Appointment Characteristics Research Articles

MP83-17 SOCIOECONOMIC FACTORS ASSOCIATED WITH PATIENT NO SHOWS IN THE AMBULATORY UROLOGY CLINIC

You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Quality Improvement & Patient Safety IV (MP83)1 Apr 2020MP83-17 SOCIOECONOMIC FACTORS ASSOCIATED WITH PATIENT NO SHOWS IN THE AMBULATORY UROLOGY CLINIC Sabine Nguyen*, Angela Massey, Dan Norez, Shiva Gautam, Karthik Tanneru, Jatinder Kumar, Umar Alam, Mark Bandyk, Hariharan Ganapathi, Robert Marino, Shiva Gautam, Shahriar Koochekpour, Joseph Costa, and Kc Balaji Sabine Nguyen*Sabine Nguyen* More articles by this author , Angela MasseyAngela Massey More articles by this author , Dan NorezDan Norez More articles by this author , Shiva GautamShiva Gautam More articles by this author , Karthik TanneruKarthik Tanneru More articles by this author , Jatinder KumarJatinder Kumar More articles by this author , Umar AlamUmar Alam More articles by this author , Mark BandykMark Bandyk More articles by this author , Hariharan GanapathiHariharan Ganapathi More articles by this author , Robert MarinoRobert Marino More articles by this author , Shiva GautamShiva Gautam More articles by this author , Shahriar KoochekpourShahriar Koochekpour More articles by this author , Joseph CostaJoseph Costa More articles by this author , and Kc BalajiKc Balaji More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000975.016AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Patient no-show to scheduled outpatient ambulatory clinic appointments adversely impacts healthcare costs and the quality of patient care. In this study, we seek to identify factors, especially those that may have socioeconomic implications, associated with patient no-shows in our county’s academic urology clinics. METHODS: A retrospective chart review was conducted on patients who were scheduled at the main urology clinics of our academic center between April 2018 and 2019. The total number of appointments scheduled and patient attendance were computed at each clinical site. Patient attendance was further analyzed according to type of scheduled appointment, type of clinician seen, and socioeconomic status based on the patient’s insurance coverage and domicile represented as Health Zones. Chi-square test and simple and multiple regression analysis were performed in order to determine the relationship between patient no-show and the variables for appointment characteristics. RESULTS: A total of 1,472 (13.8%) of the 10,709 scheduled appointments were no-show patients. Statistical significance was established between patient no-show and the multiple appointment and socioeconomic variables. There was a higher no-show rate for non-private faculty clinic than private faculty clinic (15.1% vs 9.0% respectively; p<0.0001), and appointments booked with advanced healthcare providers (16.5%) compared to with urologists (12.6%) (OR=1.20, 95% CI 1.05-1.38; p<0.0278). On regression analysis, no-show clinic patients were strongly associated with those who had no health insurance (OR=30.00, 95% CI: 23.73-37.92; p<0.0001) and those who reside in our county’s Health Zone 1 (OR=1.96, 95% CI:1.63-2.37; p<0.0001), in which the population reports a household income of 30% below poverty level and is only one-third high-school educated. Overall, the cohort of patients who are at risk for no-show are scheduled at the non-private faculty clinic with either no insurance or public insurance and reside in health zone 1. CONCLUSIONS: Identifying the cohort of patients who are at risk for no-show allows for implementation of changes to this specific target population, which may help curtail the number of future patient non-attendance to urology appointments. Source of Funding: none © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1262-e1262 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sabine Nguyen* More articles by this author Angela Massey More articles by this author Dan Norez More articles by this author Shiva Gautam More articles by this author Karthik Tanneru More articles by this author Jatinder Kumar More articles by this author Umar Alam More articles by this author Mark Bandyk More articles by this author Hariharan Ganapathi More articles by this author Robert Marino More articles by this author Shiva Gautam More articles by this author Shahriar Koochekpour More articles by this author Joseph Costa More articles by this author Kc Balaji More articles by this author Expand All Advertisement PDF downloadLoading ...

