The economics of non-attendance and the expected effect of charging a fine on non-attendees

Consumer Preferences for a Healthcare Appointment Reminder in Australia: A Discrete Choice Experiment

BACKGROUND

It is essential to consider the evidence of consumer preferences and their specific needs when determining which strategies to use to improve patient attendance at scheduled healthcare appointments.

OBJECTIVES

This study aimed to identify key attributes and elicit healthcare consumer preferences for a healthcare appointment reminder system.

METHODS

A discrete choice experiment was conducted in a general Australian population sample. The respondents were asked to choose between three options: their preferred reminder (A or B) or a 'neither' option. Attributes were developed through a literature review and an expert panel discussion. Reminder options were defined by four attributes: modality, timing, content and interactivity. Multinomial logit and mixed multinomial logit models were estimated to approximate individual preferences for these attributes. A scenario analysis was performed to estimate the likelihood of choosing different reminder systems.

RESULTS

Respondents (n = 361) indicated a significant preference for an appointment reminder to be delivered via a text message (β = 2.42, p < 0.001) less than 3 days before the appointment (β = 0.99, p < 0.001), with basic details including the appointment cost (β = 0.13, p < 0.10), and where there is the ability to cancel or modify the appointment (β = 1.36, p < 0.001). A scenario analysis showed that the likelihood of choosing an appointment reminder system with these characteristics would be 97%.

CONCLUSIONS

Our findings provide evidence on how healthcare consumers trade-off between different characteristics of reminder systems, which may be valuable to inform current or future systems. Future studies may focus on exploring the effectiveness of using patient-preferred reminders alongside other mitigation strategies used by providers.

To confirm your appointment, please press one: Examining demographic and health system interface factors that predict missed appointments in a pediatric outpatient neuropsychology clinic

Objective: Missed patient appointments have a substantial negative impact on patient care, child health and well-being, and clinic functioning. This study aims to identify health system interface and child/family demographic characteristics as potential predictors of appointment attendance in a pediatric outpatient neuropsychology clinic. Method: Pediatric patients (N = 6,976 across 13,362 scheduled appointments) who attended versus missed scheduled appointments at a large, urban assessment clinic were compared on a broad array of factors extracted from the medical record, and the cumulative impact of significant risk factors was examined. Results: In the final multivariate logistic regression model, health system interface factors that significantly predicted more missed appointments included a higher percentage of previous missed appointments within the broader medical center, missing pre-visit intake paperwork, assessment/testing appointment type, and visit timing relative to the COVID-19 pandemic (i.e. more missed appointments prior to the pandemic). Demographic characteristics that significantly predicted more missed appointments in the final model included Medicaid (medical assistance) insurance and greater neighborhood disadvantage per the Area Deprivation Index (ADI). Waitlist length, referral source, season, format (telehealth vs. in-person), need for interpreter, language, and age were not predictive of appointment attendance. Taken together, 7.75% of patients with zero risk factors missed their appointment, while 22.30% of patients with five risk factors missed their appointment. Conclusions: Pediatric neuropsychology clinics have a unique array of factors that impact successful attendance, and identification of these factors can help inform policies, clinic procedures, and strategies to decrease barriers, and thus increase appointment attendance, in similar settings.

Leveraging machine learning to create user-friendly models to mitigate appointment failure at dental school clinics

PURPOSE/OBJECTIVES

This study had a twofold outcome. The first aim was to develop an efficient, machine learning (ML) model using data from a dental school clinic (DSC) electronic health record (EHR). This model identified patients with a high likelihood of failing an appointment and provided a user-friendly system with a rating score that would alert clinicians and administrators of patients at high risk of no-show appointments. The second aim was to identify key factors with ML modeling that contributed to patient no-show appointments.

METHODS

Using de-identified data from a DSC EHR, eight ML algorithms were evaluated: simple decision tree, bagging regressor classifier, random forest classifier, gradient boosted regression, AdaBoost regression, XGBoost regression, neural network, and logistic regression classifier. The performance of each model was assessed using a confusion matrix with different threshold level of probability; precision, recall and predicted accuracy on each threshold; receiver-operating characteristic curve (ROC) and area under curve (AUC); as well as F1 score.

RESULTS

The ML models agreed on the threshold of probability score at 0.20-0.25 with Bagging classifier as the model that performed best with a F1 score of 0.41 and AUC of 0.76. Results showed a strong correlation between appointment failure and appointment confirmation, patient's age, number of visits before the appointment, total number of prior failed appointments, appointment lead time, as well as the patient's total number of medical alerts.

CONCLUSIONS

Altogether, the implementation of this user-friendly ML model can improve DSC workflow, benefiting dental students learning outcomes and optimizing personalized patient care.

Patient-reported experiences and outcomes of virtual care during COVID-19: a systematic review

INTRODUCTION

The onset of COVID-19 has caused an international upheaval of traditional in-person approaches to care delivery. Rapid system-level transitions to virtual care provision restrict the ability of healthcare professionals to evaluate care quality from the patient's perspective. This poses challenges to ensuring that patient-centered care is upheld within virtual environments. To address this, the study's objective was to review how virtual care has impacted patient experiences and outcomes during COVID-19, through the use of patient-reported experience and outcome measures (PREMs and PROMs), respectively.

METHODS

A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines to evaluate patient responsiveness to virtual care during COVID-19. Using an exhaustive search strategy, relevant peer-reviewed articles published between January 2020 and 2022 were pulled from MEDLINE, CINAHL, EMBASE, and PsychInfo databases. Study quality was independently assessed by two reviewers using the Mixed Methods Appraisal Tool. A patient partner was consulted throughout the study to provide feedback and co-conduct the review.

RESULTS

After removing duplicates, 6048 articles underwent title and abstract review, from which 644 studies were included in the full-text review stage. Following this, 102 articles were included in the study. Studies were published in 20 different countries, were predominantly cross-sectional, and reported on the delivery of virtual care in specialized adult outpatient settings. This review identified 29 validated PREMs and 43 PROMs. Several advantages to virtual care were identified, with patients citing greater convenience, (such as saving travel time and cost, less waiting experienced to see care providers) and increased protection from viral spread. Some studies also reported challenges patients and caregivers faced with virtual care, including feeling rushed during the virtual care appointment, lack of physical contact or examination presenting barriers, difficulty with communicating symptoms, and technology issues.

CONCLUSION

This review provides supportive evidence of virtual care experiences during the COVID-19 pandemic from patient and caregiver perspectives. This research provides a comprehensive overview of what patient-reported measures can be used to record virtual care quality amid and following the pandemic. Further research into healthcare professionals' perspectives would offer a supportive lens toward a strong person-centered healthcare system.

Policy-makers' conception of patient non-attendance fees in specialist healthcare: a qualitative document analysis

OBJECTIVES

Patients missing their scheduled appointments in specialist healthcare without giving notice can undermine efficient care delivery. To reduce patient non-attendance and possibly compensate healthcare providers, policy-makers have noted the viability of implementing patient non-attendance fees. However, these fees may be controversial and generate public resistance. Identifying the concepts attributed to non-attendance fees is important to better understand the controversies surrounding the introduction and use of these fees. Patient non-attendance fees in specialist healthcare have been extensively debated in Norway and Denmark, two countries that are fairly similar regarding political culture, population size and healthcare system. However, although Norway has implemented a patient non-attendance fee scheme, Denmark has not. This study aimed to identify and compare how policy-makers in Norway and Denmark have conceptualised patient non-attendance fees over three decades.

DESIGN

A qualitative document study with a multiple-case design.

