Large-Scale No-Show Patterns and Distributions for Clinic Operational Research

Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study

BACKGROUND

The phenomenon of patients missing booked appointments without canceling them-known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)-has a detrimental effect on patients' health and results in massive health care resource wastage.

OBJECTIVE

Our objective was to develop machine learning (ML) models and evaluate their performance in predicting the likelihood of DNS for hospital outpatient appointments at the MidCentral District Health Board (MDHB) in New Zealand.

METHODS

We sourced 5 years of MDHB outpatient records (a total of 1,080,566 outpatient visits) to build the ML prediction models. We developed 3 ML models using logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). Subsequently, 10-fold cross-validation and hyperparameter tuning were deployed to minimize model bias and boost the algorithms' prediction strength. All models were evaluated against accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve metrics.

RESULTS

Based on 5 years of MDHB data, the best prediction classifier was XGBoost, with an area under the curve (AUC) of 0.92, sensitivity of 0.83, and specificity of 0.85. The patients' DNS history, age, ethnicity, and appointment lead time significantly contributed to DNS prediction. An ML system trained on a large data set can produce useful levels of DNS prediction.

CONCLUSIONS

This research is one of the very first published studies that use ML technologies to assist with DNS management in New Zealand. It is a proof of concept and could be used to benchmark DNS predictions for the MDHB and other district health boards. We encourage conducting additional qualitative research to investigate the root cause of DNS issues and potential solutions. Addressing DNS using better strategies potentially can result in better utilization of health care resources and improve health equity.

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.

A patient education intervention improved rates of successful video visits during rapid implementation of telehealth

INTRODUCTION

The need to rapidly implement telehealth at large scale during the COVID-19 pandemic led to many patients using telehealth for the first time. We assessed the effect of structured pre-visit preparatory telephone calls on success of telehealth visits and examined risk factors for unsuccessful visits.

METHODS

A retrospective cohort study was carried out of 45,803 adult patients scheduled for a total of 64,447 telehealth appointments between March and July 2020 at an academic medical center. A subset of patients received a structured pre-visit phone call. Demographic factors and inclusion of a pre-visit call were analysed by logistic regression. Primary outcomes were non-completion of any visit and completion of phone-only versus audio-visual telehealth visits.

RESULTS

A pre-visit telephone call to a subset of patients significantly increased the likelihood of a successful telehealth visit (OR 0.54; 95% CI: 0.48-0.60). Patients aged 18-30 years, those with non-commercial insurance or those of Black race were more likely to have incomplete visits. Compared to age 18-30, increasing age increased likelihood of a failed video visit: 31-50 years (OR 1.31; 95% CI: 1.13-1.51), 51-70 years (OR 2.98; 2.60-3.42) and >70 years (OR 4.16; 3.58-4.82). Those with non-commercial insurance and those of Black race (OR 1.8; 95% CI 1.67-1.92) were more likely to have a failed video visit.

DISCUSSION

A structured pre-call to patients improved the likelihood of a successful video visit during widespread adoption of telehealth. Structured pre-calls to patients may be an important tool to help reduce gaps in utilization among groups.

Health care overbooking cost minimization model

Challenges in the health care industry have confounded the provision of quality services for patients. Among many relevant concerns, the drive for cost-effectiveness and efficient care have placed considerable pressure on public health care systems and insurance coverage, amid existing barriers to restructuring entrenched systems. This study closely examines the factors impacting disruptions in health care scheduling systems using a structured case study in the health care industry. The study introduces a novel model to identify optimal overbooking capacity and minimize no-show costs. To address the complexity of this issue on a smaller scale, the model is implemented using a private hospital clinical platform in Jeddah, Saudi Arabia, instituting an overbooking reservation and queuing system in 14 departments to investigate the factors that influence disruptions and inefficacy of service provision, while also introducing a strategy for covering costs. The results identify the maximum amount of overbooking that can be made for each clinic. The cost-saving plan developed is expected to save each clinic a considerable sum, as opposed to randomly overbooking without any cost assumptions. Overall, if the clinics studied implemented this strategy, a total loss of no more than SAR. 2, 408 would be incurred from overbooking, in contrast to the exponentially growing amount of SAR. 10,000 that is currently lost on scheduling errors per year. The loss model developed has practical application as a tool for decision-making that includes no-show cost minimization variables.

