Process Re-Engineering and Data Integration Using Fast Healthcare Interoperability Resources for the Multidisciplinary Treatment of Lung Cancer
Bibliographic record
Abstract
Unlabelled: Multidisciplinary team (MDT) meetings play a critical role in cancer care by fostering collaboration between different health care professionals to develop optimal treatment recommendations. However, meeting scheduling and coordination rely heavily on manual work, making information-sharing and integration challenging. This results in incomplete information, affecting decision-making efficiency and impacting the progress of MDT. This project aimed to optimize and digitize the MDT workflow by interviewing the members of an MDT and implementing an integrated information platform using the Fast Healthcare Interoperability Resources (FHIR) standard. MDT process re-engineering was conducted at a central Taiwan medical center. To digitize the workflow, our hospital adopted the NAVIFY Tumor Board (NTB), a cloud-based platform integrating medical data using international standards, including Logical Object Identifiers, Names, and Codes, Systemized Nomenclature of Medicine-Clinical Terms, M-code, and FHIR. We improved our hospital's information system using application programming interfaces to consolidate data from various systems, excluding sensitive cases. Using FHIR, we aggregated, analyzed, and converted the data for seamless integration. Using a user experience design, we gained insights into the lung cancer MDT's processes and needs. We conducted 2 phases: pre- and post-NTB integration. Ethnographic observations and stakeholder interviews revealed pain points. The affinity diagram method categorizes the pain points during the discussion process, leading to efficient solutions. We divided the observation period into 2 phases: before and after integrating the NTB with the hospital information system. In phase 1, there were 83 steps across the 6 MDT activities, leading to inefficiencies and potential delays in patient care. In phase 2, we streamlined the tumor board process into 33 steps by introducing new functions and optimizing the data entry for pathologists. We converted the related medical data to the FHIR format using 6 FHIR resources and improved our hospital information system by developing functions and application programming interfaces to interoperate among various systems; consolidating data from different sources, excluding sensitive cases; and enhancing overall system efficiency. The MDT workflow reduced steps by 60% (50/83), lowering the coordinated activity time from 30 to 5 minutes. Improved efficiency boosted productivity and coordination in each case of manager feedback. This study optimized and digitized the workflow of MDT meetings, significantly enhancing the efficiency and accuracy of the tumor board process to benefit both medical professionals and patients. Based on FHIR, we integrated the data scattered across different information systems in our hospital and established a system interoperability interface that conformed to the standard. While digitizing the work of MDT meetings, we also promoted the optimization and transformation of related information systems and improved their service quality. We recommend additional research to assess the usability of a tumor board platform.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".