Interoperable Platform to Report Polymerase Chain Reaction SARS-CoV-2 Tests From Laboratories to the Chilean Government: Development and Implementation Study
Bibliographic record
Abstract
BACKGROUND: Testing, traceability, and isolation actions are a central strategy defined by the World Health Organization to contain the COVID-19 pandemic. In this sense, the countries have had difficulties in counting the number of people infected with SARS-CoV-2. Errors in reporting results are a common factor, as well as the lack of interoperability between laboratories and governments. Approaches aimed at sending spreadsheets via email expose patients' privacy and have increased the probability of errors due to retyping, which generates a delay in the notification of results. OBJECTIVE: This study aims to design and develop an interoperable platform to report polymerase chain reaction (PCR) SARS-CoV-2 tests from laboratories to the Chilean government. METHODS: The methodology to design and develop the interoperable platform was comprised of six well-structured stages: (1) creation of a minimum data set for PCR SARS-CoV-2 tests, (2) modeling processes and end points where institutions interchange information, (3) standards and interoperability design, (4) software development, (5) software testing, and (6) software implementation. RESULTS: The interoperable Fast Healthcare Interoperability Resources (FHIR) platform to report PCR SARS-CoV-2 tests from laboratories to the Chilean government was successfully implemented. The platform was designed, developed, tested, and implemented following a structured methodology. The platform's performance to 1000 requests resulted in a response time of 240 milliseconds, throughput of 28.3 requests per second, and process management time of 131 milliseconds. The security was assured through a private network exclusive to the Ministry of Health to ensure confidentiality and integrity. The authorization and authentication of laboratories were implemented with a JavaScript Object Notation Web Token. All the PCR SARS-CoV-2 tests were accessible through an application programming interface gateway with valid credentials and the right access control list. CONCLUSIONS: The platform was implemented and is currently being used by UC Christus Laboratory. The platform is secure. It was tested adequately for confidentiality, secure authorization, authentication, and message integrity. This platform simplifies the reporting of PCR SARS-CoV-2 tests and reduces the time and probability of mistakes in counting positive cases. The interoperable solution with FHIR is working successfully and is open for the community, laboratories, and any institution that needs to report PCR SARS-CoV-2 tests.
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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 | 0.001 |
| 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".