Predictors of failed attendances in a multi-specialty outpatient centre using electronic databases

Early Access to First-Seizure Clinics, Subsequent Outcomes, and Factors Associated With Attendance

Importance

First-seizure clinics (FSCs) aim to deliver prompt specialist care to patients with new-onset undifferentiated seizure events.

Objective

To determine whether FSC attendance and time to FSC are associated with subsequent health care utilization and mortality and to investigate factors associated with FSC nonattendance.

Design, Setting, and Participants

This was a record-linkage, retrospective, cohort study of patients who booked appointments at 2 FSCs between 2007 and 2018. Patients' records were linked to state-wide administrative databases between 2000 and 2021. The setting comprised the FSCs of 2 major metropolitan public hospitals in Melbourne, Australia, providing national inpatient and outpatient adult epilepsy services. Of patients who booked appointments at the FSCs, those who were successfully linked for analysis were included in the study. Patients who recorded only canceled appointments were excluded from analysis of outcomes. Study data were analyzed from January 2000 to December 2021.

Exposure

FSC attendance.

Main Outcomes and Measures

Subsequent all-cause and seizure-related emergency department (ED) presentations and hospital admissions.

Results

Of 10 162 patients with appointments at FSCs, 9392 were linked for analysis, with mean (SD) follow-up time 6.9 (2.8) years after FSC referral. A total of 703 patients were excluded. Among 9392 linked patients, 5398 were male (57.5%; mean [SD] age, 59.7 [11.2] years). FSC attendance was associated with reduced subsequent all-cause emergency presentations (adjusted incidence rate ratio [aIRR], 0.72; 95% CI, 0.66-0.79) and all-cause hospitalization (aIRR, 0.81; 95% CI, 0.75-0.88). Those who attended at the first-scheduled appointment, compared with those who attended only a rescheduled, delayed appointment, had reduced subsequent all-cause emergency presentations (aIRR, 0.83; 95% CI, 0.76-0.91), all-cause hospitalization (aIRR, 0.71; 95% CI, 0.65-0.79), seizure-related presentations (aIRR, 0.40; 95% CI, 0.33-0.49), and mortality (hazard ratio, 0.82; 95% CI, 0.69-0.98). Male sex was associated with nonattendance (adjusted relative risk [aRR], 1.12; 95% CI, 1.03-1.22), as were injury at emergency presentation (aRR, 1.12; 95% CI, 1.01-1.24), psychiatric comorbidity (aRR, 1.68; 95% CI, 1.55-1.81), previous seizure-related presentations (aRR, 1.35; 95% CI, 1.22-1.49), and delays (>14 days) between FSC referral and appointment (aRR, 1.35; 95% CI, 1.18-1.54). Hospitalization at referral was associated with reduced nonattendance (aRR, 0.80; 95% CI, 0.72-0.90), as were non-English language preference (aRR, 0.81; 95% CI, 0.69-0.94), distance greater than 6 mi from home to clinic (aRR, 0.85; 95% CI, 0.76-0.95), and physical comorbidity (aRR, 0.80; 95% CI, 0.72-0.89).

Conclusions and Relevance

Results of this cohort study suggest that FSC attendance, particularly early attendance, was associated with reduced rates of subsequent hospital utilization. This knowledge may support adequately resourcing FSCs to improve equitable, timely access. Future study directions include assessing interventions that may support FSC attendance for at-risk groups.

Risk Factors for Missed Appointments at a Multisite Academic Urban Urogynecology Practice

IMPORTANCE

Missed appointments lead to decreased clinical productivity and poor health outcomes.

OBJECTIVES

The objectives of this study were to describe sociodemographic and clinical characteristics of patients who miss urogynecology appointments and identify risk factors for missed appointments.

STUDY DESIGN

We conducted an institutional review board-approved case-control study of women 18 years or older scheduled for a urogynecology appointment at 1 of 4 sites associated with an urban academic tertiary care center over 4 months. Patients were included in the missed appointment group if they canceled their appointments the same day or did not show up for them. For comparison, we included a control group consisting of patients immediately preceding or following the ones who missed their appointments with the same visit type. Logistic regression was used to identify risk factors for missed appointments.

RESULTS

Four hundred twenty-six women were included: 213 in the missed appointment group and 213 in the control group. Women who missed appointments were younger (60 years [interquartile range (IQR), 47-72 years] vs 69 years [IQR, 59-78 years], P < 0.0001). More women in the missed appointment group were Hispanic (24.4% vs 13.1%) and non-Hispanic Black (7.5% vs 3.8%, P = 0.009), had Medicaid (17.4% vs 6.57%, P = 0.0006), missed previous appointments (24.9% vs 11.7% P = 0.0005), waited longer for appointments (39 days [IQR, 23.5-55.5 days] vs 30.5 days [IQR, 12.8-47.0 days], P = 0.002), and made appointments for urinary incontinence (44.1% vs 26.8%, P = 0.0002). On multivariate logistic regression, women with Medicaid had significantly higher odds of missing appointments (adjusted OR, 2.11 [1.04-4.48], P = 0.044).

CONCLUSIONS

Women with Medicaid were more likely to miss urogynecology appointments. Further research is needed to address barriers this group faces when accessing care.

Pharmacist telehealth in an underserved urban population with type 2 diabetes mellitus

BACKGROUND

During the pre-vaccine months of the COVID-19 pandemic, pharmacists providing comprehensive medication management to underserved patients with type 2 diabetes mellitus at an urban Federally Qualified Healthcare Center shifted to telephone-based telehealth.

OBJECTIVES

This retrospective, observational cohort study evaluated the effectiveness of clinical pharmacist telehealth while identifying associations between patient characteristics and efficacy measures.

METHODS

Patients with uncontrolled type 2 diabetes (hemoglobin A1c (HbA1c) ≥ 8%) with a clinical pharmacist visit between April 1 and August 31, 2020, were included. Telehealth effectiveness was measured by the proportions of: 1) patients reached, 2) appointments completed, and 3) the median change in HbA1c from baseline. Interventions by the clinical pharmacist were analyzed as a secondary outcome.

RESULTS

There were 181 patients scheduled and 172 (95%) of those patients kept at least one appointment. Of the 667 appointments scheduled, 73% were kept. Median HbA1c was reduced from 10.2% to 9.2% over 5 months of follow-up, and 24.6% of patients achieved a HbA1c < 8% (n = 138, p < 0.0001 for each). Greater HbA1c changes were associated with higher baseline blood glucose (p = 0.01), higher baseline HbA1c (p < 0.0001), non-insulin medications at baseline (p = 0.007) and among those with more kept visits (p = 0.03). The healthcare quality impact of interventions during each appointment was favorable; 83.3% brought care to a higher standard, 1.9% averted major organ dysfunction and 0.4% prevented death.

CONCLUSIONS

Clinical pharmacist telehealth was effective for providing patient-centered diabetes care when in-person office visits were not an option.

Factors associated with appointment 'no-shows' at two tertiary level outpatient oculoplastic clinics

PURPOSE

Appointment no-shows in clinics can adversely impact patients and physicians alike. This study aimed to determine the rate and potential causes of missed appointments in oculoplastic clinics and compare a private practice and hospital-based academic setting.

METHODS

A retrospective review of patients who booked appointments for oculoplastic consultation, between August 2019 and January 2020 at two oculoplastic clinics was performed. Demographic and patient-specific characteristics of patients who failed to attend their appointment were identified. Data were analysed to determine and compare the no-show rates in both clinics and logistic regression was performed to determine factors associated with them.

