as well as 14 days and 48 hours before the appointment, reminding them of the appointment time and the place where they have to go. In addition, a button has been added to the e-mail so that the patient can click on it and cancel it directly in a simple way.
The possibility of canceling the appointment in the same reminder is also intended to have an impact on those who, without having forgotten their appointment, have to or want to miss it, can cancel it and thus leave the appointment free for another patient who needs it.
It seems that any type of reminder would be associated with a decrease in the no-show rate. However, it is questionable whether published figures are transferable to our setting. The variability of contexts and populations can affect the magnitude of the effect of an intervention.
After reviewing the literature, no studies were found analyzing appointment reminders and no-show rates in our country, so the investigators propose to conduct a study evaluating this impact in the Urology Department of HIBA. Although a clinical trial could be considered and the sending of reminders could be randomized, by the time the investigators decided to carry out this research, the intervention had already been implemented in all departments.
RESEARCH QUESTION
Is the e-mail reminder of a scheduled outpatient appointment to the Urology Department of HIBA effective in reducing no-show rates in patients over 18 years old?
SELECTION AND SAMPLE SIZE
During the year 2022, the Urology Department of Hospital Italiano de Buenos Aires (HIBA) performed approximately 29000 appointments in the Central Hospital and 11500 appointments in the Italian Hospital of San Justo, giving a total of 40500 appointments including spontaneous overtime and Spontaneous Demand appointments. The estimated weekly average is 779 appointments. Currently, in the Urology Service of HIBA, there is approximately a 21% no-show rate in scheduled outpatient appointments.
The investigators believe that achieving a 25% relative reduction in the proportion of no-show rates through the reminder system would be clinically relevant, i.e. from a 21% baseline no-show rate to 16%.
Using both STATA and the sample calculation tool provided free of charge by the University of San Francisco, the investigators estimated that the total N needed to estimate the calculated difference in no-show rates between groups is 1892 appointments (946 appointments per group). The parameters used were as follows: alpha (two-tailed)= 0.05, Beta= 0.20; q1 (proportion of appointments exposed)= 0.50; P0 (baseline risk of no-show rate in the unexposed group)= 0.21 and P1 (estimated risk in the exposed group)= 0.16.
The number of appointments requested before and after the implementation of the reminders is enough to test the study hypothesis.
DATA COLLECTION INSTRUMENT
For our study, the investigators will use secondary databases requested from the Department of Health Informatics after approval of the protocol by the bioethics committee of our institution. Because appointment management is centralized in a single data repository, all data corresponding to patients and appointment requests can be obtained from the entire HIBA electronic health system.
PLAN FOR DATA ANALYSIS AND REPORTING OF RESULTS
An individual approach will be used to evaluate the effect of the intervention on no-show rates. The unit of analysis is the assigned appointment. Due to the nature of the exposure and the implementation of the reminder system, all appointments before the implementation date correspond to unexposed appointments, and all appointments assigned after the implementation date correspond to exposed appointments.
Quantitative variables will be described with their absolute and relative frequency in percentage. Quantitative variables will be presented as mean and standard deviation or median and interquartile range according to the observed distribution.
For the calculation of the rates of no-shows and canceled appointments, the count of no-shows or canceled appointments, respectively, will be used as the numerator. For the no-show rate, the denominator used is the number of appointments not canceled, as is common for presenting this indicator in the literature. In the rate of canceled appointments, the total number of requested appointments will be used as the denominator. In both cases, only appointments that meet the inclusion criteria and none of the exclusion criteria during the same period will be used. The 95% CI (Confidence Interval) estimated with the normal approximation will be presented for each of the rates because the proportions are sufficiently large.
To test the null hypothesis of equality in the proportion of missed appointments between requested non-canceled appointments exposed to the reminder and requested non-canceled appointments not exposed to the reminder, a multivariate logistic regression model will be used. The investigators consider exposure to the reminder as the main exposure and no-show to the appointment as the outcome variable. Each patient will be considered as a conglomerate or cluster using robust estimators of the standard errors since the appointments of the same patient are more similar to each other than to the appointments of other patients. Time in days will be used as the adjustment covariate. The investigators will adjust for potential confounders considered relevant by the research team and the literature, which include sex, age, and medical coverage of the patient, time since the appointment request in days, time, day of the week and month of the appointment, place of care (Central Hospital, San Justo or Peripheral Center). The crude and adjusted ORs for missed appointments will be estimated with their 95%CI.