The Prediction of Outpatient No-Show Visits by using Deep Neural Network from Large Data

Patients’ no-show is one of the leading causes of increasing financial burden for healthcare organizations and is an indicator of healthcare systems' quality and performance. Patients' no-show affects healthcare delivery, workflow, and resource planning. The study aims to develop a prediction model predict no-show visits using a machine learning approach. A large volume of data was extracted from electronic health records of patient visits in outpatient clinics under the umbrella of large medical cities in Saudi Arabia. The data consists of more than 33 million visits, with an 85% no-show rate. A total of 29 features were utilized based on demographic, clinical, and appointment characteristics. Nine features were an original data element, while data elements derived 20 features. This study used and compared three machine learning algorithms; Deep Neural Network (DNN), AdaBoost, and Naive Bayes (NB). Results revealed that the DNN performed better in comparison to NB and AdaBoost. DNN achieved a weighted average of 98.2% and 94.3% of precision and recall, respectively. This study shows that machine learning has the potential to improve the efficiency and effectiveness of healthcare. The results are considered promising, and the model can be an excellent candidate for implementation.

Predictors of Wait Time in the Orthopaedic Foot and Ankle Clinic

Category:Health Sciences ResearchIntroduction/Purpose:Wait times represent a critical component of the patient experience, and prolonged waits are correlated with decreased patient satisfaction. We hypothesized that time spent waiting for radiology is the largest contributor to total patient wait time in our orthopedic foot and ankle clinic.Methods:A prospective, observational study was conducted in the outpatient orthopaedic foot and ankle clinic at a tertiary medical center. A total of 210 new and follow-up adult patients were enrolled. Patients were tracked from arrival until checkout with multiple time points being recorded by a trained observer. The time between patient arrival and first contact with the orthopaedic surgeon was broken down into five distinct categories. Total time between patient arrival and first contact with the orthopaedic surgeon was tested for association with patient and appointment characteristics using Student’s t-test.Results:The average total time spent waiting for first contact with the orthopaedic surgeon was 57.1±30.4 minutes. The largest contributor was time spent waiting for an exam room (33.1±25.5 minutes), followed by time spent waiting for radiologic imaging (21.7±19.9 minutes), time spent waiting for resident/PA (12.2±10.9 minutes), and time spent waiting for attending surgeon after seeing resident/PA (11.7±9.3 minutes). Factors contributing to a longer overall wait included obtaining x-rays at the visit (+15.4±4.2 minutes, 95% confidence interval [CI]=+7.0 to +23.8, p<0.001) and failure to complete patient paperwork beforehand (+36.9±5.3 minutes, CI=+26.4 to +47.4, p<0.001; Table 1). In contrast, overall wait time was not associated with age≥50 years, female sex, late arrival, or outside medical records needing review.Conclusion:Time spent waiting for assignment to an exam room was the largest contributor to the time between patient arrival and first contact with the attending surgeon. In order, the other contributors were time spent waiting for radiology, time spent waiting for the resident/PA, and time spent waiting for the attending surgeon after seeing the resident/PA. Obtaining x-rays increased patient wait time and completing patient paperwork beforehand decreased patient wait time. Orthopaedic foot and ankle surgeons should work to avoid unnecessary x-rays and encourage completion of patient paperwork before arrival in order to optimize clinic flow and decrease patient wait times.

With Whom Does the Surgeon Spend More Time? A Study of the Orthopaedic Foot and Ankle Clinic

