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Protected Health Information (PHI) in a Small Business

2011· book-chapter· en· W4252628021 on OpenAlexaff
James Suleiman, Terry Huston

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicLeadership and Management in Organizations
Canadian institutionsCARE Canada
Fundersnot available
KeywordsHealth Insurance Portability and Accountability ActBusinessCompliance (psychology)Protected health informationScrutinyPublic relationsLegislatureGeneral partnershipInformation securitySmall businessCertified Information Security ManagerHealth carePolitical scienceCloud computing securityComputer securitySecurity information and event managementHealth policyMarketingHRHISFinanceLawCloud computing

Abstract

fetched live from OpenAlex

Compliance with regulatory guidelines and mandates surrounding information security and the protection of privacy has been under close scrutiny for some time throughout the world. Smaller organizations have remained “out of the spotlight” and generally do not hire staff with the expertise to fully address issues of compliance. This case study examines a project partnership between an information-technology (IT) consultant who specializes in small business and a diminutive medical practice that sought support with compliance issues surrounding a research study it was conducting. Other small medical practices were contributing to the research; consequently, information sharing while concurrently adhering to the regulations of the Health Insurance Portability and Accountability Act (HIPAA) of 1996 was a significant aspect of the project. It was also critical that numerous other security and privacy legislative requirements were met. The issue of data security is often neglected in IT instruction. This case study provides a foundation for examining aspects of information security from the perspective of the small-business IT consultant.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.034
GPT teacher head0.211
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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