Massive Health Record Breaches Evidenced by the Office for Civil Rights Data
Bibliographic record
Abstract
BACKGROUND: Using data collected by the Office for Civil Rights, Department of Health and Human Services (HHS), over half of the population in the USA might have been affected by security breaches since Oct 2009. This study provided analysis of the data, presenting the numbers of individuals affected in one breach and the number of breaches. METHODS: Statistical analysis has been conducted with visualizations. Visualizations include categorized histograms and tables. Histograms are presented as bar charts with categories: location and breach type. Tables show case counts (across top 10 breaches and those with more than one million stolen records) in successive years and covered entity types. All statistics were calculated with the use of package R. Analyzed data were collected from Oct 2009 till Jun 2017. RESULTS: This study presents evidence of health data breaches taking place at an unprecedented level. Medical records of at least 173 million of people, gathered since Oct 2009, have been breached and might have adversely influenced over half of the population in the USA. CONCLUSION: Results of this study are expected to motivate public care authorities to develop similar laws and regulations as the USA while striving for better law enforcement. It takes a considerable amount of time to educate public and it takes substantial financial resources to prevent data breaches.
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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 | 0.014 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".