To test the null hypothesis of equality in the proportion of canceled appointments between requested appointments exposed to the reminder and canceled appointments among requested appointments not exposed to the reminder, the same methodology described for the previous objective will be used.
The effect of the intervention will be evaluated in pre-specified subgroups according to sex, age group, medical coverage, appointment time, day of the week, and month of the appointments, as well as according to place of care (Central Hospital, San Justo or Peripheral Center).
Probabilities of less than 0.05 will be considered statistically significant. Statistical analysis will be performed using STATA version 17 software (Texas USA).
HANDLING OF LOST DATA
The databases to be used in this analysis will contain a very low percentage of missing data. Because any missing data could be considered as missing completely at random, a complete case analysis will be used for the individual analysis.
BIASES AND LIMITATIONS OF THE STUDY
The limitations of our study, given the chosen methodology, will be mainly due to the lack of randomization of the exposure. This leads to confounding factors, which, although some will be considered a priori, cannot be considered in their totality since not all of them are known. On the other hand, the sample groups may not be balanced in terms of confounders, as may happen, for example, with time-dependent variables. However, since the intervention was implemented from a randomly defined point in time, the confounders are most likely to be balanced and, in addition, can eventually be adjusted through statistical methods.
While many retrospective studies are conducted based on incomplete or inaccurate information, many of the administrative variables in our study will be collected completely and automatically, with a standardized process that will remain stable throughout the study period. Unlike the administrative variables in the study, other variables, such as date of birth, may contain errors or incomplete data because although they are computerized, the data are originally uploaded by hospital administrators who are prone to error.
An additional limitation is all the unmeasured variables such as socioeconomic status and/or eHealth literacy of the individuals. Not all people open their e-mail every day and by not being able to measure who read the reminder the investigators may falsely believe that the intervention did not meet its objective when in fact what happens is that the patient did not open the e-mail and so was not exposed to the reminder. The investigatorsconsider it interesting in the future to compare our results with a reminder sent directly to the patient's cell phone via text messages or WhatsApp.
On the other hand, although the implementation of the intervention does not incur an additional cost to the institution for the aforementioned reasons, the investigators will not evaluate whether the intervention obtains any economic return.
Another limitation of our study is that the investigators will not evaluate the acceptability of the reminder by the users. If given a choice, it is possible that patients would choose other types of reminders according to personal preferences.
Nor will the investigators be able to estimate the impact of the intervention regarding teleconsultations, since these will be excluded from the analysis.
As far as external validity is concerned, the results obtained may not be extrapolated to other healthcare centers that do not have the structure of our hospital. To send e-mail reminders to all patients, it is necessary to have an electronic system that combines and processes information from the electronic medical record and the scheduling system and to coordinate it with a mass mailing system to be able to do it automatically. Otherwise, it is necessary to have human resources to do all this, which may not be cost-effective.
Last but not least, it is worth mentioning that since the intervention studied has two components (the reminder itself that prevents forgetfulness and the facilitator of appointment cancellation), it is not possible to estimate the effect of each component of the intervention separately.
EXPECTED IMPACT
The intervention is expected to decrease the no-show rate by 25% (or, in other words, to bring the no-show rate down to 16%) and the average waiting time for an appointment by at least one day. In addition, it is expected to increase the number of cancellations by 20%.
RESOURCES AND ESTIMATED BUDGET
The needed resources are available to complete this work. In no case will this study represent any additional cost for the patients or their medical coverage. The HIBA Medical Informatics Department already has a mass mailing system, so sending reminders does not require any financial investment or additional human resources.
The team in charge of the research and in charge of searching, processing, and analyzing the information has enough protected time during the working day to be able to complete the study within the stipulated deadlines.