RESULTS

The rate of missed appointments was 3% and 17% at the oculoplastic clinics of Lions Eye Institute (LEI, private practice) and Albany Medical Center (AMC, academic hospital-based office), respectively. Patients at the AMC clinic were more likely to be male, younger, have a lower household income, not carry private insurance, and suffer from trauma. Logistic regression analysis showed lower patient age to significantly increase the likelihood of no-shows in both clinics (p = .01 for LEI, p = .003 for AMC), and lead appointment time greater than 90 days to be a significant risk factor for no-shows at LEI (p = .01).

CONCLUSIONS

The no-show rate for oculoplastic appointments is 3% and 17% at LEI and AMC clinics, respectively. Our analysis shows that younger patients are more likely to miss appointments at both clinics, and an appointment lead time greater than 90 days is a significant risk factor for no-shows at LEI.

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.

Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures

OBJECTIVE

Avoidable case cancellations within 24 h reduce operating room (OR) efficiency, add unnecessary costs, and may have physical and emotional consequences for patients and their families. We developed and validated a prediction tool that can be used to guide same day case cancellation reduction initiatives.

DESIGN

Retrospective hospital registry study.

SETTING

University-affiliated hospitals network (NY, USA).

PATIENTS

246,612 (1/2016-6/2021) and 58,662 (7/2021-6/2022) scheduled elective procedures were included in the development and validation cohort.

MEASUREMENTS

Case cancellation within 24 h was defined as cancelling a surgical procedure within 24 h of the scheduled date and time. Our candidate predictors were defined a priori and included patient-, procedural-, and appointment-related factors. We created a prediction tool using backward stepwise logistic regression to predict case cancellation within 24 h. The model was subsequently recalibrated and validated in a cohort of patients who were recently scheduled for surgery.

MAIN RESULTS

8.6% and 8.7% scheduled procedures were cancelled within 24 h of the intended procedure in the development and validation cohort, respectively. The final weighted score contains 29 predictors. A cutoff value of 15 score points predicted a 10.3% case cancellation rate with a negative predictive value of 0.96, and a positive predictive value of 0.21. The prediction model showed good discrimination in the development and validation cohort with an area under the receiver operating characteristic curve (AUC) of 0.79 (95% confidence interval 0.79-0. 80) and an AUC of 0.73 (95% confidence interval 0.72-0.73), respectively.

CONCLUSIONS

We present a validated preoperative prediction tool for case cancellation within 24 h of surgery. We utilize the instrument in our institution to identify patients with high risk of case cancellation. We describe a process for recalibration such that other institutions can also use the score to guide same day case cancellation reduction initiatives.

Effect of COVID-19 pandemic on missed medical appointment among adults with chronic disease conditions in Northwest Ethiopia

BACKGROUND

COVID-19 had affected the health-care-seeking behavior of people with chronic medical conditions. The impact is even worse in resource-limited settings like Ethiopia. Therefore, this study was aimed to assess the extent and correlates of missed appointments among adults with chronic disease conditions before and during the COVID-19 pandemic in the Northwest Ethiopia.

METHODS

A retrospective chart review and cross-sectional survey were conducted from December 2020 to February 2021. A total of 1833 patients with common chronic disease were included by using a stratified systematic random sampling technique. Web-based data collection was done using Kobo collect. The data were explored using descriptive statistical techniques, the rate of missed appointments s before and during the COVID-19 pandemic was determined. A negative binomial regression model was fitted to identify the factors of missed appointment. An incidence rate ratio with its 95% confidence interval (CI) and p-value of the final model were reported.

RESULTS

The rate of missed appointments was 12.5% (95% CI: 11.13%, 14.20%) before the pandemic, increased to 26.8% (95% CI: 24.73%, 28.82%) during the pandemic (p-value < 0.001). Fear of COVID-19 infection and lack of transport was the most common reasons for missing appointments. Older patients (Adjusted Incidence Rate Ratio (AIRR) = 1.01, 95% CI: 1.001; 1.015), having treatment follow up more than 5 years (AIRR = 1.36, 95%CI: 1.103; 1.69), shorter frequency of follow-up (AIRR = 2.22, 95% CI: 1.63; 2.49), covering expense out of pocket (AIRR = 2.26, 95%CI: 1.41; 2.95), having a sedentary lifestyle (AIRR = 1.36, 95%CI: 1.12; 1.71), and history of missed appointments before COVID-19 pandemic (AIRR = 4.27, 95%CI: 3.35; 5.43) were positively associated with the incidence of missed appointments.

CONCLUSION

The rate of missed appointment increased significantly during the COVID-19 pandemic. Older age, longer duration of follow up, more frequent follow-up, out-of-pocket expenditure for health service, history of poor follow-up, and sedentary lifestyle had positive relationship with missed appointments during the pandemic. Therefore, it is important to give special emphasis to individuals with these risk factors while designing and implementing policies and strategies for peoples with chronic diseases to ensure the continuity of care and to avoid the long-term impact on their health.

Absenteeism of diabetics to appointments with an endocrinologist and its relationship with access to health services

Objective: To analyze the association between absenteeism and access to specialized consultations for diabetics, in the Unified Health System (SUS) in Espírito Santo (ES). Methods: Cross-sectional study conducted using primary and secondary data with 472 diabetics ≥ 18 years old scheduled in consultation with endocrinologist in the System of Regulation Centers of ES. The variables of the dimensions of access - availability, financial viability, and acceptability - were used to estimate the association with absenteeism. Logistic regression was used for the crude and adjusted analyses. Results: An association was found between absenteeism of diabetics and the variables living less than 10 km from the provider (OR: 1.81; 95%CI: 1.16 - 2.82, p = 0.01), need for transportation (OR: 4.89; 95%CI: 2.54 - 9.42, p < 0.001), and having financial expenses to attend the appointment (OR: 2.06; 95%CI: 1.23 - 3.44; p = 0.01). Conclusion: The main barriers of access to health services that contribute to the high prevalence of absenteeism from appointments with endocrinologists can be understood as a proxy for the socioeconomic status of diabetics and show close relationship with the social determinants of health.

Overbooking for physical examination considering late cancellation and set-resource relationship

BACKGROUND

Late cancellations of physical examination has severe impact on the operations of a physical examination center since it is often too late to fill vacancy. A booking control policy that considers overbooking is then one natural solution. Unlike appointment scheduling problems for clinics and hospitals, in which treating a patient mostly requires only one type of resource, a physical examination set typically requires multiple types of resources. Traditional methods that do not consider set-resource relationship thus may be inapplicable.

METHODS

We formulate a stochastic mathematical programming model that maximizes the expected net reward, which is the examination revenue minus overage cost. A complete search algorithm and a greedy search algorithm are designed to search for optimal booking limits for all examination sets. To estimate the late cancellation probability for each individual consumer, we apply logistic regression to identify significant factors affecting the probability. After clustering is used to estimate individual probabilities, Monte Carlo simulation is conducted to generate probability distributions for the number of consumers without late cancellations. A discrete-event simulation is performance to evaluate the effectiveness of our proposed solution.

RESULTS

We collaborate with a leading physical examination center to collect real data to evaluate our proposed overbooking policies. We show that the proposed overbooking policy may significantly increase the expected net reward. Our simulation results also help us understand the impact of overbooking on the expected number of customers and expected overage. A sensitivity analysis is conducted to demonstrate that the benefit of overbooking is insensitive to the accuracy of cost estimation. A Pareto efficiency analysis gives practitioners suggestions regarding policy determination considering multiple performance indications.

CONCLUSIONS

Our proposed overbooking policies may greatly enhance the overall performance of a physical examination center.

