Factors associated with non-attendance in a general practice super clinic population in regional Australia: A retrospective cohort study

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.

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.

Development and validation of nonattendance predictive models for scheduled adult outpatient appointments in different medical specialties

INTRODUCTION

Nonattendance is a critical problem that affects health care worldwide. Our aim was to build and validate predictive models of nonattendance in all outpatients appointments, general practitioners, and clinical and surgical specialties.

METHODS

A cohort study of adult patients, who had scheduled outpatient appointments for General Practitioners, Clinical and Surgical specialties, was conducted between January 2015 and December 2016, at the Italian Hospital of Buenos Aires. We evaluated potential predictors grouped in baseline patient characteristics, characteristics of the appointment scheduling process, patient history, characteristics of the appointment, and comorbidities. Patients were divided between those who attended their appointments, and those who did not. We generated predictive models for nonattendance for all appointments and the three subgroups.

RESULTS

Of 2,526,549 appointments included, 703,449 were missed (27.8%). The predictive model for all appointments contains 30 variables, with an area under the ROC (AUROC) curve of 0.71, calibration-in-the-large (CITL) of 0.046, and calibration slope of 1.03 in the validation cohort. For General Practitioners the model has 28 variables (AUROC of 0.72, CITL of 0.053, and calibration slope of 1.01). For clinical subspecialties, the model has 23 variables (AUROC of 0.71, CITL of 0.039, and calibration slope of 1), and for surgical specialties, the model has 22 variables (AUROC of 0.70, CITL of 0.023, and calibration slope of 1.01).

CONCLUSION

We build robust predictive models of nonattendance with adequate precision and calibration for each of the subgroups.

Patient factors associated with dermatology outpatient non-attendance: An analysis of racial and ethnic diversity

Non-attendance to dermatology outpatient appointments is a risk factor for poorer patient outcomes. The culturally and linguistically diverse (CALD) communities in Australia have been identified as at risk of poorer health outcomes, but there is a paucity of data assessing patient factors that may increase outpatient non-attendance. To investigate this, we performed a retrospective cross-sectional study of dermatology appointments from patients attending a tertiary, referral public hospital located in one of Australia's most racially and ethnically diverse communities. Patients within the 18-45 years age bracket were 61% more likely to not attend compared to older age groups. Those born in Oceania, Middle East Asia, and surprisingly Australia were more likely to miss an appointment, whilst those born in East and Southeast Asia were more likely to attend. Those who spoke Arabic at home were more likely to not attend, whilst those who spoke Vietnamese at home were more likely to attend. This study sheds further light on health disparities in non-attendance and emphasizes the importance of not collectively amalgamating all groups of the CALD community.

Prevalence and factors associated with missed hospital appointments: a retrospective review of multiple clinics at Royal Hospital, Sultanate of Oman

OBJECTIVES

Missed hospital appointments pose a major challenge for healthcare systems. There is a lack of information about drivers of missed hospital appointments in non-Western countries and extent of variability between different types of clinics. The aim was to evaluate the rate and predictors of missed hospital appointments and variability in drivers between multiple outpatient clinics.

SETTING

Outpatient clinics in the Royal hospital (tertiary referral hospital in Oman) between 2014 and 2018.

PARTICIPANTS

All patients with a scheduled outpatient clinic appointment (N=7 69 118).

STUDY DESIGN

Retrospective cross-sectional analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

A missed appointment was defined as a patient who did not show up for the scheduled hospital appointment without notifying or asking for the appointment to be cancelled or rescheduled. The outcomes were the rate and predictors of missed hospital appointments overall and variations by clinic. Conditional logistic regression compared patients who attended and those who missed their appointment.

RESULTS

The overall rate of missed hospital appointments was 22.3%, which varied between clinics (14.0% for Oncology and 30.3% for Urology). Important predictors were age, sex, service costs, patient's residence distance from hospital, waiting time and appointment day and season. Substantive variability between clinics in ORs for a missed appointment was present for predictors such as service costs and waiting time. Patients aged 81-90 in the Diabetes and Endocrine clinic had an adjusted OR of 0.53 for missed appointments (95% CI 0.37 to 0.74) while those in Obstetrics and Gynaecology had OR of 1.70 (95% CI 1.11 to 2.59). Adjusted ORs for longer waiting times (>120 days) were 2.22 (95% CI 2.10 to 2.34) in Urology but 1.26 (95% CI 1.18 to 1.36) in Oncology.

CONCLUSION

Predictors of a missed appointment varied between clinics in their effects. Interventions to reduce the rate of missed appointments should consider these factors and be tailored to clinic.

Dermatological services; patient profiling in a rural tertiary hospital

BACKGROUND/OBJECTIVES

There is a paucity of research available regarding the epidemiology of patients attending dermatology outpatient services in Australia. Our objective was to analyse who was attending public dermatology outpatient clinics in a Northern Territory tertiary hospital, with a particular focus on Indigenous and rural patients.

