1
|
Kissler MJ, Porter S, Knees M, Kissler K, Keniston A, Burden M. Attention Among Health Care Professionals : A Scoping Review. Ann Intern Med 2024; 177:941-952. [PMID: 38885508 DOI: 10.7326/m23-3229] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND The concept of attention can provide insight into the needs of clinicians and how health systems design can impact patient care quality and medical errors. PURPOSE To conduct a scoping review to 1) identify and characterize literature relevant to clinician attention; 2) compile metrics used to measure attention; and 3) create a framework of key concepts. DATA SOURCES Cumulated Index to Nursing and Allied Health Literature (CINAHL), Medline (PubMed), and Embase (Ovid) from 2001 to 26 February 2024. STUDY SELECTION English-language studies addressing health care worker attention in patient care. At least dual review and data abstraction. DATA EXTRACTION Article information, health care professional studied, practice environment, study design and intent, factor type related to attention, and metrics of attention used. DATA SYNTHESIS Of 6448 screened articles, 585 met inclusion criteria. Most studies were descriptive (n = 469) versus investigational (n = 116). More studies focused on barriers to attention (n = 387; 342 descriptive and 45 investigational) versus facilitators to improving attention (n = 198; 112 descriptive and 86 investigational). We developed a framework, grouping studies into 6 categories: 1) definitions of attention, 2) the clinical environment and its effect on attention, 3) personal factors affecting attention, 4) relationships between interventions or factors that affect attention and patient outcomes, 5) the effect of clinical alarms and alarm fatigue on attention, and 6) health information technology's effect on attention. Eighty-two metrics were used to measure attention. LIMITATIONS Does not synthesize answers to specific questions. Quality of studies was not assessed. CONCLUSION This overview may be a resource for researchers, quality improvement experts, and health system leaders to improve clinical environments. Future systematic reviews may synthesize evidence on metrics to measure attention and on the effectiveness of barriers or facilitators related to attention. PRIMARY FUNDING SOURCE None.
Collapse
Affiliation(s)
- Mark J Kissler
- Division of Hospital Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado (M.J.K., S.P., M.K., A.K., M.B.)
| | - Samuel Porter
- Division of Hospital Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado (M.J.K., S.P., M.K., A.K., M.B.)
| | - Michelle Knees
- Division of Hospital Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado (M.J.K., S.P., M.K., A.K., M.B.)
| | - Katherine Kissler
- College of Nursing, University of Colorado Anschutz Medical Campus, Aurora, Colorado (K.K.)
| | - Angela Keniston
- Division of Hospital Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado (M.J.K., S.P., M.K., A.K., M.B.)
| | - Marisha Burden
- Division of Hospital Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado (M.J.K., S.P., M.K., A.K., M.B.)
| |
Collapse
|
2
|
Ranjbar A, Mork EW, Ravn J, Brøgger H, Myrseth P, Østrem HP, Hallock H. Managing Risk and Quality of AI in Healthcare: Are Hospitals Ready for Implementation? Risk Manag Healthc Policy 2024; 17:877-882. [PMID: 38617593 PMCID: PMC11016246 DOI: 10.2147/rmhp.s452337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/20/2024] [Indexed: 04/16/2024] Open
Abstract
Artificial intelligence (AI) provides a unique opportunity to help meet the demands of the future healthcare system. However, hospitals may not be well equipped to handle safe and effective development and/or procurement of AI systems. Furthermore, upcoming regulations such as the EU AI Act may enforce the need to establish new management systems, quality assurance and control mechanisms, novel to healthcare organizations. This paper discusses challenges in AI implementation, particularly potential gaps in current management systems (MS), by reviewing the harmonized standard for AI MS, ISO 42001, as part of a gap analysis of a tertiary acute hospital with ongoing AI activities. Examination of the industry agnostic ISO 42001 reveals a technical debt within healthcare, aligning with previous research on digitalization and AI implementation. To successfully implement AI with quality assurance in mind, emphasis should be put on the foundation and structure of the healthcare organizations, including both workforce and data infrastructure.