ETHICAL CONSIDERATIONS
This study protocol has already been approved by our Hospital's Bioethics Committee: IRB (Institutional Review Board) Nº 6878
- Conditions
- No-Show Patients, Reminder Systems
- Being tested
- E-Mail Reminder
- Keywords
- Non-Attendance, Patient, Reminder Systems, No-Show Patients, Appointments and Schedules, Treatment Adherence and Compliance
- Medical subject headings
- No-Show Patients, Treatment Adherence and Compliance
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
E-Mail ReminderBehavioural
Patients who have an appointment (either face-to-face or teleconsultation) scheduled with a doctor at the Hospital receive an e-mail at the time of their request as well as 14 days and 48 hours prior to the appointment, reminding them of the appointment time and the place where they have to go. In addition, a button has been added to the e-mail so that the patient can click on it and cancel it directly in a simple way.
| Study group | Role | What they receive |
|---|---|---|
| Pre-Intervention | Patients who have a medical appointment with a urologist in HIBA but did not receive any reminder. | |
| Post-Intervention | Patients who have a medical appointment with a urologist in HIBA but did receive an email reminder of the appointment. Receives: Behavioral: E-Mail Reminder |
What is being measured
The primary outcome is the main question the study is designed to answer.
Proportion of scheduled appointment no-show rate
Proportion of no-show outpatient appointments
Measured: 14 days
Secondary outcomes (1)
Proportion of scheduled appointment cancelations
Proportion of canceled outpatient appointments
Measured: 14 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Nov 2023
- Study start1 Jan 2022
- Primary completion31 Dec 2023 (estimated)
- Study completion30 Mar 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for No-Show PatientsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,892 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hospital Italiano de Buenos Aires
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Diego Santillan, Hospital Italiano de Buenos Aires
- Investigators
- Diego H Giunta, PhD, Study director, Hospital Italiano de Buenos Aires
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06114602
- Sponsor's protocol ID
- 6878
- Status last verified
- Nov 2023
- First submitted
- 29 Oct 2023
- Last updated
- 7 Nov 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Parker MM, Moffet HH, Schillinger D, Adler N, Fernandez A, Ciechanowski P, Karter AJ. Ethnic differences in appointment-keeping and implications for the patient-centered medical home--findings from the Diabetes Study of Northern California (DISTANCE). Health Serv Res. 2012 Apr;47(2):572-93. doi: 10.1111/j.1475-6773.2011.01337.x. Epub 2011 Oct 27.
PubMed 22091785Nguyen DL, Dejesus RS. Increased frequency of no-shows in residents' primary care clinic is associated with more visits to the emergency department. J Prim Care Community Health. 2010 Apr 1;1(1):8-11. doi: 10.1177/2150131909359930.
PubMed 23804061Colubi MM, Perez-Elias MJ, Elias L, Pumares M, Muriel A, Zamora AM, Casado JL, Dronda F, Lopez D, Moreno S; SEAD Study Group. Missing scheduled visits in the outpatient clinic as a marker of short-term admissions and death. HIV Clin Trials. 2012 Sep-Oct;13(5):289-95. doi: 10.1310/hct1305-289.
PubMed 23134630Alyahya M, Hijazi HH, Nusairat FT. The Effects of Negative Reinforcement on Increasing Patient Adherence to Appointments at King Abdullah University Hospital in Jordan. Inquiry. 2016 Jul 20;53:0046958016660411. doi: 10.1177/0046958016660411. Print 2016.
PubMed 27444505Kheirkhah P, Feng Q, Travis LM, Tavakoli-Tabasi S, Sharafkhaneh A. Prevalence, predictors and economic consequences of no-shows. BMC Health Serv Res. 2016 Jan 14;16:13. doi: 10.1186/s12913-015-1243-z.
PubMed 26769153Giunta DH, Alonso Serena M. Nonattendance rates of scheduled outpatient appointments in a university general hospital. Int J Health Plann Manage. 2019 Oct;34(4):1377-1385. doi: 10.1002/hpm.2797. Epub 2019 May 7.
PubMed 31062463Briatore A, Tarsetti EV, Latorre A, Gonzalez Bernaldo de Quiros F, Luna D, Fuentes NA, Elizondo CM, Baum A, Alonso Serena M, Giunta DH. Causes of appointment attendance, nonattendance, and cancellation in outpatient consultations at a university hospital. Int J Health Plann Manage. 2020 Jan;35(1):207-220. doi: 10.1002/hpm.2890. Epub 2019 Aug 26.
PubMed 31448466Ramakrishnan SA, Murphy E, Barry M. Non-attendance at clinics: a waste of resource. Ir J Med Sci. 2004 Jul-Sep;173(3):172. doi: 10.1007/BF03167936. No abstract available.
