Analysis of Electronic Medical Records Data Security: Case Study in Citra Husada Sigli Hospital
Bibliographic record
Abstract
In health services, electronic medical record (E-MR) stands as tool to accelerate the provision of services to patients. However, patient’s medical record data must be kept secure, especially because it is easily hacked by unauthorized parties. This study aims to analyze the security of E-MR data at Citra Husada Hospital and identify risks that can occur. This study uses a qualitative survey with a case study design with 10 respondents that were selected by purposive sampling. The aspects of patient’s E-MR data security studied were confidentiality, integrity, authentication, availability, access control and non-repudiation. The security of E-RM data is generally good in confidentiality, authentication, availability, access control, and non-repudiation. However, some areas need improvement. While login requires a username and password, the password complexity is weak. Integrity is inadequate due to the lack of an SOP for data changes. Authentication includes digital signature related to encrypted username and password but lacks a certified electronic signature. The system is accessible only within the hospital’s intranet, ensuring availability. Access rights are well-structured. A track record ensures non-repudiation. The highest risk is unauthorized changes to patient data, highlighting the need for stronger risk management measures.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".