Factors Associated with Nonattendance to a Geriatric Clinic among Mexican Older Adults

INTRODUCTION

Outpatient appointment nonattendance (NA) represents a public health problem, increasing the risk of unfavorable health-related outcomes. Although NA is significant among older adults, little is known regarding its correlates. This study aimed to identify the correlates (including several domains from the geriatric assessment) of single and repeated NA episodes in a geriatric medicine outpatient clinic, in general and in the context of specific comorbidities.

METHODS

This is a cross-sectional study including data from 3,034 older adults aged ≥60 years with ≥1 scheduled appointments between January 1, 2016, and December 31, 2016. Appointment characteristics as well as sociodemographic, geographical, and environmental information were obtained. Univariate and multivariate multinomial regression analyses were carried out.

RESULTS

The mean age was 81.8 years (SD 7.19). Over a third (37.4%) of participants missed one scheduled appointment, and 14.4% missed ≥2. Participants with a history of stroke (OR 1.336, p = 0.041) and those with a greater number of scheduled appointments during the study time frame (OR 1.182, p < 0.001) were more likely to miss one appointment, while those with Parkinson's disease (OR 0.346, p < 0.001), other pulmonary diseases (OR 0.686, p = 0.008), and better functioning for activities of daily living (ADL) (OR 0.883, p < 0.001) were less likely to do so. High socioeconomic level (OR 2.235, p < 0.001), not having a partner (OR 1.410, p = 0.006), a history of fractures (OR 1.492, p = 0.031), and a greater number of scheduled appointments (OR 1.668, p < 0.001) increased the risk of repeated NA, while osteoarthritis (OR 0.599, p = 0.001) and hypertension (OR 0.680, p = 0.002) decreased it. In specific comorbidity populations (hypertension, type 2 diabetes mellitus, and cancer), better ADL functioning protected from a single NA, while better mobility functioning protected from repeated NA in older patients with hypertension and cancer.

DISCUSSION/CONCLUSION

Identifying geriatric factors linked to an increased probability of NA may allow one to anticipate its likelihood and lead to the design and implementation of preventive strategies and to an optimization of the use of available health resources. The impact of these factors on adherence to clinical visits requires further investigation.

Evaluation of Patient No-Shows in a Tertiary Hospital: Focusing on Modes of Appointment-Making and Type of Appointment

No-show appointments waste resources and decrease the sustainability of care. This study is an attempt to evaluate patient no-shows based on modes of appointment-making and types of appointments. We collected hospital information system data and appointment data including characteristics of patients, service providers, and clinical visits over a three-month period (1 September 2018 to 30 November 2018), at a large tertiary hospital in Seoul, Korea. We used multivariate logistic regression analyses to identify the factors associated with no-shows (Model 1). We further assessed no-shows by including the interaction term (“modes of appointment-making” X “type of appointment”) (Model 2). Among 1,252,127 appointments, the no-show rate was 6.12%. Among the modes of appointment-making, follow-up and online/telephone appointment were associated with higher odds of no-show compared to walk-in. Appointments for treatment and surgery had higher odds ratios of no-show compared to consultations. Tests for the interaction between the modes of appointment-making and type of appointment showed that follow-up for examination and online/telephone appointments for treatment and surgery had much higher odds ratios of no-shows. Other significant factors of no-shows include age, type of insurance, time of visit, lead time (time between scheduling and the appointment), type of visits, doctor’s position, and major diagnosis. Our results suggest that future approaches for predicting and addressing no-show should also consider and analyze the impact of modes of appointment-making and type of appointment on the model of prediction.

PEC e-SUS APS online appointment scheduling system: a tool to facilitate access to Primary Care in Brazil

Barriers faced by health services providing scheduled care result in high no-show rates. This article describes the main characteristics of an online appointment scheduling system incorporated into the citizens' electronic health record system (PEC e-SUS APS). Developed by the Bridge Laboratory, Federal University of Santa Catarina, which also developed the PEC e-SUS APS, the system allows patients to schedule appointments using the national patient communications hub, Conecte SUS Cidadão. The PEC e-SUS APS includes a professional's agenda module that allows patients to view available time slots and book and cancel appointments. Unfortunately, despite the benefits of online scheduling systems, their potential has been poorly exploited in Brazil. The main reasons for this include lack of information and training of health professionals on how to use the system and its potential benefits for Primary Health Care (PHC) services. Wider dissemination is needed to improve the adoption of the system and promote the routine use of this tool in health services in order to facilitate access to primary health care.

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.

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.

Patient No-Show Prediction: A Systematic Literature Review

Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.

Evaluating the Impact of Patient No-Shows on Service Quality

Purpose

Patient no-shows are long-standing issues affecting resource utilization and posing risks to the quality of healthcare services. They also lead to loss of anticipated revenue, particularly in services where resources are expensive and in great demand.

Methods

In order to address common reasons why patients miss appointments, this study reviews the current literature and investigates various tools and methods that have been implemented to mitigate such issues. Further, a case study is conducted to identify the rate of no-shows and underlying causes at a radiology department in one of the leading hospitals in the MENA region.

Results

Our results show that the no-shows are high due to multiple factors, such as patient behavior, patients’ financial situation, environmental factors and scheduling policy.

Conclusion

In conclusion, we generate a list of recommendations that can help in reducing the rate of patient no-shows, such as patient education, application of dynamic scheduling policies and effective appointment reminder systems to patients.

The Influence of the Scheduling Horizon on New Patient Arrivals

The goal of scheduling within an ambulatory enterprise is to appropriately accommodate patients; extending capacity to fulfill this aim in a large health care organization requires the management of a complex scheduling process. Understanding and handling the appointment lead time, referred to as the scheduling horizon, can positively influence capacity management. The analysis demonstrated an increased chance of nonarrived appointments of 16% for a specialty practice and 11% for a primary care practice for every 30-day delay in the scheduling horizon. By incorporating the management of the scheduling horizon, health care organizations can optimize the capacity of their ambulatory clinics.

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.

A Multipronged Initiative to Improve Productivity and Patient Access in a Federally Qualified Health Center Network

In 2012, Access Community Health Network, a Federally Qualified Health Center (FQHC) network with 36 health centers serving the greater Chicago area, embarked on a 3-year initiative to improve patient access. "Dramatic Performance Improvement" (DPI) included the adoption of modified open access scheduling and practice changes designed to improve capacity and the ability to balance supply and demand. This article describes DPI implementation, strategies, and associated outcomes, including a 20% decrease in no-show rate, a 33% drop in time to the third next available appointment (TNAA), a 37% decrease in cycle time, and a 13% increase in patient satisfaction.

Use of photograph-based telemedicine in postoperative wound assessment to diagnose or exclude surgical site infection

OBJECTIVE

This study aims to assess whether a clinician reviewing photographs of a wound was an acceptable substitute for clinical review in order to identify or exclude surgical site infection (SSI).

METHOD

We undertook a mixed methods study consisting of a qualitative public involvement exercise and a prospective, non-randomised, single-centre study of patients undergoing clean or clean-contaminated vascular surgery. For the qualitative study, two semi-structured focus group interviews were conducted. For the prospective study, patients were invited to attend a wound review at 5-7 days and 30 days postoperatively. At review, wounds were scored by a study nurse or doctor, according to the ASEPSIS scale. Anonymised wound photographs were taken and independently reviewed, and ASEPSIS scored by two independent investigators blinded to the original 'clinical review' ASEPSIS score.

