Patient-centred outcomes of imaging tests: recommendations for patients, clinicians and researchers

Abstract

BackgroundImaging tests are one of the most frequently used diagnostic modalities in healthcare, but the benefits of their direct impacts on clinical decision-making have been countered by concerns that they can be overused. Assessing the relative value of imaging tests has largely focused on measures of test accuracy, which overlooks more comprehensive benefits and risks of imaging tests, particularly their impact on patient-centred outcomes (PCOs). We present the findings of the Patient Reported Outcomes of Diagnostics (PROD) research study in response to a methodological gap in the area of diagnostic test comparative effectiveness research.MethodsOver a 3-year period, the PROD Study engaged with multiple stakeholders to identify existing conceptual models related to PCOs for imaging testing, conducted primary research and evidence synthesis, and developed consensus recommendations to describe and categorise PCOs related to imaging testing.ResultsThe PROD framework categorises PCOs from imaging studies within four main domains: information or knowledge yielded, physical impact, emotional outcomes and test burden. PCOs interact with each other and influence effects across domains, and can be modified by factors related to the patient, clinical situation, healthcare team and the testing environment.ConclusionsUsing PCOs to inform healthcare decision-making will require ways of collating and presenting information on PCOs in ways that can inform patient–provider decision-making, and developing methods to determine the relative importance of outcomes (including test accuracy) to one another.

Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes

“…Perhaps this neglect is somehow related to PCOs still currently being in its early stages—at least in the field of the clinical evaluation of AI in medicine. The first comprehensive attempt to define the PCOs of imaging tests, to which the majority of AI tools currently available after regulatory approval belong, was made only recently [ 30 ]. According to a recent systematic evaluation of research protocols for clinical trials for AI technology registered in ClinicalTrials.gov up to 2022 by Pearce et al [ 88 ], the use of PROMs in the assessment of AI health technologies as trial endpoints was observed in only 7% of clinical trials for AI technology.…”

“…Questions related to insurance coverage were designed in alignment with the National Health Insurance of the ROK. To address potential unfamiliarity with PCOs among some respondents, we provided a concise and explicit explanation of PCOs in the survey, focusing particularly on PCOs as related to imaging tests (Section B of survey questionnaire in Supplements ) [ 30 ]. Furthermore, to avoid confusion among the respondents, the survey also included explanations of the four value elements provided by AI, as outlined in Table 1 .…”

“…Simultaneously, AI’s role in improving PCOs is also highlighted. AI tools’ ability to reduce the radiation dose for computed tomography (CT) examinations and decrease the scan time required for magnetic resonance imaging (MRI) examinations through quality improvements in image acquisition exemplifies the technology’s positive impact on imaging test-related PCOs [ 30 ]. A more recent example, especially with the rapid advances in large language models based on the transformer architecture and foundational model technique [ 31 32 33 ], is the use of AI to enhance information exchange with patients in patient care—an important component of PCOs [ 34 35 ].…”

Acceptability and Adherence to Home, Kiosk, and Clinic Blood Pressure Measurement Compared to 24-H Ambulatory Monitoring

“…This illustrates the considerations that patients go through with weighing the advantages and disadvantages of diagnostic tests. 24…”