METHODS

A theory-driven qualitative analysis of policy documents (n=55) was performed.

RESULTS

Although patient non-attendance fees were seen as a measure to reduce non-attendance rates in both countries, the specific conceptualisation of the fees differed. The fees were understood as a monetary disincentive in Norwegian policy documents. In the Danish documents, the fees were framed as an educative measure to foster a sense of social responsibility, as well as serving as a monetary disincentive. The data suggest, however, a recent change in the Danish debate emphasising fees as a disincentive. In both countries, fees were partly justified as a means of compensating providers for the loss of income.

CONCLUSIONS

The results demonstrate how, as a regulative policy tool, patient non-attendance fees have been conceptualised and framed differently, even in apparently similar contexts. This suggests that a more nuanced and complex understanding of why such fees are debated is needed.

Mapping out the arguments for and against patient non-attendance fees in healthcare: an analysis of public consultation documents

BACKGROUND

Patients not attending their appointments without giving notice burden healthcare services. To reduce non-attendance rates, patient non-attendance fees have been introduced in various settings. Although some argue in narrow economic terms that behavioural change as a result of financial incentives is a voluntary transaction, charging patients for non-attendance remains controversial. This paper aims to investigate the controversies of implementing patient non-attendance fees.

OBJECTIVE

The aim was to map out the arguments in the Norwegian public debate concerning the introduction and use of patient non-attendance fees at public outpatient clinics.

METHODS

Public consultation documents (2009-2021) were thematically analysed (n=84). We used a preconceived conceptual framework based on the works of Grant to guide the analysis.

RESULTS

A broad range of arguments for and against patient non-attendance fees were identified, here referring to the acceptability of the fees' purpose, the voluntariness of the responses, the effects on the individual character and institutional norms and the perceived fairness and comparative effectiveness of patient non-attendance fees. Whereas the aim of motivating patients to keep their appointments to avoid poor utilisation of resources and increased waiting times was widely supported, principled and practical arguments against patient non-attendance fees were raised.

CONCLUSION

A narrow economic understanding of incentives cannot capture the breadth of arguments for and against patient non-attendance fees. Policy makers may draw on this insight when implementing similar incentive schemes. The study may also contribute to the general debate on ethics and incentives.

Causes of Patient Nonattendance at Medical Appointments: Protocol for a Mixed Methods Study

BACKGROUND

Approximately one-third of patient appointments in Danish health care result in failures, leading to patient risk and sizable resource waste. Existing interventions to alleviate no-shows often target the patients. The underlying reason behind these interventions is a view that attendance or nonattendance is solely the patient's problem. However, these interventions often prove to be ineffective and can perpetuate social biases and health inequalities, leaving behind patients who are more vulnerable or disadvantaged (in terms of social, economical, and linguistic factors, etc). A more holistic understanding of no-shows is needed to optimize processes, reduce waste, and support patients who are vulnerable.

OBJECTIVE

This study aims to gain a deep and more comprehensive understanding of the causes, mechanisms, and recurring patterns and elements contributing to nonattendance at Danish hospitals in the Region of Southern Denmark. It emphasizes the patient perspective and analyzes the relational and organizational processes surrounding no-shows in health care. In addition, the study aims to identify effective communicative strategies and organizational processes that can support the development and implementation of successful interventions.

METHODS

The study uses mixed quantitative-qualitative methods, encompassing 4 analytical projects focusing on nonattendance patterns, patient knowledge and behavior, the management of hospital appointments, and in situ communication. To address the complexity of no-shows in health care, the study incorporates various data sources. The quantitative data sources include the electronic patient records, Danish central registries, Danish National Patient Registry, and Register of Medicinal Product Statistics. Baseline characteristics of patients at different levels are compared using chi-square tests and Kruskal-Wallis tests. The qualitative studies involve observational data, individual semistructured interviews with patients and practitioners, and video recordings of patient consultations.

RESULTS

This paper presents the protocol of the study, which was funded by the Novo Nordisk Foundation in July 2022. Recruitment started in February 2023. It is anticipated that the quantitative data analysis will be completed by the end of September 2023, with the qualitative investigation starting in October 2023. The first study findings are anticipated to be available by the end of 2024.

CONCLUSIONS

The existing studies of nonattendance in Danish health care are inadequate in addressing relational and organizational factors leading to hospital no-shows. Interventions have had limited effect, highlighting the Danish health care system's failure to accommodate patients who are vulnerable. Effective interventions require a qualitative approach and robust ethnographic data to supplement the description and categorization of no-shows at hospitals. Obtaining comprehensive knowledge about the causes of missed patient appointments will yield practical benefits, enhancing the safety, coherence, and quality of treatment in health care.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/46227.

Behavioural economic interventions to reduce health care appointment non-attendance: a systematic review and meta-analysis

BACKGROUND

Appointment non-attendance - often referred to as "missed appointments", "patient no-show", or "did not attend (DNA)" - causes volatility in health systems around the world. Of the different approaches that can be adopted to reduce patient non-attendance, behavioural economics-oriented mechanisms (i.e., psychological, cognitive, emotional, and social factors that may impact individual decisions) are reasoned to be better suited in such contexts - where the need is to persuade, nudge, and/ or incentivize patients to honour their scheduled appointment. The aim of this systematic literature review is to identify and summarize the published evidence on the use and effectiveness of behavioural economic interventions to reduce no-shows for health care appointments.

METHODS

We systematically searched four databases (PubMed/Medline, Embase, Scopus, and Web of Science) for published and grey literature on behavioural economic strategies to reduce no-shows for health care appointments. Eligible studies met four criteria for inclusion; they were (1) available in English, Spanish, or French, (2) assessed behavioural economics interventions, (3) objectively measured a behavioural outcome (as opposed to attitudes or preferences), and (4) used a randomized and controlled or quasi-experimental study design.

RESULTS

Our initial search of the five databases identified 1,225 articles. After screening studies for inclusion criteria and assessing risk of bias, 61 studies were included in our final analysis. Data was extracted using a predefined 19-item extraction matrix. All studies assessed ambulatory or outpatient care services, although a variety of hospital departments or appointment types. The most common behaviour change intervention assessed was the use of reminders (n = 56). Results were mixed regarding the most effective methods of delivering reminders. There is significant evidence supporting the effectiveness of reminders (either by SMS, telephone, or mail) across various settings. However, there is a lack of evidence regarding alternative interventions and efforts to address other heuristics, leaving a majority of behavioural economic approaches unused and unassessed.

CONCLUSION

The studies in our review reflect a lack of diversity in intervention approaches but point to the effectiveness of reminder systems in reducing no-show rates across a variety of medical departments. We recommend future studies to test alternative behavioural economic interventions that have not been used, tested, and/or published before.