METHODS

This is a retrospective cohort study of patients who attended dermatology outpatient clinics between 1 January 2016 and 31 December 2016. Outcome measures included patient demographics (age, gender, ethnicity and postcode) and referrer details.

RESULTS

Over the 12 month study period, 923 appointments were scheduled for 500 patients. Of the appointments scheduled, 667 were attended. Twelve per cent of patients were Indigenous, and of the total appointment attendances, 10% were by Indigenous patients. Of the 923 appointments, 28% were not attended, with a higher non-attendance rate for Indigenous patients at 36%. The majority of patients seen were adults, for both groups, but a larger proportion of Indigenous children were seen. Nine per cent of patients with a recorded address were from a remote region.

CONCLUSION

Dermatology outpatient services are likely under-utilised by Indigenous, and remote patients. If we are to improve skin health in Australia, barriers such as limited access to dermatological services in remote regions must be addressed.

Which patients miss appointments with general practice and the reasons why: a systematic review

Background

Missed GP appointments have considerable time and cost implications for healthcare services.

Aim

This systematic review aims to explore the rate of missed primary care appointments, what the reported reasons are for appointments being missed, and which patients are more likely to miss appointments.

Design and setting

This study reports the findings of a systematic review. The included studies report the rate or reasons of missed appointments in a primary care setting.

Method

Databases were searched using a pre-defined search strategy. Eligible studies were selected for inclusion based on detailed inclusion criteria through title, abstract, and full text screening. Quality was assessed on all included studies, and findings were synthesised to answer the research questions.

Results

A total of 26 studies met the inclusion criteria for the review. Of these, 19 reported a rate of missed appointments. The mean rate of missed appointments was 15.2%, with a median of 12.9%. Twelve studies reported a reason that appointments were missed, with work or family/childcare commitments, forgetting the appointment, and transportation difficulties most commonly reported. In all, 20 studies reported characteristics of people likely to miss appointments. Patients who were likely to miss appointments were those from minority ethnicity, low sociodemographic status, and younger patients (<21 years).

Conclusion

Findings from this review have potential implications for targeted interventions to address missed appointments in primary care. This is the first step for clinicians to be able to target interventions to reduce the rate of missed appointments.

An Evaluation of Risk Factors for Patient "No Shows" at an Urban Joint Arthroplasty Clinic

BACKGROUND

Patient physical health and provider financial health are both affected when patients are unable to attend scheduled clinic appointments. The purpose of this study is to identify risk factors for patients missing appointments to better target interventions to improve appointment attendance.

METHODS

We reviewed scheduled arthroplasty appointments at an urban academic orthopaedic clinic over a 3-year period. We collected information including sex, race, distance to clinic, language, insurance, median income of home zip code, appointment day, time, precipitation, and temperature. Mixed-level multiple logistic regression was used to model the odds of missing appointments in Stata v14.

RESULTS

Overall, 8,185 visits for 3,081 unique patients were reviewed and 90.7% of appointments were attended. After controlling for time and day of appointment, distance from the clinic, and the primary language spoken, patients with government insurance were two times as likely to miss an appointment compared with privately insured patients. White patients were two times as likely to attend scheduled appointments compared with black/Hispanic patients. Younger patients (<50 years) and older patients (>73 years) were 2.7 times and 1.8 times, respectively, more likely to miss appointments compared with those aged between 65 and 72 years. Appointments on the most temperate days were more likely to be missed, and those on the coldest days (14°F to 36°F) and warmest days (69°F to 89°F) were less likely to be missed.

DISCUSSION

Appointment no shows are associated with sociodemographic and environmental factors. This information is valuable to help better delineate novel ways to better serve these patient populations.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

Real gains: development of a tool to measure outcomes for urban First Australian children accessing culturally responsive interprofessional therapy

Healthcare services are accountable to their clients, communities, governments and funding sources to clearly demonstrate the effectiveness of interventions. A First Australian children's therapy service delivering culturally responsive, interprofessional collaborative practice aimed to evaluate their service. However, this process was constrained by available outcome measures which lacked the flexibility necessary for meaningful use within the dynamic and relational nature of their service delivery. This paper outlines an action research process in three cycles which was used to develop the Australian Therapies Outcome Measure for Indigenous Clients (ATOMIC) with the aim of evaluating therapy outcomes for urban First Australian children engaged in culturally responsive interprofessional therapy. Interrater reliability values of 0.995 and 0.982 were established for ATOMIC pre- and post-therapy measures, respectively, during a pilot phase involving 16 participants. Participants in the main study were 80 First Australian children aged two to 16 years who attended between two and nine interprofessional therapy sessions with occupational therapists and speech pathologists. Pre- and post-therapy ATOMIC scores confirmed progress on pre-determined functional goals across a range of skill domains. Outcomes of this study demonstrated that real gains are being made in urban First Australian children's lives following interprofessional collaborative service provision.

