Personal Phone Calls Lead to Decreased Rates of Missed Appointments in an Adolescent/Young Adult Practice

Penzias, Rebecca E.; Sanabia, Virginia; Shreeve, Kyra M.; Bhaumik, Urmi; Lenz, Caitlin; Woods, Elizabeth R.; Forman, Sara F.

doi:10.1097/pq9.0000000000000192

Cited by 4 publications

(6 citation statements)

References 20 publications

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“…A review of 26 articles from diverse backgrounds found that patients who received a text notification were 23% less likely to miss their appointment than those who did not [ 42 ]. Similar results were obtained for personal phone calls in adolescents [ 39 ]. Text messages have been observed to produce similar outcomes to telephone calls, at a lower cost, in both adults [ 10 , 18 ] and pediatric patients [ 29 ].…”

Section: Introductionsupporting

confidence: 86%

“…Lacy et al [ 26 ] identified three additional issues: emotional barriers (negative emotions about going to see the doctor were greater than the sensed benefit), perceived disrespect by the health care system, and lack of understanding of the scheduling system. In pediatric appointments, other reasons include caregiver’s issues, scheduling conflicts, forgetting, transportation, public health insurance, and financial constraints [ 11 , 19 , 23 , 39 , 44 , 49 ].…”

Section: Introductionmentioning

confidence: 99%

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Predicting no-show appointments in a pediatric hospital in Chile using machine learning

Dunstan

Villena

Sanchez

et al. 2023

Health Care Manag Sci

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The Chilean public health system serves 74% of the country’s population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I and II errors based on cost-effectiveness criteria. 20.4% of the 395,963 appointments considered presented no-shows, with ophthalmology showing the highest rate among specialties at 29.1%. Patients in the most deprived socioeconomic group according to their insurance type and commune of residence and those in their second infancy had the highest no-show rate. The history of non-attendance is strongly related to future no-shows. An 8-week experimental design measured a decrease in no-shows of 10.3 percentage points when using our reminder strategy compared to a control group. Among the variables analyzed, those related to patients’ historical behavior, the reservation delay from the creation of the appointment, and variables that can be associated with the most disadvantaged socioeconomic group, are the most relevant to predict a no-show. Moreover, the introduction of new cost-effective metrics significantly impacts the validity of our prediction models. Using a prototype to call patients with the highest risk of no-shows resulted in a noticeable decrease in the overall no-show rate.

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Section: Introductionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Predicting no-show appointments in a pediatric hospital in Chile using machine learning

Dunstan

Villena

Sanchez

et al. 2023

Health Care Manag Sci

View full text Add to dashboard Cite

show abstract

“…These reminders are for record purposes to avoid unreasonable termination. This method is proven to significantly improve the appointment rate and reduce the number of missed appointments (Penzias et al, 2019). With proper appointment policies, reminders and patient's motivation, a missed appointment can be effectively controlled.…”

Section: Discussionmentioning

confidence: 99%

“…The prevalence of missed dental appointments varied across studies between 9% to 40% (Awartani, 2003;Holtzman et al, 2013;Albaloushi and Da'ar, 2015;Storrs et al, 2016;Tandon et al, 2016). The most common excuse for missed dental appointments among patients was forgetfulness, busy with other matters and not brought by a parent for regular dental care (Tandon et al, 2016;Onyejaka et al, 2018;Kirby and Harris, 2019;Penzias et al, 2019). Factors that contributed to the missed dental appointments include age, gender and occupation status (Storrs et al, 2016;West et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Prevalence and reasons for missed dental appointments among patients treated by undergraduate dental students of Universiti Sains Islam Malaysia

Khairuddeen

Nazari

Al-Kadhim

et al. 2022

IJOHS

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Missed dental appointments drastically affect treatment proficiency and productivity of dental care facility. This study aimed to determine the prevalence and reasons for missed dental appointments among patients treated by clinical dental students of Universiti Sains Islam Malaysia (USIM). A total of 202 folders from year 2018 to 2020 treated by year 3 to year 5 dental students were retrieved from a pool of patient’s record files using simple random sampling methods. Information on missed appointments was recorded. Other patients’ information was kept confidential. The percentage of missed dental appointments among patients under clinical dental students’ care was 37.1% (95% CI=30.7, 44.0). The majority of patients who missed dental appointments were male (37.6%), adults (38.7%) and employee (40.2%) patients. The most reported reason for the missed dental appointment was due to personal matters (36%) and the least was forgetfulness (0.6%) and miscommunication (0.6%). In conclusion, missed dental appointment commonly occurs among patients and frequently it was due to personal matters. A proper explanation by the students before scheduling an appointment is crucial to avoid any future missed dental appointments.

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“…Patient-related factors include forgetting the appointment, not receiving a reminder, emotional barriers, feeling disrespected by providers, and a lack of understanding of the scheduling system have emerged as common reasons cited by patients in previous research studies [24,30,20,37,28,1]. Additionally, transportation problems, work commitments, difficulties in taking time off from work, scheduling conflicts, issues related to health insurance, and financial concerns have been identified as significant factors influencing patients' ability to keep appointments [9,28,32]. Abu Lekham et al ( 2021) applied text mining to analyze the root causes of patient no-shows at an outpatient primary care medical organization serving rural areas in the Finger Lakes area of New York.…”

Section: Reasons For No-showsmentioning

confidence: 99%

Ensemble Learning for Addressing Class Imbalance in Cardiology Appointment Scheduling and Overbooking

Aghaeifar,

Servis,

Khasawneh

2023

Preprint

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Purpose Outpatient appointment scheduling is a critical aspect of healthcare services; however, the unpredictable nature of patient behavior poses challenges. This study focuses on predicting patient behavior in cardiology appointment scheduling in an outpatient cardiology practice within the Mount Sinai Health System (MSHS) to optimize overbooking strategies. Methods By reviewing the literature and conducting exploratory data analysis, significant features influencing patient behavior were identified. An ensemble learning model for an imbalanced class was developed to accurately predict the likelihood of no-show appointments and enable strategic overbooking decisions. Results The findings demonstrate that an increased lead time is associated with a greater probability of appointment no-shows. Patient-initiated rescheduling and cancellations were identified as the primary reasons for appointment changes. Moreover, while average wait time increases with age, younger individuals tend to have longer appointments. A stacking ensemble model for imbalanced classes and three machine-learning approaches were evaluated. The stacking ensemble model outperformed traditional techniques with an impressive F1 score of 92.3% and an AUC of 91%. Conclusion The proposed model enables allocating appointments based on patient preferences and characteristics, optimizing resource use, and reducing the number of cancellations and no-shows.

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Personal Phone Calls Lead to Decreased Rates of Missed Appointments in an Adolescent/Young Adult Practice

Abstract: Supplemental Digital Content is available in the text.

Cited by 4 publications

References 20 publications

Predicting no-show appointments in a pediatric hospital in Chile using machine learning

Predicting no-show appointments in a pediatric hospital in Chile using machine learning

Prevalence and reasons for missed dental appointments among patients treated by undergraduate dental students of Universiti Sains Islam Malaysia

Ensemble Learning for Addressing Class Imbalance in Cardiology Appointment Scheduling and Overbooking

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