Assessing the Legal Aspects of Information Security Requirements for Health Care in 3 Countries: Scoping Review and Framework Development
Bibliographic record
Abstract
BACKGROUND: The loss of human lives from cyberattacks in health care is no longer a probabilistic quantification but a reality that has begun. In addition, the threat scope is also expanding to involve a threat of national security, among others, resulting in surging data breaches within the health care sector. For that matter, there have been provisions of various legislation, regulations, and information security governance tools such as policies, standards, and directives toward enhancing health care information security-conscious care behavior among users. Meanwhile, in a research scenario, there are no comprehensive required security practices to serve as a yardstick in assessing security practices in health care. Moreover, an analysis of the holistic view of the requirements that need more concentration of management, end users, or both has not been comprehensively developed. Thus, there is a possibility that security practice research will leave out vital requirements. OBJECTIVE: The objective of this study was to systematically identify, assess, and analyze the state-of-the-art information security requirements in health care. These requirements can be used to develop a framework to serve as a yardstick for measuring the future real security practices of health care staff. METHODS: A scoping review was, as a result, adopted to identify, assess, and analyze the information security requirement sources within health care in Norway, Indonesia, and Ghana. RESULTS: Of 188 security and privacy requirement sources that were initially identified, 130 (69.1%) were fully read by the authors. Subsequently, of these 188 requirement documents, 82 (43.6%) fully met the inclusion criteria and were accessed and analyzed. In total, 253 security and privacy requirements were identified in this work. The findings were then used to develop a framework to serve as a benchmark for modeling and analyzing health care security practices. CONCLUSIONS: On the basis of these findings, a framework for modeling, analyzing, and developing effective security countermeasures, including incentivization measures, was developed. Following this framework, research results of health care security practices would be more reliable and effective than relying on incomprehensive security requirements.
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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 | 0.080 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.074 | 0.055 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| 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".