RESULTS

In the qualitative study, three female patients were interviewed across two dates. Emerging themes included the burden of SSI, hospital follow-up and telemedical follow-up. A total of 37 patients with a mean age of 61.14 years were included in the quantitative analysis. There was a total of 53 wound reviews. There was >85% agreement between photograph and clinical reviewers in all categories except erythema. The specificity of photograph review for diagnosis of SSI was 90%. The intraclass correlation coefficient for total ASEPSIS score was R=0.806 (95% CI 0.694, 0.881), indicating strong reliability between reviewers.

CONCLUSION

Our data shows that, in the assessment of SSI, there is good correlation between face-to-face clinical and remote photographic review. Incorporating this method of wound assessment into a postoperative follow-up care pathway may save patients and clinicians from unnecessary hospital visits, particularly when conducting health research.

Enhancing outpatient appointment scheduling system performance when patient no-show percent and lateness rates are high

Purpose High lateness and no-show percentages pose great challenges on the patient scheduling process. Usually this is addressed by optimizing the time between patients in the scheduling process and the percent of extra patients scheduled to account for absent patients. However, since the patient no-show and lateness is highly stochastic we might end up with many patients showing up on time which leads to crowded clinics and high waiting times. The clinic might end up as well with low utilization of the doctor time. The purpose of this paper is to study the effect of scheduled overload percentages and the patient interval on the waiting time, overtime, and the utilization. Design/methodology/approach Actual data collection and statistical modeling are used to model the distribution for common dentist procedures. Simulation and validation are used to model the treatment process. Then algorithm development is used to model and generate the patient arrival process. The simulation is run for various values of basic interval scheduled time between arrivals for the patients. Further, 3D graphical illustration for the objectives is prepared for the analysis. Findings This work initially reports on the statistical distribution for the common procedures in dentist clinics. This can be used for developing a scheduling system and for validating the scheduling algorithms developed. This work also suggest a model for generating patient arrivals in simulation. It was found that the overtime increases excessively when coupling both high basic interval and high overloading percentage. It was also found that: to obtain low overtime we must reduce the basic interval. Waiting time increases when reducing the basic scheduled appointment interval and increase the scheduled overload percentage. Also doctors' utilization is increased when the basic interval is reduced. Research limitations/implications This work was done at a local clinic and this might limit the value of the modeled procedure times. Practical implications This work presents a statistical model for the various procedures and a detailed technique to model the operations of the clinics and the patient arrival time which might assist researches and developers in developing their own model. This work presents a procedure for troubleshooting scheduling problems in outpatient clinics. For example, a clinic suffering from high patient waiting time is directly instructed to slightly increase their basic scheduled interval between patients or slightly reduce the overloading percentage. Social implications This work is targeting an extremely important constituent of the health-care system which is the outpatient clinics. It is also targeting multiple objectives namely waiting times, utilization overtime, which in turn is related to the economics and doctor utilization. Originality/value This work presents a detailed modeling procedure for the outpatient clinics under high lateness and no-show and addresses the modeling procedure for the patient arrivals. This 3D graphical charting for the objectives includes a study of the multiple objectives that are of high concern to outpatient clinic scheduling interested parties in one paper.

Demand, capacity, and access of the outpatient clinic: A framework for analysis and improvement

RATIONALE

While theoretical frameworks for optimization of the outpatient processes are abundant, practical step-by-step analyses to give leads for improvement, to forecast capacity, and to support decision making are sparse.

AIMS AND OBJECTIVES

This article demonstrates how to evaluate and optimize the triad of demand, (future) capacity, and access time of the outpatient clinic using a structured six-step method.

METHODS

All individual logistical patient data of an orthopaedic outpatient clinic of one complete year were analysed using a 6-step method to evaluate demand, supply, and access time. Trends in the data were retrospectively analysed and evaluated for potential improvements. A model for decision making was tested. Both the analysis of the method and actual results were considered as main outcomes.

RESULTS

More than 25 000 appointments were analysed. The 6-step method showed to be sufficient to result in valuable insights and leads for improvement. While the overall match between demand and capacity was considered adequate, the variability in capacity was much higher than in demand, thereby leading to delays in access time. Holidays and subsequent weeks showed to be of great influence for demand, capacity, and access time. Using the six-step method, several unfavourable characteristics of the outpatient clinic were revealed and a better match between demand, supply, and access time could have been reached with only minor adjustments. Last, a clinic specific prediction and decision model for demand and capacity was made using the 6-step method.

CONCLUSIONS

The 6-step analysis can successfully be applied to redesign and improve the outpatient health care process. The results of the analysis showed that national holidays and variability in demand and capacity have a big influence on the outpatient clinic. Using the 6-step method, practical improvements in outpatient logistics were easily found and leads for future decision making were contrived.

Understanding Why Patients No-Show: Observations of 2.9 Million Outpatient Imaging Visits Over 16 Years

PURPOSE

To understand why patients "no-show" for imaging appointments, and to provide new insights for improving resource utilization.

MATERIALS AND METHODS

We conducted a retrospective analysis of nearly 2.9 million outpatient examinations in our radiology information system from 2000 to 2015 at our multihospital academic institution. No-show visits were identified by the "reason code" entry "NOSHOW" in our radiology information system. We restricted data to radiography, CT, mammography, MRI, ultrasound, and nuclear medicine examinations that included all studied variables. These variables included modality, patient age, appointment time, day of week, and scheduling lead time. Multivariate logistic regression was used to identify factors associated with no-show visits.

RESULTS

Out of 2,893,626 patient visits that met our inclusion criteria, there were 94,096 no-shows during the 16-year period. Rates of no-show visits varied from 3.36% in 2000 to 2.26% in 2015. The effect size for no-shows was strongest for modality and scheduling lead time. Mammography had the highest modality no-show visit rate of 6.99% (odds ratio [OR] 5.38, P < .001) compared with the lowest modality rate of 1.25% in radiography. Scheduling lead time greater than 6 months was associated with more no-show visits than scheduling within 1 week (OR 3.18, P < .001). Patients 60 years and older were less likely to miss imaging appointments than patients under 40 (OR 0.70, P < .001). Mondays and Saturdays had significantly higher rates of no-show than Sundays (OR 1.52 and 1.51, P < .001).

CONCLUSION

Modality type and scheduling lead time were the most predictive factors of no-show. This may be used to guide new interventions such as targeted reminders and flexible scheduling.

Using overbooking to manage no-shows in an Italian healthcare center

Background

In almost all healthcare systems, no-shows (scheduled appointments missed without any notice from patients) have a negative impact on waiting lists, costs and resource utilization, impairing the quality and quantity of cares that could be provided, as well as the revenues from the corresponding activity. Overbooking is a tool healthcare providers can resort to reduce the impact of no-shows.

Methods

We develop an overbooking algorithm, and we assess its effectiveness using two methods: an analysis of the data coming from a practical implementation in an healthcare center; a simulation experiment to check the robustness and the potential of the strategy under different conditions. The data of the study, which includes personal and administrative information of patients, together with their scheduled and attended examinations, was taken from the electronic database of a big outpatient center. The attention was focused on the Magnetic Resonance (MR) ward because it uses expensive equipment, its services need long execution times, and the center has actually used it to implement an overbooking strategy aimed at reducing the impact of no-shows. We propose a statistical model for the patient’s show/no-show behavior and we evaluate the ensuing overbooking procedure implemented in the MR ward. Finally, a simulation study investigates the effects of the overbooking strategy under different scenarios.