Collapse
Affiliation(s)
- Arian Ranjbar
- Medical Technology and E-Health, Akershus University Hospital, Lørenskog, Norway
| | | | - Jesper Ravn
- Medical Technology and E-Health, Akershus University Hospital, Lørenskog, Norway
| | - Helga Brøgger
- Group Research and Development, DNV AS, Høvik, Norway
| | - Per Myrseth
- Group Research and Development, DNV AS, Høvik, Norway
| | | | - Harry Hallock
- Group Research and Development, DNV AS, Høvik, Norway
| |
Collapse
|
3
|
Ding S, Huang X, Sun R, Yang L, Yang X, Li X, Liu J, Yang H, Zhou H, Huang X, Su F, Shu L, Zheng X, Wang X. The relationship between alarm fatigue and burnout among critical care nurses: A cross-sectional study. Nurs Crit Care 2023; 28:940-947. [PMID: 37070292 DOI: 10.1111/nicc.12899] [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: 06/21/2022] [Revised: 01/16/2023] [Accepted: 02/28/2023] [Indexed: 04/19/2023]
Abstract
BACKGROUND Alarm fatigue has significant negative impacts on nurses and patient safety. However, the relationship between alarm fatigue and burnout is still unclear. AIMS This study aimed to explore the relationship between alarm fatigue and burnout among critical care nurses. STUDY DESIGN A descriptive-analytical cross-sectional study design was used. Data were collected from five hospitals in mainland China between January 2022 and March 2022. A general information questionnaire, the Chinese version of the Intensive Care Unit Nurse Alarm Fatigue Questionnaire, and the Chinese version of the Maslach Burnout Inventory were used. RESULTS A total of 236 critical care nurses were enrolled in this study. The mean score of alarm fatigue among critical care nurses was 21.11 ± 6.83. The results showed that critical care nurses experienced moderate alarm fatigue levels, and most nurses had moderate to high levels of burnout. The multiple linear regression analyses showed that alarm fatigue was independently associated with emotional exhaustion, depersonalization dimensions, and reduced personal accomplishment dimension. CONCLUSIONS Alarm fatigue was associated with burnout among critical care nurses. Reducing critical care nurses' alarm fatigue may help to alleviate burnout. RELEVANCE TO CLINICAL PRACTICE Managers should provide comprehensive training for nurses and promote the application of artificial intelligence technology in alarm management to reduce alarm fatigue and improve burnout among critical care nurses.
Collapse
Affiliation(s)
- Shenglan Ding
- Pediatric Intensive Care Unit, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiuhua Huang
- Surgery Intensive Care Unit, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Rong Sun
- Adult Intensive Care Unit, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Lili Yang
- Surgery Intensive Care Unit, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaorong Yang
- Department of Rehabilitation, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xue Li
- Surgery Intensive Care Unit, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianhua Liu
- Adult Intensive Care Unit, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hui Yang
- Pediatric Intensive Care Unit, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Zhou
- Neonatal Intensive Care Unit, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoming Huang
- Pediatric Intensive Care Unit, West China Second University Hospital, Sichuan University/West China Women's and Children's Hospital, Chengdu, China
| | - Fenghua Su
- Intensive Care Unit 1, Fujian Provincial Hospital, Fuzhou, China
| | - Lingzhi Shu
- Intensive Care Unit, The Affiliated Hospital of Guizhou Medical University
| | - Xingli Zheng
- Pediatric Intensive Care Unit, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Wang
- Department of Nursing, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
4
|
Cánovas-Segura B, Morales A, Juarez JM, Campos M. Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework. J Biomed Inform 2023:104397. [PMID: 37245656 DOI: 10.1016/j.jbi.2023.104397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/11/2023] [Accepted: 05/15/2023] [Indexed: 05/30/2023]
Abstract
Alerts are a common functionality of clinical decision support systems (CDSSs). Although they have proven to be useful in clinical practice, the alert burden can lead to alert fatigue and significantly reduce their usability and acceptance. Based on a literature review, we propose a unified framework consisting of a set of meaningful timestamps that allows the use of state-of-the-art measures for alert burden, such as alert dwell time, alert think time, and response time. In addition, it can be used to investigate other measures that could be relevant as regards dealing with this problem. Furthermore, we provide a case study concerning three different types of alerts to which the framework was successfully applied. We consider that our framework can easily be adapted to other CDSSs and that it could be useful for dealing with alert burden measurement thus contributing to its appropriate management.
