A Hybrid Density-Based Outlier Detection Model for Privacy in Electronic Patient Record system

To systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical activities. In July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research. Eighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy. While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis. EHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.

The creation of an electronic patient record (EPR) system with a user-friendly interface based on the concept of the episode of care was considered an urgent priority in the present Greek context, where a Health Care Reform program is in progress. This paper reports the procedures of developing an EPR system, and outlines some of its essentials and key issues. We performed a systematic review and analyzed the perceptions and patterns of use of existing EPR systems among Greek general practitioners. On the basis of this analysis, Transhis was selected using defined criteria for appropriateness, efficiency, and feasibility for general practice as a prototype, for creating a Windows-based EPR system using the International Classification of Primary Care (ICPC-2) and International Classification of Diseases (ICD-10) as classifications. The new EPR system seems appropriate for use within the current Greek primary care setting. Further studies are required for its evaluation.

ObjectivesTo develop and test a novel machine learning approach for monitoring impact of computerized clinical decision support (CDS) tools on clinicians’ electronic health record (EHR) activities.Materials and MethodsOur CDS monitoring approach leverages topic modeling, a latent-variable statistical machine learning method, to infer health providers’ EHR activities from EHR audit logs. We applied this approach to monitor the impact of a tobacco cessation support CDS tool newly implemented in 5 cancer clinics (2018-2021). We trained the topic model on EHR audit log data from 3445 encounters (pre-CDS-implementation: 1734, post-CDS-implementation: 1711) for patients with active smoking status. The number of topics was automatically determined based on within-topic coherence and across-topic divergence, and the identified topics were assigned clinically relevant EHR activity labels by 4 domain experts.ResultsThe topic model identified 2 distinct activities focusing on CDS (act on CDS, bypass/postpone CDS), 2 activities related to CDS (review patient records and address alerts, use note templates and acknowledge the completion of CDS), 6 related to accessing (access patient station) and reviewing patient data (external records, synopsis data, snapshot of patient data, problem list/diagnosis/notes, treatment plan), and 4 related to modifying EHR (modify diagnosis/problem lists, document visit with record review, perform administrative activities for visit and billing, and document follow-up care plan). Comparing matched 1-hour after-check-in windows post-implementation (n = 841) versus pre-implementation (n = 841) of CDS, the mean prevalence (expressed as proportions out of 1.0) of providers’ EHR-use activity increased on CDS-focused activities (0.073, 95% CI, 0.066-0.079) and CDS-related activities (0.098, 95% CI, 0.089-0.106) and decreased on modifying EHR (−0.113, 95% CI, −0.124 to −0.102) and reviewing patient data (−0.058, 95% CI, −0.072 to −0.044).DiscussionOur topic model-based CDS monitoring approach can identify shifts in prevalence of EHR-use activities pre-implementation versus post-implementation. This approach can be applied to detect unintended changes in EHR activities on a large population scale following CDS implementation, providing valuable insights to guide focused qualitative investigations for CDS improvement or de-implementation.ConclusionOur approach offers a scalable, data-driven framework for evaluating the real-world impact of EHR-embedded CDS tools. Built on a generic machine learning framework, this approach could be adapted to explore impact of other healthcare quality improvement strategies using EHR-integrated CDS interventions.

Switzerland's Electronic Patient Record (EPR) system faces a critical adoption crisis: less than 2% of the population has opened an account despite years of investment. To understand this failure from the citizen's perspective, we launched the first "expedition" in which 120 volunteers ("salutonauts") systematically tested Switzerland's current digital health infrastructure by opening EPRs, requesting their health data and documenting their experiences. This represents the first real-world, citizen-led assessment of Switzerland's health data infrastructure. Our findings reveal three fundamental insights. First, citizens appreciate the concept of personally controlled health records governed by a trustworthy national framework. Second, the current EPR implementation is both impractical and economically untenable under existing conditions: opening an EPR nationwide would cost CHF 400 million (calculated from required man-hours alone), while uploading historic documents would cost CHF 1 billion, and obtaining insurance claims data would cost over CHF 5 billion in lost productivity. Third, the recently announced draft of the new Electronic Health Dossier Law (E-GD) comes at a critical moment, since evidence-based redesign of the EPR system is essential. 95% of participants valued comprehensive medical checkups producing structured data, demonstrating citizens' appetite for high-quality digital health records when they provide tangible benefits. Our expedition methodology, which is agile, citizen-centred and involves real-world testing, offers a replicable model for evaluating digital health initiatives before massive implementation costs are incurred. With the E-GD revision underway, Switzerland has a unique opportunity to develop an EPR system that serves patients and physicians above all. Successful implementation requires EPR opening at birth by default (with the ability to opt-out), the AI-powered automation of data collection and uploading, seamless healthcare professional interfaces, and using the EPR as the billing data gateway. The alternative - i.e. continuing the current approach - virtually guarantees failure based on economic factors alone.