Using Nudges to Reduce Missed Appointments in Primary Care and Mental Health: a Pragmatic Trial

BACKGROUND

Missed appointments ("no-shows") are a persistent and costly problem in healthcare. Appointment reminders are widely used but usually do not include messages specifically designed to nudge patients to attend appointments.

OBJECTIVE

To determine the effect of incorporating nudges into appointment reminder letters on measures of appointment attendance.

DESIGN

Cluster randomized controlled pragmatic trial.

PATIENTS

There were 27,540 patients with 49,598 primary care appointments, and 9420 patients with 38,945 mental health appointments, between October 15, 2020, and October 14, 2021, at one VA medical center and its satellite clinics that were eligible for analysis.

INTERVENTIONS

Primary care (n = 231) and mental health (n = 215) providers were randomized to one of five study arms (four nudge arms and usual care as a control) using equal allocation. The nudge arms included varying combinations of brief messages developed with veteran input and based on concepts in behavioral science, including social norms, specific behavioral instructions, and consequences of missing appointments.

MAIN MEASURES

Primary and secondary outcomes were missed appointments and canceled appointments, respectively.

STATISTICAL ANALYSIS

Results are based on logistic regression models adjusting for demographic and clinical characteristics, and clustering for clinics and patients.

KEY RESULTS

Missed appointment rates in study arms ranged from 10.5 to 12.1% in primary care clinics and 18.0 to 21.9% in mental health clinics. There was no effect of nudges on missed appointment rate in primary care (OR = 1.14, 95%CI = 0.96-1.36, p = 0.15) or mental health (OR = 1.20, 95%CI = 0.90-1.60, p = 0.21) clinics, when comparing the nudge arms to the control arm. When comparing individual nudge arms, no differences in missed appointment rates nor cancellation rates were observed.

CONCLUSIONS

Appointment reminder letters incorporating brief behavioral nudges were ineffective in improving appointment attendance in VA primary care or mental health clinics. More complex or intensive interventions may be necessary to significantly reduce missed appointments below their current rates.

TRIAL NUMBER

ClinicalTrials.gov, Trial number NCT03850431.

Sociodemographic and Clinical Factors Associated with Nonattendance at the Hepatology Clinic

BACKGROUND

Absenteeism from clinic appointments reduces efficiency, wastes resources, and contributes to longer wait times. There are limited data regarding factors associated with nonattendance in hepatology clinics. Identifying factors related to appointment nonattendance may help in the design of interventions for reducing absenteeism.

METHODS

We aim to identify sociodemographic, clinical, and appointment-related factors associated with absenteeism following referral to a liver clinic in a tertiary academic center located in the US Midwest. We designed a case-control study using data from electronic medical records of patients scheduled for appointments between January 2016 and December 2021. Cases were defined as patients who canceled appointments on the same day or resulting in no-shows, and controls were those who completed the referral visit. Information about patients' sociodemographic characteristics, appointment details, and etiology of liver disease were recorded. Hierarchical logistic regression was used to analyze factors related to nonattendance.

RESULTS

Of 3404 scheduled appointments, 460 (13.5%) missed visits were recorded. In the multivariable logistic regression models, hepatitis C and alcohol-associated liver disease were associated with greater odds of nonattendance [odds ratio (OR) 4.0 (95% CI 3.2-4.9), OR 2.7 (1.7-4.2), respectively] compared to those with other liver disease. Sociodemographic characteristics associated with risk of nonattendance included being Black [OR 2.6, (1.8-3.7)], Medicaid insurance or no insurance [OR 2.3 (1.7-2.9), OR 2.5 (1.6-3.7), respectively], non-English speaking [OR 1.8 (1.1-3.1)], being unmarried [OR 1.8 (1.4-2.2)], and longer wait time (> 30 days) until appointments [OR 1.8 (1.5-2.2)].

