Factors associated with non-attendance in the Irish national diabetic retinopathy screening programme (INDEAR study report no. 2).

AIMS: We aimed to determine the patient and screening-level factors that are associated with non-attendance in the Irish National Diabetic Retinal screening programme (Diabetic RetinaScreen). To accomplish this, we modelled a selection of predictors derived from the historical screening records of patients with diabetes. METHODS: In this cohort study, appointment data from the national diabetic retinopathy screening programme (RetinaScreen) were extracted and augmented using publicly available meteorological and geospatial data. A total of 653,969 appointments from 158,655 patients were included for analysis. Mixed-effects models (univariable and multivariable) were used to estimate the influence of several variables on non-attendance to screening appointments. RESULTS: All variables considered for analysis were statistically significant. Variables of note, with meaningful effect, were age (OR: 1.23 per decade away from 70; 95% CI: [1.22-1.24]), type 2 diabetes (OR: 1.10; 95% CI: [1.06-1.14]) and socio-economic deprivation (OR: 1.12; 95% CI: [1.09-1.16]). A majority (52%) of missed appointments were from patients who had missed three or more appointments. CONCLUSIONS: This study is the first to outline factors that are associated with non-attendance within the Irish national diabetic retinopathy screening service. In particular, when corrected for age and other factors, patients with type 2 diabetes had higher rates of non-attendance. Additionally, this is the first study of any diabetic screening programme to demonstrate that weather may influence attendance. This research provides unique insight to guide the implementation of an optimal and cost-effective intervention strategy to improve attendance.

Appointment cancellations and no shows: To charge or not to charge?

Every day patients make appointments with doctors in order for both to be able to schedule their time accordingly. All is well unless one of the parties cancels the appointment. In the case of a cancellation that is within 2 hours of a general practitioner visit or 24 hours of a specialist visit, the patient is usually charged for either the full consultation or part thereof. Doctors may also have reasons to cancel and rearrange their appointments with patients, yet there is no penalty placed on the doctor for such behaviour. There appears to be a mismatch between the disincentives for the patient not to cancel v. those of the doctor not to cancel. In this article, the legal and ethical aspects of charging for a missed appointment will be dealt with in order to determine the current situation in South Africa. Furthermore, research into missed appointments will be discussed to ascertain the major causes and provide recommendations to prevent missed appointments from occurring.

No-shows in ambulatory clinics and non-utilized appointments for elective operations in selected surgical departments at a tertiary hospital in Israel.

BACKGROUND: The phenomenon of a patient missing a medical appointment without notification is called a "no-show". In contrast, "non-utilized appointments" are a broader phenomenon including all appointments that didn't occur as registered - whether due to actions taken by providers or patients. Both no-shows and non-utilized appointments can lead to reduced quality of care, loss in productivity, financial losses and impaired patient outcomes. METHODS: The study was carried out between August 2016 and January 2017 in the ENT, Orthopedics and General Surgery Departments of the Jerusalem-based Shaare Zedek Medical Center. The study team sought to examine the reasons for non-utilized appointments in elective operations. The study team also interviewed no-show ambulatory care patients regarding the causes of the no-show and reviewed medical records of no-show patients to determine the nature of the missed appointments. RESULTS: The rate of non-utilization of appointments for elective operations was 6%. The leading reasons for non-utilization of these appointments were: patient health issues, patient surgery postponement and surgery schedule overload (together accounting for 52% of cases and 72% of known reasons). The no-show rate for ambulatory clinic appointments was approximately 15%. The leading reasons for ambulatory clinic no-shows were: administrative issues, illness and forgetfulness (together accounting for 58% of all reasons). The leading types of appointments missed were:post-operation follow-ups and chronic illness follow-up (together accounting for 46% of cases and 63% of known reasons). CONCLUSIONS: In this study, the non-utilized appointment rate for elective operations was found to be lower than those noted in the medical literature, while the no-show rate for ambulatory visits was found to be similar to that found in the literature. There is room to question the necessity of certain types of postoperative follow-up appointments since they are at "high risk" for no-show. One promising way to reduce the no-show rate would involve improving the hospital's information and computing systems in order to identify patients who are susceptible to a no-show incident.

Reducing the Impact of Same-Day Missed Appointments.