Category:Health Sciences ResearchIntroduction/Purpose:In the era of decreasing reimbursement and increasing financial pressure on the orthopaedic foot and ankle surgeon, improving clinic efficiency has value. The purpose of this study was to identify which patients and which types of clinical visits consume the greatest amounts of an attending orthopaedic foot and ankle surgeon’s time.Methods:A prospective, observational study was conducted in an outpatient orthopaedic foot and ankle clinic at a tertiary medical center. 210 adult patients were enrolled from the clinics of two fellowship-trained, board certified orthopaedic surgeons. Time spent in the exam room with the attending surgeon was the primary outcome. Independent variables included patient and appointment characteristics (i.e. age, sex, new/follow-up appointment, etc.). Linear regression was used to test for association between the independent variables and the primary outcome.Results:Mean time spent by the attending surgeon in the exam room (± standard deviation) was 7.4±4.4 minutes (range, 1-20). Predictors of greater time spent in the exam room included patient age≥50 years (+2.0 minutes, 95% confidence interval [CI]=+0.8 to +3.1, p=0.001), female sex (+1.7 minutes, 95% CI=+0.5 to +2.9, p=0.005), outside medical records reviewed (+2.4 minutes, 95% CI=+0.6 to +4.2, p= 0.010), and new (versus follow-up) patient appointment (+1.7 minutes, 95% CI=+0.5 to +2.9; Table 1). In contrast, time spent in the exam room was not associated with the patient arriving late, completion of patient paperwork before the appointment, whether the patient obtained x-rays at the visit, or the type of provider that saw the patient prior to the attending (resident/physician assistant; p>0.05 for each).Conclusion:Patients who are age=≥50, identify as female, bring outside medical records for review, and are presenting to the surgeon for the first time consume the greatest amounts of a surgeon’s time in the examination room. Surgeons can anticipate spending more time in the room with these types of patients and should schedule their clinics accordingly.

Wait Times in Musculoskeletal Patients: What Contributes to Patient Satisfaction.

Introduction:The purpose of this study was to determine how wait time duration is associated with patient satisfaction and how appointment characteristics relate to wait time duration and patient satisfaction in the orthopedic surgery clinic.Methods:Two hundred sixty-four patients visiting one of 3 ambulatory orthopedic surgery clinics were asked to estimate their wait time and to rate their satisfaction with the visit. The associations between appointment characteristics, wait time, and satisfaction were analyzed using t tests, 1-way analysis of variance, and Pearson correlation coefficients.Results:Wait times were significantly different based on visit type, appointment time, whether an X-ray was required, and whether a trainee was involved (P < .001). Patients with wait times less than 30 minutes had higher satisfaction scores (P < .001). Satisfaction ratings were significantly different based on the surgeon’s management recommendation (P = .0211), but were not significantly different based on sex, age, office location, visit type, appointment time subsection, or time spent with the physician (P > .05).Conclusion:Wait times negatively correlated with satisfaction. New patient visits, appointment times in the later third of the day, appointments requiring an X-ray, and appointments involving a trainee had significantly longer wait times. Care should be taken to inform patients with visits involving these characteristics that they may experience longer than average wait times.

Factors of missed appointments at an academic medical center in Taiwan.

Missed appointments mean appointments neither attended nor canceled by patients. Missed appointments belong to one of the important subjects of hospital management because they would incur the inactivity of medical professionals and devices, occupy the health resources for other patients, and thus impair the quality of healthcare services. The aim of this study was to explore the factors of missed appointments at the outpatient department of an academic medical center in Taiwan. This was a cross-sectional study based on registration records of an academic medical center in Northern Taiwan in 2015. Fifteen variables of patients, appointments, and weathers were taken into analysis. Logistic regression was used to calculate the adjusted odds ratio of each variable. For nonfirst visits, we further built a logistic regression model with the five most influential variables and the personal attendance pattern of the previous three appointments. Of 2 132 577 eligible appointments in 2015, the overall no-show rate was 16.9%. The influential factors included the following: (1) patient characteristics: younger than 40 years, ≤6 visits, and a no-show rate between 50% and 75% in the previous year; (2) appointment characteristics: Saturdays, evenings, on the last third of the waiting list, only one appointment on the same day, online appointments, appointment-to-visit intervals (wait time) in 7 to 14 days, appointments to obstetrics/gynecology or pediatrics, first-time appointments, and the interval between the appointed visit and the previous visit in <7 days; and (3) weather characteristics: warm weathers and heavy rains. For nonfirst appointments, the influences in decreasing order were heavy rain, shorter interval between the appointed visit and the previous visit to the same department, higher no-show rate in the previous year, total absence in the personal attendance pattern of the previous three appointments, longer wait time, and children. The factors of missed appointments were multifaceted. Further measures could be undertaken accordingly to enhance healthcare efficiency.

Correlation of Appointment Times and Subspecialty With the No-Show Rates in an Orthopedic Ambulatory Clinic.