PubMed 15693391Wilson R, Winnard Y. Causes, impacts and possible mitigation of non-attendance of appointments within the National Health Service: a literature review. J Health Organ Manag. 2022 Aug 4;ahead-of-print(ahead-of-print). doi: 10.1108/JHOM-11-2021-0425.
PubMed 35918282Wolthers OD. Non-attendance in a secondary paediatric referral centre. Dan Med J. 2018 Nov;65(11):A5515.
PubMed 30382021Car J, Gurol-Urganci I, de Jongh T, Vodopivec-Jamsek V, Atun R. Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database Syst Rev. 2012 Jul 11;(7):CD007458. doi: 10.1002/14651858.CD007458.pub2.
PubMed 22786507Boksmati N, Butler-Henderson K, Anderson K, Sahama T. The Effectiveness of SMS Reminders on Appointment Attendance: a Meta-Analysis. J Med Syst. 2016 Apr;40(4):90. doi: 10.1007/s10916-016-0452-2. Epub 2016 Feb 6.
PubMed 26852337
Study at a glance
From the registry record, last updated by the sponsor on 7 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Impact Analysis of Email Reminders on No-Show Rates in Scheduled Ambulatory Appointments in the Urology Department of Hospital Italiano de Buenos Aires
- ClinicalTrials.gov ID
- NCT06114602
- Acronym / sponsor ID
- 6878
- Conditions
- No-Show Patients, Reminder Systems
- Keywords
- Non-Attendance, Patient, Reminder Systems, No-Show Patients, Appointments and Schedules, Treatment Adherence and Compliance
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,892 participants (estimated)
- Lead sponsor
- Hospital Italiano de Buenos AiresUniversity, hospital or other
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 30 Mar 2024 (estimated)
- First posted
- 2 Nov 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
In this observational study, the investigators will analyze all patients who have scheduled appointments in the Urology Department from twelve months before the start date of the e-mail reminder dispatch (01/02/2023) to twelve months after (01/01/2022 to 31/12/2023). The investigators will divide them into two groups based on whether they have received the reminder or not. The investigators are going to compare the rate of no-show rates in both groups and then obtain the relative risk of the association between appointment reminders and no-show rates.
Detailed description
JUSTIFICATION
The Outpatient Department and the Medical Informatics Department of the Hospital Italiano de Buenos Aires (HIBA) designed a joint strategy based on e-mail reminders for scheduled outpatient appointments. Patients who have an appointment (either face-to-face or teleconsultation) scheduled with a doctor at the Hospital receive an e-mail at the time of their request as well as 14 days and 48 hours before the appointment, reminding them of the appointment time and the place where they have to go. In addition, a button has been added to the e-mail so that the patient can click on it and cancel it directly in a simple way.
The possibility of canceling the appointment in the same reminder is also intended to have an impact on those who, without having forgotten their appointment, have to or want to miss it, can cancel it and thus leave the appointment free for another patient who needs it.
It seems that any type of reminder would be associated with a decrease in the no-show rate. However, it is questionable whether published figures are transferable to our setting. The variability of contexts and populations can affect the magnitude of the effect of an intervention.
After reviewing the literature, no studies were found analyzing appointment reminders and no-show rates in our country, so the investigators propose to conduct a study evaluating this impact in the Urology Department of HIBA. Although a clinical trial could be considered and the sending of reminders could be randomized, by the time the investigators decided to carry out this research, the intervention had already been implemented in all departments.
RESEARCH QUESTION
Is the e-mail reminder of a scheduled outpatient appointment to the Urology Department of HIBA effective in reducing no-show rates in patients over 18 years old?
SELECTION AND SAMPLE SIZE
During the year 2022, the Urology Department of Hospital Italiano de Buenos Aires (HIBA) performed approximately 29000 appointments in the Central Hospital and 11500 appointments in the Italian Hospital of San Justo, giving a total of 40500 appointments including spontaneous overtime and Spontaneous Demand appointments. The estimated weekly average is 779 appointments. Currently, in the Urology Service of HIBA, there is approximately a 21% no-show rate in scheduled outpatient appointments.
The investigators believe that achieving a 25% relative reduction in the proportion of no-show rates through the reminder system would be clinically relevant, i.e. from a 21% baseline no-show rate to 16%.
