As part of a PhD study, Claire Currie, Senior Lecturer at Glasgow Caledonian University is evaluating the facilitators and barriers to using Clinical Decision Support (CDS) for radiology referrals. This is a real-time, evidence-based solution, integrating software with the Royal College of Radiologists’ (RCR) iRefer guidance1, helping healthcare professionals request the most appropriate imaging for their patients. To gather valuable insights, we are inviting health care professionals (NHS or private) who request, vet, and justify any type of radiology referral using the CDS tool or traditional methods to participate in an online survey that explores the experiences and perceptions of CDS. Whether you use CDS or not, your experience matters – please complete our survey.
We understand that radiology services are experiencing unprecedented demand, driven by an aging population, evolving clinical pathways, new guidelines, and increased screening efforts aimed at early diagnosis. Complex imaging modalities, particularly CT and MRI, have seen significant growth, with examinations increasing by 30% from 2014 to 2019. This surge is largely due to heightened acute hospital activity and urgent cancer referrals, necessitating effective strategies to manage this demand2.
To request any radiology examination, referrers can use appropriate use criteria or guidelines such as iRefer1. The current systems involve electronic, or paper referrals received by radiology departments, where they are vetted for appropriateness and justified by practitioners, as per IR(ME)R (2017). This process can result in time delays in the patient’s pathway if referrals are rejected due to incomplete information or other reasons. While regulations do not apply to non-ionising radiation modalities like ultrasound (US) and MRI, the principles of accurate referral and justification are upheld as good practice throughout radiology.
CDS systems aim to eliminate inappropriate referrals and missing request information. These systems guide clinicians' decision-making at the point of care, improving patient safety and reducing human error. Initially rule-based, CDS systems now incorporate machine learning and AI, enhancing their capabilities. From its initial development over 50 years ago, CDSS was designed to improve patient safety by mitigating human error. Berner & La Lande3 claim that when CDS system is used properly, it can transform healthcare education and practice. Within the last decade, more complex CDS systems have emerged to support radiology request decisions. These systems integrate guidelines with patient data, presenting referrers with the best recommended tests and alternatives. The guidelines serve as the knowledge base, forming associations between signs, symptoms, and imaging tests. The inference engine combines these rules with actual patient data, resulting in the production of radiology referrals. This process ensures patients receive the right test the first time, improving care quality and minimising unnecessary radiation exposure.
A CDS system can be considered to improve the quality of patient care foremost, followed by potential workflow efficiencies as well as efficient management of resources. For quality of care, CDS could ensure that patients receive the right test the first time, allowing or enabling clinicians to provide or take patients to the best diagnostic pathway and, by extension, minimising any necessary radiation to the patient. There is no doubt that radiographers are professionals who are well versed in digital literacy and competency, and this continues to be supported by NHS England Framework for Allied Health Professionals4 and the Scottish Government’s digital health and care strategy5. This NHS England Framework recognises that digital health solutions can improve quality, safety and efficiency. Two of the seven ‘effective digital capabilities’ for AHP services include using CDS: orders and results management, and decision support; indeed the framework recognises that a key profession using these digital tools is radiographers: “Compared to other AHPs, radiographers will increasingly be exposed to some of the more advanced developments in digital diagnostic capabilities through the evolution of artificial intelligence and machine learning innovation in clinical decision support.” Many of potential transformations of services fit well with CDS, recognising the goal to standardise practice, reduce unwarranted variation and reduce unnecessary testing. The Scottish Government digital health and care strategy made a commitment to supporting digital leadership to further embed digital technology and literacy across organisations, ensure training so staff understand data-driven recommendations, decision support tools and AI.
The survey study forms part of a broader series evaluating the impact of CDS on the patient pathway. The online survey aims to:
- Explore the experiences and perceptions of CDS.
- Gather referrers’ perspectives on CDS.
- Understand radiology staffs’ perspectives on CDS.
The survey compares experiences of those who use CDS with those do not use CDS, assessing changes in attitudes, behaviours, and willingness to implement CDS in routine practice. It takes approximately 10-15 minutes, and responses are anonymous. This research has ethical approval from Glasgow Caledonian University (AHP/A24/007) and is partially funded by CoRIPS. Your participation in the survey will provide valuable insights into the experiences and perceptions of CDS, helping to shape future practices and improve patient outcomes. Thank you in advance for your time and help. Whether you use CDS or not, your experience matters – please complete our survey.
In conclusion clinical decision support systems represent a potential advancement to the referral process in radiology, offering numerous benefits in patient care, workflow efficiency, and resource management. By integrating guidelines with patient data, CDS systems ensuring patients get the right test at the right time.
References
- iRefer: Making the Best Use of Clinical Radiology. 8th ed. London: Royal College of Radiologists, 2017.
- Richards M., 2020 Diagnostics: Recovery and Renewal Report of the Independent Review of Diagnostic Services for NHS England Available from https://www.england.nhs.uk/publication/diagnostics-recovery-and-renewal-report-of-the-independent-review-of-diagnostic-services-for-nhs-england/
- Berner, E.& La Lande, T., 2007. Overview of clinical decision support systems. In: Berner, ES. La Lande TJ ed., Clinical Decision Support Systems New York: Springer, pp. 3-22.
- NHS England Publications 000378, 2019 A digital framework for allied health professionals, NHS England. Available from https://www.england.nhs.uk/wp-content/uploads/2019/04/a-digital-framework-for-allied-health-professionals.pdf
- Scottish Government (2021) Digital health and care strategy ISBN 9781802015768 Available from: https://www.gov.scot/publications/scotlands-digital-health-care-strategy/pages/7/