Effectiveness and Consequences of Direct Access in Physiotherapy: A Systematic Review

Background. Direct access in physiotherapy (DAPT) occurs when a patient has the ability to self-refer to physical therapy without physician referral. This model of care in musculoskeletal diseases (MSDs) has shown better outcomes than the traditional-based medical model of care that requires physician referral to access physiotherapist services. This traditional physician referral often results in a delay in care. Unfortunately, DAPT is still not permitted in many countries. Objectives. The primary objective of this systematic review was to compare the effectiveness, safety, and the accuracy of DAPT compared to the physician-led model of care for the management of patients with musculoskeletal disorders. The secondary objective of the present study is to define the physiotherapists' characteristics or qualifications involved in DAPT. Materials and methods. Databases searched included: Medline, Scopus and Web of Science. Databases were searched from their inception to July 2022. Research strings were developed according to the PICO model of clinical questions (patient, intervention, comparison, and outcome). Free terms or synonyms (e.g., physical therapy; primary health care; direct access; musculoskeletal disease; cost-effectiveness) and when possible MeSH (Medical Subject Headings) terms were used and combined with Boolean operators (AND, OR, NOT). Risk of bias assessment was carried out through Version 2 of the Cochrane risk-of-bias tool (ROB-2) for randomized controlled trials (RCTs) and the Newcastle Ottawa Scale (NOS) for observational studies. Authors conducted a qualitative analysis of the results through narrative analysis and narrative synthesis. The narrative analysis was provided for an extraction of the key concepts and common meanings of the different studies, while the summary narrative provided a textual combination of data. In addition, a quantitative analysis was conducted comparing the analysis of the mean and differences between the means. Results. Twenty-eight articles met the inclusion criteria and were analyzed. Results show that DAPT had a high referral accuracy and a reduction in the rate of return visits. The medical model had a higher use of imaging, drugs, and referral to another specialist. DAPT was found to be more cost-effective than the medical model. DAPT resulted in better work-related outcomes and was superior when considering patient satisfaction. There were no adverse events noted in any of the studies. In regard to health outcomes, there was no difference between models. ROB-2 shows an intermediate risk of bias risk for the RCTs with an average of 6/9 points for the NOS scale for observational studies. Conclusion. DAPT is a safe, less expensive, reliable triage and management model of care that results in higher levels of satisfaction for patients compared to the traditional medical model. Prospero Registration Number: CRD42022349261.

To charge or not to charge: reducing patient no-show

BACKGROUND

In order to reduce patient no-show, the Israeli government is promoting legislation that will allow Health Plans to require a co-payment from patients when reserving an appointment. It is hoped that this will create an incentive for patients to cancel in advance rather than simply not show up. The goal of this policy is to improve patient access to medical care and ensure that healthcare resources are utilized effectively. We explore this phenomenon to support evidence-based decision making on this issue, and to determine whether the proposed legislation is aligned with the findings of previous studies.

MAIN BODY

No-show rates vary across countries and healthcare services, with several strategies in place to mitigate the phenomenon. There are three key stakeholders involved: (1) patients, (2) medical staff, and (3) insurers/managed care organizations, each of which is affected differently by no-shows and faces a different set of incentives. The decision whether to impose financial penalties for no-shows should take a number of considerations into account, such as the fine amount, service type, the establishment of an effective fine collection system, the patient's socioeconomic status, and the potential for exacerbating disparities in healthcare access. The limited research on the impact of fines on no-show rates has produced mixed results. Further investigation is necessary to understand the influence of fine amounts on no-show rates across various healthcare services. Additionally, it is important to evaluate the implications of this proposed legislation on patient behavior, access to healthcare, and potential disparities in access.

CONCLUSION

It is anticipated that the proposed legislation will have minimal impact on attendance rates. To achieve meaningful change, efforts should focus on enhancing medical service availability and improving the ease with which appointments can be cancelled or alternatively substantial fines should be imposed. Further research is imperative for determining the most effective way to address the issue of patient no-show and to enhance healthcare system efficiency.

Evaluation of an Automated Reminder System for Reducing Missed MRI Appointments

Background: The high frequency of missed appointments continues to be a burden on healthcare providers, leading to decreased productivity, quality of service, and quality of outcome. The purpose of this study is to evaluate the effectiveness of Televox's automated appointment reminder service in reducing the missed appointment rate for MRI (magnetic resonance imaging). The appointment reminders were sent 72 h in advance. The total and no-show numbers were tallied to calculate missed appointment rates. Comparison of the missed appointment rate with and without Televox implementation and different payment types was performed. Temporal comparisons were also made across the corresponding time periods in order to control for seasonal fluctuations. Results: An insignificant decline in missed appointment rates was found in locations implementing Televox (P = .495) overall, although a significant decrease in missed appointments was found among Medicaid patients (P = .0381). Conclusion: Implementation of Televox appointment reminder systems did not significantly affect appointment attendance overall, but could be more useful specifically for encouraging Medicaid patients to attend MRI appointments.

Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning

In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets. We also implemented multivariate Random Forest (RF) models to predict the endpoints. We achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.82 and 0.73 for predicting no-show and long waiting room time endpoints, respectively. The univariate LR analysis on DI appointments uncovered the effect of the time of appointment during the day/week, and patients' demographics such as income and the number of caregivers on the no-shows and long waiting room time endpoints. For predicting no-show, we found age, time slot, and percentage of single caregiver to be the most critical contributors. Age, distance, and percentage of non-English speakers were the most important features for our long waiting room time prediction models. We found no sex discrimination among the scheduled pediatric DI appointments. Nonetheless, inequities based on patient features such as low income and language barrier did exist.

Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Background

Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model.

Methods

The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment.

Results

Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively.

Conclusions

The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07865-y.

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.

Lost in Follow-Up: Predictors of Patient No-Shows to Clinic Follow-Up After Abdominal Injury

BACKGROUND

The aim of this study is to evaluate risk factors for non-attendance to post-discharge, hospital follow-up appointments for traumatically injured patients who underwent exploratory laparotomy.

METHODS

This is a retrospective chart review of patients who underwent exploratory laparotomy for traumatic abdominal injury at an urban, Midwestern, level I trauma center with clinic follow-up scheduled after discharge. Clinically, relevant demographic characteristics, patients' distance from hospital, and the presence of staples, sutures, and drains requiring removal were collected. Descriptive statistics of categorical variables were calculated as totals and percentages and compared with a chi-squared test or Fisher's exact when appropriate.

RESULTS

The sample included 183 patients who were largely assaultive trauma survivors (68%), male (80%), and black (53%) with a mean age of 35.4 ± 14.9 years. Overall, 18.5% no-showed for their follow-up appointment. On multivariate analysis for clinic no-show; length of stay (odds ratio = 0.92 [0.84-0.99], P = 0.04) and the need for suture, staple, or drain removal were protective for clinic attendance (odds ratio = 5.59 [1.07-7.01], P = 0.04). Overall, 12 patients (6.4%) were readmitted. Forty patients (18.3%) had their follow-up in the emergency department (ED). On multivariate regression of risk factors for ED visits, the only statistically significant factors (P < 0.05) were clinic appointment no-show (OR = 2.81) and self-pay insurance (OR = 4.78).

CONCLUSIONS

Abdominal trauma patients are at high risk of no-show for follow-up appointments and no-show visits are associated with ED visits. Future work is needed evaluating interventions to improve follow-up.

Cost, profile, and postoperative resource use for surgically managed acute hand and wrist injuries with emergency department presentation

STUDY DESIGN

Retrospective cost-of-illness study.

INTRODUCTION

Injuries to the hand and wrist are common. Most uncomplicated and stable upper extremity injuries recover with conservative management; however, some require surgical intervention. The economic burden on the health care system from such injuries can be considerable.

PURPOSE OF THE STUDY

To estimate the economic implications of surgically managed acute hand and wrist injuries at one urban health care network.

METHODS

Using 33 primary diagnosis ICD-10 codes involving the hand and wrist, 453 consecutive patients from 2014 to 2015 electronic billing records who attended the study setting emergency department and received consequent surgical intervention and outpatient follow-up were identified. Electronic medical records were reviewed to extract demographic data. Costs were calculated from resource use in the emergency department, inpatient, and outpatient settings. Results are presented by demographics, injury type, mechanism of injury, and patient pathway.

