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As information technology permeates healthcare (particularly
provider-facing systems), maximizing system effectiveness requires the
ability to document and analyze tricky or troublesome usage scenarios.
However, real-world medical applications are typically replete with
privacy-sensitive data regarding patients, diagnoses, clinicians, and
EMR user interface details; any instrumentation for screen capture
(capturing and recording the scenario depicted on the screen)
needs to respect these privacy constraints. Furthermore, real-world
medical informatics systems are typically composed of modules from
many sources, mission-critical and often closed-source; any
instrumentation for screen capture cannot rely on access to structured
output or software internals.
In this paper, we present a solution: a system that combines keyboard video mouse (KVM) capture with automatic text redaction (and interactively selectable unredaction) to produce precise technical content that can enrich stakeholder communications and improve end-user influence on system evolution. KVM-based capture makes our system both application and operating-system independent because it eliminates software-interface dependencies on capture targets. Using a corpus of EMR screenshots, we present empirical measurements of redaction effectiveness and processing latency to demonstrate system performances. We discuss how these techniques can translate into instrumentation systems that improve real-world medical informatics deployments. END:
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Josephy Cooley and Sean Smith, "Privacy-Preserving Screen Capture: Closing the Loop for Medical Informatics Usability." Dartmouth Computer Science Technical Report TR2012-725, July 2012.
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