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Moy AJ, Cato KD, Withall J, Kim EY, Tatonetti N, Rossetti SC. Using Time Series Clustering to Segment and Infer Emergency Department Nursing Shifts from Electronic Health Record Log Files. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2023; 2022:805-814. [PMID: 37128367 PMCID: PMC10148355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Few computational approaches exist for abstracting electronic health record (EHR) log files into clinically meaningful phenomena like clinician shifts. Because shifts are a fundamental unit of work recognized in clinical settings, shifts may serve as a primary unit of analysis in the study of documentation burden. We conducted a proof- of-concept study to investigate the feasibility of a novel approach using time series clustering to segment and infer clinician shifts from EHR log files. From 33,535,585 events captured between April-June 2021, we computationally identified 43,911 potential shifts among 2,285 (74.2%) emergency department nurses. On average, computationally-identified shifts were 10.6±3.1 hours long. Based on data distributions, we classified these shifts based on type: day, evening, night; and length: 12-hour, 8-hour, other. We validated our method through manual chart review of computationally-identified 12-hour shifts achieving 92.0% accuracy. Preliminary results suggest unsupervised clustering methods may be a reasonable approach for rapidly identifying clinician shifts.
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Affiliation(s)
- Amanda J Moy
- Columbia University Department of Biomedical Informatics, NY, NY, USA
| | - Kenrick D Cato
- Columbia University Irving Medical Center Department of Emergency Medicine, NY, NY, USA
- Columbia University School of Nursing, NY, NY, USA
| | | | - Eugene Y Kim
- Columbia University Irving Medical Center Department of Emergency Medicine, NY, NY, USA
| | | | - Sarah C Rossetti
- Columbia University Department of Biomedical Informatics, NY, NY, USA
- Columbia University School of Nursing, NY, NY, USA
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Knox MK, Mehta PD, Dorsey LE, Yang C, Petersen LA. A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload. Appl Clin Inform 2023; 14:76-90. [PMID: 36473498 PMCID: PMC9891851 DOI: 10.1055/a-1993-7627] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE The aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow. METHODS Using 1 year (October 1, 2014-September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time. RESULTS As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional R2 = 0.237; marginal R2 = 0.199; intraclass correlation = 0.05). On average, an RN and a licensed practical nurse (LPN) with four patients, each with six medications, would be expected to take 70 and 74 minutes, respectively, to complete the medication pass. On a unit with median 10 patients per LPN, the median duration (127 minutes) represents untimely medication administration on more than half of staff days. With each additional patient assigned to a nurse, average start time was earlier by 4.2 minutes for RNs and 1.4 minutes for LPNs. CONCLUSION Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.
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Affiliation(s)
- Melissa K. Knox
- Michael E. DeBakey VA Medical Center, Houston, Texas, United States
- Center for Innovations in Quality, Effectiveness, and Safety, Houston, Texas, United States
- Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
| | - Paras D. Mehta
- Department of Medicine, University of Houston, Houston, Texas, United States
| | | | - Christine Yang
- Michael E. DeBakey VA Medical Center, Houston, Texas, United States
- Center for Innovations in Quality, Effectiveness, and Safety, Houston, Texas, United States
- Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
| | - Laura A. Petersen
- Michael E. DeBakey VA Medical Center, Houston, Texas, United States
- Center for Innovations in Quality, Effectiveness, and Safety, Houston, Texas, United States
- Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
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Hwang GJ, Chang PY, Tseng WY, Chou CA, Wu CH, Tu YF. Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020. Comput Inform Nurs 2022; 40:814-824. [PMID: 36516032 DOI: 10.1097/cin.0000000000000897] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The present study referred to the technology-based learning model to conduct a systematic review of the dimensions of nursing activities, research samples, research methods, roles of artificial intelligence, applied artificial intelligence algorithms, evaluation measure of algorithms, and research foci. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedure, this study obtained and analyzed a total of 102 high-quality artificial intelligence-associated nursing activities studies published from 2001 to 2020 in the Web of Science database. The results showed: (1) In terms of nursing activities, nursing management was explored the most, followed by nursing assessment; (2) quantitative methods were most frequently adopted in artificial intelligence-associated nursing activities studies to investigate issues related to patients, followed by nursing staff; (3) the most adopted roles of artificial intelligence in artificial intelligence-associated nursing activities studies were profiling and prediction, followed by assessment and evaluation; (4) artificial intelligence-associated nursing activities studies frequently mixed applied artificial intelligence algorithms and evaluation measure of algorithms; (5) in the dimension of research foci, most studies mainly paid attention to the design or evaluation of the artificial intelligence systems/instruments, followed by investigating the correlation and affect issues. Based on the findings, several recommendations are raised as a reference for future researchers, educators, and policy makers.