Results

The first contribution is a list of variables to identify the factors performing the best to predict no-shows. We classified the variables in three groups: “Patient’s intrinsic factors”, “Exogenous factors” and “Factors associated with the examination”. The second contribution is a predictive model of no-shows, which is estimated on context-specific data using the variables just discussed. Such a model represents a fundamental ingredient of the overbooking strategy we propose to reduce the negative effects of no-shows. The third contribution is the assessment of that strategy by means of a simulation study under different scenarios in terms of number of resources and no-show rates. The same overbooking strategy was also implemented in practice (giving the opportunity to consider it as a quasi-experiment) to reduce the negative impact caused by non attendance in the MR ward. Both the quasi-experiment and the simulation study demonstrated that the strategy improved the center’s productivity and reduced idle time of resources, although it increased slightly the patient’s waiting time and the staff’s overtime. This represents an evidence that overbooking can be suitable to improve the management of healthcare centers without adversely affecting their costs and the quality of cares offered.

Conclusions

We shown that a well designed overbooking procedure can improve the management of medical centers, in terms of a significant increase of revenue, while keeping patient’s waiting time and overtime under control. This was demonstrated by the results of a quasi-experiment (practical implementation of the strategy in the MR ward) and a simulation study (under different scenarios). Such positive results took advantage from a predictive model of no-show carefully designed around the medical center data.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-2979-z) contains supplementary material, which is available to authorized users.

[Absenteeism and associated factors in scheduled visits to a Preventive Medicine outpatient clinic]

BACKGROUND

Patient absenteeism in outpatient clinics represents a significant obstacle to the cost-effectiveness of healthcare. The aim of this study was to assess the frequency of absence of patients and its associated factors in scheduled visits to a Preventive Medicine department.

PATIENTS AND METHODS

The cross-sectional study was carried out in the Service of Preventive Medicine of the Lozano Blesa University Clinical Hospital of Zaragoza. It included all the visits scheduled from 3 January to 31 March 2017. For each visit, the date and time were registered, together with the type (first or consecutive appointments), age, gender, town of residence, country of birth, and underlying disease. The Chi-squared test was used to determine the association between the variables and making the visit, with a multiple logistic regression analysis being performed on the variables in which a significant association was found.

RESULTS

Of the total of 582 appointments studied, the absenteeism rate was 12.5% (73 out of 582; 13.7% for first appointments and 11.7% for consecutive appointments). Variables that revealed a significant association with patients not attending were: time (9.00-11:15 a. m.; OR=1.84; 95%CI: 1.10-3.08), day of the week (Mondays-Thursdays; OR=3.19; 95%CI: 1.12-9.07), country of birth (outside of Spain; OR=2.09; 95%CI:1.09-3.99), vaccination group (chronic kidney disease during pre-dialysis or dialysis; OR=3.59; 95%CI: 1.57-8.18), and age group (under 52 years old; OR=1.85; 95%CI: 1.08-3.19).

CONCLUSIONS

The rate of absenteeism is at an intermediate position compared to the outpatient visits for other departments. The detection of associated factors makes it possible to plan specific measures for improvements that may reduce absences.

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%).

Dynamic Scheduling for Veterans Health Administration Patients using Geospatial Dynamic Overbooking

The Veterans Health Administration (VHA) is plagued by abnormally high no-show and cancellation rates that reduce the productivity and efficiency of its medical outpatient clinics. We address this issue by developing a dynamic scheduling system that utilizes mobile computing via geo-location data to estimate the likelihood of a patient arriving on time for a scheduled appointment. These likelihoods are used to update the clinic's schedule in real time. When a patient's arrival probability falls below a given threshold, the patient's appointment is canceled. This appointment is immediately reassigned to another patient drawn from a pool of patients who are actively seeking an appointment. The replacement patients are prioritized using their arrival probability. Real-world data were not available for this study, so synthetic patient data were generated to test the feasibility of the design. The method for predicting the arrival probability was verified on a real set of taxicab data. This study demonstrates that dynamic scheduling using geo-location data can reduce the number of unused appointments with minimal risk of double booking resulting from incorrect predictions. We acknowledge that there could be privacy concerns with regards to government possession of one's location and offer strategies for alleviating these concerns in our conclusion.

If You Book It, Will They Come? Attendance at Postdischarge Follow-Up Visits Scheduled by Inpatient Providers

BACKGROUND

Postdischarge follow-up visits (PDFVs) are widely recommended to improve inpatient-outpatient transitions of care.

OBJECTIVE

To measure PDFV attendance rates.

DESIGN

Observational cohort study.

SETTING

Medical units at an academic quaternary-care hospital and its affiliated outpatient clinics.

PATIENTS

Adult patients hospitalized between April 2014 and March 2015 for whom at least 1 PDFV with our health system was scheduled. Exclusion criteria included nonprovider visits, visits cancelled before discharge, nonaccepted health insurance, and visits scheduled for deceased patients.

MEASUREMENTS

The study outcome was the incidence of PDFVs resulting in no-shows or same-day cancellations (NS/SDCs).

RESULTS

Of all hospitalizations, 6136 (52%) with 9258 PDFVs were analyzed. Twenty-five percent of PDFVs were NS/SDCs, 23% were cancelled before the visit, and 52% were attended as scheduled. In multivariable regression models, NS/SDC risk factors included black race (odds ratio [OR] 1.94, 95% confidence interval [CI], 1.63-2.32), longer lengths of stay (hospitalizations ≥15 days: OR 1.51, 95% CI, 1.22- 1.88), and discharge to facility (OR 2.10, 95% CI, 1.70-2.60). Conversely, NS/SDC visits were less likely with advancing age (age ≥65 years: OR 0.39, 95% CI, 0.31-0.49) and driving distance (highest quartile: OR 0.65, 95% CI, 0.52-0.81). Primary care visits had higher NS/SDC rates (OR 2.62, 95% CI, 2.03-3.38) than oncologic visits. The time interval between discharge and PDFV was not associated with NS/SDC rates.

CONCLUSIONS

PDFVs were scheduled for more than half of hospitalizations, but 25% resulted in NS/SDCs. New strategies are needed to improve PDFV attendance.

Reducing Missed Primary Care Appointments in a Learning Health System: Two Randomized Trials and Validation of a Predictive Model

BACKGROUND

Collaborations between clinical/operational leaders and researchers are advocated to develop "learning health systems," but few practical examples are reported.

OBJECTIVES

To describe collaborative efforts to reduce missed appointments through an interactive voice response and text message (IVR-T) intervention, and to develop and validate a prediction model to identify individuals at high risk of missing appointments.

RESEARCH SUBJECTS AND DESIGN

Random assignment of 8804 adults with primary care appointments to a single IVR-T reminder or no reminder at an index clinic (IC) and 7497 at a replication clinic (RC) in an integrated health system in Denver, CO.

MEASURES

Proportion of missed appointments; demographic, clinical, and appointment-specific predictors of missed appointments.

RESULTS

Patients receiving IVR-T had a lower rate of missed appointments than those receiving no reminder at the IC (6.5% vs. 7.5%, relative risk=0.85, 95% confidence interval, 0.72-1.00) and RC (8.2% vs. 10.5%, relative risk=0.76, 95% confidence interval, 0.65-0.89). A 10-variable prediction model for missed appointments demonstrated excellent discrimination (C-statistic 0.90 at IC, 0.89 at RC) and calibration (P=0.99 for Osius and McCullagh tests). Patients in the 3 lowest-risk quartiles missed 0.4% and 0.4% of appointments at the IC and RC, respectively, whereas patients in the highest-risk quartile missed 24.1% and 28.9% of appointments, respectively.

CONCLUSIONS

A single IVR-T call reduced missed appointments, whereas a locally validated prediction model accurately identified patients at high risk of missing appointments. These rigorous studies promoted dissemination of the intervention and prompted additional research questions from operational leaders.

