{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/45545"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/45545","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Facilitating patient and administrator analyses of electronic health record accesses","abstract":"The past two decades in the United States have ushered in an era of increasing ubiquity of digitized healthcare as the speed and sophistication of technology follows an ever-growing trend. Electronic health records (EHRs) are an integral part of the growing healthcare industry which offers ease of access and new functionality while simultaneously causing worries over their privacy and security. In an effort to address these concerns, much legislation has been enacted in order to tighten the oversight and requirements for accessing protected health information (PHI). Most recently, the Department of Health and Human Services has released rulemaking which requires providers utilizing EHRs to comply with patients’ requests for logs of the accesses to their records. In this work, we outline our system for complying with this regulation while easing the burden of compli- ance for providers and simultaneously providing patients with informative and satisfying information about why their accounts were accessed. We implement a system called the Multiview Audit Interface (MAI) which utilizes recent research in the data mining and anomaly detection communities to provide a unified interface for conveniently using these algorithms for patients and administrators. We then test this system on a de- identified access log from Northwestern Memorial Hospital containing months of audit data. We construct a framework for implementing these algorithms as modules, thereby recycling existing code, encouraging multi-faceted comprehensions of their results, and offering an easy-to-use interface that administrators and patients can use alike. We demonstrate the the power of three modules currently implemented and show how the extensibility of the framework can be harvested to develop modules in the future.","abstract_html":"The past two decades in the United States have ushered in an era of increasing ubiquity of digitized healthcare as the speed and sophistication of technology follows an ever-growing trend. Electronic health records (EHRs) are an integral part of the growing healthcare industry which offers ease of access and new functionality while simultaneously causing worries over their privacy and security. In an effort to address these concerns, much legislation has been enacted in order to tighten the oversight and requirements for accessing protected health information (PHI). Most recently, the Department of Health and Human Services has released rulemaking which requires providers utilizing EHRs to comply with patients’ requests for logs of the accesses to their records. In this work, we outline our system for complying with this regulation while easing the burden of compli- ance for providers and simultaneously providing patients with informative and satisfying information about why their accounts were accessed. We implement a system called the Multiview Audit Interface (MAI) which utilizes recent research in the data mining and anomaly detection communities to provide a unified interface for conveniently using these algorithms for patients and administrators. We then test this system on a de- identified access log from Northwestern Memorial Hospital containing months of audit data. We construct a framework for implementing these algorithms as modules, thereby recycling existing code, encouraging multi-faceted comprehensions of their results, and offering an easy-to-use interface that administrators and patients can use alike. We demonstrate the the power of three modules currently implemented and show how the extensibility of the framework can be harvested to develop modules in the future.","abstract_has_math":false,"creators":["Duffy, Eric"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Gunter, Carl A."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-08-22T16:47:17Z","date_published":"2013-08-22T16:47:17Z","updated_at":"2026-07-22T22:25:36Z","subjects":["Electronic Health Records","Software Engineering","Audit Logs","Anomaly Detection","Healthcare Security"],"languages":["en"],"rights":["Copyright 2013 Eric Duffy"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/45545","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gunter, Carl A."]},{"key":"dc:creator","label":"Author","values":["Duffy, Eric"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-08-22T16:47:17Z","2015-08-22T10:00:25Z","2013-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electronic Health Records","Software Engineering","Audit Logs","Anomaly Detection","Healthcare Security"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Eric Duffy"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/45545"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The past two decades in the United States have ushered in an era of increasing ubiquity of digitized healthcare as the speed and sophistication of technology follows an ever-growing trend. 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We implement a system called the Multiview Audit Interface (MAI) which utilizes recent research in the data mining and anomaly detection communities to provide a unified interface for conveniently using these algorithms for patients and administrators. We then test this system on a de- identified access log from Northwestern Memorial Hospital containing months of audit data. We construct a framework for implementing these algorithms as modules, thereby recycling existing code, encouraging multi-faceted comprehensions of their results, and offering an easy-to-use interface that administrators and patients can use alike. 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We implement a system called the Multiview Audit Interface (MAI) which utilizes recent research in the data mining and anomaly detection communities to provide a unified interface for conveniently using these algorithms for patients and administrators. We then test this system on a de- identified access log from Northwestern Memorial Hospital containing months of audit data. We construct a framework for implementing these algorithms as modules, thereby recycling existing code, encouraging multi-faceted comprehensions of their results, and offering an easy-to-use interface that administrators and patients can use alike. 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