RESULTS

Two hundred and twenty-six individuals (n 1⁄4 264 surgeries) were included. The total cost of all injuries was $1,204,606. The median cost per injury for non-compensable cases (n = 191) was $4508 [IQR $3993-$6172] and $5057 [IQR $3957-$6730] for compensable cases (n = 35). The median number of postoperative appointments with a surgeon was 2.00 (IQR 1.00-3.00) for both compensable and non-compensable cases. The number of hand therapy appointments for non-compensable cases and compensable cases was 4 [IQR 2-6] and 2 [IQR 1-3], respectively.

DISCUSSION

Findings of this investigation highlight opportunities for health promotion strategies for reducing avoidable injuries and present considerations for reducing cost burden by addressing high fail to attend (FTA) appointment rates.

CONCLUSION

Surgically managed hand and wrist injuries contribute to a significant financial burden on the health care system. Further research using stringent data collection methods are required to establish epidemiological data and national estimates of cost burden.

The effect of a mobile health app on adherence to physical health treatment

BACKGROUND

Adhering to prescribed medical interventions predicts the efficacy of the treatment. In the physical health clinics, not adhering to prescribed therapy can take the form of not attending a scheduled clinic visit (no-show appointment) or prematurely terminating treatment against the advice of the provider (self-discharge). A variety of interventions, including mobile phone apps have been introduced with patients to increase their adherence with attending scheduled clinic visits. Limited research has examined the impact of a mobile phone app among patients attending a chiropractic and rehabilitation clinic visits.

OBJECTIVE

The purpose of this study was to compare adherence with prescribed physical health treatment among patients attending a chiropractic and rehabilitation clinic who did and did not choose to adopt a phone-based app to complement their treatment.

METHODS

The medical records of new patients who presented for care during 2019 and 2020 at five community-based chiropractic and rehabilitation clinics were reviewed for the number of kept and no-show appointments and if the patient was provider discharged or self-discharged. During this 24-month study 36.3% of the 4,126 patients seen in the targeted clinics had downloaded the Kanvas App to their mobile phone while the remaining patients chose not to download the app (Usual Care Group). The gamification component of the Kanvas App provided the patient with a point every time they attended their visits which could be redeemed for an incentive.

RESULTS

During both 2019 and 2020 respectively the Kanvas App Group were provider discharged at a greater rate than the Usual-Care Group. The Kanvas App Group kept a similar number of appointments compared to the Usual-Care Group in 2019 but kept significantly more appointments than the Usual-Care Group in 2020. During 2019 both groups exhibited a similar number of no-show appointments but in 2020 the Kanvas App Group demonstrated more no-show appointments than the Usual Care Group. When collapsed across years and self-discharged the Kanvas App Group had a greater number of kept appointments compared to the Usual Care Group. When provider discharged, both groups exhibited a similar number of kept appointments. The Kanvas App Group and the Usual Care Group were similar in the number of no-show appointments when provider discharged and when self-discharged the Kanvas App Group had more no-show appointments compared to the Usual Care Group.

CONCLUSIONS

Patients who did or did not have access to the Kanvas App and were provider discharged, exhibited a similar number of kept appointments and no-show appointments. When subjects were self-discharged and received the Kanvas App they exhibited 3.2 more kept appointments and .94 more no-show appointments than self-discharged Usual Care Group.

Characteristics Associated With New Patient Appointment No-Shows at an Academic Ophthalmology Department in the United States

PURPOSE

This study aimed to identify patient and appointment characteristics associated with no-shows to new patient appointments at a US academic ophthalmology department.

DESIGN

Cross-sectional study.

METHODS

This was a study of all adult patients with new patient appointments scheduled with an attending ophthalmologist at Penn State Eye Center between January 1^st and December 31^st of 2019. A multiple logistic regression model was used to assess the association between characteristics and no-show status.

RESULTS

Of 4,628 patients, 759 (16.4%) were no-shows. From the multiple logistic regression model, characteristics associated with no-shows were age (Odds Ratio (OR) for 18-40 years vs. >60 years: 3.41, 95% Confidence Interval (CI) 2.57, 4.51, p <0.001 and OR for 41-60 years vs. >60 years: 2.14, 95% CI 1.67, 2.74, p<0.001), median household income (OR for <$35,667 vs. >$59,445: 1.59, 95% CI 1.08, 2.34, p<0.001), insurance (OR for None vs. Medicare: 6.92, 95% CI 4.41, 10.86, p<0.001 and OR for Medicaid vs. Medicare: 1.54, 95% CI 1.18, 2.01, p=0.002), race (OR for Black vs. White: 2.62, 95% CI 2.00, 3.43, p<0.001 and OR for Other vs. White: 2.02, 95% CI 1.58, 2.59, p<0.001), and commute distance (OR for 5-10 mi vs. ≤5 mi: 1.73, 95% CI 1.17, 2.55, p=0.006). Appointments with longer lead times and scheduled with glaucoma or retina specialists were also significantly associated with greater no-shows.

CONCLUSION

Certain patient and appointment characteristics were associated with no-show status. These findings may assist in the development of targeted interventions at the patient, practice, and health system levels to improve appointment attendance.

Factors associated with missed appointments by adults with type 2 diabetes mellitus: a systematic review

Keeping regular medical appointments is a key indicator of patient engagement in diabetes care. Nevertheless, a significant proportion of adults with type 2 diabetes mellitus (T2DM) miss their regular medical appointments. In order to prevent and delay diabetes-related complications, it is essential to understand the factors associated with missed appointments among adults with T2DM. We synthesized evidence concerning factors associated with missed appointments among adults with T2DM. Using five electronic databases, including PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, PsycINFO and Web of Science, a systematic literature search was done to identify studies that describe factors related to missed appointments by adults with T2DM. A total of 18 articles met the inclusion criteria. The majority of studies included in this review were cohort studies using medical records. While more than half of the studies were of high quality, the operational definitions of missed appointments varied greatly across studies. Factors associated with missed appointments were categorized as patient characteristics, healthcare system and provider factors and interpersonal factors with inconsistent findings. Patient characteristics was the most commonly addressed category, followed by health system and provider factors. Only three studies addressed interpersonal factors, two of which were qualitative. An increasing number of people live with one or more chronic conditions which require more careful attention to patient-centered care and support. Future research is warranted to address interpersonal factors from patient perspectives to better understand the underlying causes of missed appointments among adults with T2DM.

The impact of common variables on non-attendance at a radiology centre in Singapore

INTRODUCTION

With the increasing demand for medical imaging, non-attendance inhibits private and public radiology practices in Singapore from providing timely care and achieving maximal efficiency. Missed radiological appointments adversely affect clinical and economic outcomes and strain the finite healthcare resources. We examined the prevalence and predictors of patient non-attendance for radiological services at a regional public hospital in Singapore and compared them against other medical imaging centres globally.

METHODS

Outpatient records of patients who were scheduled for specialised medical imaging obtained from Radiological Information System (RIS) were retrospectively reviewed. Analysed variables include patient demographics, radiology modalities, visit statuses and appointment lead times where Pearson's chi-square test and Fisher's exact test were used for categorical variables, and independent sample t-test was used for continuous variables. The association between each patient characteristic and non-attendance status was assessed using Binary Logistics Regression. Variables that showed statistical significance in univariate analysis were included in the multivariate logistic regression model to identify the independent risk factors associated with non-attendance.

RESULTS

Among the 59,748 outpatient appointments with medical imaging requests, 15.5% did not turn up for their appointments. Logistic regression indicated that patient's age, ethnicity, subsidy status, house ownership, living vicinity to regional hospital cluster, appointment wait times, appointment hours and appointment months were significant factors associated with the failure to attend scheduled radiological examinations.