CONCLUSION

Several sociodemographic and administrative characteristics, as well as hepatitis C and alcohol-associated liver disease were associated with appointment nonattendance. Targeted future interventions may help to decrease nonattendance.

Pediatric Practices' Perceptions of Text Message Communication with Families: An American Academy of Pediatrics (AAP), Pediatric Research in Office Settings (PROS) Study

Background

Text messages can be an effective and low-cost mechanism for patient reminders; however, they are yet to be consistently integrated into pediatric primary care.

Objective

The aim of this study was to explore pediatric primary care clinician and staff perceptions of pediatric office text message communication with families.

Methods

As part of the National Institutes of Health-funded Flu2Text randomized controlled trial of second-dose influenza vaccine text message reminders, we conducted 7 focus groups and 4 individual interviews in July-August 2019 with primary care pediatric clinicians and staff (n = 39). Overall, 10 Pediatric Research in Office Settings (PROS) pediatric practices in 10 states were selected using stratified sampling. Semi-structured discussion guides included perspectives on possible uses, perceived usefulness, and ease of use of text messages; practices' current text messaging infrastructure; and perceived barriers/facilitators to future use of texting. Two investigators independently coded and analyzed transcripts based on the technology acceptance model using NVIVO 12 Plus (intercoder reliability, K = 0.86).

Results

Overall, participants were supportive of text reminders for the second-dose influenza vaccine, other vaccines, and appointments and perceived texting as a preferred method of communication for caregivers. Health information privacy and patient confidentiality were the main concerns cited. Only respondents from practices with no internal appointment text message reminder system prior to the study expressed concerns about technology implementation logistics, time, and cost.

Conclusion

Text message reminders, for various uses, appear to be well accepted among a group of geographically widespread pediatric practices after participation in a trial of influenza vaccine text message reminders. Privacy, confidentiality, and resource barriers need to be addressed to facilitate successful implementation.

The Health Belief Model Modifying Factors Associated with Missed Clinic Appointments among Individuals with Sickle Cell Disease in the Jazan Province, Saudi Arabia

In treating chronic illnesses, such as sickle cell disease (SCD), outpatient care is essential; poor adherence in attending clinic appointments can lead to serious outcomes. SCD is highly prevalent in Saudi Arabia, and patients with SCD are advised to follow up with their treating physician in order to control this disease manifestation and to better forecast its complications. Studies evaluating missed appointments among patients with SCD are rare. Therefore, the current study aimed to use the health belief model's modifying factors in order to evaluate the variables associated with poor adherence in attending appointments. A total of 381 participants with SCD from various regions in the Jazan Province, southwestern Saudi Arabia, were included. The survey instrument included socioeconomic determinants, factors associated with poor adherence in attending outpatient appointments, and solutions under the conceptual framework of the health belief model. A descriptive analysis was conducted and the factors that impacted adherence in attending the appointments were evaluated. In the current sample, respondents with SCD from 21 to 30 years represented 41%, which was followed by participants who were 11 to 20 years at 21.5%. In addition, about 60% of the participants were women. Further, approximately 62% of the patients admitted were missing one or more outpatient appointments in the previous year, which was significantly related to various factors, such as socioeconomic characteristics and patient residence. Forgetting the appointment was the main reason for skipping outpatient appointments for patients with SCD; as such, reminders appear to be a good solution for most participants. Our findings indicated that modifying components of the health belief model, including age, level of education, income, patients' residence, and lacking cues to action (such as reminders) are important in explaining the reason for poor adherence in attending appointments. Thus, efforts are needed to address these factors and to ensure that SCD patients uphold their appointments. Future studies should examine the clinical, psychological, and epidemiological aspects that are linked with missed consultations.

Impact of United States 2017 Immigration Policy changes on missed appointments at two Massachusetts Safety-Net Hospitals

INTRODUCTION

Studies have shown mixed findings regarding the impact of immigration policy changes on immigrants' utilization of primary care.

