Forecasting the demand for radiology services

Côté, Murray J.; Smith, Marlene A.

doi:10.1080/20476965.2017.1390056

Cited by 12 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accurately forecasting the demand for healthcare resources is a complex process involving many variables. These forecasts can be utilized in staffing, space, and equipment models for resource utilization across a healthcare system [13]. Our data shows a consistent decrease in ED radiologic studies across multiple academic tertiary institutions following statewide government-mandated self-isolation protocol and consistent decreases in trauma imaging volumes, suggesting decreased demands on the emergency healthcare system.…”

Section: Discussionsupporting

confidence: 52%

Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic

et al. 2020

View full text Add to dashboard Cite

Purpose The coronavirus disease 2019 (COVID-19) pandemic has led to significant disruptions in the healthcare system including surges of infected patients exceeding local capacity, closures of primary care offices, and delays of non-emergent medical care. Government-initiated measures to decrease healthcare utilization (i.e., "flattening the curve") have included shelterin-place mandates and social distancing, which have taken effect across most of the USA. We evaluate the immediate impact of the Public Health Messaging and shelter-in-place mandates on Emergency Department (ED) demand for radiology services. Methods We analyzed ED radiology volumes from the five University of California health systems during a 2-week time period following the shelter-in-place mandate and compared those volumes with March 2019 and early April 2019 volumes. Results ED radiology volumes declined from the 2019 baseline by 32 to 40% (p < 0.001) across the five health systems with a total decrease in volumes across all 5 systems by 35% (p < 0.001). Stratifying by subspecialty, the smallest declines were seen in non-trauma thoracic imaging, which decreased 18% (p value < 0.001), while all other non-trauma studies decreased by 48% (p < 0.001). Conclusion Total ED radiology demand may be a marker for public adherence to shelter-in-place mandates, though ED chest radiology demand may increase with an increase in COVID-19 cases.

show abstract

Section: Discussionsupporting

confidence: 52%

Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Unfortunately, this approach has been undermined by several factors: heterogeneous practices, lack of compatibility of information and technology tools, time, or free exchange platforms for medical specialists. The rare time series analyses involving imaging mostly depicted trends in general or emergency radiological activity and sometimes developed forecasting models [18][19][20]. Conversely, analysis of the radiological activity during COVID-19 pandemic has generally consisted of examining changes compared to usual general or emergency activity, but the predictive value of these changes in terms of public health has been poorly investigated, with cohorts of fewer than 5000 patients [21][22][23].…”

Section: Discussionmentioning

confidence: 99%

“…The terms in bold correspond to the regression part of the model, and the other terms to the error η(t) which can be expressed with an auto-regressive integrated moving average (ARIMA) model with ε(t) an uncorrelated error term (i.e. white noise) following a normal law N with variance in parentheses CT(x), where x in {t, t − 1, t − 2}, corresponds to the number of CT-scans performed in the COVID- 19 The centralised organisation of our teleradiology structure has favoured the implementation of early standardised COVID-19 practices. This COVID-19 workflow label has enabled our support teams to constantly monitor teleradiology activity since the week of 2020-03-09 (i.e.…”

Section: Table 4 Final Predictive Modelsmentioning

confidence: 99%

Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France

et al. 2021

View full text Add to dashboard Cite

Background COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution in France. We incorporated two sources of data from March to November 2020: an open-source epidemiological dataset, collecting daily hospitalisations, intensive care unit admissions, hospital deaths and discharges, and a teleradiology dataset corresponding to the weekly number of CT-scans performed in 65 emergency centres and interpreted remotely. CT-scans specifically requested for COVID-19 suspicion were monitored. Teleradiological and epidemiological time series were aligned. Their relationships were estimated through a cross-correlation function, and their extremes and breakpoints were compared. Dynamic linear models were trained to forecast the weekly hospitalisations based on teleradiological activity predictors. Results A total of 100,018 CT-scans were included over 36 weeks, and 19,133 (19%) performed within the COVID-19 workflow. Concomitantly, 227,677 hospitalisations were reported. Teleradiological and epidemiological time series were almost perfectly superimposed (cross-correlation coefficients at lag 0: 0.90–0.92). Maximal number of COVID-19 CT-scans was reached the week of 2020-03-23 (1 086 CT-scans), 1 week before the highest hospitalisations (23,542 patients). The best valid forecasting model combined the number of COVID-19 CT-scans and the number of hospitalisations during the prior two weeks and provided the lowest mean absolute percentage (5.09%, testing period: 2020-11-02 to 2020-11-29). Conclusion Monitoring COVID-19 CT-scan activity in emergencies accurately and instantly predicts hospitalisations and helps adjust medical resources, paving the way for complementary public health indicators.

show abstract

“…Even so, generating nonseasonal forecasting models that have predictive capability on a blind withhold set at the proper level of aggregation can provide decision support for supply- and demand-side interventions. These types of models have found support in many areas of health care such as radiology [14] and Alzheimer disease [15].…”

Section: Methodsmentioning

confidence: 99%

Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models

Fulton

Kruse

2019

J Med Internet Res

View full text Add to dashboard Cite

BackgroundHospital-based back surgery in the United States increased by 60% from January 2012 to December 2017, yet the supply of neurosurgeons remained relatively constant. During this time, adult obesity grew by 5%.ObjectiveThis study aimed to evaluate the demand and associated costs for hospital-based back surgery by geolocation over time to evaluate provider practice variation. The study then leveraged hierarchical time series to generate tight demand forecasts on an unobserved test set. Finally, explanatory financial, technical, workload, geographical, and temporal factors as well as state-level obesity rates were investigated as predictors for the demand for hospital-based back surgery.MethodsHospital data from January 2012 to December 2017 were used to generate geospatial-temporal maps and a video of the Current Procedural Terminology codes beginning with the digit 63 claims. Hierarchical time series modeling provided forecasts for each state, the census regions, and the nation for an unobserved test set and then again for the out-years of 2018 and 2019. Stepwise regression, lasso regression, ridge regression, elastic net, and gradient-boosted random forests were built on a training set and evaluated on a test set to evaluate variables important to explaining the demand for hospital-based back surgery.ResultsWidespread, unexplained practice variation over time was seen using geographical information systems (GIS) multimedia mapping. Hierarchical time series provided accurate forecasts on a blind dataset and suggested a 6.52% (from 497,325 procedures in 2017 to 529,777 in 2018) growth of hospital-based back surgery in 2018 (529,777 and up to 13.00% by 2019 [from 497,325 procedures in 2017 to 563,023 procedures in 2019]). The increase in payments by 2019 are estimated to be US $323.9 million. Extreme gradient-boosted random forests beat constrained and unconstrained regression models on a 20% unobserved test set and suggested that obesity is one of the most important factors in explaining the increase in demand for hospital-based back surgery.ConclusionsPractice variation and obesity are factors to consider when estimating demand for hospital-based back surgery. Federal, state, and local planners should evaluate demand-side and supply-side interventions for this emerging problem.

show abstract

Forecasting the demand for radiology services

Cited by 12 publications

References 30 publications

Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic

Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic

Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France

Hospital-Based Back Surgery: Geospatial-Temporal, Explanatory, and Predictive Models

Contact Info

Product

Resources

About