The quality of life of a patient post-surgical intervention is important for the long-term monitoring and treatment of patient disease. In addition to a patient’s journey in terms of what...

Electronic patient record (EPR) systems are widely used. This study explores the context and use of systems to provide insights into improving their use in clinical practice. We used video to observe 163 consultations by 16 clinicians using four EPR brands. We made a visual study of the consultation room and coded interactions between clinician, patient, and computer. Few patients (6.9%, n=12) declined to participate. Patients looked at the computer twice as much (47.6 s vs 20.6 s, p<0.001) when it was within their gaze. A quarter of consultations were interrupted (27.6%, n=45); and in half the clinician left the room (12.3%, n=20). The core consultation takes about 87% of the total session time; 5% of time is spent pre-consultation, reading the record and calling the patient in; and 8% of time is spent post-consultation, largely entering notes. Consultations with more than one person and where prescribing took place were longer (R(2) adj=22.5%, p<0.001). The core consultation can be divided into 61% of direct clinician-patient interaction, of which 15% is examination, 25% computer use with no patient involvement, and 14% simultaneous clinician-computer-patient interplay. The proportions of computer use are similar between consultations (mean=40.6%, SD=13.7%). There was more data coding in problem-orientated EPR systems, though clinicians often used vague codes. The EPR system is used for a consistent proportion of the consultation and should be designed to facilitate multi-tasking. Clinicians who want to promote screen sharing should change their consulting room layout.

BackgroundThe recent, rapid development of digital technologies offers new possibilities for more efficient implementation of electronic health record (EHR) and personal health record (PHR) systems. A growing volume of healthcare data has been the hallmark of this digital transformation. The large healthcare datasets' complexity and their dynamic nature pose various challenges related to processing, analysis, storage, security, privacy, data exchange, and usability. Materials and MethodsWe performed a systematic review of systematic reviews to assess technological progress in EHR and PHR systems. We searched MEDLINE, Cochrane, Web of Science, and Scopus for systematic literature reviews on technological advancements that support EHR and PHR systems published between January 1, 2010, and October 06, 2020. ResultsThe searches resulted in a total of 2,448 hits. Of these, we finally selected 23 systematic reviews. Most of the included papers dealt with information extraction tools and natural language processing technology (n = 10), followed by studies that assessed the use of blockchain technology in healthcare (n = 8). Other areas of digital technology research included EHR and PHR systems in austere settings (n = 1), de-identification methods (n = 1), visualization techniques (n = 1), communication tools within EHR and PHR systems (n = 1), and methodologies for defining Clinical Information Models that promoted EHRs and PHRs interoperability (n = 1). ConclusionsTechnological advancements can improve the efficiency in the implementation of EHR and PHR systems in numerous ways. Natural language processing techniques, either rule-based, machine-learning, or deep learning-based, can extract information from clinical narratives and other unstructured data locked in EHRs and PHRs, allowing secondary research (i.e., phenotyping). Moreover, EHRs and PHRs are expected to be the primary beneficiaries of the blockchain technology implementation on Health Information Systems. Governance regulations, lack of trust, poor scalability, security, privacy, low performance, and high cost remain the most critical challenges for implementing these technologies.

