Forecasting the demand for radiology services

Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics

The COVID-19 pandemic resulted in an unprecedented burden on intensive care units (ICUs). With increased demands and limited supply, critical care resources, including dialysis machines, became scarce, leading to the undertaking of value-based cost-effectiveness analyses and the rationing of resources to deliver patient care of the highest quality. A high proportion of COVID-19 patients admitted to the ICU required dialysis, resulting in a major burden on resources such as dialysis machines, nursing staff, technicians, and consumables such as dialysis filters and solutions and anticoagulation medications. Artificial intelligence (AI)-based big data analytics are now being utilized in multiple data-driven healthcare services, including the optimization of healthcare system utilization. Numerous factors can impact dialysis resource allocation to critically ill patients, especially during public health emergencies, but currently, resource allocation is determined using a small number of traditional factors. Smart analytics that take into account all the relevant healthcare information in the hospital system and patient outcomes can lead to improved resource allocation, cost-effectiveness, and quality of care. In this review, we discuss dialysis resource utilization in critical care, the impact of the COVID-19 pandemic, and how AI can improve resource utilization in future public health emergencies. Research in this area should be an important priority.

The role of teleradiology during COVID-19 outbreak: Saudi radiologists' perspectives

OBJECTIVES

To evaluate the role of teleradiology during the COVID-19 pandemic from Saudi radiologists' perspectives to improve the radiology quality service.

METHODS

A cross-sectional study was carried out in Saudi Arabia among radiologists working at local hospitals from October to November 2021. It contains 21 questions involved demographic information; general information on teleradiology services; and the impact of teleradiology during COVID-19. One-way ANOVA was used to compare demographic groups. Chi-square test was used to compare demographic groups regarding their distribution of responses. All tests were carried out <0.05 level of significance.

RESULTS

A total of 102 radiologists participated in this study (56% males, 44% females), 58.8% of them were sub-specialized in chest radiology. Regarding the general status of teleradiology, 69.6% of participants believed that teleradiology is a helpful tool for imaging interpretation. However, 44% of them were uncertain on the impact of teleradiology on patients' confidentiality. Approximately 87% of participants agreed that there is a positive contribution of teleradiology during COVID-19, which enables decreasing risk of infection and workload. There was a significant difference between professional degrees and overall participant responses (p<0.05). Academicians agreed that it enhances radiology departments' work (mean=17.78, SD=1.86).

CONCLUSION

Concerns raised on complicated cases that require physical presence of patients, cannot be performed by teleradiology. Additionally, it might provide insufficient communication with other professionals to discuss images.

Smart Health

In recent decades, healthcare organizations around the world have increasingly appreciated the value of information technologies for a variety of applications. Three of the new technological advancements that are impacting smart health are metaverse, artificial intelligence (AI), and data science. The metaverse is the intersection of three major technologies — AI, augmented reality (AR), and virtual reality (VR). Metaverse provides new possibilities and potential that are still emerging. The increased work efficiency enabled by artificial intelligence and data science in hospitals not only improves patient care but also cuts costs and workload for healthcare providers. Artificial intelligence, coupled with machine learning, is transforming the healthcare industry. The availability of big data enables data scientists to use the data for descriptive, predictive, and prescriptive analytics. This article reviews multiple case studies and the literature on AI and data science applications in hospital administration. The article also presents unresolved research questions and challenges in the applications of the metaverse, AI, and data science in the smart health context. For researchers, in addition to providing a good synopsis of the development and applications of the metaverse, AI, and data science in the healthcare area, this article identifies possible future research directions and discusses the possibilities of the metaverse, artificial intelligence, and data science in smart health. For practitioners, this article provides both hospital decision-makers and healthcare workers with practical guidelines and a smart health management model.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01040-3.

Google Trends Data of Radiologists Who Accept Medicare: A Potential Tool for Predicting State Demand

PURPOSE

To identify and analyze the demand for radiologists who accept Medicare per state from 2004 to 2009, as reflected by volume of Google searches, and to place such demand in context with other available data by state.

METHODS

The number of radiologists who accept Medicare by state was divided by each state's population to achieve the radiologist density per 10,000 residents. Relative search volume (RSV) for the term "radiologist" was collected from Google Trends from 2004 to 2009. The Radiologist Demand Index (RDI) for each state was then calculated by dividing each state's RSV by the radiologist density for that state. To standardize values, each state's RDI was divided by the largest RDI to generate the Relative Radiologist Demand Index (RRDI). Utilization of medical imaging per 1000 Medicare beneficiaries in each state, overall health of a population in each state, and percentage of the population enrolled in Medicare in each state were used to compare trends with the RRDI.

RESULTS

West Virginia had the greatest curiosity about radiologists who accept Medicare (as represented by proportion of Google searches) (RSV=100), followed by Mississippi (RSV=95), and Arkansas (RSV=87). Oregon demonstrated the lowest level of curiosity about radiologists who accept Medicare, by having the lowest proportion of google searches (RSV=43), followed by Vermont (RSV=49), California (RSV=50), and Colorado (RSV=50). The highest radiologist densities per population were found in Montana, D.C., and Wyoming (3.25, 1.56, 1.11, respectively). The lowest radiologist densities were found in Oklahoma, Texas, and Utah (0.4, 0.4, 0.41, 0.41, respectively). The RRDI was greatest in Louisiana (100), Arkansas (94.8), and Texas (86.3), and smallest in Montana (10.6), D.C. (17.7) and Wyoming (28.4). Positive trends between utilization of medical imaging per 1000 Medicare beneficiaries and state overall health and the RRDI were recognized. No trend between each state's RRDI and percentage of population enrolled in Medicare was noted.

CONCLUSION

Imaging studies performed, an indirect measure of demand, showed trends with RRDI. Higher RRDI and imaging per 1000 Medicare beneficiaries trended with lower health scores for a state's general population. RRDI may be a useful tool reflecting each state's demand for radiologist who accepts Medicare.

A hybrid analytical model for an entire hospital resource optimisation

Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.

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

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 shelter-in-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.

Electronic supplementary material

The online version of this article (10.1007/s10140-020-01797-y) contains supplementary material, which is available to authorized users.

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

Background

Hospital-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%.

Objective

This 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.

Methods

Hospital 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.

Results

Widespread, 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.

Conclusions

Practice 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.