Predicting Patient No-show Behavior: a Study in a Bariatric Clinic

PURPOSE

No-shows of patients to their scheduled appointments have a significant impact on healthcare systems, including lower clinical efficiency and higher costs. The purpose of this study was to investigate the factors associated with patient no-shows in a bariatric surgery clinic.

MATERIALS AND METHODS

We performed a retrospective study of 13,230 records for 2660 patients in a clinic located in Rio de Janeiro, Brazil, over a 17-month period (January 2015-May 2016). Logistic regression analyses were conducted to explore and model the influence of certain variables on no-show rates. This work also developed a predictive model stratified for each medical specialty.

RESULTS

The overall proportion of no-shows was 21.9%. According to multiple logistic regression, there is a significant association between the patient no-shows and eight variables examined. This association revealed a pattern in the increase of patient no-shows: appointment in the later hours of the day, appointments not in the summer months, post-surgery appointment, high lead time, higher no-show history, fewer numbers of previous appointments, home address 20 to 50 km away from the clinic, or scheduled for another specialty other than a bariatric surgeon. Age group, forms of payment, gender, and weekday were not significant predictors. Predictive models were developed with an accuracy of 71%.

CONCLUSION

Understanding the characteristics of patient no-shows allows making improvements in management practice, and the predictive models can be incorporated into the clinic dynamic scheduling system, allowing the use of a new appointment policy that takes into account each patient's no-show probability.

Nonattendance rates of scheduled outpatient appointments in a university general hospital

OBJECTIVE

We aimed to estimate nonattendance to scheduled medical ambulatory appointments rates globally and by subgroups.

DESIGN AND PARTICIPANTS

We designed a retrospective cohort of all adult outpatients over 18 years old who requested at least one scheduled ambulatory medical appointment from 1 January 2015 to 31 December 2016.

SETTING

Hospital Italiano de Buenos Aires is a university general hospital in the Autonomous City of Buenos Aires, Argentina. It includes an integrated health care network that is formed by two high complexity hospitals and 22 primary care centers.

RESULTS

The age median was 60.4 years, and 31.33% of the appointments were scheduled by men; 2 526 549 appointments fulfilled selection criteria, belonging to 348 420 patients. The global nonattendance rate was 27.84% (95% CI, 27.79-27.9). The nonattendance rate to general practitioner appointments was 25.53% (95% CI, 25.42-25.63); to clinical specialties, 27.78% (95% CI, 27.68-27.87); and to surgical specialties, 29.31% (95% CI, 29.23-29.4).

CONCLUSIONS

Because of the consistent variability of nonattendance in different settings, it is strongly recommended that local estimates are used in the design of effective interventions to improve adherence with outpatient healthcare scheduled appointments.

Development and validation of a patient no-show predictive model at a primary care setting in Southern Brazil

Patient no-show is a prevalent problem in health care services leading to inefficient resources allocation and limited access to care. This study aims to develop and validate a patient no-show predictive model based on empirical data. A retrospective study was performed using scheduled appointments between 2011 and 2014 from a Brazilian public primary care setting. Fifty percent of the dataset was randomly assigned to model development, and 50% was assigned to validation. Predictive models were developed using stepwise naïve and mixed-effect logistic regression along with the Akaike Information Criteria to select the best model. The area under the ROC curve (AUC) was used to assess the best model performance. Of the 57,586 scheduled appointments in the period, 70.7% (n = 40,740) were evaluated including 5,637 patients. The prevalence of no-show was 13.0% (n = 5,282). The best model presented an AUC of 80.9% (95% CI 80.1-81.7). The most important predictors were previous attendance and same-day appointments. The best model developed from data already available in the scheduling system, had a good performance to predict patient no-show. It is expected the model to be helpful to overbooking decision in the scheduling system. Further investigation is needed to explore the effectiveness of using this model in terms of improving service performance and its impact on quality of care compared to the usual practice.

Factors associated with appointment non-attendance at a medical imaging department in regional Australia: a retrospective cohort analysis

INTRODUCTION

Appointment non-attendance contributes added cost to the healthcare sector through wasted resource allocations. Medical imaging departments commonly schedule appointments for most modalities; however, no study has quantified patient attendance rates in the Australian regional setting. This is despite evidence that regional, rural and remote Australians tend to demonstrate poorer health than metropolitan counterparts. This study aims to identify the factors that influence appointment non-attendance at a teaching hospital in regional Australia.