Radiologist reimbursement cuts over the last few years have driven outpatient imaging centers to find innovative ways to cut costs and improve efficiency while maintaining high image quality. A California-based outpatient imaging center has been tackling the problem of same-day missed appointments since 2014. The lack of meaningful change with each experiment in the last 4 years has led the organization to change direction and look for ways to actively maintain a desired utilization level. An interim solution to cut examination times in modalities with high cancellation rates allows schedulers to schedule more examinations every day. This article covers the history of continuous improvement around reducing same-day missed appointments and eventually realizing the need to open capacity, and it makes the argument for an integrated system that predicts the probability of patients cancelling their examination to facilitate proactive measures.

Estimating willingness-to-pay for health care: A discrete choice experiment accounting for non-attendance to the cost attribute.

RATIONALE, AIMS, AND OBJECTIVES: The literature suggests that discrete choice experiments (DCEs) are a preferable method for estimating willingness-to-pay (WTP) values, as they may avoid some biases often observed in contingent valuation surveys, such as protest and strategic behaviour. However, the choice studies have demonstrated that attribute non-attendance may be a serious issue that leads to biased WTP estimates. METHOD: A DCE was used to estimate patients' WTP for dental care at the dental school clinic. The endogenous attribute attendance (EAA) model was used to estimate WTP for dental care attributes, while accounting for non-attendance to the cost attribute. RESULTS: The EAA model revealed that almost every second respondent had ignored the cost attribute when making choices, indicating that patients were very sensitive to other characteristics of dental care. Dental care providers should pay particular attention to providing a detailed explanation of treatment to their patients, as this was the most valued attribute of dental care. The welfare estimates from the EAA model, which accounted for non-attendance to the cost attribute, were more than two times lower than welfare estimates from a traditional multinomial logit model and mixed logit model estimated in WTP space. CONCLUSIONS: The findings raise concerns for derived WTP estimates in DCE studies that did not consider non-attendance to the cost attribute during the estimation process. Non-attendance to the cost attribute overestimates WTP values, even if being motivated by the true preferences of respondents.

Effectiveness and cost-effectiveness of fining non-attendance at public hospitals: a randomised controlled trial from Danish outpatient clinics.

OBJECTIVES: Fines have been proposed as means for reducing non-attendance in healthcare. The empirical evidence of the effect of fines is however limited. The objective of this study is to investigate the effectiveness and cost-effectiveness of fining non-attendance at outpatient clinics. DESIGN, PARTICIPANTS AND SETTING: 1:1 randomised controlled trial of appointments for an outpatient clinic, posted to Danish addresses, between 1 May 2015 and 30 November 2015. Only first appointment for users was included. Healthcare professionals and investigators were masked. INTERVENTION: A fine of DKK250 (€34) was issued for non-attendance. Users were informed about the fine in case of non-attendance by the appointment letter, and were able to reschedule or cancel until the appointment. A central administration office administered the fine system. MAIN OUTCOME MEASURES: The main outcome measures were non-attendance of non-cancelled appointments, fine policy administration costs, net of productivity consequences and probability of fining non-attendance being cost-effective over no fining for a range of hypothetical values of reduced non-attendance. RESULTS: All of the 6746 appointments included were analysed. Of the 3333 appointments randomised to the fine policy, 130 (5%) of non-cancelled appointments were unattended, and of the 3413 appointments randomised to no-fine policy, 131 (5%) were unattended. The cost per appointment of non-attendance was estimated at DKK 56 (SE 5) in the fine group and DKK47 (SE 4) in the no-fine group, leading to a non-statistically significant difference of DKK10 (95% CI -9 to 22) per appointment attributable to the fine policy. The probability of cost-effectiveness remained around 50%, irrespective of increased values of reduced non-attendance or various alternative assumptions used for sensitivity analyses. CONCLUSIONS: At a baseline level of around 5%, fining non-attendance does not seem to further reduce non-attendance. Future studies should focus on other means for reduction of non-attendance such as nudging or negative reinforcement. TRIAL REGISTRATION NUMBER: ISRCTN61925912.

Modeling Patient No-Show History and Predicting Future Outpatient Appointment Behavior in the Veterans Health Administration.