METHODS

We used a difference-in-differences analysis to compare changes in missed primary care appointments over time across two groups: patients who received care in Spanish, Portuguese, or Haitian Creole, and non-Hispanic, white patients who received care in English.

RESULTS

After adjustment for age, sex, race, insurance, hospital system, and presence of chronic conditions, immigration policy changes were associated with an absolute increase in the missed appointment prevalence of 0.74 percentage points (95% confidence interval: 0.34, 1.15) among Spanish, Portuguese and Haitian-Creole speakers. We estimated that missed appointments due to immigration policy changes resulted in lost revenue of over $185,000.

CONCLUSIONS

We conclude that immigration policy changes were associated with a significant increase in missed appointments among patients who receive medical care in languages other than English.

Machine learning approaches to predicting no-shows in pediatric medical appointment

Patients’ no-shows, scheduled but unattended medical appointments, have a direct negative impact on patients’ health, due to discontinuity of treatment and late presentation to care. They also lead to inefficient use of medical resources in hospitals and clinics. The ability to predict a likely no-show in advance could enable the design and implementation of interventions to reduce the risk of it happening, thus improving patients’ care and clinical resource allocation. In this study, we develop a new interpretable deep learning-based approach for predicting the risk of no-shows at the time when a medical appointment is first scheduled. The retrospective study was conducted in an academic pediatric teaching hospital with a 20% no-show rate. Our approach tackles several challenges in the design of a predictive model by (1) adopting a data imputation method for patients with missing information in their records (77% of the population), (2) exploiting local weather information to improve predictive accuracy, and (3) developing an interpretable approach that explains how a prediction is made for each individual patient. Our proposed neural network-based and logistic regression-based methods outperformed persistence baselines. In an unobserved set of patients, our method correctly identified 83% of no-shows at the time of scheduling and led to a false alert rate less than 17%. Our method is capable of producing meaningful predictions even when some information in a patient’s records is missing. We find that patients’ past no-show record is the strongest predictor. Finally, we discuss several potential interventions to reduce no-shows, such as scheduling appointments of high-risk patients at off-peak times, which can serve as starting point for further studies on no-show interventions.

No-show after extracorporeal shock wave lithotripsy treatment in endourology clinic: Can we build a typical patient profile?

Objectives

Patients “no‐show” in outpatient clinics is a worldwide challenge. Healthcare providers and patients suffer from negative impacts that include increased expenditure, clinical management ineffectiveness, and decreased access to care. This study aims to evaluate no‐show rate among extracorporeal shock wave lithotripsy patients visiting endourology clinic and to identify the demographic and clinical predictors of no‐show.

Methods

A cross‐sectional and historical cohort study using electronic medical records. We included 790 patients aged >18 years old referred for endourology clinic following shock wave lithotripsy during 2010–2017 at Hadassah Medical Center in Israel. We predicted no‐show rate following shock wave lithotripsy by various patient characteristics by a multivariate logistic regression model.

Results

Overall, 291 (36.8%) patients did not arrive for postoperative clinic. Of these, 91 (11.52%) patients referred to Emergency Department. Patients who were younger in age (odds ratio 1.49, 95% confidence interval 1.08–2.04), patients who underwent hospitalization ≥3 days (odds ratio 1.63, 95% confidence interval 1.11–2.41) and patients who had undergone a stent‐free shock wave lithotripsy (odds ratio 5.71, 95% confidence interval 2.40–13.57) were significantly associated with higher no‐show rate. Larger stone size was associated with reduction in no‐show rate with every millimeter increase of stone diameter was associated with a reduction of 6.1% probability for no‐show (odds ratio 0.94, 95% confidence interval 0.89–0.99).

Conclusions

Predicting patients' characteristics and no‐show patterns is necessary to improve clinical management efficiency, access to care, and costs. We showed that patients who were younger, patients who underwent stent‐free shock wave lithotripsy, patients who had a smaller stone, and patients who underwent a longer hospitalization were more prone to miss their appointment. Paying attention to the characteristics of individual patients may assist in implementing intervening program of patient scheduling.