Collapse
Affiliation(s)
| | - Antonio Morales
- AIKE Research Group (INTICO), University of Murcia, Murcia, Spain.
| | - Jose M Juarez
- AIKE Research Group (INTICO), University of Murcia, Murcia, Spain.
| | - Manuel Campos
- AIKE Research Group (INTICO), University of Murcia, Murcia, Spain; Murcian Bio-Health Institute (IMIB-Arrixaca), Murcia, Spain.
| |
Collapse
|
5
|
Loeb RG. Incremental advances will improve medical device alarm sounds. Br J Anaesth 2023; 130:401-403. [PMID: 36792387 DOI: 10.1016/j.bja.2023.01.014] [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: 11/25/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 02/16/2023] Open
Abstract
The March issue contains a laboratory study of auditory perception, which is an unusual topic for this journal. A perspective is provided on how the study relates to recent research on clinical auditory alarms and displays. Techniques used in the study are explored and explained, such as enrolment of non-clinician volunteer participants, use of coordinate response measure phrase stimuli, presentation of sound loudness levels using the decibel scale, and analysis using signal detection theory. Such efforts to improve the safety, efficacy, and tolerability of modern medical device alarms are critical for improved patient safety.
Collapse
Affiliation(s)
- Robert G Loeb
- The University of Florida College of Medicine, Gainesville, FL, USA; University of Queensland, St Lucia, QNS, Australia.
| |
Collapse
|
6
|
Kim D, Jin BT. Development and Comparative Performance of Physiologic Monitoring Strategies in the Emergency Department. JAMA Netw Open 2022; 5:e2233712. [PMID: 36169956 PMCID: PMC9520367 DOI: 10.1001/jamanetworkopen.2022.33712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Accurate and timely documentation of vital signs affects all aspects of triage, diagnosis, and management. The adequacy of current patient monitoring practices and the potential to improve on them are poorly understood. OBJECTIVE To develop measures of fit between documented and actual patient vital signs throughout the visit, as determined from continuous physiologic monitoring, and to compare the performance of actual practice with alternative patient monitoring strategies. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study evaluated 25 751 adult visits to continuously monitored emergency department (ED) beds between August 1, 2020, and December 31, 2021. A series of monitoring strategies for the documentation of vital signs (heart rate [HR], respiratory rate [RR], oxygen saturation by pulse oximetry [Spo2], mean arterial pressure [MAP]) was developed and the strategies' ability to capture physiologic trends and vital sign abnormalities simulated. Strategies included equal spacing of charting events, charting at variable intervals depending on the last observed values, and discrete optimization of charting events. MAIN OUTCOMES AND MEASURES Coverage was defined as the proportion of monitor-derived vital sign measurements (at 1-minute resolution) that fall within the bounds of nursing-charted values over the course of an ED visit (HR ± 5 beats/min, RR ± 3 breaths/min, Spo2 ± 2%, and MAP ± 6 mm Hg). Capture was defined as the documentation of a vital sign abnormality detected by bedside monitor (tachycardia [HR >100 beats/min], bradycardia [HR <60 beats/min], hypotension [MAP <65 mm Hg], and hypoxia [Spo2 <95%]). RESULTS Median patient age was 60 years (IQR, 43-75 years), and 13 329 visits (51.8%) were by women. Monitored visits had a median of 4 (IQR, 2-5) vital sign charting events per visit. Compared with actual practice, a simple rule, which observes vital signs more frequently if the last observation fell outside the bounds of the previous values, and using the same number of observations as actual practice, produced relative coverage improvements of 31.5% (95% CI, 30.5%-32.5%) for HR, 31.0% (95% CI, 30.0%-32.0%) for MAP, 16.8% (95% CI, 16.0%-17.6%) for RR, and 7.8% (95% CI, 7.3%-8.3%) for Spo2. The same strategy improved capture of abnormalities by 38.9% (95% CI, 26.8%-52.2%) for tachycardia, 38.1% (95% CI, 29.0%-47.9%) for bradycardia, 39.0% (95% CI, 24.2%-55.7%) for hypotension, and 123.1% (95% CI, 110.7%-136.3%) for hypoxia. Analysis of optimal coverage suggested an additional scope for improvement through more sophisticated strategies. CONCLUSIONS AND RELEVANCE In this cross-sectional study, actual documentation of ED vital signs was variable and incomplete, missing important trends and abnormalities. Alternative monitoring strategies may improve on current practice without increasing the overall frequency of patient monitoring.
