Reducing radiation dose and streamlining clinical operations

King Fahd University Hospital and Imam Abdulrahman Bin Faisal University, Saudi Arabia

With the current pressure on healthcare facilities to provide optimal clinical care in a safe and reliable way, hospitals and clinics face a continuous challenge to balance efficiency and

high quality of care. In addition, medical regulatory agencies have started regulating radiation emitting devices that may affect human health. In the Kingdom of Saudi Arabia, the Saudi FDA (SFDA) established the National Diagnostic Reference Level (NDRL) to ensure and promote the efficiency of radiation sources and to minimize radiation risks for patients.

King Fahd Hospital of the University (KFHU) of Imam Abdulrahman Bin Faisal University is one of the largest tertiary hospitals in the Eastern Province of Saudi Arabia. It is a 550 bed hospital that is a level I Trauma center and a large stroke referral center providing world-class medical care to about 2 million people of the surrounding population. KFHU strives to be the best in the field by meeting the highest standards set forth by the Joint Commission International Accreditation (JCIA).

To be able to succeed with the above challenge, optimal use of data is key! With existing systems, the majority of operational data is either not efficiently used, fragmented or simply lost – all of which prevents users from being able to derive improvement measures. It is hence critical to have access to proper and reliable data to implement effective change.

The Department of Radiology at KFHU took on this challenge with the help of Siemens Healthineers teamplay performance management applications. teamplay is a cloud based software platform enabling digital applications that grant instant access to analytics, derived from operational data

from our imaging fleet. The teamplay receiver software serves as a DICOM node and fetches data from all our modalities. Regardless of the manufacturer, our entire imaging devices (MRI, CT, SPECT, X-ray, mammography, interventional radiology, etc.) were connected and monitored remotely. Data collection and analysis included imaging throughput, radiation dose levels, staff and room

utilization, etc. – down to each device and procedure. This allowed us to simplify reporting and gain insights to reveal improvement potentials.

The data was applied in real-time, intuitive, user-friendly, and adaptable depending on the scenario or issue being analyzed. This allowed us to understand the root cause of

issues, identify deviations, explore potential levers, and hence enhance operational efficiency and improve quality of care by leveraging operational, technical, and clinical data.

teamplay performance management applications were activated at KFHU in April 2021 which allowed us to have instant access to our radiology performance data and allowed us to have a holistic transparent and clear overview of:

  1. Radiation dose data through teamplay Dose, which monitored radiation doses over time, reported dose outlier events and gave insights into the issues leading to those deviations from national reference levels.
  2. Key performance indicators (KPIs) for imaging device utilization through teamplay Usage, like patient change time, exam duration, table occupancy, protocol variations, etc.

After 3 months of continuous data collection, all data was analyzed by a consortium quality improvement team in our department headed by our chairman, with the goal of identifying areas of improvement. Through our analysis, we found 3 major areas of concern:

  1. In teamplay Usage, we found that our MRI machines were under-utilized with an average utilization of 32% for one of our MRI machines and 27% for our second MRI machine, with an average of 2.66 exams per hour on both machines.
  2. Also, in teamplay Usage, we found that our technologists used multiple different protocols for the same anatomic region on our CT scanners.
  3. In teamplay Dose, we found that around 7.9% of total CT scan exams were exceeding the SFDA NDRL reference doses.
CT dose outliers per month

Fig. 1 Radiation doses in CT showed that the dose outliers in June re presented 7.9% of the total number of CT scans. After implementing changes in the month of July, the dose outliers has dropped to 2.8% by the month of September.

We drilled down to every radiation dose outlier event we had in our department during the data collection period and analyzed the reasons that have caused the dose event. We found that the majority of reasons were related to scan over-coverage, patients who were not placed in the iso-center of the machine, obese patients or wrong protocol parameters. We also identified the top protocols with the highest dose outliers (exceeding SFDA NDRL reference levels).

Regarding protocol variations, we found that there were multiple protocols for the same body region. For example, “head_routine_2021” and “head_ routine_2020” with great differences in the average Computed Tomography Dose Index (CTDI) dose for each protocol.

To overcome this, the department administration took the initiative to implement immediate change. With the help of Siemens Healthineers, a CT applications specialist and our lead CT technologist unified all protocols on our CT machines to a single protocol for each body region/clinical indication and the scan parameters were optimized to reduce radiation doses, whilst still delivering acceptable results in terms of image quality by our radiologists. In

addition, all our CT technologists went through an extensive refresher training regarding the importance of radiation protection, proper patient positioning, and troubleshooting

scan parameters to lower radiation dose levels. 

After implementing all changes, data was collected for another 2 months, and the results showed that dose outliers were lowered from 7.9% in June to 2.8% in September.

In addition, the average Dose Length Product (DLP) was lowered by about 15% across all CT exams.

Analyzing the root cause behind MRI under-utilization, teamplay Usage showed us an inhomogeneous throughput of MRI schedule during working hours. This was more evident during the morning time and late afternoon. This was because the morning slots were reserved for in-patients and the slots in the afternoon were reserved in-patients who had infections. If there were no in-patients in the morning or afternoon requiring MRI scans, the MRI machines were not in use, thus wasting valuable capacity. We realized that increasing patient throughput in those time slots can result in increasing patient throughput and decrease our MRI backlog. Through teamplay, we identified MRI exams that have the shortest exam duration (e.g., lumbar spine MRI, knee MRI, etc.) and we scheduled those types of exams in our under-utilized time slots.
After implementing these changes, we found that our number of exams have increased by 49%, the number of exams per hour increased by about 17% and our MRI machine utilization increased by about 40% on both our MRI machines.

King Fahd University Hospital and Imam Abdulrahman Bin Faisal University, Saudi Arabia

These results show how the teamplay performance management applications helped us at KFHU to face the challenges in our hospital, to increase system efficiency, and to improve patient care. By making use of the data of our entire imaging fleet, we were able to identify and understand improvement potentials and achieved great results by optimizing our operations accordingly. This also results in tangible benefits for our patients, such as higher quality of care through lower radiation doses and shorter wait times thanks to more efficient MRI operations.