Factors Contributing to Missed Appointments in a Pediatric Otolaryngology Clinic

OBJECTIVE

To understand why pediatric otolaryngology patients do not attend scheduled clinic appointments and identify factors correlated with no-show status.

STUDY DESIGN

Retrospective cohort study.

METHODS

This is a retrospective cohort study that uses medical record data extraction of patients that was scheduled to attend new patient appointments at a pediatric otolaryngology clinic in 2018.

RESULTS

Factors associated with no-shows included complex psychiatric history (OR (95% CI) 0.789 (0.71-0.88), P < .001), increased appointment lead time (OR (95% CI) 0.981 (0.976-0.987), P < .001), afternoon appointments (OR (95% CI) 0.783 (0.64-0.99), P = .038), and complex maternal medical history (OR (95% CI) 0.987 (0.979-0.996), P < .005). In contrast, factors associated with attendance included complex patients' medical history (OR (95% CI) 1.058 (0.98-1.02), P < .001), primary care physician at the same hospital (OR (95% CI) 2.766 (2.25-3.39), P < .001), and primary language being Spanish (OR (95% CI) 2.536 (1.75-3.67) P < .001). The factors of distance from the hospital (OR (95% CI) 1.001 (0.99-1.01), P = .868), season of appointment (P = .997), race (P = .623), and ethnicity (P = .804) were not associated with attendance or no-shows.

CONCLUSION

Patient and maternal medical problems, mental health history, primary care location, appointment lead time, hour of appointment, and primary language, all contribute to appointment attendance, while appointment timing, race, and ethnicity are not associated with attendance. Further work must be performed to overcome these barriers to minimize healthcare risks and improve patient outcomes.

QUALITY OF EVIDENCE

Level 3 Laryngoscope, 2021.

Enhancing Usability of Appointment Reminders: Qualitative Interviews of Patients Receiving Care in the Veterans Health Administration

BACKGROUND

No-shows are a persistent and costly problem in all healthcare systems. Because forgetting is a common cause of no-shows, appointment reminders are widely used. However, qualitative research examining appointment reminders and how to improve them is lacking.

OBJECTIVE

To understand how patients experience appointment reminders as part of intervention development for a pragmatic trial of enhanced appointment reminders.

DESIGN

Qualitative content analysis PARTICIPANTS: Twenty-seven patients at a single Department of Veterans Affairs hospital and its satellite clinics APPROACH: We conducted five waves of interviews using rapid qualitative analysis, in each wave continuing to ask veterans about their experience of reminders. We double-coded all interviews, used deductive and inductive content analysis to identify themes, and selected quotations that exemplified three themes (limitations, strategies, recommendations).

KEY RESULTS

Interviews showed four limitations on the usability of current appointment reminders which may contribute to no-shows: (1) excessive information within reminders; (2) frustrating telephone systems when calling in response to an appointment reminder; (3) missing or cryptic information about clinic logistics; and (4) reminder fatigue. Patients who were successful at keeping appointments often used specific strategies to optimize the usability of reminders, including (1) using a calendar; (2) heightening visibility; (3) piggybacking; and (4) combining strategies. Our recommendations to enhance reminders are as follows: (1) mix up their content and format; (2) keep them short and simple; (3) add a personal touch; (4) include specifics on clinic location and contact information; (5) time reminders based on the mode of delivery; and (6) hand over control of reminders to patients.

CONCLUSIONS

Appointment reminders are vital to prevent no-shows, but their usability is not optimized for patients. There is potential for healthcare systems to modify several aspects of the content, timing, and delivery of appointment reminders to be more effective and patient-centered.

Exploratory analysis to enhance operational efficiency of new patient screenings

OBJECTIVE

The aims of this study were to 1) evaluate patient demographic data for new patient exams (NPE) and 2) analyze lead response time for checked-in and no-show appointments in predoctoral clinics in a dental school.