The impact of electronic medical record systems on outpatient workflows: A longitudinal evaluation of its workflow effects

Objective: To evaluate the continuity and completeness of electronic health record (EHR) data, and the concordance of select clinical outcomes and baseline comorbidities between EHR and linked claims data, from three healthcare delivery systems in Taiwan. Methods: We identified oral hypoglycemic agent (OHA) users from the Integrated Medical Database of National Taiwan University Hospital (NTUH-iMD), which was linked to the National Health Insurance Research Database (NHIRD), from June 2011 to December 2016. A secondary evaluation involved two additional EHR databases. We created consecutive 90-day periods before and after the first recorded OHA prescription and defined patients as having continuous EHR data if there was at least one encounter or prescription in a 90-day interval. EHR data completeness was measured by dividing the number of encounters in the NTUH-iMD by the number of encounters in the NHIRD. We assessed the concordance between EHR and claims data on three clinical outcomes (cardiovascular events, nephropathy-related events, and heart failure admission). We used individual comorbidities that comprised the Charlson comorbidity index to examine the concordance of select baseline comorbidities between EHRs and claims. Results: We identified 39,268 OHA users in the NTUH-iMD. Thirty-one percent (n = 12,296) of these users contributed to the analysis that examined data continuity during the 6-month baseline and 24-month follow-up period; 31% (n = 3,845) of the 12,296 users had continuous data during this 30-month period and EHR data completeness was 52%. The concordance of major cardiovascular events, nephropathy-related events, and heart failure admission was moderate, with the NTU-iMD capturing 49–55% of the outcome events recorded in the NHIRD. The concordance of comorbidities was considerably different between the NTUH-iMD and NHIRD, with an absolute standardized difference >0.1 for most comorbidities examined. Across the three EHR databases studied, 29–55% of the OHA users had continuous records during the 6-month baseline and 24-month follow-up period. Conclusion: EHR data continuity and data completeness may be suboptimal. A thorough evaluation of data continuity and completeness is recommended before conducting clinical and translational research using EHR data in Taiwan.

We integrate literature- and data-driven task analysis methods to derive an initial task taxonomy for electronic health record (EHR) and electronic medical record (EMR) data analysis. An EHR (EMR) is a digital and longitudinal version of a patients health(medical) information and may include all key clinical events relevant to that persons health (medical) history, such as provider, demographics, progress notes, medicine, diagnosis, etc. Our goal is to arrive a task taxonomy for analyzing EHR (EMR) datasets because tasks play an important role in the design and evaluation of visualization techniques. Our method has three stages: data collection, task modelling, and task taxonomy summary. In data collection, we first survey related literature from the past two decades and extract typical tasks and corresponding data by extracting goals and scenarios of the particular work. We introduce multiple continuous relations to describe specific binary or multiple continuous relation-seeking tasks. Finally, we arrive an initial set of task types for EHR/EMR analysis that guide the design and evaluation of visualization techniques.

Background:The Electronic Health Record (EHR) system is a systematic collection of health information based on electronics and integrated with information systems in the hospital network. The use of the EHR system is very important in a comprehensive strategy to improve the quality of health and patient safety, especially in the operating room. Purpose: to find out the benefits of using EHR on operating room efficiency which includes treatment costs, OR efficiency metrics, turn over time, documentation time and nursing quality. Method: This literature review was searched through relevant evidence-based studies usingdatabase at PROQUEST, Science Direct, Scopus, Elsevier from 2017-2021. Results:The EHR system has a cost-saving impact on the average operating room cost of 36-37 dollars per minute and can reduce costs during pre, intra and post surgery. For the FPIR value from the data of 27,403 cases after post-implementation to 9,630 cases, it means that there are no missing patients to enter the operating room, while at the time of delay the FCOTSin pre-implementation of EHR was 1972 cases or (7.20%) and post-implementation 377 cases (3.91%). Meanwhile, the turn over time (TOT) after post implementation showed a significant increase of 53 minutes, p &lt; 0.001. The introduction of the EHR also had an impact on increasing documentation time for nurses from 23% to 35%. In addition, it also provides benefits to the quantity and quality of nursing. Conclusion: The usefulness of the EHR system contributes to increasing the efficiency of the operating room so that it can provide effectiveness in service and quality of care. Nurses are expected to be able to input the EHR system and service agencies can implement these technological developments

Changes in Electronic Health Record Use Time and Documentation over the Course of a Decade

ObjectiveTo derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time.Materials and MethodsA cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time.ResultsFive out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month −0.01 hours; practicing cardiology −1.30 hours; medical subspecialties −0.89 hours (except gastroenterology, P = .002); neurology/psychiatry −2.60 hours; obstetrics/gynecology −1.88 hours; pediatrics −1.05 hours (P = .001); sports/physical medicine and rehabilitation −3.25 hours; and surgical specialties −3.65 hours.ConclusionsFor every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.