CONCLUSION

Even though predictors of non-attendance remained consistent across medical imaging centres worldwide, Singapore reported a higher prevalence of missed appointments calling for future exploratory studies to understand the population's health-seeking behaviours and ordering patterns of clinicians.

IMPLICATIONS FOR PRACTICE

Comparison and identification of these predictors will assist in the design of targeted interventions that may improve patient's adherence and utilisation of imaging services.

Using business intelligence and data visualization to understand the characteristics of failed appointments in dental school clinics

PURPOSE/OBJECTIVES

Broken appointments are an important cause of waste in health care. Patients who fail to attend incur costs to providers, deny trainees learning opportunities, and impact their own health as well as that of other patients who are waiting for care.

METHODS

A total of 410,000 appointment records over 3 years were extracted from our electronic health record. We conducted exploratory data analysis and assessed correlations between appointment no-shows and other attributes of the appointment and the patient. The University of Michigan Medical School's Committee on Human Research reviewed the study and deemed that no Institutional Review Board oversight was necessary for this quality improvement project that was, retrospectively, turned into a study with previously de-identified data.

RESULTS

The patient's previous attendance record is the single most significant correlation with attendance. We found that patients who said they are "scared" of dental visits were 62% as likely to attend as someone reporting "no problem." Patients over 65 years of age have better attendance rates. There was a positive association between receiving email/text confirmation and attendance. A total of 94.9% of those emailed a reminder and 92.2% of those who were texted attended their appointment.

CONCLUSION(S)

We were able to identify relationships of several variables to failed and attended appointments that we were previously unknown to us. This knowledge enabled us to implement interventions to support better attendance at Dental Clinics at the University of Michigan, improving patient health, student training, and efficient use of resources.

Using machine learning tools to investigate factors associated with trends in 'no-shows' in outpatient appointments

Missed appointments are estimated to cost the UK National Health Service (NHS) approximately £1 billion annually. Research that leads to a fuller understanding of the types of factors influencing spatial and temporal patterns of these so-called "Did-Not-Attends" (DNAs) is therefore timely. This research articulates the results of a study that uses machine learning approaches to investigate whether these factors are consistent across a range of medical specialities. A predictive model was used to determine the risk-increasing and risk-mitigating factors associated with missing appointments, which were then used to assign a risk score to patients on an appointment-by-appointment basis for each speciality. Results show that the best predictors of DNAs include the patient's age, appointment history, and the deprivation rank of their area of residence. Findings have been analysed at both a geographical and medical speciality level, and the factors associated with DNAs have been shown to differ in terms of both importance and association. This research has demonstrated how machine learning techniques have real value in informing future intervention policies related to DNAs that can help reduce the burden on the NHS and improve patient care and well-being.

Changing Patterns of Medical Visits and Factors Associated with No-show in Patients with Rheumatoid Arthritis during COVID-19 Pandemic

BACKGROUND

The main barrier to the effective rheumatoid arthritis (RA) therapy is poor adherence. Coronavirus disease 2019 (COVID-19) pandemic have led to a significant change in the pattern and the number of medical visits. We assessed changing patterns of medical visits and no-show, and identified factors associated with no-show in patients with RA during COVID-19 pandemic.

METHODS

RA patients treated with disease-modifying antirheumatic drugs at least 6 months who had been in remission or those with mild disease activity were observed for 6 months from February to July 2020. No-show was defined as a missed appointment that was not previously cancelled by the patient and several variables that might affect no-show were examined.

RESULTS

A total of 376 patients and 1,189 appointments were evaluated. Among 376 patients, 164 patients (43.6%) missed appointment more than one time and no-show rate was 17.2% during COVID-19 pandemic. During the observation, face-to-face visits gradually increased and no-show gradually decreased. The logistic regression analysis identified previous history of no-show (adjusted odds ratio [OR], 2.225; 95% confidence interval [CI], 1.422-3.479; P < 0.001) and fewer numbers of comorbidities (adjusted OR, 0.749; 95% CI, 0.584-0.961; P = 0.023) as the independent factors associated with no-show.

CONCLUSION

Monthly analysis showed that the no-show rate and the pattern of medical visits gradually changed in patients with RA during COVID-19 pandemic. Moreover, we found that previous history of no-show and fewer numbers of comorbidities as the independent factors associated with no-show.

Into the Storm: a Mixed Methods Evaluation of Reasons for Non-attendance of Appointments in the Free Clinic Setting

Non-attendance of healthcare appointments impact individual health outcomes and the capacity and financial stability of clinics. While non-attendance of appointments has been associated with a variety of factors, interventions to increase attendance have had mixed success. The most widely used intervention, reminder systems like phone calls or text messages, generally improves attendance rates but is insufficient for many clinics as a sole intervention. This study of underresourced patients who did not attend appointments at two clinics for uninsured individuals describes the multifactorial, individualized, and interacting reasons for non-attendance among these methods: Forty-three patients were interviewed by phone within 3 weeks of missing a clinic appointment using a scripted interview based on the literature. Responses were coded and analyzed. For 57% of respondents, a competing priority such as work or caregiving was a reason for missing an appointment. Forgetting about the appointment was a barrier for 38% of participants despite reminder systems being in place. Contributions to non-attendance were identified through thematic analysis: emotional and physical exhaustion, prioritization of work over healthcare, unreliable transportation, financial stress, and being unaware of an appointment. These findings demonstrate the need to test multiple patient-centered interventions, particularly in the context of underresourced communities.

Efficiency of Text Message Contact on Medical Safety in Outpatient Surgery: Retrospective Study

BACKGROUND

Establishing pre- and postoperative contact with patients is part of successful medical management in outpatient surgery. In France, this is mostly done via telephone. Automated information with short message service (SMS) reminders might be an interesting alternative to increase the rate of compliance with preoperative instructions, but no study has shown the safety of this approach.

OBJECTIVE

The objective of this study was to evaluate the impact of pre- and postoperative automated information with SMS reminders on medical safety in outpatient surgery.

METHODS

We conducted a retrospective, single-center, nonrandomized, controlled study with a before-after design. All adult patients who had outpatient surgery between September 2016 and December 2017 in our university hospital center were included. Before April 2017, patients were contacted by telephone by an outpatient surgery nurse. After April 2017, patients were contacted by SMS reminder. All patients were contacted the day before and the day after surgery. Patients contacted by SMS reminder were also contacted on day 7 after surgery. The primary end point was the conversion rate to full-time hospitalization. Secondary end points were hospitalization causes (anesthetic, surgical, organizational) and hospitalization costs.

RESULTS

A total of 4388 patients were included, 2160 before and 2228 after the introduction of SMS reminders. The conversion rate to full-time hospitalization was 34/4388 (0.77%) with a difference between SMS group (8/2228, 0.36%) and telephone group (26/2160, 1.20%). The cost of SMS reminders was estimated as half that of telephone calls.

CONCLUSIONS

In this work, we report a decrease in the rate of conversion to full-time hospitalization with the use of pre- and postoperative SMS reminders. This new approach could represent a safe and cost-effective method in an outpatient surgery setting.

Fine for non-attendance in public hospitals in Denmark: A survey of non-attenders' reasons and attitudes

OBJECTIVE

To investigate non-attending patients' reasons for non-attendance and their general and specific attitudes towards a non-attendance fine.

DATA SOURCES

Non-attenders at two hospital departments participating in a trial of fine for non-attendance from May 2015 to January 2017.

DESIGN

A quantitative questionnaire study was conducted among non-attenders.

