Generating unique IDs from patient identification data using security models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The use of electronic health records (EHRs) has continued to increase within healthcare systems in the developed and developing nations. EHRs allow for increased patient safety, grant patients easier access to their medical records, and offer a wealth of data to researchers. However, various bioethical, financial, logistical, and information security considerations must be addressed while transitioning to an EHR system. The need to encrypt private patient information for data sharing is one of the foremost challenges faced by health information technology. METHOD: We describe the usage of the message digest-5 (MD5) and secure hashing algorithm (SHA) as methods for encrypting electronic medical data. In particular, we present an application of the MD5 and SHA-1 algorithms in encrypting a composite message from private patient information. RESULTS: The results show that the composite message can be used to create a unique one-way encrypted ID per patient record that can be used for data sharing. CONCLUSION: The described software tool can be used to share patient EMRs between practitioners without revealing patients identifiable data.
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.
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 | 0.001 | 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 | 0.005 |
| Open science | 0.001 | 0.001 |
| 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 it