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Affiliation(s)
- Gwo-Jen Hwang
- Author Affiliations : Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology (Dr Hwang, Ms Chang, Ms Tseng, Mr Chou, and Ms Wu); and Department of Library and Information Science, Bachelor's Program in Information Innovation and Digital life, Research and Development Center for Physical Education, Health, and Information Technology, Fu Jen Catholic University (Dr Tu), Taiwan
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Lo B, Sequeira L, Strudwick G, Jankowicz D, Almilaji K, Karunaithas A, Hang D, Tajirian T. Accuracy of Physician Electronic Health Record Usage Analytics using Clinical Test Cases. Appl Clin Inform 2022; 13:928-934. [PMID: 36198309 PMCID: PMC9534596 DOI: 10.1055/s-0042-1756424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/25/2022] [Indexed: 11/02/2022] Open
Abstract
Usage log data are an important data source for characterizing the potential burden related to use of the electronic health record (EHR) system. However, the utility of this data source has been hindered by concerns related to the real-world validity and accuracy of the data. While time-motion studies have historically been used to address this concern, the restrictions caused by the pandemic have made it difficult to carry out these studies in-person. In this regard, we introduce a practical approach for conducting validation studies for usage log data in a controlled environment. By developing test runs based on clinical workflows and conducting them within a test EHR environment, it allows for both comparison of the recorded timings and retrospective investigation of any discrepancies. In this case report, we describe the utility of this approach for validating our physician EHR usage logs at a large academic teaching mental health hospital in Canada. A total of 10 test runs were conducted across 3 days to validate 8 EHR usage log metrics, finding differences between recorded measurements and the usage analytics platform ranging from 9 to 60%.
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Affiliation(s)
- Brian Lo
- Information Management Group, Centre for Addiction and Mental Health, Toronto, Canada
- Centre for Complex Interventions (Digital Interventions Unit), Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Lydia Sequeira
- Information Management Group, Centre for Addiction and Mental Health, Toronto, Canada
- Centre for Complex Interventions (Digital Interventions Unit), Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Gillian Strudwick
- Information Management Group, Centre for Addiction and Mental Health, Toronto, Canada
- Centre for Complex Interventions (Digital Interventions Unit), Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Damian Jankowicz
- Information Management Group, Centre for Addiction and Mental Health, Toronto, Canada
| | - Khaled Almilaji
- Information Management Group, Centre for Addiction and Mental Health, Toronto, Canada
| | - Anjchuca Karunaithas
- Information Management Group, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Health and Society, University of Toronto Scarborough, Scarborough, Canada
| | - Dennis Hang
- Information Management Group, Centre for Addiction and Mental Health, Toronto, Canada
- Health Information Science, University of Victoria, Victoria, British Columbia, Canada
| | - Tania Tajirian
- Information Management Group, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Family and Community Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
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Womack DM, Miech EJ, Fox NJ, Silvey LC, Somerville AM, Eldredge DH, Steege LM. Coincidence Analysis: A Novel Approach to Modeling Nurses' Workplace Experience. Appl Clin Inform 2022; 13:794-802. [PMID: 36044917 PMCID: PMC9433166 DOI: 10.1055/s-0042-1756368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 07/13/2022] [Indexed: 11/02/2022] Open
Abstract
OBJECTIVES The purpose of this study is to identify combinations of workplace conditions that uniquely differentiate high, medium, and low registered nurse (RN) ratings of appropriateness of patient assignment during daytime intensive care unit (ICU) work shifts. METHODS A collective case study design and coincidence analysis were employed to identify combinations of workplace conditions that link directly to high, medium, and low RN perception of appropriateness of patient assignment at a mid-shift time point. RN members of the study team hypothesized a set of 55 workplace conditions as potential difference makers through the application of theoretical and empirical knowledge. Conditions were derived from data exported from electronic systems commonly used in nursing care. RESULTS Analysis of 64 cases (25 high, 24 medium, and 15 low) produced three models, one for each level of the outcome. Each model contained multiple pathways to the same outcome. The model for "high" appropriateness was the simplest model with two paths to the outcome and a shared condition across pathways. The first path comprised of the absence of overtime and a before-noon patient discharge or transfer, and the second path comprised of the absence of overtime and RN assignment to a single ICU patient. CONCLUSION Specific combinations of workplace conditions uniquely distinguish RN perception of appropriateness of patient assignment at a mid-shift time point, and these difference-making conditions provide a foundation for enhanced observability of nurses' work experience during hospital work shifts. This study illuminates the complexity of assessing nursing work system status by revealing that multiple paths, comprised of multiple conditions, can lead to the same outcome. Operational decision support tools may best reflect the complex adaptive nature of the work systems they intend to support by utilizing methods that accommodate both causal complexity and equifinality.
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Affiliation(s)
- Dana M. Womack
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | | | - Nicholas J. Fox
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | - Linus C. Silvey
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | - Anna M. Somerville
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | - Deborah H. Eldredge
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | - Linsey M. Steege
- School of Nursing, University of Wisconsin–Madison, Madison, Wisconsin, United States
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Chopannejad S, Sadoughi F, Bagherzadeh R, Shekarchi S. Predicting major adverse cardiovascular events in acute coronary syndrome: A scoping review of machine learning approaches. Appl Clin Inform 2022; 13:720-740. [PMID: 35617971 PMCID: PMC9329142 DOI: 10.1055/a-1863-1589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. OBJECTIVE The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. METHODS In order to predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The findings of the studies are presented in the form of a narrative synthesis of evidence. RESULTS According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N=20) achieved a high Area under the ROC Curve between 0.8 to 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. CONCLUSION Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.
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Affiliation(s)
- Sara Chopannejad
- Student Research Committee, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Farahnaz Sadoughi
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Rafat Bagherzadeh
- English Language Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Sakineh Shekarchi
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
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