BACKGROUND: Missed appointments reduce the efficiency of the health care system and negatively impact access to care for all patients. Identifying patients at risk for missing an appointment could help health care systems and providers better target interventions to reduce patient no-shows. OBJECTIVES: Our aim was to develop and test a predictive model that identifies patients that have a high probability of missing their outpatient appointments. METHODS: Demographic information, appointment characteristics, and attendance history were drawn from the existing data sets from four Veterans Affairs health care facilities within six separate service areas. Past attendance behavior was modeled using an empirical Markov model based on up to 10 previous appointments. Using logistic regression, we developed 24 unique predictive models. We implemented the models and tested an intervention strategy using live reminder calls placed 24, 48, and 72 hours ahead of time. The pilot study targeted 1,754 high-risk patients, whose probability of missing an appointment was predicted to be at least 0.2. RESULTS: Our results indicate that three variables were consistently related to a patient's no-show probability in all 24 models: past attendance behavior, the age of the appointment, and having multiple appointments scheduled on that day. After the intervention was implemented, the no-show rate in the pilot group was reduced from the expected value of 35% to 12.16% (p value < 0.0001). CONCLUSIONS: The predictive model accurately identified patients who were more likely to miss their appointments. Applying the model in practice enables clinics to apply more intensive intervention measures to high-risk patients.

Lead Time to Appointment and No-Show Rates for New and Follow-up Patients in an Ambulatory Clinic.

High rates of no-shows in outpatient clinics are problematic for revenue and for quality of patient care. Longer lead time to appointment has variably been implicated as a risk factor for no-shows, but the evidence within pediatric clinics is inconclusive. The goal of this study was to estimate no-show rates and test for association between appointment lead time and no-show rates for new and follow-up patients. Analyses included 534 new and 1920 follow-up patients from pulmonology and gastroenterology clinics at a freestanding children's hospital. The overall rate of no-shows was lower for visits scheduled within 0 to 30 days compared with 30 days or more (23% compared with 47%, P < .0001). Patient type significantly modified the association of appointment lead time; the rate of no-shows was higher (30%) among new patients compared with (21%) follow-up patients with appointments scheduled within 30 days (P = .004). For appointments scheduled 30 or more days' lead time, no-show rates were statistically similar for new patients (46%) and follow-up patients (0.48%). Time to appointment is a risk factor associated with no-shows, and further study is needed to identify and implement effective approaches to reduce appointment lead time, especially for new patients in pediatric subspecialties.

The financial impact of clinic no-show rates in an academic pediatric otolaryngology practice.

PURPOSE: To investigate determinants of no-show rates in an academic pediatric otolaryngology practice including appointment time, age, sex, new patient status, payer mix, and median household income by zip code. MATERIALS AND METHODS: Retrospective chart review of clinic no-show rates and patient demographics in a free standing children's hospital and affiliated outpatient clinics across eight providers in a one-year period. RESULTS: Analysis shows that the overall no-show rate across all providers was 15% with the highest rate of 19% in the zip code with the lowest median income. Highest no-shows are in June, but overall, seasons did not play a significant role in no-show rates. Male gender, morning appointments, and having public insurance appear to significantly predict no-shows. Lost revenue on no-shows range from $191K to $384K per year. The average percentage of the amount billed paid by insurance range from the lowest by out-of-state Medicaid at 16% to the highest by managed care at 54%. CONCLUSIONS: No-show rates account for a significant portion of lost revenue in the outpatient setting for an academic practice, and can be predicted by lower median income, male gender, morning appointments, and public insurance. Such patients may need different appointment reminders. Future clinic templates should be optimized for no-shows to increase productivity and access to care.

The effect of fines on nonattendance in public hospital outpatient clinics: study protocol for a randomized controlled trial.