DATA COLLECTION

Non-attending patients in the intervention group were invited to complete the questionnaire. The response rate was 39% and the total number of respondents was 71 individuals.

PRINCIPAL FINDINGS

The main reason for non-attendance was technical challenges with the digital appointment and with cancelation. The main part of the respondents was generally positive towards a fine for non-attendance. However, approximately the half had a negative attitude towards the actual fine issued.

CONCLUSIONS

Technical challenges with appointments and cancelation should get special attention when addressing non-attendance. Danish non-attending patients are primarily positive towards the general principle of issuing a fine for non-attendance. However, a significant proportion of the generally positive, reported a negative specific attitude to the specific fine issued to them. This, however, did not affect their general attitude.

'I've always done what I was told by the medical people': a qualitative study of the reasons why older adults attend multifactorial falls risk assessments mapped to the Theoretical Domains Framework

OBJECTIVES

Multifactorial falls risk assessments reduce the rate of falls in older people and are recommended by international guidelines. Despite their effectiveness, their potential impact is often constrained by barriers to implementation. Attendance is an issue. The aim of this study was to explore why older people attend community-based multifactorial falls risk assessment clinics, and to map these reasons to a theoretical framework.

DESIGN

This is a qualitative study. Semi-structured interviews were conducted and analysed thematically. Each theme and subtheme were then mapped onto the Theoretical Domains Framework (TDF) to identify the determinants of behaviour.

PARTICIPANTS

Older adults (aged 60 and over) who attended community-based multifactorial falls risk assessments.

RESULTS

Sixteen interviews were conducted. Three main themes explained participants' reasons for attending the multifactorial risk assessment; being that 'type of person', being 'linked in' with health and community services and having 'strong social support'. Six other themes were identified, but these themes were not as prominent during interviews. These were knowing what to expect, being physically able, having confidence in and being positive towards health services, imagining the benefits given previous positive experiences, determination to maintain or regain independence, and being 'crippled' by the fear of falling. These themes mapped on to nine TDF domains: 'knowledge', 'skills', 'social role and identity', 'optimism', 'beliefs about consequences', 'goals', 'environmental context and resources', 'social influences' and 'emotion'. There were five TDF domains that were not relevant to the reasons for attending.

CONCLUSIONS

These findings provide theoretically based factors that influence attendance which can be used to inform the development of interventions to improve attendance to falls prevention programmes.

Profile and cost of sport and exercise-related hand and wrist injuries with Emergency Department presentation

OBJECTIVES

Injuries to the hand and wrist from sport and exercise are common and costly. This cost-of-illness analysis was performed to estimate the economic implications of hand and wrist injuries that were sustained as a result of participation during sport or exercise.

PERSPECTIVE

Cost estimates were calculated from resource use in the emergency, inpatient and outpatient settings from the perspective of one public healthcare service.

SETTING

Alfred Health, a large public health service with two emergency departments located in Victoria, Australia.

METHODS

This descriptive epidemiological study used ICD-10 diagnostic codes and electronic billing records to identify 778 potential cases for inclusion. Electronic medical records were screened and reviewed to extract demographic and patient care journey data.

RESULTS

692 individuals, (n=761 individual zone of injuries), were included. Australian Rules Football (ARF) was the largest contributor to injuries (20.2%) followed by riding bicycles (15.9%. The total cost of all injuries was $790,325, with a median cost per case of $278 [IQR $210-$282] in the Emergency Department n=692, $3328 [IQR $2242-$6441] in the inpatient setting n=76 and $630 [IQR $460-$870] in the outpatient setting n=244.

CONCLUSIONS

Hand and wrist injuries sustained from sport and exercise contribute to a significant financial burden on the healthcare system. Future research that considers the costs that occur outside of the public healthcare service is required estimate the burden associated with these injuries comprehensively. Injury prevention programs may mitigate the observed injury trends.

Absenteísmo de usuários como fator de desperdício: desafio para sustentabilidade em sistema universal de saúde

RESUMO O absenteísmo de usuários em consultas e exames é considerado um problema mundial na assistência à saúde, gerando desperdício de recursos tanto no setor público como no setor privado. O objetivo deste estudo foi o de estimar o desperdício de recursos monetários vinculado ao absenteísmo em procedimentos especializados no Sistema Único de Saúde (SUS) na Região de Saúde Metropolitana do Espírito Santo (RSM-ES) entre os anos de 2014 e 2016. Analisaram-se 1.002.719 procedimentos, sendo 666.182 consultas e 336.537 exames especializados. Os dados de absenteísmo foram retirados dos registros administrativos do Sistema de Regulação do ES (SisReg-ES), fornecidos pela Secretaria Estadual de Saúde. Os valores monetários foram obtidos por meio da Tabela SUS, da tabela complementar de convênios e da tabela de custos estimados, segundo o tipo de prestador envolvido no atendimento. A taxa média de absenteísmo para consultas foi de 38,6% (257.025 consultas), gerando um total estimado de R$3.558.837,88; e para exames especializados, foi de 32,1% (108.103 exames), em um total estimado de R$15.007.624,15. Os valores totais desperdiçados são significativos e evidenciam o desafio constante na agenda dos gestores na busca pela SUStentabilidade em sistemas universais de saúde.

Predictors of No-show Rate in the GI Endoscopy Suite at a Safety Net Academic Medical Center

BACKGROUND/OBJECTIVES

Noncompliance with physician and procedure appointments is associated with poor disease control and worse disease outcomes. This also impacts the quality of care, decreases efficiency, and affects revenue. Studies have shown that no-show rates are higher in clinics caring for underserved populations and may contribute to poorer health outcomes in this group.

METHODS

We performed a 17-month retrospective observational cohort study of patients scheduled for outpatient procedures in the Gastroenterology endoscopy suite at the University of Florida Health, Jacksonville. Multivariate logistic regression analysis was performed to evaluate associations between attendance and predictors of no-show.

RESULTS

In total, 6157 patients were scheduled to undergo different GI procedures during the study period. A total of 4388 (71%) patients completed their procedure, whereas 2349 (29%) failed to attend their appointment and were considered "no-show". There was a significant relationship between the visit attendance and race, insurance, gender, and marital status. Males had a higher no-show rate compared with females (30% vs. 28%; P<0.05). African Americans had the highest no-show rate (32%; P<0.05) amongst different races. Patients scheduled for surveillance colonoscopy (ie, history of polyps, IBD, Colon cancer) were more likely to show (78%) than those obtaining initial colorectal cancer screening (74%) or other indications (71%) (P<0.05).In the logistic regression model, patients with commercial insurance are more likely to show for their appointments than those with noncommercial insurance (eg, Medicare, Medicaid, City contract etc) [odds ratio (OR), 2.6; 95% confidence interval (CI), 2.2-3.0]. The odds of showing up are 1.7 times higher for married men compared with single men (OR, 1.7; 95% CI, 1.3-2.0). Similarly, married females are more likely to show up for appointment than single females (OR, 1.1; 95% CI, 0.9-1.3). We did not find significant association between the type of GI procedure (eg, colonoscopy vs. esophagogastroduodenoscopy vs. advanced endoscopic procedures) (P>0.05).

CONCLUSIONS

Predictors of no-shows for endoscopic gastrointestinal procedures included unpartnered or single patients, African American race and noncommercial insurance providers. Patients scheduled for surveillance colonoscopy had better adherence than initial screening. Further studies are required to better characterize these factors and improve adherence to the outpatient appointments based on the identified predictors.

