155 Is the widespread adoption of interconnected electronic health records (EHR’s) contributing to the overdiagnosis problem? Establishing a research agenda
Bibliographic record
Abstract
Justification for the Proposed Workshop: As more medical practices adopt the use of interconnected Electronic Health Records (EHR’s), the issue of overdiagnosis may become increasingly impactful as a single EHR may be accessed by multiple care providers in distinct clinical settings, each with variable pre-existing relationships to the patient. In such contexts, overdiagnosed conditions applied to a patient’s problem list may result in unintended downstream consequences in excess of those that might be expected from the use of more traditional, site-specific paper charts. As such, the widespread use of interconnected EHR’s may exacerbate long-term patient labelling associated with an overdiagnosed condition. Evidencing the need for caution regarding overdiagnosis and the use of EHR’s are studies which have previously shown that patients who have had a misdiagnosed and/or overdiagnosed antibiotic hypersensitivity applied to their EHR are less likely to receive optimal therapy in the future and are therefore exposed to potentially worse clinical outcomes. Following the recent inclusion of the term ‘overdiagnosis’ to the list of searchable medical subject headings (MeSH) of the US National Library of Medicine, researchers are now able to research the topic of overdiagnosis more efficiently. We propose that a workshop be conducted to draw upon the collective knowledge Preventing Overdiagnosis 2022 Conference attendees with the goal of formalizing a proposed research agenda for topics related to how the use of EHR’s may be contributing to the overdiagnosis problem. As a team of facilitators, the authors of this proposal would plan to lead the attendees of this workshop into separate breakout group discussions each aimed at exploring suggested research topics and their associated ethical considerations. Suggested Topics for Discussion: What might patient preferences be regarding the duration that a diagnosis remains on the patient’s problem list? Might these preferences change depending on the diagnosis? Should approaches be adopted to limit the lifespan of a diagnostic label within an EHR? What are the potential benefits of a diagnostic label persisting indefinitely within a patient’s EHR? What are the potential harms of a diagnostic label persisting indefinitely within a patient’s EHR? Projected Outcomes of the Proposed Workshop: Attendees will gather an increased understanding of this topic through direct discussion with colleagues and subject-matter-experts. The authors of this proposal would collect the deliberations and conclusions accumulated through this interactive workshop to develop a commentary for publication outlining the specific agenda-items suggested for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.194 | 0.317 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.039 | 0.027 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".