A quality improvement project to reduce waiting time for pediatric outpatient referral clinics in Singapore

Background:

The long waiting time in our General Pediatric Clinic results in the delay in diagnosis and treatment leading to increase in morbidity and dissatisfaction rate among sick children who seek treatment at KK Women’s and Children’s Hospital, Singapore. The mean ± SD waiting time in our General Pediatric Clinic increased from 40 ± 4 days in 2008 and 45 ± 7 days in 2009 to 50 ± 5 days in 2010.

Objectives:

To reduce long waiting times in our General Pediatric Clinic by 20%.

Methods:

The Reducing Waiting Time Working Group was formed to tackle the waiting time problem in the General Pediatric Clinic. Extensive literature was searched and reviewed, brainstorming and discussions were carried out and strategies were developed. A series of implementations were carried out sequentially including clearing of the backlog of cases, development of general and fast track referral guidelines, triaging system and ‘5 Days SMS and 48 Hours Call’ system.

Results:

By clearing the backlog cases, waiting time was reduced from 57 days to 44 days. Through effective triaging system and ‘5 Days SMS and 48 Hours Call’ system, the mean ± SD waiting time was further reduced by 30% from 50 ± 5 days to 35 ± 7 days. By June 2012, the mean waiting time was 27 days.

Conclusions:

Waiting time is one of the important indicators of healthcare quality provided by any healthcare institution. Through these implementations, waiting time was successfully reduced by 30%. These methods may serve as viable options to improve waiting time in other healthcare settings.

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

BACKGROUND

Accurately predicting the risk of no-show for a scheduled colonoscopy can help target interventions to improve compliance with colonoscopy, and thereby reduce the disease burden of colorectal cancer and enhance the utilization of resources within endoscopy units.

OBJECTIVES

We aimed to utilize information available in an electronic medical record (EMR) and endoscopy scheduling system to create a predictive model for no-show risk, and to simultaneously evaluate the role for natural language processing (NLP) in developing such a model.

DESIGN

This was a retrospective observational study using discovery and validation phases to design a colonoscopy non-adherence prediction model. An NLP-derived variable called the Non-Adherence Ratio ("NAR") was developed, validated, and included in the model.

PARTICIPANTS

Patients scheduled for outpatient colonoscopy at an Academic Medical Center (AMC) that is part of a multi-hospital health system, 2009 to 2011, were included in the study.

MAIN MEASURES

Odds ratios for non-adherence were calculated for all variables in the discovery cohort, and an Area Under the Receiver Operating Curve (AUC) was calculated for the final non-adherence prediction model.

KEY RESULTS

The non-adherence model included six variables: 1) gender; 2) history of psychiatric illness, 3) NAR; 4) wait time in months; 5) number of prior missed endoscopies; and 6) education level. The model achieved discrimination in the validation cohort (AUC= =70.2 %). At a threshold non-adherence score of 0.46, the model's sensitivity and specificity were 33 % and 92 %, respectively. Removing the NAR from the model significantly reduced its predictive power (AUC = 64.3 %, difference = 5.9 %, p < 0.001).

CONCLUSIONS

A six-variable model using readily available clinical and demographic information demonstrated accuracy for predicting colonoscopy non-adherence. The NAR, a novel variable developed using NLP technology, significantly strengthened this model's predictive power.

Noncompletion of referrals to outpatient specialty clinics among patients discharged from the emergency department: a prospective cohort study

Objective:

We sought to characterize patients who are referred from the emergency department (ED) to specialty clinics but do not complete the referral, and to identify reasons for their failure to follow up.

Methods:

A prospective cohort study was carried out over 3 months of patients who were discharged from the ED of a teaching hospital with referral to internal medicine, cardiology or neurology clinics, but who did not complete the referral. Information on demographics, barriers to care and reasons for not completing the referral was obtained through a standardized telephone interview.

Results:

Of 171 ED referrals, 42 (24.6%) were not completed. Interviews were completed for 71.4% (30 patients). Of the nonattenders, 80% were functional in English and most had high school (73.1%) or university (60.7%) education. Virtually all (93.0%) interviewees could get to hospital by themselves or have someone take them. Only 42.9% (12 patients) understood why the emergency physician (EP) requested consultation, and 42.9% (12 patients) described EP instructions as poor or fair. Primary reasons for noncompletion of consult were patient choice (46.7%, 95% confidence interval [CI] 27.1%–66.2%), physical or social barriers (13.3%, 95% CI 0.0%–27.2%), communication failure (20%, 95% CI 4.0%–36.0%) and consultant's refusal of the consultation (20% [95% CI 4.0%–36.0%]). All consultant refusals were from one internal medicine clinic, representing 42% (8/19) of ED referrals to that clinic. None of the 6 patients interviewed who were declined consultation was aware that their consultation had been refused.

Conclusion:

Patients discharged by the EP with referral to specialty clinics frequently do not complete the consultation. Causes for failure to follow up relate to patient decision, inadequate or poorly understood discharge information, and system factors. Institutional audits of patients who fail to complete follow-up may reveal unanticipated barriers to care.

Patient navigation based on predictive modeling decreases no-show rates in cancer care

BACKGROUND

Patient adherence to appointments is key to improving outcomes in health care. "No-show" appointments contribute to suboptimal resource use. Patient navigation and telephone reminders have been shown to improve cancer care and adherence, particularly in disadvantaged populations, but may not be cost-effective if not targeted at the appropriate patients.

METHODS

In 5 clinics within a large academic cancer center, patients who were considered to be likely (the top 20th percentile) to miss a scheduled appointment without contacting the clinic ahead of time ("no-shows") were identified using a predictive model and then randomized to an intervention versus a usual-care group. The intervention group received telephone calls from a bilingual patient navigator 7 days before and 1 day before the appointment.

RESULTS

Over a 5-month period, of the 40,075 appointments scheduled, 4425 patient appointments were deemed to be at high risk of a "no-show" event. After the patient navigation intervention, the no-show rate in the intervention group was 10.2% (167 of 1631), compared with 17.5% in the control group (280 of 1603) (P<.001). Reaching a patient or family member was associated with a significantly lower no-show rate (5.9% and 3.0%, respectively; P<.001 and .006, respectively) compared with leaving a message (14.7%: P = .117) or no contact (no-show rate, 21.6%: P = .857).

CONCLUSIONS

Telephone navigation targeted at those patients predicted to be at high risk of visit nonadherence was found to effectively and substantially improve patient adherence to cancer clinic appointments. Further studies are needed to determine the long-term impact on patient outcomes, but short-term gains in the optimization of resources can be recognized immediately.

Characteristics and outcomes of advanced cancer patients who miss outpatient supportive care consult appointments

BACKGROUND

Missed appointments (MA) are frequent, but there are no studies on the effects of the first MA at supportive care outpatient clinics on clinical outcomes.

METHODS

We determined the frequency of MA among all patients referred to our clinic from January-December 2011 and recorded the clinical and demographic data and outcomes of 218 MA patients and 217 consecutive patients who kept their first appointments (KA).