The effects of a pretreatment educational group programme on mental health treatment outcomes: a randomized controlled trial

Background

Patients dropping out of mental health treatment is considered a widespread and significant obstacle to providing effective treatment, thus reducing the probability of patients achieving the desired improvement. Here, relative to ordinary treatment, we investigate the effects of providing an educational group programme before mental health treatment on mental health symptomatology and the risk of patients dropping out or prematurely discontinuing treatment.

Methods

A randomized controlled trial in which adults referred to a community mental health center were randomized to either a Control Group (n = 46) or a pretreatment educational programme followed by treatment as usual (Intervention Group, n = 45). The primary outcome was self-reported mental health symptomatology assessed with BASIS-32. Data were analyzed by multilevel linear regression and Cox’s regression.

Results

We recruited 93 patients during a 26-month period. Assessments were performed before (0 month, baseline) and after the intervention (1 month, before treatment initiation), and after 4 and 12 months. The net difference in BASIS-32 score between 0 and 1-month was − 0.27 (95% confidence interval CI] -0.45 to − 0.09) in favor of the intervention group. Although both groups had a significant and continuous decline in psychopathology during the treatment (from 1 month and throughout the 4- and 12-month follow-up assessments), the group difference detected before treatment (between 0 and 1 month) persisted throughout the study. Premature treatment discontinuation was partially prevented. The dropout risk was 74% lower in the Intervention Group than in the Control Group (hazard ratio 0.26, 95% CI = 0.07–0.93).

Conclusions

A brief educational intervention provided before mental health treatment seems to have an immediate and long-lasting effect on psychopathology, supplementary to traditional treatment. Such an intervention might also have a promising effect on reducing treatment dropout.

Trial registration

NCT00967265, clinicaltrials.gov. Registered August 27, 2009, retrospectively registered.

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.

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.

Choosing and booking-and attending? Impact of an electronic booking system on outpatient referrals and non-attendances

Patient non-attendance can lead to worse health outcomes and longer waiting times. In the English National Health Service, around 7% of patients who are referred by their general practice for a hospital outpatient appointment fail to attend. An electronic booking system (Choose and Book-C&B) for general practices making hospital outpatient appointments was introduced in England in 2005 and by 2009 accounted for 50% of appointments. It was intended, inter alia, to reduce the rate of non-attendance. Using a 2004-2009 panel with 7,900 English general practices, allowing for the relaxation of constraints on patient of hospital, and for the potential endogeneity of use of C&B, we estimate that the introduction of C&B reduced non-attendance by referred patients in 2009 by 72,160 (8.7%).

Why don't patients attend their appointments? Maintaining engagement with psychiatric services

Patients miss about 20% of scheduled appointments for mental health treatment, almost twice the rate in other medical specialties. Up to 50% of patients who miss appointments drop out of scheduled care. Many who miss appointments because of slips and lapses later rearrange their appointments without adverse consequences. Those that do not are at risk of further deterioration, relapse and hospital readmission. Predictors of non-attendance are complex and linked with the predictors of missed medication. Service barriers and administrative errors are common but are often overlooked in the absence of feedback from patients. Of prime importance are the therapeutic alliance and degree of ‘helpfulness’ of the clinician but again these are rarely measured routinely. Useful markers of engagement include patient-rated trust, satisfaction and degree of perceived participation in treatment decisions. Much can be done to improve attendance in most services. Simple measures such as offering prompt, convenient appointments, offering reminders and augmenting with telephone contact have a reasonable evidence base. Scales to assess therapeutic alliance are now available. Complex interventions need to be evaluated carefully in order that the overall benefits outweigh costs. We suggest that clinicians consider accessibility, discharge policies and patient feedback when examining local rates of non-attendance.

Reducing Non-Attendance Rates for Assessment at an Eating Disorders Service: A Quality Improvement Initiative

Rates of non-attendance at initial appointments within community eating disorder (ED) services are frequently high, although this has received relatively little research attention and no reports of interventions designed to address this. The current report describes outcomes following a change of procedure introducing a 'partial booking' system. Attendance rates at first appointments (N = 1260) were audited following introduction of a system designed to reduce non-attendance in January 2013 within a UK ED service. Rates were compared following implementation of the new system, using a historical control group for comparison, and showed a decline from 20.4 to 15.1%, a medium-sized effect. Use of a system asking patients to book an appointment reduced non-attendance at initial appointments and may be of use to similar services experiencing high non-attendance rates. Opt-in initiatives can reduce burden resulting from long waiting times and can be easily adapted to individual services.

Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record

PURPOSE

To test whether data elements available in the electronic medical record (EMR) can be effectively leveraged to predict failure to attend a scheduled radiology examination.

MATERIALS AND METHODS

Using data from a large academic medical center, we identified all patients with a diagnostic imaging examination scheduled from January 1, 2016, to April 1, 2016, and determined whether the patient successfully attended the examination. Demographic, clinical, and health services utilization variables available in the EMR potentially relevant to examination attendance were recorded for each patient. We used descriptive statistics and logistic regression models to test whether these data elements could predict failure to attend a scheduled radiology examination. The predictive accuracy of the regression models were determined by calculating the area under the receiver operator curve.

RESULTS

Among the 54,652 patient appointments with radiology examinations scheduled during the study period, 6.5% were no-shows. No-show rates were highest for the modalities of mammography and CT and lowest for PET and MRI. Logistic regression indicated that 16 of the 27 demographic, clinical, and health services utilization factors were significantly associated with failure to attend a scheduled radiology examination (P ≤ .05). Stepwise logistic regression analysis demonstrated that previous no-shows, days between scheduling and appointments, modality type, and insurance type were most strongly predictive of no-show. A model considering all 16 data elements had good ability to predict radiology no-shows (area under the receiver operator curve = 0.753). The predictive ability was similar or improved when these models were analyzed by modality.

CONCLUSION

Patient and examination information readily available in the EMR can be successfully used to predict radiology no-shows. Moving forward, this information can be proactively leveraged to identify patients who might benefit from additional patient engagement through appointment reminders or other targeted interventions to avoid no-shows.

A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression*. An Application in Joint Prediction of Appointment Miss-opportunities across Multiple Clinics

OBJECTIVES

Whether they have been engineered for it or not, most healthcare systems experience a variety of unexpected events such as appointment miss-opportunities that can have significant impact on their revenue, cost and resource utilization. In this paper, a multi-way multi-task learning model based on multinomial logistic regression is proposed to jointly predict the occurrence of different types of miss-opportunities at multiple clinics.

METHODS

An extension of L₁ / L₂ regularization is proposed to enable transfer of information among various types of miss-opportunities as well as different clinics. A proximal algorithm is developed to transform the convex but non-smooth likelihood function of the multi-way multi-task learning model into a convex and smooth optimization problem solvable using gradient descent algorithm.

RESULTS

A dataset of real attendance records of patients at four different clinics of a VA medical center is used to verify the performance of the proposed multi-task learning approach. Additionally, a simulation study, investigating more general data situations is provided to highlight the specific aspects of the proposed approach. Various individual and integrated multinomial logistic regression models with/without LASSO penalty along with a number of other common classification algorithms are fitted and compared against the proposed multi-way multi-task learning approach. Fivefold cross validation is used to estimate comparing models parameters and their predictive accuracy. The multi-way multi-task learning framework enables the proposed approach to achieve a considerable rate of parameter shrinkage and superior prediction accuracy across various types of miss-opportunities and clinics.

CONCLUSIONS

The proposed approach provides an integrated structure to effectively transfer knowledge among different miss-opportunities and clinics to reduce model size, increase estimation efficacy, and more importantly improve predictions results. The proposed framework can be effectively applied to medical centers with multiple clinics, especially those suffering from information scarcity on some type of disruptions and/or clinics.