Using both STATA and the sample calculation tool provided free of charge by the University of San Francisco, the investigators estimated that the total N needed to estimate the calculated difference in no-show rates between groups is 1892 appointments (946 appointments per group). The parameters used were as follows: alpha (two-tailed)= 0.05, Beta= 0.20; q1 (proportion of appointments exposed)= 0.50; P0 (baseline risk of no-show rate in the unexposed group)= 0.21 and P1 (estimated risk in the exposed group)= 0.16.
The number of appointments requested before and after the implementation of the reminders is enough to test the study hypothesis.
DATA COLLECTION INSTRUMENT
For our study, the investigators will use secondary databases requested from the Department of Health Informatics after approval of the protocol by the bioethics committee of our institution. Because appointment management is centralized in a single data repository, all data corresponding to patients and appointment requests can be obtained from the entire HIBA electronic health system.
PLAN FOR DATA ANALYSIS AND REPORTING OF RESULTS
An individual approach will be used to evaluate the effect of the intervention on no-show rates. The unit of analysis is the assigned appointment. Due to the nature of the exposure and the implementation of the reminder system, all appointments before the implementation date correspond to unexposed appointments, and all appointments assigned after the implementation date correspond to exposed appointments.
Quantitative variables will be described with their absolute and relative frequency in percentage. Quantitative variables will be presented as mean and standard deviation or median and interquartile range according to the observed distribution.
For the calculation of the rates of no-shows and canceled appointments, the count of no-shows or canceled appointments, respectively, will be used as the numerator. For the no-show rate, the denominator used is the number of appointments not canceled, as is common for presenting this indicator in the literature. In the rate of canceled appointments, the total number of requested appointments will be used as the denominator. In both cases, only appointments that meet the inclusion criteria and none of the exclusion criteria during the same period will be used. The 95% CI (Confidence Interval) estimated with the normal approximation will be presented for each of the rates because the proportions are sufficiently large.
To test the null hypothesis of equality in the proportion of missed appointments between requested non-canceled appointments exposed to the reminder and requested non-canceled appointments not exposed to the reminder, a multivariate logistic regression model will be used. The investigators consider exposure to the reminder as the main exposure and no-show to the appointment as the outcome variable. Each patient will be considered as a conglomerate or cluster using robust estimators of the standard errors since the appointments of the same patient are more similar to each other than to the appointments of other patients. Time in days will be used as the adjustment covariate. The investigators will adjust for potential confounders considered relevant by the research team and the literature, which include sex, age, and medical coverage of the patient, time since the appointment request in days, time, day of the week and month of the appointment, place of care (Central Hospital, San Justo or Peripheral Center). The crude and adjusted ORs for missed appointments will be estimated with their 95%CI.
To test the null hypothesis of equality in the proportion of canceled appointments between requested appointments exposed to the reminder and canceled appointments among requested appointments not exposed to the reminder, the same methodology described for the previous objective will be used.
The effect of the intervention will be evaluated in pre-specified subgroups according to sex, age group, medical coverage, appointment time, day of the week, and month of the appointments, as well as according to place of care (Central Hospital, San Justo or Peripheral Center).
Probabilities of less than 0.05 will be considered statistically significant. Statistical analysis will be performed using STATA version 17 software (Texas USA).
HANDLING OF LOST DATA
The databases to be used in this analysis will contain a very low percentage of missing data. Because any missing data could be considered as missing completely at random, a complete case analysis will be used for the individual analysis.
BIASES AND LIMITATIONS OF THE STUDY
The limitations of our study, given the chosen methodology, will be mainly due to the lack of randomization of the exposure. This leads to confounding factors, which, although some will be considered a priori, cannot be considered in their totality since not all of them are known. On the other hand, the sample groups may not be balanced in terms of confounders, as may happen, for example, with time-dependent variables. However, since the intervention was implemented from a randomly defined point in time, the confounders are most likely to be balanced and, in addition, can eventually be adjusted through statistical methods.
While many retrospective studies are conducted based on incomplete or inaccurate information, many of the administrative variables in our study will be collected completely and automatically, with a standardized process that will remain stable throughout the study period. Unlike the administrative variables in the study, other variables, such as date of birth, may contain errors or incomplete data because although they are computerized, the data are originally uploaded by hospital administrators who are prone to error.