RESULTS

Of 1,352 advanced-cancer patients referred to our clinic, 218 (16 %) had an MA. The MA patients' median age was 57 years (interquartile range, 49-67). The mean time between referral and appointment was 7.4 days (range, 0-71) for KA patients vs. 9.1 days (range, 0-89) for MA patients (P = 0.006). Reasons for missing included admission to the hospital (17/218 [8 %]), death (4/218 [2 %]), appointments with primary oncologists (37/218 [18 %]), other appointments (19/218 [9 %]), visits to the emergency room (ER) (9/218 [9 %]), and unknown (111/218 [54 %]). MA patients visited the ER more at 2 weeks (16/214 [7 %] vs. 5/217 [2 %], P = 0.010) and 4 weeks (17/205 [8 %] vs. 8/217 [4 %], P = 0.060). Median-survival duration for MA patients was 177 days (range, 127-215) vs. 253 days (range, 192-347) for KA patients (P = 0.013). Multivariate analysis showed that MAs were associated with longer time between referral and scheduled appointment (odds ratio [OR], 1.026/day, P = 0.030), referral from targeted therapy services (OR, 2.177, P = 0.004), living in Texas/Louisiana regions (OR, 2.345, P = 0.002), having an advanced directive (OR, 0.154, P < 0.0001), and being referred for symptom control (OR, 0.024, P = 0.0003).

CONCLUSION

MA patients with advanced cancer have worse survival and increased ER utilization than KA patients. Patients at higher risk for MA should undergo more aggressive follow-up. More research is needed.

Injury type and emergency department management of orthopaedic patients influences follow-up rates

BACKGROUND

Orthopaedic clinic follow-up is required to ensure optimal management and outcome for many patients who present to the emergency department (ED) with an orthopaedic injury. While several studies have shown that demographic variables influence patient follow-up after discharge from the ED, the objective of this study was to examine orthopaedic-related and other factors associated with the failure to return for orthopaedic outpatient management, so-called "no-show," after an ED visit.

METHODS

A chart review was conducted at a large academic public hospital. Four hundred and sixty-four consecutive adult patients who received an orthopaedic consult in the ED with subsequent referral to the orthopaedic clinic from January through June, 2011, were included. With use of chi-square and Mann-Whitney univariate tests, data regarding injury type and management were analyzed for association with no-show. Variables with p < 0.25 were included in a multivariate stepwise forward logistic regression analysis.

RESULTS

The overall no-show rate was 26.1%. Logistic regression modeling revealed significant differences in no-show rates based on cause of injury (odds ratio [OR] 7.51; 95% confidence interval [CI], 2.27 to 25.1), with assault victims having the highest no-show rate. Anatomic region of injury significantly influenced no-show rates (OR 6.61; 95% CI, 1.45 to 30.5), with patients with a spine or back complaint having the highest no-show rate. Follow-up rates were influenced by the orthopaedic resident provider consulted (OR 10.8; 95% CI, 4.11 to 31.1), and this was not related to level of training (p = 0.25). The type of bracing applied influenced the no-show rate (OR 2.46; 95% CI, 1.58 to 3.96), and the easier it was to remove the brace (splint), the worse the follow-up (p = 0.0001). Several demographic variables were also predictive of clinic nonattendance, including morbid obesity (OR 15.0; 95% CI, 4.83 to 51.6) and current tobacco use (OR 5.56; 95% CI, 2.19 to 15.4).

CONCLUSIONS

This study supports previous evidence of high no-show rates with scheduled orthopaedic follow-up among patients treated in the ED. The data highlight distinct orthopaedic-related factors associated with nonattendance. These findings are useful in identifying patients at high risk for no-show to scheduled orthopaedic follow-up appointments and may influence disposition and management decisions for these patients.

A longitudinal linear model of patient characteristics to predict failure to attend an inner-city chronic pain clinic

Patients often fail to attend appointments in chronic pain clinics for unknown reasons. We hypothesized that certain patient characteristics predict failure to attend scheduled appointments, pointing to systematic barriers to accessing chronic pain services for certain underserved populations. We collected retrospective data from a longitudinal observational cohort of patients at an academic pain clinic in Newark, New Jersey. To examine the effect of demographic factors on appointment status, we fit a marginal logistic regression using generalized estimating equations with exchangeable correlation. A total of 1,394 patients with 3,488 total encounters between January 1, 2006, and December 31, 2009, were included. Spanish spoken as a primary language (alternatively Hispanic or other race) and living between 5 and 10 miles from the clinic were associated with reduced odds of arriving for an appointment; making an appointment for a particular complaint such as cancer pain or back pain, an interventional pain procedure scheduled in connection with the appointment, unemployed status, and continuity of care (as measured by office visit number) were associated with increased odds of arriving. Spanish spoken as a primary language and distance to the pain clinic predicted failure to attend a scheduled appointment in our cohort. If these constitute systematic barriers to access, they may be amenable to targeted interventions.

PERSPECTIVE

We identified certain patient characteristics, specifically Spanish spoken as a primary language and geographic distance from the clinic, that predict failure to attend an inner-city chronic pain clinic. These identified barriers to accessing chronic pain services may be modifiable by simple cost-effective interventions.

Predictors of attendance at an obesity clinic and subsequent weight change

BACKGROUND

There is conflicting evidence regarding characteristics of patients most likely to have poor outcomes after referral to a multidisciplinary weight loss clinic. The aim of this study was to identify patient characteristics associated with poor attendance and poor weight outcomes at a weight management clinic based in an Australian tertiary hospital.

METHODS

Patient characteristics including age, sex, referral source, postcode of residence, weight, body mass index (BMI) and the presence of specific comorbidities were recorded. Outcome measures included questionnaire return following referral (a requirement prior to a first appointment being scheduled), percentage of appointments attended and rate of weight change (kg/month). Continuous variables were expressed as mean ± standard deviation and compared using a t-test. Categorical data were presented as proportions and a chi-squared test was used to test significance. Statistical significance was set as p < 0.05.

RESULTS

Of 502 patients referred to the Comprehensive Metabolic Care Centre (CMCC), 231 (46%) did not return their questionnaire. Patients referred by their GP, compared to those with only internal hospital referrals, were more likely to return their questionnaire (86.0% cf. 77.9%; p = 0.02) as were those who had their BMI recorded in their referral letter (58% cf 45% p = 0.011). 28.1% of patients attended half or less of their scheduled appointments at the CMCC but none of the parameters analysed was associated with attendance. Weight loss was associated with residence in a rural location (p = 0.016) and hypercholesterolaemia (p = 0.03) and weight gain was associated with obstructive sleep apnoea (p = 0.04).

CONCLUSIONS

A large proportion of the patients referred to a weight management clinic never had an appointment scheduled. Clinicians should not anticipate greater compliance in one patient demographic than another; all groups need focus, particularly at the referral stage, and likely poor compliance must be anticipated and better managed.

Into the abyss: diabetes process of care indicators and outcomes of defaulters from a Canadian tertiary care multidisciplinary diabetes clinic

Background

Continuity of care is essential for good quality diabetes management. We recently found that 46% of patients defaulted from care (had no contact with the clinic for 18 months after a follow-up appointment was ordered) in a Canadian multidisciplinary tertiary care diabetes clinic. The primary aim was to compare characteristics, diabetes processes of care, and outcomes from referral to within 1 year after leaving clinic or to the end of the follow-up period among those patients who defaulted, were discharged or were retained in the clinic.

Methods

Retrospective cohort study of 193 patients referred to the Foustanellas Endocrine and Diabetes Center (FEDC) for type 2 diabetes from January 1, 2005 to June 30, 2005. The FEDC is the primary academic referral centre for the Ottawa Region and provides multidisciplinary diabetes management. Defaulters (mean age 58.5 ± 12.5 year, 60% M) were compared to patients who were retained in the clinic (mean age 61.4 ± 10.47 years, 49% M) and those who were formally discharged (mean age 61.5 ± 13.2 years, 53.3% M). The chart audit population was then individually linked on an individual patient basis for laboratory testing, physician visits billed through OHIP, hospitalizations and emergency room visits using Ontario health card numbers to health administrative data from the Ministry of Health and Long-Term Care at the Institute for Clinical and Evaluative Sciences (ICES).