Text Message Reminders Reduce Outpatient Radiology No-Shows But Do Not Improve Arrival Punctuality

PURPOSE

To assess whether text-based appointment reminders are a cost-effective strategy to decrease patient no-shows and improve arrival punctuality in the setting of outpatient radiology imaging.

METHODS AND MATERIALS

From July 2016 through October 2016, all patients scheduled for MRI imaging at two outpatient locations were randomly assigned to a texting or nontexting arm based on the day. On texting days, patients scheduled for MRI received both the traditional phone call reminder as well as a text-based reminder of their MRI examination. On nontexting days, patients scheduled for MRI received only the traditional phone call reminder. All patients were evaluated based on whether they attended the MRI appointment and, if attended, whether they arrived 30 minutes before the MRI appointment as requested in the text message. Potential associations between the text reminder and examination attendance and punctuality were assessed by χ² test with associations considered significant at P ≤ .05.

RESULTS

A total of 6,989 patients were eligible for analysis, 3,086 in the texting group and 3,903 in the nontexting group. In the texting group, 67.5% (2,083/3,086) of patients were successfully texted with an appointment reminder, with the other 32.5% not having text accessibility. The percent of no-shows was significantly decreased for the texting group compared with the nontexting group (3.8% versus 5.1%, P = .02, odds ratio 0.75, 95% confidence interval 0.59 to 0.94). There was no significant difference between the percent of patients arriving the requested 30 minutes before the MRI appointment between the texting and nontexting groups (60.0% versus 58.5%, P = .25).

CONCLUSION

Text message appointment reminders are an effective strategy for decreasing radiology no-shows, even in the presence of traditional phone reminders, but do not improve patient arrival punctuality.

"Nudge" and the epidemic of missed appointments

Purpose - Missed appointments constitute a significant problem in the UK National Health Service (NHS) and this remains an area where improvements could yield substantial efficiency savings. The purpose of this paper is to suggest that nudge policies based on behavioural theories may help target interventions to improve patient motivation to attend appointments. Design/methodology/approach - The authors propose two policies to reduce missed appointments. The first attempts to empower patients through making the appointment system more individualised to them and utilising their intrinsic feelings of social responsibility. The second policy utilises a financial commitment given by the patient at the time of booking. The different mechanisms of influencing patient behaviour are based on two different views of what motivates individuals' actions. The first policy is based on individuals being "knights". They are altruistic and have well-intentioned values. The second policy option is constructed on the premise that an individual is governed by self-interest, and they are in fact "knaves". Findings - A policy, which avoids the use of financial penalties is likely to be more culturally acceptable within the NHS. It could also prevent the phenomenon of "crowding out" whereby the desire to act dutifully gets displaced by the motivation to avoid incurring a monetary fine. Originality/value - Testing both strategies would provide insight into patient attitudes towards health care and society. This would help optimise behavioural strategies which may influence not only appointment attendances but also have wider implications for encouraging rational health care consumption.

[Analysis of economic cost of missed outpatient appointments]

AIM

To estimate the economic costs of missed Outpatient appointments by the Costa del Sol Health Agency (ASCS).

METHOD

An analysis was performed on the costs arising from missed outpatient appointments (first appointment and examinations) of each of the specialities in the Centres belonging to the ASCS. A formula was used to determine the unit cost per appointment and per centre and speciality. This involved the direct imputation of the controllable costs and the indirect imputation of the service costs, together with an estimated cost of re-appointments based on a previous case-control study.

RESULTS

The cost of missed appointments per centre in the Costa del Sol Hospital was €2,475,640, with a failure rate of 14.2% (256,377 appointments). In the Benalmádena High Resolution Hospital it was €515,936, with an absence rate of 12.2% (44,848 appointments), and in the Mijas High Resolution Centre, a cost of €395,342 with an absence rate of the 13.5% (99,536 appointments). The mean extra cost of a re-appointment was €12.95. The specialities with a higher medium cost were Digestive Diseases, Internal Medicine, and Rehabilitation.

CONCLUSIONS

The economic cost of patients not turning up for scheduled appointments in the ASCS was greater than 3 million Euros for a non-attendance rate of the 13.8%, with Mijas High Resolution Centre being the centre that showed the lowest mean unitary cost per medical appointment.

Targeted Reminder Phone Calls to Patients at High Risk of No-Show for Primary Care Appointment: A Randomized Trial

BACKGROUND

No-shows, or missed appointments, are a problem for many medical practices. They result in fragmented care and reduce access for all patients.

OBJECTIVE

To determine whether telephone reminder calls targeted to patients at high risk of no-show can reduce no-show rates.

DESIGN

Single-center randomized controlled trial.

PARTICIPANTS

A total of 2247 primary care patients in a hospital-based primary care clinic at high risk of no-show (>15 % risk) for their appointment in 7 days.

INTERVENTION

Seven days prior to their appointment, intervention arm patients were placed in a calling queue to receive a reminder phone call from a patient service coordinator. Coordinators were trained to engage patients in concrete planning. All patients received an automated phone call (usual care).

MAIN MEASURES

Primary outcome was no-show rate. Secondary outcomes included arrival rate, cancellation rate, reschedule rate, time to cancellation, and change in revenue.

KEY RESULTS

The no-show rate in the intervention arm (22.8 %) was significantly lower (absolute risk difference -6.4 %, p < 0.01, 95 % CI [-9.8 to -3.0 %]) than that in the control arm (29.2 %). Arrival, cancellation, and reschedule rates did not differ significantly. In the intervention arm, rescheduling and cancellations occurred further in advance of the appointment (mean difference, 0.35 days; 95 % CI [0.07-0.64]; p = 0.01). Reimbursement did not differ significantly.

CONCLUSIONS

A phone call 7 days prior to an appointment led to a significant reduction in no-shows and increased reimbursement among patients at high risk of no-show. The use of targeted interventions may be of interest to practices taking on increased accountability for population health.

Preventing Endoscopy Clinic No-Shows: Prospective Validation of a Predictive Overbooking Model

OBJECTIVES

Patient absenteeism for scheduled visits and procedures ("no-show") occurs frequently in healthcare systems worldwide, resulting in treatment delays and financial loss. To address this problem, we validated a predictive overbooking system that identifies patients at high risk for missing scheduled gastrointestinal endoscopy procedures ("no-shows" and cancellations), and offers their appointments to other patients on short notice.

METHODS

We prospectively tested a predictive overbooking system at a Veterans Administration outpatient endoscopy clinic over a 34-week period, alternating between traditional booking and predictive overbooking methods. For the latter, we assigned a no-show risk score to each scheduled patient, utilizing a previously developed logistic regression model built with electronic health record data. To compare booking methods, we measured service utilization-defined as the percentage of daily total clinic capacity occupied by patients-and length of clinic workday.

RESULTS

Compared to typical booking, predictive overbooking resulted in nearly all appointment slots being filled-2.5 slots available during control weeks vs. 0.35 slots during intervention weeks, t(161)=4.10, P=0.0001. Service utilization increased from 86% during control weeks to 100% during intervention weeks, allowing 111 additional patients to undergo procedures. Physician and staff overages were more common during intervention weeks, but less than anticipated (workday length of 7.84 h (control) vs. 8.31 h (intervention), t(161)=2.28, P=0.02).

CONCLUSIONS

Predictive overbooking may be used to maximize endoscopy scheduling. Future research should focus on adapting the model for use in primary care and specialty clinics.