Electronic Health Records and Pulmonary Function Data: Developing an Interoperability Roadmap. An Official American Thoracic Society Workshop Report
Bibliographic record
Abstract
Abstract A workshop “Electronic Health Records and Pulmonary Function Data: Developing an Interoperability Roadmap” was held at the American Thoracic Society 2019 International Conference. “Interoperability” is defined as is the ability of different information-technology systems and software applications to directly communicate, exchange data, and use the information that has been exchanged. At present, pulmonary function test (PFT) equipment is not required to be interoperable with other clinical data systems, including electronic health records (EHRs). For this workshop, we assembled a diverse group of experts and stakeholders, including representatives from patient-advocacy groups, adult and pediatric general and pulmonary medicine, informatics, government and healthcare organizations, pulmonary function laboratories, and EHR and PFT equipment and software companies. The participants were tasked with two overarching Aobjectives: 1) identifying the key obstacles to achieving interoperability of PFT systems and the EHR and 2) recommending solutions to the identified obstacles. Successful interoperability of PFT data with the EHR impacts the full scope of individual patient health and clinical care, population health, and research. The existing EHR–PFT device platforms lack sufficient data standardization to promote interoperability. Cost is a major obstacle to PFT–EHR interoperability, and incentives are insufficient to justify the needed investment. The current vendor–EHR system lacks sufficient flexibility, thereby impeding interoperability. To advance the goal of achieving interoperability, next steps include identifying and standardizing priority PFT data elements. To increase the motivation of stakeholders to invest in this effort, it is necessary to demonstrate the benefits of PFT interoperability across patient care and population health.
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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 | 0.147 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".