Collapse
Affiliation(s)
- David Kim
- Department of Emergency Medicine, Stanford University, Palo Alto, California
| | - Boyang Tom Jin
- Department of Computer Science, Stanford University, Palo Alto, California
| |
Collapse
|
7
|
Alsuyayfi S, Alanazi A. Impact of clinical alarms on patient safety from nurses’ perspective. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
|
8
|
Sowan AK, Staggers N, Reed CC, Austin T, Chen Q, Xu S, Lopez E. State of Science in Alarm System Safety: Implications for Researchers, Vendors, and Clinical Leaders. Biomed Instrum Technol 2022; 56:19-28. [PMID: 35213681 PMCID: PMC8979078 DOI: 10.2345/0899-8205-56.1.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Alarm fatigue is a complex phenomenon that needs to be assessed within the context of the clinical setting. Considering that complexity, the available information on how to address alarm fatigue and improve alarm system safety is relatively scarce. This article summarizes the state of science in alarm system safety based on the eight dimensions of a sociotechnical model for studying health information technology in complex adaptive healthcare systems. The summary and recommendations were guided by available systematic reviews on the topic, interventional studies published between January 2019 and February 2022, and recommendations and evidence-based practice interventions published by professional organizations. The current article suggests implications to help researchers respond to the gap in science related to alarm safety, help vendors design safe monitoring systems, and help clinical leaders apply evidence-based strategies to improve alarm safety in their settings. Physiologic monitors in intensive care units-the devices most commonly used in complex care environments and associated with the highest number of alarms and deaths-are the focus of the current work.
Collapse
Affiliation(s)
- Azizeh K. Sowan
- Azizeh K. Sowan, PhD, RN, MSN, MSDA, MBA, FAAN is an associate professor in the School of Nursing at the University of Texas Health at San Antonio.
| | - Nancy Staggers
- Nancy Staggers, PhD, RN, FAAN is a professor in the School of Nursing and Department of Biomedical Informatics at the University of Utah in Salt Lake City.
| | - Charles C. Reed
- Charles C. Reed, PhD, RN, CNRN, is a vice president and associate chief nursing officer at the University Health System in San Antonio, TX.
| | - Tommye Austin
- Tommye Austin, PhD, MBA, RN, NEA-BC, is a senior vice president and chief nurse executive at the University Health System in San Antonio.
| | - Qian Chen
- Qian Chen, PhD, is an assistant professor in the Department of Computer Science at the University of Texas at San Antonio.
| | - Shouhuai Xu
- Shouhuai Xu, PhD, was affiliated with The University of Texas at San Antonio at the time this work was conducted; he currently is a professor in the Department of Computer Science at the University of Colorado Colorado Springs.
| | - Emme Lopez
- Emme Lopez is a librarian in the School of Nursing at the University of Texas Health at San Antonio.
| |
Collapse
|
9
|
Bollepalli SC, Sevakula RK, Au-Yeung WTM, Kassab MB, Merchant FM, Bazoukis G, Boyer R, Isselbacher EM, Armoundas AA. Real-Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks. J Am Heart Assoc 2021; 10:e023222. [PMID: 34854319 PMCID: PMC9075394 DOI: 10.1161/jaha.121.023222] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach. Methods and Results This study involves a total of 953 independent life-threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid- convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5-fold cross-validation for 5 times and obtained an accuracy of 87.5%±0.5%, and a score of 81%±0.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability. Conclusions Our method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.
Collapse
Affiliation(s)
| | - Rahul K Sevakula
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | | | - Mohamad B Kassab
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | | | - George Bazoukis
- Second Department of Cardiology Evangelismos General Hospital of Athens Athens Greece
| | - Richard Boyer
- Anesthesia Department Massachusetts General Hospital Boston MA
| | | | - Antonis A Armoundas
- Cardiovascular Research Center Massachusetts General Hospital Boston MA.,Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA
| |
Collapse
|
10
|
Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. J Med Internet Res 2021; 23:e26522. [PMID: 34847057 PMCID: PMC8669587 DOI: 10.2196/26522] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/21/2021] [Accepted: 10/08/2021] [Indexed: 12/23/2022] Open
Abstract
Background Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios. Objective This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care. Methods Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria. Results The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail. Conclusions The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.