METHODS

The data for the study were collected from the predoctoral clinics at Tufts University School of Dental Medicine (TUSDM) for patients with NPE appointments with American Dental Association's (ADA) diagnostic code of D100. A total of 26,826 appointments and 24,419 unique patients were reviewed from January 1, 2015 to December 31, 2019. Patient demographic variables such as age, gender, zip codes, and lead response time were analyzed.

RESULTS

From 26,826 total number of appointments, 10,454 appointments were categorized as no-show appointments (38.97%). In the no-show appointments, the sex distribution was 59.93% female and 40.07% male, and in checked-in category, the sex distribution was 53.75% female and 46.25% male. As the lead response time increased over 5 days, the no-show rate increased to 49.79%. Approximately 55% of the entire NPE was from Greater Boston area.

CONCLUSION

The association between lead time and no-show rate was shown that when lead time was reduced, no-show rate decreased. By identifying the no-show appointments and lead time, schools and clinics can improve operational efficiency, reduce financial loss, and maintain continuation of care by supporting patients who need access to care and creating secondary automated recall system to maximize communication and chair use.

"If You Call Them, They Will Come": A Telephone Call Reminder to Decrease the No-Show Rate in an Outpatient Palliative Medicine Clinic

INTRODUCTION

A high outpatient clinic no-show rate affects clinical outcomes, increases healthcare costs, and reduces both access to care and provider productivity. In an effort to reduce the no-show rate at a busy palliative medicine outpatient clinic, a quality improvement project was launched consisting of a telephone call made by clinic staff prior to appointments. The study aimed to determine the effect of this intervention on the no-show rate, and assess the financial impact of a decreased no-show rate.

METHODS AND MATERIALS

The outpatient clinic no-show rate was measured from September 1 to December 31, 2015. Data from the first 8 months of the calendar year was removed since these could not be verified. Starting January 1, 2016, patients received a telephone call reminder 24 hours prior to their scheduled outpatient appointment for confirmation. No-show rate was again measured for the calendar year 2016. Opportunity costs were calculated for unfulfilled clinic visits.

RESULTS

Of the 1224 completed visits from September 1 to December 31, 2015, 271 were no-shows with an average rate of 11.8%. After the intervention, there were 4368 completed visits and 562 no-shows. The no-show rate for 2016 averaged 6.9% (p < 0.001), down 4.9% from the last 4 months of 2015. Estimated opportunity costs were about 396 no-show visits avoided, equivalent to an annual savings of about $79,200.

CONCLUSION

A telephone call reminder to patients 24 hours prior to their appointment decreased the no-show rate in an outpatient palliative medicine clinic. Avoiding unfulfilled visits resulted in substantial opportunity costs.

Patient-Centric Scheduling With the Implementation of Health Information Technology to Improve the Patient Experience and Access to Care: Retrospective Case-Control Analysis

Background

Cancellations and rescheduling of doctor’s appointments are common. An automated rescheduling system has the potential to facilitate the rescheduling process so that newly opened slots are promptly filled by patients who need and can take the slot. Building on an existing online patient portal, a large health care system adopted an automated rescheduling system, Fast Pass, that sends out an earlier appointment offer to patients via email or SMS text messaging and allows patients to reschedule their appointment through the online portal.

Objective

We examined the uptake of Fast Pass at its early stage of implementation. We assessed program features and patient and visit characteristics associated with higher levels of Fast Pass utilization and the association between Fast Pass use and no-show and cancellation rates.

Methods

This study was a retrospective analysis of Fast Pass offers sent between July and December 2018. Multivariable logistic regression was used to assess the independent contribution of program, patient, and visit characteristics on the likelihood of accepting an offer. We then assessed the appointment outcome (completion, cancellation, or no-show) of Fast Pass offered appointments compared to appointments with the same patient and visit characteristics, but without an offer.

