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Rates of COVID-19 in the population are driving government decisions regarding social distancing and patient behavior. More accurate predictions of numbers of cases will allow for more optimal staffing adjustments for both volume increases and decreases.
Staffing decision makers at a local imaging facility receive data from this algorithm that forecasts the volume of imaging studies by CPT code. This informs their decisions for setting the schedule.
For example, a second wave of COVID-19 impacts a community served by a radiology group whose volume changed significantly during the first wave that necessitated changes in staffing levels. The algorithm maps the trajectory of infection relative to the first wave and predicts trends in imaging demand as the wave progresses and evolves. Staffing decision makers are then able to adjust policy and scheduling to accommodate for appropriate staffing levels for the second wave.
Based on predicted volumes by modality and CPT codes, staffing suggestions can be accommodated to the subspecialist level. If the algorithm predicts a surge in neuroimaging by 50% but a decrease in screening breast examinations by 70%, the facility is able to adjust their specialist staffing and potential for redeployment to accommodate for these changes.
Algorithm processes input data including prior and current local number of COVID-19 cases, expected number of cases by whichever model is deemed appropriate, and the pre-pandemic imaging volume. Inputs such as local policy, current non-imaging patient volumes, and population behavior can also optionally be included to further refine the predictions. This information is synthesized to forecast volume across different imaging modalities and is sent to policy and staffing decision makers to make appropriate adjustments.
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Data Element |
Data Type |
Description |
Notes |
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COVID-19 Cases/Deaths by geographic region |
Tabular |
Number of reported cases each day by county or city |
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Expected COVID-19 Cases/Deaths by geographic region |
Tabular |
Expected number of cases each day by county or city by appropriate model |
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Facility volume by CPT |
Tabular |
Pre-pandemic imaging activity for the facility by CPT code. |
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First wave COVID+ cases |
Tabular |
Volume of COVID+ cases during the first wave. Patient recovery/outcome. |
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Public Transportation Data (optional) |
Tabular |
Report on public transit - whether it's operational and any changes in frequency or access |
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Local business policy data (optional) |
Tabular |
Report on the businesses that are open to the public and the trends in traffic across different types of businesses |
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Community mobility reports (optional) |
Tabular |
Report on movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential |
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Local mask requirement (optional) |
Categorical |
Are masks required for public activity in the local area? |
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Population health data (optional) |
Tabular |
health metrics for the local population |
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Similar facility data (optional) |
Tabular |
imaging demand data for similar facilities |
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Local hospitalization/ ICU numbers |
Tabular |
number of local hospitalization/ ICU usage with respiratory symptoms |
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Referral data (optional) |
Tabular |
Number of office visits, operations, and inpatient census of relevant services: e.g. pulmonologist and cardiovascular office visit numbers for chest/cardiac division, orthopedic office visit numbers and orthopedic surgery cases for musculoskeletal division. |
Predicted Imaging Volume by CPT Code
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Definition |
The predicted imaging volume grouped by CPT code for a given period of time |
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Data Type |
Tabular |
|
Value Set |
N/A |
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Units |
Predicted studies/ CPT code for a selected date |
Predicted Imaging Volume by Modality
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Definition |
The predicted imaging volume grouped by modality (e.g., radiography, fluoroscopy, CT, MRI, ultrasound, nuclear medicine, interventional) for a given period of time |
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Data Type |
Tabular |
|
Value Set |
N/A |
|
Units |
Predicted studies/ modality for a selected date |
Predicted Number of COVID-19 Patients
|
Definition |
The predicted number of COVID-19 patients for a given period of time |
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Data Type |
Tabular |
|
Value Set |
N/A |
|
Units |
Number of Patients |
Predicted Number of COVID-19 Patients by Modality
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Definition |
The predicted number of COVID-19 patients grouped by modality (e.g., radiography, fluoroscopy, CT, MRI, ultrasound, nuclear medicine, interventional) for a given period of time |
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Data Type |
Tabular |
|
Value Set |
N/A |
|
Units |
Predicted number of patients/modality for a selected date |