Collapse
Affiliation(s)
- Kathrin Seibert
- Institute of Public Health and Nursing Research, High Profile Area Health Sciences, University of Bremen, Bremen, Germany
| | - Dominik Domhoff
- Institute of Public Health and Nursing Research, High Profile Area Health Sciences, University of Bremen, Bremen, Germany
| | - Dominik Bruch
- Auf- und Umbruch im Gesundheitswesen UG, Bonn, Germany
| | - Matthias Schulte-Althoff
- School of Business and Economics, Department of Information Systems, Freie Universität Berlin, Einstein Center Digital Future, Berlin, Germany
| | - Daniel Fürstenau
- Department of Digitalization, Copenhagen Business School, Frederiksberg, Denmark.,Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Biessmann
- Faculty VI - Informatics and Media, Beuth University of Applied Sciences, Einstein Center Digital Future, Berlin, Germany
| | - Karin Wolf-Ostermann
- Institute of Public Health and Nursing Research, High Profile Area Health Sciences, University of Bremen, Bremen, Germany
| |
Collapse
|
11
|
Au-Yeung WTM, Sevakula RK, Sahani AK, Kassab M, Boyer R, Isselbacher EM, Armoundas AA. Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:437-445. [PMID: 34604758 PMCID: PMC8482048 DOI: 10.1093/ehjdh/ztab058] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/13/2021] [Accepted: 06/20/2021] [Indexed: 01/29/2023]
Abstract
AIMS This work attempts to develop a standalone heart rhythm alerting system for the intensive care unit (ICU), where life-threatening arrhythmias have to be identified/alerted more precisely and more instantaneously (i.e. with lower latency) than existing bedside monitors. METHODS AND RESULTS We use the dataset from the PhysioNet 2015 Challenge, which contains records that led to true and false arrhythmic alarms in the ICU. These records have been re-annotated as one of eight classes, namely (i) asystole, (ii) extreme bradycardia, (iii) extreme tachycardia, (iv) ventricular fibrillation (VF), (v) ventricular tachycardia (VT), (vi) normal sinus rhythm, (vii) sinus tachycardia, and (viii) noise/artefacts. Arrhythmia-specific features and features that measure the signal quality were extracted from all the records. To improve VF detection, an improved, over an existing, single-lead R-wave detection was developed that takes into account the R-waves detected in all electrocardiographic (ECG) leads. To avoid false R-wave detection due to pacing spikes, ECG signals were filtered with a low pass filter prior to R-wave detection, while the raw signals were used for feature extraction. Random forest was used as the classifier, and 10-time five-fold cross-validation, resulted in a macro-average sensitivity of 81.54%. CONCLUSIONS In conclusion, comparing with the bedside monitors used in the PhysioNet 2015 competition, we find that our method achieves higher positive predictive values for asystole, extreme bradycardia, VT, and VF; furthermore, our method is able to alert the presence of arrhythmia instantaneously, i.e. up to 4 s earlier.
Collapse
Affiliation(s)
- Wan-Tai M Au-Yeung
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129, USA
| | - Rahul K Sevakula
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129, USA
| | - Ashish K Sahani
- Center for Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab 14001, India
| | - Mohamad Kassab
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129, USA
| | - Richard Boyer
- Anesthesia Department, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Eric M Isselbacher
- Healthcare Transformation Lab, Massachusetts General Hospital, 50 Staniford St, Boston, MA 02114, USA
| | - Antonis A Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129, USA,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 45 Carleton St, Cambridge, MA 02142, USA,Corresponding author. Tel: +1 617-726-0930,
| |
Collapse
|
12
|
Scott JB. The Conundrum of Mechanical Ventilation Alarms. Respir Care 2021; 66:699-700. [PMID: 33782139 PMCID: PMC9993998 DOI: 10.4187/respcare.09028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- J Brady Scott
- Department of Cardiopulmonary SciencesDivision of Respiratory CareRush UniversityChicago, Illinois
| |
Collapse
|
13
|
Duclos C, Bouaud J. Pragmatic Considerations on Clinical Decision Support from the 2019 Literature. Yearb Med Inform 2020; 29:155-158. [PMID: 32823309 PMCID: PMC7442518 DOI: 10.1055/s-0040-1702016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Objectives
: To summarize significant research contributions published in 2019 in the field of computerized clinical decision support and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook.
Methods
: Two bibliographic databases were searched for papers referring to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. From search results, section editors established a list of candidate best papers, which were then peer-reviewed by external reviewers. The IMIA Yearbook editorial committee finally selected the best papers on the basis of all reviews including the section editors’ evaluation.
Results
: A total of 1,378 articles were retrieved. Fifteen best paper candidates were selected, the reviews of which resulted in the selection of three best papers. One paper reports on a guideline modeling approach based on clinical decision trees, both clinically interpretable and suitable for implementation in CDSSs. In another paper, authors promote the use of extended Timed Transition Diagrams in CDSSs to formalize consistently recurrent medical processes for chronic diseases management. The third paper proposes a conceptual framework and a grid for assessing the performance of predictive tools based on the critical appraisal of published evidence.