An additional limitation is all the unmeasured variables such as socioeconomic status and/or eHealth literacy of the individuals. Not all people open their e-mail every day and by not being able to measure who read the reminder the investigators may falsely believe that the intervention did not meet its objective when in fact what happens is that the patient did not open the e-mail and so was not exposed to the reminder. The investigatorsconsider it interesting in the future to compare our results with a reminder sent directly to the patient's cell phone via text messages or WhatsApp.
On the other hand, although the implementation of the intervention does not incur an additional cost to the institution for the aforementioned reasons, the investigators will not evaluate whether the intervention obtains any economic return.
Another limitation of our study is that the investigators will not evaluate the acceptability of the reminder by the users. If given a choice, it is possible that patients would choose other types of reminders according to personal preferences.
Nor will the investigators be able to estimate the impact of the intervention regarding teleconsultations, since these will be excluded from the analysis.
As far as external validity is concerned, the results obtained may not be extrapolated to other healthcare centers that do not have the structure of our hospital. To send e-mail reminders to all patients, it is necessary to have an electronic system that combines and processes information from the electronic medical record and the scheduling system and to coordinate it with a mass mailing system to be able to do it automatically. Otherwise, it is necessary to have human resources to do all this, which may not be cost-effective.
Last but not least, it is worth mentioning that since the intervention studied has two components (the reminder itself that prevents forgetfulness and the facilitator of appointment cancellation), it is not possible to estimate the effect of each component of the intervention separately.
EXPECTED IMPACT
The intervention is expected to decrease the no-show rate by 25% (or, in other words, to bring the no-show rate down to 16%) and the average waiting time for an appointment by at least one day. In addition, it is expected to increase the number of cancellations by 20%.
RESOURCES AND ESTIMATED BUDGET
The needed resources are available to complete this work. In no case will this study represent any additional cost for the patients or their medical coverage. The HIBA Medical Informatics Department already has a mass mailing system, so sending reminders does not require any financial investment or additional human resources.
The team in charge of the research and in charge of searching, processing, and analyzing the information has enough protected time during the working day to be able to complete the study within the stipulated deadlines.
ETHICAL CONSIDERATIONS
This study protocol has already been approved by our Hospital's Bioethics Committee: IRB (Institutional Review Board) Nº 6878
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Pre-Intervention | – | –Patients who have a medical appointment with a urologist in HIBA but did not receive any reminder. |
| Post-Intervention | – | Behavioral: E-Mail ReminderPatients who have a medical appointment with a urologist in HIBA but did receive an email reminder of the appointment. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The target population of the study corresponds to scheduled outpatient appointments at the urology department. The accessible population are the appointments requested for care in the Urology Deparment of HIBA, with an assigned date between January 1, 2022 and December 31, 2023 inclusive (24 months in total).
Key inclusion criteria (1 listed)
- We will include all patients over 18 years who have been assigned appointments or overappointments, scheduled or with note, in the Urology Department of HIBA between January 1, 2022 and December 31, 2023.
Key exclusion criteria (1 listed)
- Teleconsultation appointments and face-to-face consultations made through spontaneous overappointments or spontaneous demand will be excluded. The latter two cases are not considered to be appointments assigned to the patient prior to the consultation, but are assigned at the same time the patient presents his or her request. The electronic information system has no way of knowing in advance that this patient is going to be seen and therefore has no way of sending a reminder.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| E-Mail ReminderPatients who have an appointment (either face-to-face or teleconsultation) scheduled with a doctor at the Hospital receive an e-mail at the time of their request as well as 14 days and 48 hours prior to the appointment, reminding them of the appointment time and the place where they have to go. In addition, a button has been added to the e-mail so that the patient can click on it and cancel it directly in a simple way. | Behavioural | Post-Intervention |
Primary outcome
Proportion of scheduled appointment no-show rate
Time frame: 14 days
Proportion of no-show outpatient appointments
1 secondary outcome
Proportion of scheduled appointment cancelations
Time frame: 14 days
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Nov 2023.
- Background: PubMed 22091785Parker MM, Moffet HH, Schillinger D, Adler N, Fernandez A, Ciechanowski P, Karter AJ. Ethnic differences in appointment-keeping and implications for the patient-centered medical home--findings from the Diabetes Study of Northern California (DISTANCE). Health Serv Res. 2012 Apr;47(2):572-93. doi: 10.1111/j.1475-6773.2011.01337.x. Epub 2011 Oct 27.
