Learning relational policies from electronic health record access logs

Malin, Bradley; Nyemba, Steve; Paulett, J.D.

doi:10.1016/j.jbi.2011.01.007

Cited by 36 publications

(26 citation statements)

References 40 publications

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“…12 The access logs embedded in EHRs offer an unobtrusive way to study physicians' use of time. Previous research has used access logs to examine how physicians use EHRs, [13][14][15][16][17][18] to develop local access policies, 19 and to detect suspicious access. 20 We report here on an innovative use of the access log to study primary care physician work effort.…”

mentioning

confidence: 99%

Electronic Health Record Logs Indicate That Physicians Split Time Evenly Between Seeing Patients And Desktop Medicine

Tai-Seale¹,

et al. 2017

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Time spent by physicians is a key resource in health care delivery. This study used data captured by the access time stamp functionality of an electronic health record (EHR) to examine physician work effort. This is a potentially powerful, yet unobtrusive, way to study physicians' use of time. We used data on physicians' time allocation patterns captured by over thirty-one million EHR transactions in the period 2011-14 recorded by 471 primary care physicians, who collectively worked on 765,129 patients' EHRs. Our results suggest that the physicians logged an average of 3.08 hours on office visits and 3.17 hours on desktop medicine each day. Desktop medicine consists of activities such as communicating with patients through a secure patient portal, responding to patients' online requests for prescription refills or medical advice, ordering tests, sending staff messages, and reviewing test results. Over time, log records from physicians showed a decline in the time allocated to face-to-face visits, accompanied by an increase in time allocated HHS Public AccessAuthor manuscript Health Aff (Millwood). Author manuscript; available in PMC 2017 August 07. Author Manuscript Author ManuscriptAuthor Manuscript Author Manuscript to desktop medicine. Staffing and scheduling in the physician's office, as well as provider payment models for primary care practice, should account for these desktop medicine efforts.Physician time is a key resource in health services delivery. Understanding how physicians spend their clinical time is essential, given the need to understand practical capacity; guide staffing, scheduling, and support models in the physician's office; and improve the accuracy of payment for physician services. Fee-for-service payments are intended to reflect resources (captured as relative value units, or RVUs) used before, during, and after clinical encounters associated with face-to-face ambulatory care visits. 1-3 Questions have been raised about whether the reports underlying the RVU estimates are accurate and representative of true physician effort in providing patient care services. In the age of electronic health records (EHRs) with patient portals, patients often request services (such as prescription refills and medical advice) online, without face-to-face visits. Physician effort in addressing these online requests was absent from the original RVU calculations. [1][2][3] In addition to physician reports of their own efforts, 2,4,5 researchers have used time-andmotion studies 6 and video 7 and audio recordings. 8 While these methods capture significant physician effort, 6,9-11 they are costly to use and often evaluate only a limited number of physicians. Even the resource-based relative values scale (RBRVS) was built, for some specialties, on survey responses to vignettes from about twenty physicians. 1 This scale is still the chief tool used to determine the periodic updates to the Medicare Fee Schedule. Furthermore, concerns about the Hawthorne effect preclude ongoing, broad-based direct observa...

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confidence: 99%

Electronic Health Record Logs Indicate That Physicians Split Time Evenly Between Seeing Patients And Desktop Medicine

Tai-Seale¹,

et al. 2017

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“…The first dataset corresponds to the real access logs of the Vanderbilt University Medical Center (VUMC) EHR system. This system has been in application for over a decade and is well-ingrained in healthcare operations [14]. The logs document when an authenticated VUMC employee accessed a patient’s record.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Leveraging social networks to detect anomalous insider actions in collaborative environments

Chen

Nyemba

Zhang

et al. 2011

Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics

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Collaborative information systems (CIS) enable users to coordinate efficiently over shared tasks. T hey are often deployed in complex dynamic systems that provide users with broad access privileges, but also leave the system vulnerable to various attacks. Techniques to detect threats originating from beyond the system are relatively mature, but methods to detect insider threats are still evolving. A promising class of insider threat detection models for CIS focus on the communities that manifest between users based on the usage of common subjects in the system. However, current methods detect only when a user’s aggregate behavior is intruding, not when specific actions have deviated from expectation. In this paper, we introduce a method called specialized network anomaly detection (SNAD) to detect such events. SNAD assembles the community of users that access a particular subject and assesses if similarities of the community with and without a certain user are sufficiently different. We present a theoretical basis and perform an extensive empirical evaluation with the access logs of two distinct environments: those of a large electronic health record system (6,015 users, 130,457 patients and 1,327,500 accesses) and the editing logs of Wikipedia (2,388,955 revisors, 55,200 articles and 6,482,780 revisions). We compare SNAD with several competing methods and demonstrate it is significantly more effective: on average it achieves 20–30% greater area under an ROC curve.

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“…This work extends the analysis in [8], which focused on daily and weekly patterns, by considering session-level features. Our study examines three aspects: 1) system-level behavior, 2) user-level behavior and migration over sessions, and 3) user-record access relationship.…”

Section: Introductionmentioning

confidence: 80%

“…The system has been in operation for over a decade and is well-ingrained in the daily healthcare and business operations of the medical center [8]. StarChart is an integrated, longitudinal EMR system for both inpatient and outpatient settings.…”

Section: System Description and Modelmentioning

confidence: 99%

Towards understanding the usage pattern of web-based electronic medical record systems

Malin

2011

2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks

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The benefits and importance of electronic medical record (EMR) systems have been well recognized in the healthcare industry. Yet, their wide adoption still face significant barriers in providing on-demand secure medical information access while preserving patients’ privacy. Understanding the usage pattern of an EMR system is the first essential step towards building such environment. This paper conducts an in-depth trace analysis of a large-scale EMR system that has been in operation for more than a decade at the Vanderbilt Medical Center. Our study demonstrates several important characteristics of EMR system usage from the perspective of user-initiated sessions. First, the workload of the EMR system is highly stable and consistent with a weekly pattern. Second, EMR behavior varies between users, but each user’s behavior tends to be consistent with a slow rate of migration across sessions. Finally, the degree of access between users and medical records is sparse, echoing the limits of patient-caregiver relationships that manifest in real healthcare operations. We believe these observations can assist in the development of system security measures, such as EMR-specific anomaly detection systems, and facilitate system performance optimization.

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Learning relational policies from electronic health record access logs

Cited by 36 publications

References 40 publications

Electronic Health Record Logs Indicate That Physicians Split Time Evenly Between Seeing Patients And Desktop Medicine

Electronic Health Record Logs Indicate That Physicians Split Time Evenly Between Seeing Patients And Desktop Medicine

Leveraging social networks to detect anomalous insider actions in collaborative environments

Towards understanding the usage pattern of web-based electronic medical record systems

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