Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

Huang, Yu-Li; Hanauer, David A.

doi:10.4338/aci-2014-04-ra-0026

Cited by 114 publications

(55 citation statements)

References 52 publications

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“…Medium-scale studies (ranging from 6,000 to 8,000 patients) focused on a few patient characteristics or a single (eg, time) component. 11 - 13 For example, a large-scale no-show modeling of a Veterans Affairs (VA) outpatient clinic included 555,183 patients, which scheduled 25,050,479 appointments; however, the study only considered a few variables such as the patient gender, the date of the appointment, and new versus established patients. 14 Most studies developed regression models to predict appointment nonadherence.…”

Section: Introductionmentioning

confidence: 99%

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

Mohammadi

Türkcan

et al. 2018

J Prim Care Community Health

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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.

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Section: Introductionmentioning

confidence: 99%

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

Mohammadi

Türkcan

et al. 2018

J Prim Care Community Health

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“…Incomplete study rates vary from an average of 2.3% (2) to as high as 25–39% in selected anxious populations, even when larger bore claustrophobia-friendly open scanners are used (3). No-shows and incompletions result in ineffective utilization and financial losses (4, 5). Word of mouth from dissatisfied patients may also potentially dissuade friends and family members as future patients (6, 7).…”

Section: Introductionmentioning

confidence: 99%

Effect of team training on improving MRI study completion rates and no‐show rates

Norbash

Yucel

Yuh

et al. 2016

Magnetic Resonance Imaging

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Purpose MR is a high-cost imaging modality, and an optimized encounter ideally provides high quality care, patient satisfaction and capacity utilization. Our purpose was to assess the effectiveness of team training and its impact on patient show-up and completion rates for their MRI examinations. Methods A total of 97,712 patient visits from 3 tertiary academic medical centers over one-year intervals were evaluated, totaling 49,733 visits at baseline and 47,979 after training. Each centers’ MRI team received team training skill training including advanced communication and team training techniques training. This training included onsite instruction including case simulation with scenarios requiring appropriate behavioral and communicative interventions. Orientation and training also utilized customized online tools, and proctoring. The study completion rate and patient show-up rate during consecutive year-long intervals before and after team training were compared to assess its effectiveness. Two-sided Chi-Square tests for proportions using were applied at a 0.05 significance level. Results Despite differing no-show rates (5–22.2%) and study incompletions rates (0.7–3.7%) at the three academic centers, the combined patients’ data showed significant (p<0.0001) improvement in the patients’ no-show rates (combined decreases from 11.2% to 8.7%) and incompletion rates (combined decreases from 2.3 to 1.4%). Conclusion Our preliminary results suggest training of imaging team can improve the no-show and incompletion rates of the MRI service, positively affecting throughput and utilization. Team training can be readily implemented and may help address the needs of the current cost-conscious and consumer sensitive healthcare environment.

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“…To ascertain uninterruptible treatment procedures and control for contant workstations, by implementing novel work ows to continue essential treatment planning and delivery in the outpatient and inpatient setting. Underutilizition of medical resources hold negative impacts by increasing healthcare costs, decreasing access to care, and reducing e ciency and productivity of care providers [7]. As the most common reasons for missing medical appointments are forgetting (35.5.%) and miscommunication (31.5%) [35], it is recommended to and proactively schedule patients to diminish negative impacts of patient no-shows [8].…”

Section: Discussionmentioning

confidence: 99%

“…As the most common reasons for missing medical appointments are forgetting (35.5.%) and miscommunication (31.5%) [35], it is recommended to and proactively schedule patients to diminish negative impacts of patient no-shows [8]. While predictive models propose overbooking approaches to signi cantly reduce patient waiting by at least 6%, 27% on overtime, and 3% on total costs compared to at-overbooking methods [7], our Department early focused on controlling appointment compliance while avoiding over-booking. Analysing calender weeks 12-19 of years 2019 and 2020 year-to-date, we found that while reducing the overall number of patients presenting to the Department of Radiation Oncology by 10.3%, the resulting daily number of patients starting radiation therapy was increased by 18.5% (7.5 vs. 8.8; SD 2.93 vs. 0.63) p=0.026 year to date.…”

Section: Discussionmentioning

confidence: 99%

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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Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

Cited by 114 publications

References 52 publications

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

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

Effect of team training on improving MRI study completion rates and no‐show rates

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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