BACKGROUND: Nonattendance at scheduled appointments in public hospitals presents a challenge for efficient resource use and may ultimately affect health outcomes due to longer waiting times. Seven percent of all scheduled outpatient appointments in the United Kingdom are estimated to be nonattended. Various reminder systems have been shown to moderately reduce nonattendance, although the effect of issuing fines for nonattendance has not yet been tested in a randomized context. However, such use of financial incentives could impact access to care differently across the different socioeconomic groups. The aim of this study is to assess the effect of fines on hospital outpatient nonattendance. METHODS/DESIGN: A 1:1 randomized controlled trial of scheduled outpatient appointments was used, with follow-ups until the date of appointment. The setting is an orthopedic clinic at a regional hospital in Denmark. Appointments for users who are scheduled for diagnostics, treatment, surgery, or follow-ups were included from May 2015 to November 2015. Appointments assigned to the intervention arm include an attachment of the appointment letter explaining that a fine will be issued in the case of nonattendance without prior notice. Appointments assigned to the control arm follow usual practice (same system but no letter attachment). The primary outcome is the proportion of nonattendance. Secondary outcomes are proportions of cancellations, sociodemographics, and health-problem characteristics. Furthermore, the intervention costs and production value of nonattended appointments will be measured. An analysis of effect and cost-effectiveness will be conducted based on a 5 % significance level. DISCUSSION: The study is initiated and funded by the Danish Regions, which have the responsibility for the Danish public healthcare sector. The results are expected to inform future decisions about the introduction of fines for nonattendance at public hospitals. TRIAL REGISTRATION: Current Controlled Trials, ISRCTN61925912 . Registered on 6 July 2015.

The role of a nurse telephone call to prevent no-shows in endoscopy.

BACKGROUND AND AIMS: Preventing missed appointments, or "no-shows," is an important target in improving efficient patient care and lowering costs in gastrointestinal endoscopy practices. We aimed to investigate whether a nurse telephone call would reduce no-show rates for endoscopic appointments, and to determine if hiring and maintaining a nurse dedicated to pre-endoscopy phone calls is economically advantageous. Our secondary aim was to identify predictors of no-shows to endoscopy appointments. METHODS: We hired and trained a full-time licensed nurse to make a telephone call to patients 7 days before their scheduled upper endoscopy or colonoscopy. We compared this intervention with a previous reminder system involving mailed reminders. The effect of the intervention and impact of other predictors of no-shows were analyzed in 2 similar preintervention and postintervention patient cohorts. A mixed effects logistic regression model was used to estimate the association of the odds of being a no-show to the scheduled appointment and the characteristics of the patient and visit. An analysis of costs was performed that included the startup and maintenance costs of the intervention. RESULTS: We found that a nurse phone call was associated with a 33% reduction in the odds of a no-show visit (odds ratio, 0.67; 95% confidence interval, 0.50-0.91), adjusting for gender, age, partnered status, insurer type, distance from the endoscopy center, and visit type. The recovered reimbursement during the study period was $48,765, with net savings of $16,190 when accounting for the maintenance costs of the intervention; this resulted in a net revenue per annum of $43,173. CONCLUSIONS: We found that endoscopy practices may increase revenue, improve scheduling efficiency, and maximize resource utilization by hiring a nurse to reduce no-shows. Predictors of no-shows to endoscopy included unpartnered or single patients, commercial or managed care, being scheduled for colonoscopy as opposed to upper endoscopy, and being scheduled for a screening or surveillance colonoscopy.

Prevalence, predictors and economic consequences of no-shows.

BACKGROUND: Patients not attending to clinic appointments (no-show) significantly affects delivery, cost of care and resource planning. We aimed to evaluate the prevalence, predictors and economic consequences of patient no-shows. METHOD: This is a retrospective cohort study using administrative databases for fiscal years 1997-2008. We searched administrative databases for no-show frequency and cost at a large medical center. In addition, we estimated no-show rates and costs in another 10 regional hospitals. We studied no-show rates in primary care and various subspecialty settings over a 12-year period, the monthly and seasonal trends of no-shows, the effects of implementing a reminder system and the economic effects of missed appointments. RESULTS: The mean no-show rate was 18.8% (2.4%) in 10 main clinics with highest occurring in subspecialist clinics. No-show rate in the women clinic was higher and the no-show rate in geriatric clinic was lower compared to general primary care clinic (PCP). The no-show rate remained at a high level despite its reduction by a centralized phone reminder (from 16.3% down to 15.8%). The average cost of no-show per patient was $196 in 2008. CONCLUSIONS: Our data indicates that no-show imposed a major burden on this health care system. Further, implementation of a reminder system only modestly reduced the no-show rate.