The temporal progression and workload-related causal contributors to physician burnout are not well-understood. To characterize burnout's time course and evaluate the effect of time-varying workload on burnout and medical errors. Six-month longitudinal cohort study with measurements of burnout, workload, and wrong-patient orders every 4 weeks. Seventy-five intern physicians in internal medicine, pediatrics, and anesthesiology at a large academic medical center. Burnout was measured using the Professional Fulfillment Index survey. Workload was collected from electronic health record (EHR) audit logs and summarized as follows: total time spent on the EHR, after-hours EHR time, patient load, inbox time, chart review time, note-writing time, and number of orders. Wrong-patient orders were assessed using retract-and-reorder events. Seventy-five of 104 interns enrolled (72.1%) in the study. A total of 337 surveys and 8,863,318 EHR-based actions were analyzed. Median burnout score across the cohort across all time points was 1.2 (IQR 0.7-1.7). Individual-level burnout was variable (median monthly change 0.3, IQR 0.1-0.6). In multivariable analysis, increased total EHR time (β=0.121 for an increase from 54.5 h per month (25th percentile) to 123.0 h per month (75th percentile), 95%CI=0.016-0.226), increased patient load (β=0.130 for an increase from 4.9 (25th percentile) to 7.1 (75th percentile) patients per day, 95%CI=0.053-0.207), and increased chart review time (β=0.096 for an increase from 0.39 (25th percentile) to 0.59 (75th percentile) hours per patient per day, 95%CI=0.015-0.177) were associated with an increased burnout score. After adjusting for the total number of ordering sessions, burnout was not statistically associated with an increased rate of wrong-patient orders (rate ratio=1.20, 95%CI=0.76-1.89). Burnout and recovery were associated with recent clinical workload for a cohort of physician trainees, highlighting the elastic nature of burnout. Wellness interventions should focus on strategies to mitigate sustained elevations of work responsibilities.

Background: Efficient population-level surveillance of youth-onset diabetes is essential for public health planning. Early surveillance approaches predated the use of electronic health records (EHR) and required manual chart reviews of all potential cases. Approaches utilizing EHR data present the potential for significant gains in cost efficiency, but the degree of precision remains unclear. Objective: Identify characteristics of diabetes patients &amp;lt;20 years in Colorado who were missed or misclassified by an EHR algorithm compared with patients identified by a traditional approach including manual chart review. Methods: We compared incident diabetes patients diagnosed in 2014-2018 in Colorado by SEARCH for Diabetes in Youth and ascertained using manual chart review to determine diabetes status, type, and date of diagnosis to those identified by date of first diabetes diagnosis per EHR data extracts. Both approaches used similar and multiple case sources (networks of providers and hospitals) designed to cover all of Colorado. Results: SEARCH ascertained 2206 patients with incident diabetes in 2014-2018. The EHR-based algorithm correctly identified incident diabetes status for 81% with 8.7% not identified as a diabetes case, and 10.3% assigned the wrong incident year. Patients missed or misclassified by the algorithm were more likely to have type 2 diabetes (36.1% incorrect or missing) or other diabetes types (36.4% incorrect or missing), to identify as Hispanic of any race (24.0% incorrect or missing) or other race (45.5% incorrect or missing) or were first identified by SEARCH at sources that did not use EHRs (75.8% incorrect or missing). Conclusion: Algorithmic misclassification from EHR system data using date of first diagnosis may be more profound for minority race/ethnic groups and those without type 1 diabetes. Improved understanding of who is missed or misclassified by such approaches may assist with bias correction efforts to provide valid, generalizable estimates of the diagnosed diabetes population. Disclosure A. Bellatorre: None. S. Burgett: None. T. Anderson: None. R.B. Conway: None. B.A. Shiferaw: None. D. Dabelea: None. T.L. Crume: None. Funding Centers for Disease Control and Prevention and National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (U18DP006139)