Conclusions
: As showed by the number and the variety of works related to decision support, research in the field is very active. This year’s selection highlighted pragmatic works that promote transparency and trust required by decision support tools.
Collapse
Affiliation(s)
- C Duclos
- Université Sorbonne Paris Nord, Sorbonne Université, INSERM, UMR_S 1142, LIMICS, Paris, France.,AP-HP, Hôpital Avicenne, Bobigny, France
| | - J Bouaud
- AP-HP, Delegation for Clinical Research and Innovation, Paris, France.,Université Sorbonne Paris Nord, Sorbonne Université, INSERM, UMR_S 1142, LIMICS, Paris, France
| | | |
Collapse
|
14
|
Sbaffi L, Walton J, Blenkinsopp J, Walton G. Information Overload in Emergency Medicine Physicians: A Multisite Case Study Exploring the Causes, Impact, and Solutions in Four North England National Health Service Trusts. J Med Internet Res 2020; 22:e19126. [PMID: 32716313 PMCID: PMC7418008 DOI: 10.2196/19126] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 05/22/2020] [Accepted: 06/11/2020] [Indexed: 12/19/2022] Open
Abstract
Background Information overload is affecting modern society now more than ever because of the wide and increasing distribution of digital technologies. Social media, emails, and online communications among others infuse a sense of urgency as information must be read, produced, and exchanged almost instantaneously. Emergency medicine is a medical specialty that is particularly affected by information overload with consequences on patient care that are difficult to quantify and address. Understanding the current causes of medical information overload, their impact on patient care, and strategies to handle the inflow of constant information is crucial to alleviating stress and anxiety that is already crippling the profession. Objective This study aims to identify and evaluate the main causes and sources of medical information overload, as experienced by emergency medicine physicians in selected National Health Service (NHS) trusts in the United Kingdom. Methods This study used a quantitative, survey-based data collection approach including close- and open-ended questions. A web-based survey was distributed to emergency physicians to assess the impact of medical information overload on their jobs. In total, 101 valid responses were collected from 4 NHS trusts in north England. Descriptive statistics, principal component analysis, independent sample two-tailed t tests, and one-way between-group analysis of variance with post hoc tests were performed on the data. Open-ended questions were analyzed using thematic analysis to identify key topics. Results The vast majority of respondents agreed that information overload is a serious issue in emergency medicine, and it increases with time. The always available culture (mean 5.40, SD 1.56), email handling (mean 4.86, SD 1.80), and multidisciplinary communications (mean 4.51, SD 1.61) are the 3 main reasons leading to information overload. Due to this, emergency physicians experience guideline fatigue, stress and tension, longer working hours, and impaired decision making, among other issues. Aspects of information overload are also reported to have different impacts on physicians depending on demographic factors such as age, years spent in emergency medicine, and level of employment. Conclusions There is a serious concern regarding information overload in emergency medicine. Participants identified a considerable number of daily causes affecting their job, particularly the traditional culture of emergency departments being always available on the ward, exacerbated by email and other forms of communication necessary to maintain optimal, evidence-based practice standards. However, not all information is unwelcome, as physicians also need to stay updated with the latest guidelines on conditions and treatment, and communicate with larger medical teams to provide quality care.
Collapse
Affiliation(s)
- Laura Sbaffi
- Information School, University of Sheffield, Sheffield, United Kingdom
| | - James Walton
- Leeds Teaching Hospitals Trust, Leeds, United Kingdom
| | - John Blenkinsopp
- North Tees and Hartlepool NHS Foundation Trust, Stockton-on-Tees, United Kingdom
| | - Graham Walton
- Centre for Information Management, Loughborough University, Loughborough, United Kingdom
| |
Collapse
|
15
|
Fernandes C, Miles S, Lucena CJP. Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation. JMIR Med Inform 2020; 8:e15407. [PMID: 32432551 PMCID: PMC7270842 DOI: 10.2196/15407] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/15/2019] [Accepted: 01/26/2020] [Indexed: 11/13/2022] Open
Abstract
Background Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. Objective This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. Methods We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. Results The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. Conclusions Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.
Collapse
Affiliation(s)
- Chrystinne Fernandes
- Department of Informatics, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil
| | - Simon Miles
- Department of Informatics, King's College London, London, United Kingdom
| | - Carlos José Pereira Lucena
- Department of Informatics, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil
| |
Collapse
|