- Background: PubMed 23804061Nguyen DL, Dejesus RS. Increased frequency of no-shows in residents' primary care clinic is associated with more visits to the emergency department. J Prim Care Community Health. 2010 Apr 1;1(1):8-11. doi: 10.1177/2150131909359930.
- Background: PubMed 23134630Colubi MM, Perez-Elias MJ, Elias L, Pumares M, Muriel A, Zamora AM, Casado JL, Dronda F, Lopez D, Moreno S; SEAD Study Group. Missing scheduled visits in the outpatient clinic as a marker of short-term admissions and death. HIV Clin Trials. 2012 Sep-Oct;13(5):289-95. doi: 10.1310/hct1305-289.
- Background: PubMed 27444505Alyahya M, Hijazi HH, Nusairat FT. The Effects of Negative Reinforcement on Increasing Patient Adherence to Appointments at King Abdullah University Hospital in Jordan. Inquiry. 2016 Jul 20;53:0046958016660411. doi: 10.1177/0046958016660411. Print 2016.
- Background: PubMed 26769153Kheirkhah P, Feng Q, Travis LM, Tavakoli-Tabasi S, Sharafkhaneh A. Prevalence, predictors and economic consequences of no-shows. BMC Health Serv Res. 2016 Jan 14;16:13. doi: 10.1186/s12913-015-1243-z.
- Background: PubMed 31062463Giunta DH, Alonso Serena M. Nonattendance rates of scheduled outpatient appointments in a university general hospital. Int J Health Plann Manage. 2019 Oct;34(4):1377-1385. doi: 10.1002/hpm.2797. Epub 2019 May 7.
- Background: PubMed 31448466Briatore A, Tarsetti EV, Latorre A, Gonzalez Bernaldo de Quiros F, Luna D, Fuentes NA, Elizondo CM, Baum A, Alonso Serena M, Giunta DH. Causes of appointment attendance, nonattendance, and cancellation in outpatient consultations at a university hospital. Int J Health Plann Manage. 2020 Jan;35(1):207-220. doi: 10.1002/hpm.2890. Epub 2019 Aug 26.
- Background: PubMed 15693391Ramakrishnan SA, Murphy E, Barry M. Non-attendance at clinics: a waste of resource. Ir J Med Sci. 2004 Jul-Sep;173(3):172. doi: 10.1007/BF03167936. No abstract available.
- Background: PubMed 35918282Wilson R, Winnard Y. Causes, impacts and possible mitigation of non-attendance of appointments within the National Health Service: a literature review. J Health Organ Manag. 2022 Aug 4;ahead-of-print(ahead-of-print). doi: 10.1108/JHOM-11-2021-0425.
- Background: PubMed 30382021Wolthers OD. Non-attendance in a secondary paediatric referral centre. Dan Med J. 2018 Nov;65(11):A5515.
- Background: PubMed 22786507Car J, Gurol-Urganci I, de Jongh T, Vodopivec-Jamsek V, Atun R. Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database Syst Rev. 2012 Jul 11;(7):CD007458. doi: 10.1002/14651858.CD007458.pub2.
- Background: PubMed 26852337Boksmati N, Butler-Henderson K, Anderson K, Sahama T. The Effectiveness of SMS Reminders on Appointment Attendance: a Meta-Analysis. J Med Syst. 2016 Apr;40(4):90. doi: 10.1007/s10916-016-0452-2. Epub 2016 Feb 6.
- Individual participant dataSharing planned: No
Related trials for No-Show Patients
Active, not recruiting
Improving Show Rates in Dental Care Settings Serving Underserved Populations
Dental Care
Not yet recruiting
Impact of Appointment Reminder Strategies on Smoking Cessation Clinic Attendance
Behavioral Intervention, Nicotine Addiction
Enrolling by invitation
Data Science and Qualitative Research for Decision Support in the HIV Care Cascade
Human Immunodeficiency Virus, Treatment Adherence
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Diego H Giunta, PhD study director
Hospital Italiano de Buenos Aires
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of No-Show Patients and Reminder Systems, run by Hospital Italiano de Buenos Aires at 1 site in Argentina.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 7 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Argentina. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Diego Santillan, MD (+5491162504033, diego.santillan@hospitalitaliano.org.ar), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Proportion of scheduled appointment no-show rate (time frame: 14 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06114602, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Nov 2023.