Building a high quality medical data architecture for multiple uses in an integrated health care environment
Bibliographic record
Abstract
Objective: The aim was to create a reliable information provisioning system in healthcare for both care and research processes, based on existing data standards and standardized electronic messages. The research question is: How can a Clinical Data Ware House (CDWH) be developed for standardized basic patient data, generic nursing data and data about oncology nursing, allowing management of Electronic Health Record data, electronic data exchange and data analytics? Materials and methods: The main instrument used was the Detailed Clinical Model (DCM) data standardization approach. Further, data communication utilized HealthLevel7v3 (HL7v3) messages, transported by Mirth Connect. In an incremental, design-oriented research project, CDWH-prototypes were constructed using Enterprise Architect, a HL7v3 generator plug-in, SQL Server technology and PostgreSQL-based CDWH-technology. Results: The project resulted in 16 existing DCMs selected and 6 new DCMs defined. From those DCMs, a HL7v3 message structure was generated and a CDWH created. Implementing specialized Data Marts (DM) a connection between the CDWH and one Electronic Health Record system was built. Discussion: Detailed Clinical Models improve data quality, yet building them consumes time and resources. Some required data codes could not be identified in time and dummy codes were used instead. The existence of unstructured medical data in legacy systems may proof to be an obstacle in the future. Conclusion: The project shows that using Detailed Clinical Models as the sole source for system development leads to a sound design for a CDWH and HL7v3 messages, supporting a standards based health information system, suitable for multiple uses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".