Unexpectedly missed appointments ("no-shows") cause clinic inefficiency, lost time and revenue, wasted healthcare resources, and provider dissatisfaction. No-shows can be associated with miscommunication, transportation difficulties, employment status, age, race, and socioeconomic status. This study investigates the association between no-show rates and patient, appointment time, and provider characteristics. Data for all scheduled appointments in a single orthopedic multispecialty institution during calendar year 2016 were obtained. Data points included patient age, gender, and race; hour; month; and subspecialty. Chi-square testing was used to compare no-show and kept appointments with respect to patient and appointment characteristics. Logistic regression was used to calculate differences in no-show rates between orthopedic subspecialties. The overall no-show rate was 11.5%. Race, age, and subspecialties were all found to be associated with higher no-show rates. No significant differences were observed for gender, appointment time, or month of appointment. The authors suggest that patients at higher risk of not showing up for scheduled appointments may need extra effort from providers to accommodate the patients' schedules when making appointments, to confirm their appointments a few days before, and/or to incentivize patients to minimize no-shows.

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

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

Outpatient Office Wait Times And Quality Of Care For Medicaid Patients.

The time patients spend in a doctor's waiting room prior to a scheduled appointment is an important component of the quality of the overall health care experience. We analyzed data on twenty-one million outpatient visits obtained from electronic health record systems, which allowed us to measure time spent in the waiting room beyond the scheduled appointment time. Median wait time was a little more than four minutes. Almost one-fifth of visits had waits longer than twenty minutes, and 10percent were more than thirty minutes. Waits were shorter for early-morning appointments, for younger patients, and at larger practices. Median wait time was 4.1 minutes for privately insured patients and 4.6 minutes for Medicaid patients. After adjustment for patient and appointment characteristics, Medicaid patients were 20percent more likely than the privately insured patients to wait longer than twenty minutes, with most of this disparity explained by differences in practices and providers they saw. Wait times for Medicaid patients relative to privately insured patients were longer in states with relatively lower Medicaid reimbursement rates. The study complements other work that suggests that Medicaid patients face some additional barriers in the receipt of care.

How does corporate control affect the appointment, auditing expertise and reputation of independent directors? Evidence from Taiwan

Abstract This study examines the influence of corporate control on the appointment and quality characteristics of independent directors. Our findings, based upon a dataset of related appointments within listed companies in Taiwan, suggest that the preferences of firms to voluntarily appoint independent directors has a positive association with the control right of the controlling shareholder, stemming from the cash flow rights, not from the deviation between ownership and control after decomposing the control. Most importantly, we find that the firms with high control right and cash flow rights of the controlling shareholders are likely to voluntarily appoint independent directors with good auditing expertise and reputation.

Time dependent patient no-show predictive modelling development.

Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

Postdischarge Behavioral Health Treatment and 6-Month Reattempt Rate for Veterans Hospitalized for Suicide Attempt.

The highest risk for suicide occurs immediately after psychiatric discharge. The U.S. Department of Veteran's Affairs' (VA) suicide prevention program emphasizes suicide surveillance and frequent contact after suicide attempt. To describe the 6-month reattempt rate and appointment characteristics for veterans after VA hospitalization for suicide attempt. This retrospective observational study identified veterans hospitalized for suicide attempt (N = 504). Comparisons of patient characteristics and treatment delivery were conducted between veteran groups. The sample (N = 504) was predominantly White (82%) and male (91%), with a median age of 50 years. The 6-month reattempt rate was 6%. Timing of first appointment was earlier in the reattempt group (n = 20) versus the no-reattempt group (n = 467). Appointment intensity, especially telephone appointments, was greater in the reattempt group. The effect of postdischarge treatment on preventing suicide cannot be determined by evaluating only treatment timing and intensity. Future studies should measure the treatment quality and clinical severity.

Outpatient appointment scheduling given individual day-dependent no-show predictions

This paper examines the combined use of predictive analytics, optimization, and overbooking to schedule outpatient appointments in the presence of no-shows. We tackle the problem of optimally overbooking appointments given no-show predictions that depend on the individual appointment characteristics and on the appointment day. The goal is maximizing the number of patients seen while minimizing waiting time and overtime. Our analysis leads to the definition of a near-optimal and simple heuristic which consists of giving same-day appointments to likely shows and future-day appointments to likely no-shows. We validate our findings by performing extensive simulation tests based on an empirical data set of nearly fifty thousand appointments from a real outpatient clinic. The results suggest that our heuristic can lead to a substantial increase in performance and that it should be preferred to open access under most parameter configurations. Our paper will be of great interest to practitioners who want to improve their clinic performance by using individual no-show predictions to guide appointment scheduling.