METHODS

Categories restricted to age, gender, indigenous status, distance from investigation site, referral source and imaging modality were collected for all appointments (N = 13,458) referred to the medical imaging department in 2015. The likelihood of each of these factors correlating with a patient not attending a scheduled appointment was calculated using the chi-squared analysis and binary logistic regression.

RESULTS

Gender, indigenous status as well as specific imaging modalities, referral sources and age categories were significantly associated with non-attendance. Overall, male patients were 1.57 (P < 0.001) times more likely to miss a scheduled appointment than female patients. Patients who identified as Aboriginal and Torres Strait Islander were 2.66 (P < 0.001) times more likely to miss a scheduled appointment than patients who did not identify as Aboriginal and Torres Strait Islander.

CONCLUSIONS

Several key factors appear to affect medical imaging appointment non-attendance. Key factors include indigenous status, gender, image modality, referral source and age. Further improvement is required to better meet the needs of underrepresented patient demographics.

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.

When colocation is not enough: a case study of General Practitioner Super Clinics in Australia

Developed nations are implementing initiatives to transform the delivery of primary care. New models have been built around multidisciplinary teams, information technology and systematic approaches for chronic disease management (CDM). In Australia, the General Practice Super Clinic (GPSC) model was introduced in 2010. A case study approach was used to illustrate the development of inter-disciplinary CDM over 12 months in two new, outer urban GPSCs. A social scientist visited each practice for two 3-4-day periods. Data, including practice documents, observations and in-depth interviews (n=31) with patients, clinicians and staff, were analysed using the concept of organisational routines. Findings revealed slow, incremental evolution of inter-disciplinary care in both sites. Clinic managers found the facilitation of inter-disciplinary routines for CDM difficult in light of competing priorities within program objectives and the demands of clinic construction. Constraints inherent within the GPSC program, a lack of meaningful support for transformation of the model of care and the lack of effective incentives for collaborative care in fee-for-service billing arrangements, meant that program objectives for integrated multidisciplinary care were largely unattainable. Findings suggest that the GPSC initiative should be considered a program for infrastructure support rather than one of primary care transformation.

The walk-in clinic model improves access to psychiatry in primary care

OBJECTIVE

Missed appointments decrease clinic capacity and negatively affect health outcomes. The objective of this study was to increase the proportion of filled initial psychiatry appointments in an urban, hospital-based primary care practice.

METHODS

Patients were identified as having a high or low risk of missing their initial psychiatry appointments based on prior missed medical appointments. High-risk patients were referred to a walk-in clinic instead of a scheduled appointment. The primary outcome was ratio of filled appointments to booked appointments. We used a statistical process control chart (p chart) to measure improvement. Secondary outcomes were percentages of patients from historically underserved groups who received an initial psychiatry evaluation before and after the intervention.

RESULTS

The average ratio of filled to booked initial appointments increased from 59% to 77% after the intervention, and the p chart confirmed that this change represented special cause variation. No statistically significant demographic differences between the patients who received psychiatric evaluations before and after the intervention were found.

CONCLUSIONS

Missed initial psychiatry appointments can be accurately predicted by prior missed medical appointments. A referral-based walk-in clinic is feasible and does not reduce access to care for historically underserved patient groups.

High quality, patient centred and coordinated care for Alstrom syndrome: a model of care for an ultra-rare disease

Background

Patients with rare and ultra-rare diseases make heavy demands on the resources of both health and social services, but these resources are often used inefficiently due to delays in diagnosis, poor and fragmented care. We analysed the national service for an ultra-rare disease, Alstrom syndrome, and compared the outcome and cost of the service to the standard care.

Methods

Between the 9th and 26th of March 2014 we undertook a cross-sectional study of the UK Alstrom syndrome patients and their carers. We developed a semi-structured questionnaire to assess our rare patient need, quality of care and costs incurred to patients and their careers. In the UK all Alstrom syndrome patients are seen in two centres, based in Birmingham, and we systematically evaluated the national service and compared the quality and cost of care with patients’ previous standard of care.

Results

One quarter of genetically confirmed Alstrom syndrome UK patients were enrolled in this study. Patients that have access to a highly specialised clinical service reported that their care is well organised, personalised, holistic, and that they have a say in their care. All patients reported high level of satisfaction in their care. Patient treatment compliance and clinic attendance was better in multidisciplinary clinic than the usual standard of NHS care. Following a variable costing approach based on personnel and consumables’ cost, our valuation of the clinics was just under £700/patient/annum compared to the standard care of £960/patient/annum. Real savings, however, came in terms of patients’ quality of life. Furthermore there was found to have been a significant reduction in frequency of clinic visits and ordering of investigations since the establishment of the national service.

Conclusions

Our study has shown that organised, multidisciplinary “one stop” clinics are patient centred and individually tailored to the patient need with a better outcome and comparable cost compared with the current standard of care for rare disease. Our proposed care model can be adapted to several other rare and ultra-rare diseases.