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Purpose |
Predict the risk of COVID-19 infection in patients during the imaging scheduling process through the analysis of clinical symptoms and environmental and occupational risk factors. Staff would then be alerted of patients at high-risk for COVID-19 infection who are being scheduled for upcoming imaging. This risk information would then be used to ensure that high-risk patients are scheduled at specific times, at specific facilities, and on specific reserved equipment and that any staff handling these patients takes appropriate protective precautions. |
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Tag(s) |
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Panel |
Non-Interpretive, COVID-19 |
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Define-AI ID |
20160005 |
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Originator |
COVID-19 Sub-Panel |
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Panel Chair |
Woojin Kim, Ben Wandtke |
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Panel Reviewers |
Rob Ambrosini |
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License |
Creative Commons 4.0 |
| Status | Public Comment |
COVID-19 and other viral infections can result in significant illness and even death. Highly contagious infections, such as COVID-19, are transmitted mainly through droplets, but also by contact with infected surfaces and other modes of transmission. Effective prevention of viral spread includes measures like social distancing (keeping 6 ft/2 meters or more between persons), wearing personal protective equipment, frequent handwashing, and cleaning of surfaces.
Knowledge of patients at high-risk for COVID-19 infection during the imaging scheduling process can help reduce the exposure of other patients and limit the potential infection spread. Additionally, when high-risk patients present for imaging exams, receptionists, technologists, and other imaging staff can take necessary precautions to protect themselves and other patients from potential infection. Infection risk mitigation can be accomplished by scheduling high-risk patients for specific times, at specific facilities, and/or on specific equipment reserved for high-risk patients. The goal is to avoid crowding in patient waiting and service areas, and this has to take into account additional time needed for exam room turnover.
Clinical Scenario 1: An 80-year-old man presents for outpatient CT imaging to follow up previously treated lung cancer. The patient is a nursing home resident with recent development of a cough and sore throat. The patient has not been tested for COVID-19 infection.
Clinical Scenario 2: A 65-year-old woman presents for pre-surgical breast MRI prior to cancer resection. The patient flew back from a country with a level 3 travel advisory within the last 5 days.
Clinical Scenario 3: A 25-year-old woman presents for outpatient abdominal CT for abdominal pain. She works as a nurse in the COVID-19 unit at her hospital.
Clinical Scenario 4: A 45-year-old male presents to the emergency room after a fall from a ladder with loss of consciousness and worsening mental status change, needing a head CT. The patient also reports fever, cough, shortness of breath, and loss of smell over the past few days, raising suspicion for COVID-19 infection. The patient is now a person under investigation (PUI) for COVID-19.
Clinical scenario 5: A 45-year-old woman presents for a mammogram screening exam. She is extremely anxious because her friend was just diagnosed with breast cancer, and she experiences the same discomfort in her breasts that her friend did.
Clinica scenario 6: A 14-year-old boy presents with a 1st time febrile seizure. He is otherwise healthy and has no risk factors for COVID-19.
Clinical signs and symptoms and risk factor data are collected from manual patient screening (facilitated by telephone questionnaires completed by the scheduling team) and automated screening via search of the electronic medical record for a patient during the imaging scheduling process. This search should identify patients who have had or are scheduled for a COVID-19 test and the result of COVID-19 testing, if the test result is available. This data is processed by the AI model. The model determines if the patient is high-risk for being infected with COVID-19. A message is then sent to the clinical staff through the radiology information system regarding the patient’s high-risk status and the patient is scheduled accordingly. An annotation is made on the patient’s scheduled appointment to alert the reception staff, technologists, and other imaging staff of the patient’s high-risk status at the time of their presentation to the imaging center.
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Data Element |
Data Type |
Description |
Notes |
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Fever |
Categorical |
Patient presents with fever. (Y/N) |
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Body temperature |
Numerical |
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Chills |
Categorical |
Patient presents with chills. (Y/N) |
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Cough |
Categorical |
Patient presents with a cough. (Y/N) |
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Shortness of breath or difficulty breathing |
Categorical |
Patient presents with shortness of breath or difficulty breathing. (Y/N) |
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Respiratory rate |
Numerical |
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Fatigue |
Categorical |
Patient presents with fatigue. (Y/N) |
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Muscles or body aches |
Categorical |
Patient presents with body aches. (Y/N) |
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Headache |
Categorical |
Patient presents with a headache. (Y/N) |
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New loss of taste or smell |
Categorical |
Patent presents with a new loss of taste or smell. (Y/N) |
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Sore throat |
Categorical |
Patient presents with a sore throat. (Y/N) |
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Congestion or runny nose |
Categorical |
Patient presents with congestion or a runny nose. (Y/N) |
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Nausea or vomiting |
Categorical |
Patient presents with nausea or vomiting. (Y/N) |
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Diarrhea |
Categorical |
Patient presents with diarrhea. (Y/N) |
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Nursing/Group home resident |
Categorical |
Patient is a resident of nursing or group home. (Y/N) |
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Medical worker |
Categorical |
Patient is a medical worker. (Y/N) |
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Known exposure to COVID-19 positive individual or location |
Categorical |
Patient has interacted with someone with a known COVID-19 diagnosis or visited a location with a relatively higher prevalence of COVID-19 cases. (Y/N) |
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Travel history |
List |
List of patient’s recent travel history |
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Clinical urgency |
Categorical |
Category of clinical urgency |
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Patient is an essential worker |
Categorical |
Indicates whether the patient is an essential worker during the outbreak. Essential vs. non-essential. |
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Age |
Integer |
Patient’s age at the time of registration |
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Comorbidities |
Categorical |
List of comorbidities for COVID-19
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Ref 1 |
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Sex |
Categorical |
Value representing the sex of the patient |
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Recent imaging |
Categorical |
Recent imaging exam results with evidence or suggestion of COVID-19 infection |
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Patient anxiety |
Categorical |
If the patient suffers from anxiety over a postponed imaging study, and their anxiety outweighs the risk of infection while at the imaging center, the study should be performed |
Categorical Risk that Patient is COVID-19 Positive
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Definition |
Patient’s COVID-19 positive risk based on clinical signs and symptoms as well as risk factors |
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Data Type |
Categorical |
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Value Set |
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Units |
N/A |
Categorical Risk that Patient is COVID-19 Positive
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Definition |
Patient’s COVID-19 positive risk based on clinical signs and symptoms as well as risk factors |
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Data Type |
Categorical |
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Value Set |
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Units |
N/A |
Numerical Risk that Patient is COVID-19 Positive
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Definition |
Scale of risk that patient is COVID-19 positive |
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Data Type |
Numeric |
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Value Set |
N/A |
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Units |
N/A |
Risk Factors
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Definition |
Risk factors predisposing of worse outcomes with COVID-19 infection |
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Data Type |
Categorical |
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Value Set |
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Units |
N/A |
Clinical Indication
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Definition |
Level of urgency for obtaining the study |
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Data Type |
Categorical |
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Value Set |
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Units |
N/A |
People Who Are at Increased Risk for Severe Illness. (2020, June 25). Available at: <https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-at-increased-risk.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fcoronavirus%2F2019-ncov%2Fneed-extra-precautions%2Fpeople-at-higher-risk.html>
Catalyst.nejm.org. 2020. Identifying Patients With Increased Risk Of Severe Covid-19 Complications: Building An Actionable Rules-Based Model For Care Teams | Catalyst Non-Issue Content. Available at: <https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0116>.