Including illness behavior in the assessment of endocrine patients.

The concept of illness behavior was introduced by Mechanic and Volkart in the 1960s and refers to the ways in which individuals experience, perceive, evaluate and respond to their own health status (1). Accordingly, behavioral responses to the same illness or the same physical symptom may vary widely among subjects and greatly affect the disease course and outcome. In past decades, important lines of research have been concerned with illness perception, coping, risk behavior, attendance at medical facilities, treatment-seeking behavior, and treatment adherence. They have, however, mostly investigated single aspects separately. We have recently suggested that the concept of illness behavior may provide a unifying framework to observations and findings that would otherwise remain scattered and unrelated in the medical literature (1). Aspects of illness behavior that have important clinical implications have been investigated in the setting of diabetes but have been otherwise understudied in endocrine disorders. Some issues are worth considering to improve effectiveness in the longitudinal management of endocrine patients. Delayed diagnosis is a challenging matter in clinical endocrinology. Early recognition of many endocrine disorders may be impaired by symptoms that are initially subtle and nonspecific as well as by the presence of psychiatric symptoms. Stern et al (2) found that 19.9% of patients with Graves’ disease received an accurate diagnosis more than 6 months after they had sought medical care. Indeed, several psychiatric symptoms such as irritability, anxiety, and sleep difficulties were found to be presenting manifestations of this condition (2). These symptoms are likely to be considered by the physician an expression of psychological disturbance and make the differential diagnosis difficult. Similarly, a patient’s interpretation of symptoms may also contribute to a delay in the diagnosis. In the same study (2), 35.1% of patients with Graves’ disease took more than 6 months to seek medical care after they had experienced their initial symptoms because they were interpreting them as a sign of psychological distress and were therefore reluctant to seek help. Once a subject receives a diagnosis of an endocrine disease, he/she is usually required to undergo regular follow-up visits. According to some preliminary findings, nonattendance at outpatient visits is an important phenomenon in endocrinology. In a large patient population at the Taiwan University Hospital where this issue was examined over a period of 6 months in endocrinology and metabolism outpatient clinics, booked visits were not attended at a rate of 73% (3). In the same study, nonattendance was predicted by features concerning both the patient (eg, younger age) and the physician (eg, teaching status) as well as some appointment characteristics (eg, first-time visit) (3). However, the role of psychosocial variables (eg, patients’ illness attitudes) as potential predictors of nonattendance was not explored. In some instances, decreased motivation by the patient to attend follow-up visits may be determined by a lack of perceived symptoms. In a study by Kasuki et al (4), 17.6% of acromegalic patients were not attending follow-up visits at their reference centers for more than 1 year. Many of them stopped attending the acromegalic outpatient clinic because they were not experiencing symptoms and were not feeling sick, even though they were found to still have active acromegaly. There were no significant differences between

Patient no-show predictive model development using multiple data sources for an effective overbooking approach.

Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows. Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost. From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods. This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient's show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.

Outpatient Appointment Scheduling Given Individual Day-Dependent No-Show Predictions

This paper examines the combined use of predictive analytics, optimization, and overbooking to schedule outpatient appointments in the presence of no-shows. We tackle the problem of optimally overbooking appointments given no-show predictions that depend on the individual appointment characteristics and on the appointment day. The goal is maximizing the number of patients seen while minimizing waiting time and overtime. Our analysis leads to the definition of a near-optimal and simple heuristic which consists of giving same-day appointments to likely shows and future-day appointments to likely no-shows. We validate our findings by performing extensive simulation tests based on an empirical data set of nearly fifty thousand appointments from a real outpatient clinic. The results suggest that our heuristic can lead to a substantial increase in performance and that it should be preferred to open access under most parameter configurations. Our paper will be of great interest to practitioners who want to improve their clinic performance by using individual no-show predictions to guide appointment scheduling.

