CODEX Digest - 4.30.26

1) 2026 Physician Survey on Augmented Intelligence.

American Medical Association; 2026.

Physicians' adoption of AI is vital for its safe, effective implementation. This nationwide survey reveals an increase from 65 to over 75% of physicians having a positive view of AI for healthcare, particularly for diagnosis. The survey also reviews physician comfort with patient AI use.

2) Developing Patient-Reported Outcome Measures of Timely Experience of Diagnosis (PROMOTE-Dx) for Cancer: Survey and Quality Measure Development Report.

Anhang Price R, Berdahl CT, Bradley MA, et al. RAND; 2026.

Patient reporting is crucial for flagging problems to inform diagnostic accuracy improvement efforts. This report describes the development and evaluation of PROMOTE-Dx, a survey-based patient-reported measure for assessing timely cancer diagnosis. Healthcare organizations can use PROMOTE-Dx to evaluate and enhance the timeliness of cancer diagnoses.

3) Patient, community, and clinician beliefs about multi-cancer detection tests: implications for the evidence needed to recommend routine use.

Brooks EM, Villalobos G, Miller CA, et al. J Public Health (Oxf). 2026;48(1):206-215.

Multi-cancer detection tests (MCDs) may help reduce cancer deaths by enabling early diagnosis. This focus group study finds that participants valued early detection and screening convenience with a single blood test but were concerned about limited clinical validation and risks from false positives, overdiagnosis, and overtreatment. Of importance, the implementation of MCD testing at the NHS identifed safety and feasibility issues, showing the importance of more large-scale trials and clinical validation for any future implementation.

4) Barriers and facilitators to the fidelity of delirium screening in the emergency department: an ethnographic approach.

Carpenter KP, Bellolio F, Bartolacci M, et al. J Am Geriatr Soc. 2026;74(1):43-54.

Evidence-based screening tools for delirium in older adults are available but often underused. This ethnographic study found incomplete screenings and identified barriers such as staff prioritization of other duties, limited awareness of the tool's value, and language barriers. Facilitators included input from care partners on baseline mental status and nurses introducing the screening to boost patient participation.

5) Potential missed diagnostic opportunities after low dose CT screening for lung cancer: results from the National Lung Cancer Screening Trial.

Hassoon A, Hussain S, Galiatsatos P, et al. Diagnosis (Berl). Epub Mar 20.

Low dose CT (LDCT) is an important screening method, but many lung cancer patients face diagnostic uncertainty after a positive initial screen. This UK study investigates potential missed diagnostic opportunities (PMDO), defined as no lung cancer diagnosis following a positive LDCT, later followed by another positive annual LDCT that confirms the disease. Over 37% of patients diagnosed with lung cancer and considered at risk for PMDO experienced at least one PMDO, highlighting an important area of focus for timely lung CA diagnosis.

6) Safe for kids? AI medical devices in radiology overlook paediatric suitability.

Kelly BS, Lee J, Antram E, et al. Eur Radiol. 2026;36(3):2083-2090.

Evaluating the suitability of diagnostic tests and devices for children is fundamental for their safe use. This review explores whether radiology AI devices including software approved in Europe specify pediatric use, revealing many do not. The authors recommend a standardized safety indicator to help clinicians choose appropriate devices.

7) Cumulative incidence of stroke disability and mortality following emergency department discharge for dizziness: a cohort study.

Kerber KA, Sangha N, Burke JF, et al. Ann Emerg Med. 2026;87(4):463-473.

Diagnosing stroke in emergency department patients presenting mainly with dizziness is difficult. This retrospective study estimates stroke-related disability or mortality after such visits, finding these outcomes are rare when patients are discharged home. The authors address challenges in accurate stroke diagnosis during initial assessments and suggest decision support tools to help identify strokes in dizziness cases.

8) Pricing models for diagnostic AI based on qualitative insights from healthcare decision makers.

Kirchhoff J, Berns F, Schieder C, et al. NPJ Digital Med. Epub 2026 Mar 6.

AI-driven diagnostic support systems can boost accuracy and efficiency, but opaque, usage-based pricing hinders adoption. This European interview study explores ways to make pricing clearer and more predictable, considering technical and organizational factors. Ten themes reveal resistance to usage-based pricing and a desire for transparency. The authors offer design principles and recommendations for pricing models that support procurement, reimbursement, and scaling.

9) Between doubt and diagnosis: patient experiences of emotional harm from diagnostic delays.

McCleskey S, Hero J, Berdahl C, et al. Patient Educ Couns. 2026;149:109610.

For patients, delayed diagnosis carries deep emotional and relational costs, eroding trust and future care engagement. This interview study of patients who experienced a delayed diagnosis reveals three key themes: feeling dismissed, leading to frustration and self-doubt; relief and validation upon finally receiving a diagnosis; and lasting mistrust in healthcare despite that validation. These harms span ages and conditions, underscoring the need for patient-centered, validating communication and partnership to improve diagnostic safety.

10) “I didn’t know, I definitely guessed.” Exploring pre-registration podiatry students’ approach to identifying dermatological conditions in different skin tones, a mixed methods study.

Otter S, Funnell F, Sykes A, et al. J Foot Ankle Res. 2026;19(2):e70144.

People of color face higher rates of delayed or incorrect dermatologic diagnoses, worsening health inequalities. This UK mixed-methods study of podiatry students found low confidence in diagnosing dermatological conditions, especially on dark skin tones. It highlights educational gaps in podiatry education, addressing a critical research need at the intersection of training and health equity.

11) Large language model-based identification of venous thromboembolism diagnostic delays.

Schaye V, Sartori DJ, Signoriello L, et al. J Hosp Med. 2026;21(4):391-401.

Delayed venous thromboembolism (VTE) diagnosis is an impactful harm in hospitals, but identifying cases is difficult and feedback is limited. This retrospective real-world study shows that a LLM-based electronic trigger outperforms traditional electronic triggers in detecting VTE delays; although positive predictive value was only 48% and observed incidence of VTE Dx delay was quite low.

12) Leveraging artificial intelligence to accelerate competency development in diagnostic reasoning of medical students.

Sur M, Dissaneevate S, Yuenyongviwat A, et al. Med Teach. Epub 2026 Mar 17.

Illness scripts are one element of teaching clinical reasoning, and AI chatbots may improve illness script quality more effectively than conventional teaching. This Thai study compared chatbot-enabled feedback and script co-construction with traditional instruction, finding that AI-enhanced personalized feedback within a structured framework boosts medical students’ diagnostic reasoning skill development.