Results

Retained and defaulted patients had significantly longer duration of diabetes, more microvascular complications, were more likely to be on insulin and less likely to have a HbA1c < 7.0% than patients discharged from clinic. A significantly lower proportion of patients who defaulted from tertiary care received recommended monitoring for their diabetes (HbA1c measurements, lipid measurements, and periodic eye examinations), despite no difference in median number of visits to a primary care provider (PCP). Emergency room visits were numerically higher in the defaulters group.

Conclusions

Patients defaulting from a tertiary care diabetes hospital do not receive the recommended monitoring for their diabetes management despite attending PCP appointments. Efforts should be made to minimize defaulting in this group of individuals.

Preference based load balancing as an outpatient appointment scheduling aid

Load balancing is a performance improvement aid in various applications of distributed systems. In this paper we propose a preference based load balancing strategy as a scheduling aid in an outpatient clinic of an online medical consultation system. The performance objectives are to maximizing throughout and minimizing waiting time. Patients will provide a standard set of preferences prior to scheduling an appointment. The preferences are rated on to a scale and each service request will have a respective preference score. The available doctors will also be classified into classes based on their clinical expertise and the nature of the past diagnosis and the types of patients consulted. The preference scores will then be mapped on to each class and the appointment will be scheduled. The proposed scheme was modeled as a queuing system in Matlab. Matlab SimEvents library modules were used for constructing the model. Performance was analysed based on the average waiting time and utilization. The results revealed that the preference based load balancing scheme markedly reduce the waiting time and significantly improve the utilization under different load conditions.

Defaulters among lung cancer patients in a suburban district in a developing country

INTRODUCTION

This study was carried out to determine the prevalence, patient's characteristic and reasons for defaulting follow-up and treatment among patients with lung cancer.

METHODS

Patients with histologically confirmed lung cancer were recruited. Patient's detailed demographic data, occupation, socioeconomic status, and educational level of both the patients and their children were recorded. Defaulters were classified as either intermittent or persistent defaulters. By using Chi-square test, defaulter status was compared with various demographic and disease characteristic factors. The reasons for default were determined.

RESULTS

Ninety five patients were recruited. Among them, 81.1% patients were males; 66.3% were Malays. The mean age (SD) was 60 ± 10.5 years. About 46.3% of the patients had Eastern Cooperation Oncology Group (ECOG) functional status 0/1 and 96.8% of the patients presented with advanced stage (Stage 3b or 4). Overall, 20 patients (21.1%) were defaulters (35.0% intermittent defaulters; 65.0% persistent defaulters). Among the intermittent defaulters, 8 patients defaulted once and one patient defaulted 3 times. Among the 20 defaulters, only 2 (10%) patients turned up for the second follow-up appointment after telephone reminder. Two main reasons for default were 'too ill to come' (38.5.5%) and logistic difficulties (23.1%). No correlation was found between patient education, children education, income, ECOG status, stage of the disease, race, and gender with the defaulter rate.

CONCLUSION

Defaulter rate among lung cancer patients was 21.1%. Children education level is the only significant factor associated with the defaulter rate.

Policy challenges for the pediatric rheumatology workforce: Part I. Education and economics

For children with rheumatic conditions, the available pediatric rheumatology workforce mitigates their access to care. While the subspecialty experiences steady growth, a critical workforce shortage constrains access. This three-part review proposes both national and international interim policy solutions for the multiple causes of the existing unacceptable shortfall. Part I explores the impact of current educational deficits and economic obstacles which constrain appropriate access to care. Proposed policy solutions follow each identified barrier.Challenges consequent to obsolete, limited or unavailable exposure to pediatric rheumatology include: absent or inadequate recognition or awareness of rheumatic disease; referral patterns that commonly foster delays in timely diagnosis; and primary care providers' inappropriate or outdated perception of outcomes. Varying models of pediatric rheumatology care delivery consequent to market competition, inadequate reimbursement and uneven institutional support serve as additional barriers to care.A large proportion of pediatrics residency programs offer pediatric rheumatology rotations. However, a minority of pediatrics residents participate. The current generalist pediatrician workforce has relatively poor musculoskeletal physical examination skills, lacking basic competency in musculoskeletal medicine. To compensate, many primary care providers rely on blood tests, generating referrals that divert scarce resources away from patients who merit accelerated access to care for rheumatic disease. Pediatric rheumatology exposure could be enhanced during residency by providing a mandatory musculoskeletal medicine rotation that includes related musculoskeletal subspecialties. An important step is the progressive improvement of many providers' fixed referral and laboratory testing patterns in lieu of sound physical examination skills.Changing demographics and persistent reimbursement disparities will require workplace innovation and legislative reform. Reimbursement reform is utterly essential to extending patient access to subspecialty care. In practice settings characterized by a proportion of Medicaid-subsidized patients in excess of the national average (> 41%), institutional support is vital. Accelerating access to care will require the most efficient deployment of existing, limited resources. Practice redesign of such resources can also improve access, e.g., group appointments and an escalating role for physician extenders. Multidisciplinary, team-oriented care and telemedicine have growing evidence basis as solutions to limited access to pediatric rheumatology services.

Effect of two-level provider capacities on the performance of open access clinics

The successful implementation of open access scheduling requires the match of daily healthcare provider capacity and patient demand at the high level of total capacity and the low levels of individual capacities for different types of appointments. In this paper, we introduce 12 scheduling rules for determining the two-level provider capacities and compare them in terms of four performance metrics: the probabilities of granting requests for fixed and open appointments, and the expectation and the variance of the number of patients consulted. Our analytical results show that adjusting low level provider capacities can reduce the difference between the two probabilities. When the ratios of low level capacities to the high level provider capacity are fixed, the two probabilities increase with the increase in the high level capacity. Meanwhile, our numerical results demonstrate that the expectation and the variance of the number of patients consulted increase with the increase in the high level capacity. The results provide insights in determining optimal two-level provider capacities to match daily patient demand. Potential approaches to optimality are also proposed based on the results.

Non-English speakers attend gastroenterology clinic appointments at higher rates than English speakers in a vulnerable patient population

GOALS

We sought to identify factors associated with gastroenterology clinic attendance in an urban safety net healthcare system.

BACKGROUND

Missed clinic appointments reduce the efficiency and availability of healthcare, but subspecialty clinic attendance among patients with established healthcare access has not been studied.

STUDY

We performed an observational study using secondary data from administrative sources to study patients referred to, and scheduled for an appointment in, the adult gastroenterology clinic serving the safety net healthcare system of San Francisco, CA. Our dependent variable was whether subjects attended or missed a scheduled appointment. Analysis included multivariable logistic regression and classification tree analysis. A total of 1833 patients were referred and scheduled for an appointment between May 2005 and August 2006. Prisoners were excluded. All patients had a primary care provider.

RESULTS

Six hundred eighty-three patients (37.3%) missed their appointment; 1150 patients (62.7%) attended. Language was highly associated with attendance in the logistic regression; non-English speakers were less likely than English speakers to miss an appointment [adjusted odds ratio 0.42 (0.28, 0.63) for Spanish, 0.56 (0.38, 0.82) for Asian language, P<0.001]. Other factors were also associated with attendance, but classification tree analysis identified language to be the most highly associated variable.

CONCLUSIONS

In an urban safety net healthcare population, among patients with established healthcare access and a scheduled gastroenterology clinic appointment, not speaking English was most strongly associated with higher attendance rates. Patient-related factors associated with not speaking English likely influence subspecialty clinic attendance rates, and these factors may differ from those affecting general healthcare access.