Impact of batch appointments on no-show rates: a public vs private clinic perspective

Much of the appointment-scheduling literature has characterized the impact of no-show rates with respect to individual appointment requests. However, little is known about the impact of appointments that are grouped by household. This study is concerned with understanding the prevalence of these family group appointments and identifying the appointment characteristics that significantly influence no-show rates. Using historical data from one public and one private pediatric clinic, multiway frequency analysis is used to characterize the association between appointment characteristics and appointment size. A logistic regression model is also developed to identify the factors that contribute to no-show rates for both clinics. The results of the study indicate that more than one-third of the appointments scheduled by both clinics were associated with batch appointment requests. In addition, no-show rates for batch appointments in the public clinic were higher than those of the private clinic. We identify appointment size as a significant predictor in determining no-show rates for both public and private clinics. Since no-show rates adversely impact clinic efficiency, scheduling coordinators should consider the impact of grouping-related appointments when determining how to best allocate resources.

Influence of Patient Financial Account Status on Orthodontic Appointment Attendance

To test the null hypothesis that the financial status of a patient's orthodontic contract and other patient and appointment characteristics have no influence on appointment attendance. During a 6-week period, the last appointment of each active, non-Medicaid patient (n = 538) was recorded as either kept or missed. The financial status of the patient's contract, age, sex, method by which the appointment was made, and the type of appointment scheduled were recorded. Associations between each of these factors and appointment attendance were evaluated using chi(2) analysis. In the case of patients who missed their appointment, attendance at the subsequent reappointment was also evaluated. The overall appointment failure rate was 12.3%. Males (16.2%) were significantly more likely than females (9.5%) to miss appointments (P = .02). Also, appointments made by postcard (28.2%) were more likely to be missed than those made in person (11.8%) or by phone (6.5%) (P = .003). The most significant factor affecting appointment attendance was the patient's financial status (P = .0001). Patients with accounts that were overdue (33.3%) or in collections (28.6%) were more likely to miss appointments than those whose accounts were current (10.5%). Of patients who missed their appointment (n = 66), 30% also missed the subsequent reappointment. The null hypothesis was rejected. Patients with delinquent financial accounts were three times as likely to miss an orthodontic appointment as those whose contracts were current. Increased rates of missed orthodontic appointments were also found for males, patients scheduled by mailing a postcard, and patients who had missed their previous appointment.

Missed appointments at a Swiss university outpatient clinic

To assess the appointment conditions and characteristics of patients who miss their appointments ('no-shows'); this will aid in the formulation of intervention methods to reduce no-show rates. During a one-month period, data on all no-shows at the general internal medicine outpatient clinic of the Geneva University Hospitals were collected. Control patients were matched for appointment time and gender. Patient and appointment characteristics were collated on 13 parameters, and these were compared between no-shows and controls. Two hundred and six of 1296 appointments were no-shows (15.8%). Compared with controls, no-shows were younger, born earlier in the year, more often were not Europeans, more often had a common language with the physician or translator (no communication problems), and more often had a follow-up (not first) appointment. Other parameters were not significant (appointment day of week and time of day, gender, residency status, insurance coverage, family physician, medical consequences, covert addiction). The no-show rate was within the range for comparable settings. Several parameters associated with no-shows reflected specifics of a hospital-based adult outpatient clinic that mainly serves middle-to-low socio-economic classes and is a referral clinic for refugees in a middle-sized European city with a high percentage of foreigners with different backgrounds and languages. Planned interventions should consider local factors.

Gender Differences in Employment and Salaries of Psychologists Within Medical School Settings

A survey of 3,894 psychologists employed within medical schools and academic health centers was conducted in 1997 by the Research Office of the American Psychological Association (APA) in collaboration with the Association of Medical School Psychologists (AMSP). This survey, published as the 1997 Employment Characteristics and Salaries of Medical School Psychologists, included inquiries about various aspects of employment (e.g., academic rank, tenure status, employment activities, appointment characteristics, department affiliations, demographic characteristics) and earnings. Employment characteristics and salary information were compared by gender. The findings indicated that men had more favorable employment circumstances and higher base salaries than their female counterparts. These differences existed across all academic ranks and were found at each level of experience. This article discusses these findings and their implications.