Results

Of 177,311 Fast Pass offers sent, 14,717 (8.3%) were accepted. Overall, there was a 1.3 percentage point (38%) reduction in no-show rates among Fast Pass accepted appointments compared to other appointments with matching characteristics (P<.001). The offers were more likely to be accepted if they were sent in the evening (versus early morning), the first (versus repeated) offer for the same appointment, for a slot 1-31 days ahead (versus same-day), for later in a day (versus before 10am), for a primary care (versus specialty) visit, sent via SMS text messaging (versus email only), for an appointment made through the online patient portal (versus via phone call or in-person), or for younger adults aged 18-49 years (versus those aged 65 years or older; all at P<.001). Factors negatively associated with offer acceptance were a higher number of comorbidities (P=.02) and visits scheduled for chronic conditions (versus acute conditions only; P=.002).

Conclusions

An automated rescheduling system can improve patients’ access by reducing wait times for an appointment, with an added benefit of reducing no-shows by serving as a reminder of an upcoming appointment. Future modifications, such as increasing the adoption of SMS text messaging offers and targeting older adults or patients with complex conditions, may make the system more patient-centered and help promote wider utilization.

Data Analytics and Modeling for Appointment No-show in Community Health Centers

Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.

Factors of missed appointments at an academic medical center in Taiwan

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

The factors of missed appointments were multifaceted. Further measures could be undertaken accordingly to enhance healthcare efficiency.

Diagnoses and other predictors of patient absenteeism in an outpatient neurology clinic

Background

We sought to determine the neurologic diagnosis or diagnostic categories that are associated with a higher probability of honoring a scheduled follow-up visit in the outpatient clinic.

Methods

We conducted a retrospective analysis of patients evaluated over a 3-year period (July 2014-June 2017) at a single neurology clinic in an urban location. Adult patients who honored an initial scheduled outpatient appointment were included. Only diagnoses with a ≥0.5% prevalence at our center were analyzed. Mixed-effects logistic regression was used to determine association of independent variables and honored follow-up visits.

Results

Of 61,232 scheduled outpatient subsequent encounters for 20,729 unique patients, the overall absenteeism rate was 12.5% (95% confidence interval [CI] 12.2%-12.8%). Independent risk factors associated with absenteeism included younger age, black or Latino race/ethnicity, Medicaid/Medicare payor status, and longer delay from appointment scheduling to appointment date. In mixed-effects logistic regression, diagnoses associated with the lowest odds of showing were medication overuse headache (show rate 79.2%, odds ratio [OR] for honoring appointment 0.67, 95% CI 0.48-0.93) and depression (rate 85.9%, OR 0.82, 95% CI 0.70-0.97), whereas the diagnoses associated with the greatest odds of showing included Charcot-Marie-Tooth disease (rate 96.3%, OR 2.54, 95% CI 1.44-4.49) and aphasia (rate 95.9%, OR 2.34, 95% CI 1.28-4.30).

Conclusions

Certain chronic neurologic diseases, such as medication overuse headache and depression, were associated with a significantly lower odds of honoring scheduled follow-up conditions. As these conditions influence quality of life and productivity, patients with these illnesses may benefit from selective targeting to encourage adherence with scheduled follow-up appointments.

Why Do Patients Miss Dental Appointments in Eastern Province Military Hospitals, Kingdom of Saudi Arabia?

No-shows for scheduled appointments are a frequent occurrence, creating unused appointment slots and reducing patient quality of care and access to services while increasing loss to follow-up and medical costs. The aim of our study was to determine the factors that lead to patients missing their dental appointments in Eastern Province Military Hospitals, Kingdom of Saudi Arabia. The study population included military personnel and their families attending the dental clinics of these hospitals. In our study, the percentage of missed appointments was 58.1%, while 54.4% of participants canceled dental appointments in the past. Thirty-six percent preferred morning appointments while 56% preferred an afternoon appointment and were likely to miss a morning appointment if given one. The most common reasons for missing an appointment were forgetting about it (24.3%) and the inability to get time off either from work or school (15.4%); 1.5% of patients stated they had a bad dental experience and feared dental treatment while the unavailability of transport accounted for 0.7% of patients. Of the reasons given for canceling an appointment, the inability to get time off from work/school was the most common (22.1%) while a dislike for treatment was the least common (0.7%). Canceling an appointment was significantly correlated with missing an appointment among the surveyed sample (P=0.00). In our research, 60.3% of participants still relied on their personal diary to remember appointments, which could be a reason for the high rate of missed appointments. Fifty-nine percent of respondents felt that missing an appointment was important to them, while 72% stated that missed appointments could affect the work of the clinic but still believed that automatic appointments should be given to patients who missed them and a change be made accordingly. Since major factors included a lack of a reminder message and appointments scheduled at inconvenient timings, some steps that can help reduce the frequency of missed appointments include sending a reminder message to patients, giving preference to their schedules for appointments, giving patients shorter appointments, reducing intervals between subsequent appointments, and educating patients regarding the treatment plan, to reduce anxiety.

