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

Rosenbaum, Joshua; Mieloszyk, Rebecca J.; Hall, Christopher S.; Hippe, Daniel S.; Gunn, Martin L.; Bhargava, Puneet

doi:10.1016/j.jacr.2018.03.053

Cited by 44 publications

(32 citation statements)

References 22 publications

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“…Besides, no-show of first-time visiting patients could be due to the resolution of symptoms or transportation barriers. 12 Additional causes include the wrong beliefs of the patient about the disease or the test, 13 long waiting time between the actual and scheduled appointment, insurance coverage, language and transportation difficulties. 14 Studies also suggested that reasons can differ depending on the type of healthcare settings and nature of medical tests.…”

Section: Literature Review No-show Rate and Underlying Reasonsmentioning

confidence: 99%

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<p>Evaluating the Impact of Patient No-Shows on Service Quality</p>

Marbouh¹,

Khaleel²,

Shanqiti³

et al. 2020

RMHP

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Patient no-shows are long-standing issues affecting resource utilization and posing risks to the quality of healthcare services. They also lead to loss of anticipated revenue, particularly in services where resources are expensive and in great demand. Methods: In order to address common reasons why patients miss appointments, this study reviews the current literature and investigates various tools and methods that have been implemented to mitigate such issues. Further, a case study is conducted to identify the rate of no-shows and underlying causes at a radiology department in one of the leading hospitals in the MENA region. Results: Our results show that the no-shows are high due to multiple factors, such as patient behavior, patients' financial situation, environmental factors and scheduling policy. Conclusion: In conclusion, we generate a list of recommendations that can help in reducing the rate of patient no-shows, such as patient education, application of dynamic scheduling policies and effective appointment reminder systems to patients.

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Section: Literature Review No-show Rate and Underlying Reasonsmentioning

confidence: 99%

“…For instance, it was found that the high no-show rate in the mammography department is affected due to the anxiety and discomfort of patients. 13 In fact, the examinations for less serious conditions had high rates of no-shows since patients believe that postponing tests would be harmless.…”

Section: Literature Review No-show Rate and Underlying Reasonsmentioning

confidence: 99%

<p>Evaluating the Impact of Patient No-Shows on Service Quality</p>

Marbouh¹,

Khaleel²,

Shanqiti³

et al. 2020

RMHP

View full text Add to dashboard Cite

show abstract

“…This work contributes to the existing literature on no-show modeling in Radiology [3,9,15,21,22,24,[30][31][32][33] in several ways. First, by corroborating ndings on no-show predictors in previous works and compiling such results (see Additional le 4: Table S3).…”

Section: Discussionmentioning

confidence: 94%

Modeling the No-Show of Patients to Exam Appointments of Computed Tomography

Silva

Fogliatto

Garcia

et al. 2020

Preprint

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Background: No-shows of patients have negative impacts on healthcare systems, such as resources’ underutilization, efficiency loss, and cost increase. Predicting no-show is key to develop strategies that counteract its effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital.Methods: We carried out a retrospective study on 8,382 appointments made to computed tomography (CT) exams between January and December 2017. Penalized logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients’ no-show. The predictive capabilities of the models were evaluated analyzing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC).Results: The no-show rate in computerized tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalized logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analyzed appearing as significant. One of the variables included in the model (number of exams scheduled in previous year) had not been previously reported in the related literature.Conclusions: Our findings may be used to guide the development of strategies to reduce the no-show of patients to exam appointments.

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“…According to Rosenbaum et. al, [17] modality, younger patient age, appointment time, day of week, and scheduling lead time are independent variables associated with no-show visits in Radiology. Delays in diagnosis and treatment can lead to increased morbidity and mortality [18].…”

Section: Discussionmentioning

confidence: 97%

Managing Patient Flows in Radiation Oncology - Reworking Existing Treatment Designs to Prevent Infections During the COVID-19 Pandemic at a German University Hospital

Akuamoa-Boateng

Wegen

Ferdinandus

et al. 2020

Preprint

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Purpose Implementing active Patient Flow Management practices in a large University Hospital Radiation Oncology Department to prevent nosocomial infections of patients and personnel during the COVID-19 pandemic, while safely enable continuation of essential therapies and optimizing infrastructure utilization.Patients and methods Year-to-date intervention analyses of calender weeks 12 to 19 comparing years 2019 and 2020 inpatient and outpatient procedures evaluating active patient flow management effects on overall treatment and facility capacities while analysing COVID-19 nosocomial infections.Results Implementing changes in active patient flow management first visit overall appointment compliance maintained above 85.5%. Appointment reduction of publicly insured patients by 10.3% daily (p=0.004) significantly increased downstream planning CT appointment scheduling (p=0.00001) and performing (p=0.0001) resulting in absolute 20.1% (p=0.009) increment of CT performance. Daily treatment inception was significantly increased by absolute 18.5% (p=0.026). Hypofractionation and acelleration was significantly increased (p=0.0043). Integrating strict testing guidelines, distancing regimen for staff and patients, strict hygiene regulations and precise appointment scheduling, no SARS-CoViD-2 infection in 164 tested Radiation Oncology Service inpatients was observed.Conclusion In times, where medical infrastructure capacities are shortened and resources reduced, controlling infrastructural time per patient, optimizing facility utilization, personnel workload and machine occupancy rates during therapy evaluation, planning and irradiation in the inpatient and outpatient setting can help increasing appointment compliance and securing time for quality management. Avoiding recurrent and preventable exposure to health care institutions has potential health benefits and might avert nosocomial infections during a pandemic such as the current COVID-19 pandemic enabling Radiation Oncologists continue essential treatments safely.

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

Cited by 44 publications

References 22 publications

<p>Evaluating the Impact of Patient No-Shows on Service Quality</p>

<p>Evaluating the Impact of Patient No-Shows on Service Quality</p>

Modeling the No-Show of Patients to Exam Appointments of Computed Tomography

Managing Patient Flows in Radiation Oncology - Reworking Existing Treatment Designs to Prevent Infections During the COVID-19 Pandemic at a German University Hospital

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