Schedule-based metrics for the evaluation of clinic performance and potential recovery of cancelled appointments

BACKGROUND

Assessment of outpatient clinic performance is important to optimize patient access. Metrics based on schedule data may assist with assessment of operational efficiency and recovering cancelled appointments.

OBJECTIVES

To define schedule-based characteristics of clinic operations and to evaluate potential for recovery of cancelled appointments.

METHODS

Sixty-seven weekly cardiology clinics from a single provider over 18 months at an academic medical center were analyzed. Parameters included clinic slots eligible to have patients scheduled (available), slots occupied by appointments (scheduled), and slots for which patients attended the associated visit (appeared). Rates of usage (scheduled/available), appearance (appeared/scheduled), and utilization (appeared/available=usage rate*appearance rate) were calculated. Surplus slots were defined as the difference between available slots and slots occupied by patients that appeared. Cancellation lag-time was defined as the interval between a cancellation and the appointment time. If a patient did not notify the clinic regarding a non-appearance, cancellation lag-time was set to zero. To quantify the impact of a change in clinic operations on efficiency, these metrics were used to evaluate a different cardiologist's clinic before and after its physical location moved.

RESULTS

For approximately 900 patient visits, usage and appearance rates were∼80%, yielding a utilization rate of ∼2/3. On average, there were nearly 8 surplus slots per clinic. Approximately 30% of cancellation lag-times had positive values and nearly half of positive cancellation lag-times were >3h, indicating potential for recovery of those appointments. The intervention analysis showed that usage rate decreased and surplus slots per clinic increased significantly after a change in clinic location.

CONCLUSIONS

Schedule-based analysis provides a framework to assess changes to clinic operations, identify mechanisms underlying inefficiency, and suggest solutions for improving clinic performance (i.e. more schedulers in response to low usage rates). Cancellation lag-time analysis suggests recovering a portion of same-day cancellations is plausible.

Cancelled Primary Care Appointments: A Prospective Cohort Study of Diabetic Patients

Patients scheduled for primary care appointments often cancel or no show. For diabetic patients, nonattendance can affect continuity of care and result in higher emergency department (ED) and hospital use. Nonattendance also impacts appointment scheduling, patient access, and clinic work load. While no show has received significant attention, little research has addressed the prevalence and impact of appointment cancellation. Data on 46,710 appointments for 7586 adult diabetic patients was used to conduct a prospective cohort study examining primary care appointment behavior. The independent variable was the status of the INDEX appointment, which was attended, cancelled, or no showed. Dependent variables included the dates of (1) the last attended appointment, (2) scheduling the NEXT appointment, (3) the next attended follow-up appointment, and (4) ED visits and hospitalizations within six months of the INDEX. Cancellation was more prevalent than no show (17.7% vs 12.2%). Of those who cancelled and scheduled a next appointment, 28.8% experienced over 30 days delay between the INDEX and NEXT appointment dates, and 59.9% delayed rescheduling until on or after the cancelled appointment date. Delay in rescheduling was associated with an 18.6% increase in days between attended appointments and a 26.0% increase in ED visits. For diabetic patients, cancellation with late rescheduling is a prevalent and unhealthy behavior. Although more work is necessary to address the health, intervention, and cost issues, this work suggests that cancellation, like no show, may be problematic for many clinics and patients.