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James M, Gabhart JM, Galletto M, Vitale-McDowell T. The Most Vulnerable Population: Our Ethical Responsibility to Improve Pediatric Triage. CLIN NURSE SPEC 2024; 38:159-162. [PMID: 38889055 DOI: 10.1097/nur.0000000000000829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Affiliation(s)
- Michelle James
- Author Affiliations: Clinical Quality Consultant (James), Kaiser Foundation Hospital and Health Plans, Regional Clinical Quality, Northern California, Oakland; Assistant Chief of Pediatric Hospital Medicine, Newborn Nursery Physician Lead, and KP NCAL Pediatric Sepsis Clinical Lead (Gabhart), South Sacramento Service Area, The Permanente Medical Group, California; Quality & Safety Improvement Consultant VI, Clinical Quality Consulting (KFH/HP), and NCAL Site Coordinator-Virtual Pediatric Systems (Galletto), Kaiser Foundation Hospital and Health Plans, Regional Clinical Quality, Northern California, Oakland, Califonia; and Service Director and Pediatric Emergency Care Coordinator (Vitale-McDowell), Kaiser San Rafael Emergency Department, California
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2
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Wilcox A. Understanding the opportunity and application of synthetic data in healthcare. Paediatr Perinat Epidemiol 2023; 37:301-302. [PMID: 36970808 DOI: 10.1111/ppe.12970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/05/2023] [Indexed: 05/10/2023]
Affiliation(s)
- Adam Wilcox
- Center for Applied Clinical Informatics, Institute for Informatics, School of Medicine, Washington University, St Louis, Missouri, USA
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Chen Q, Li R, Lin C, Lai C, Huang Y, Lu W, Li L. SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis. Appl Clin Inform 2023; 14:65-75. [PMID: 36452980 PMCID: PMC9876660 DOI: 10.1055/a-1990-3037] [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: 07/20/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND The lack of information interoperability between different devices and systems in the intensive care unit (ICU) hinders further utilization of data, especially for early warning of specific diseases in the ICU. OBJECTIVES We aimed to establish a data integration system. Based on this system, the sepsis prediction module was added to compose the Sepsis PREdiction System (SEPRES), where real-time early warning of sepsis can be implemented at the bedside in the ICU. METHODS Data are collected from bedside devices through the integration hub and uploaded to the integration system through the local area network. The data integration system was designed to integrate vital signs data, laboratory data, ventilator data, demographic data, pharmacy data, nursing data, etc. from multiple medical devices and systems. It integrates, standardizes, and stores information, making the real-time inference of the early warning module possible. The built-in sepsis early warning module can detect the onset of sepsis within 5 hours preceding at most. RESULTS Our data integration system has already been deployed in Ruijin Hospital, confirming the feasibility of our system. CONCLUSION We highlight that SEPRES has the potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention.
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Affiliation(s)
- Qiyu Chen
- Division of Applied Mathematics, Fudan University, Shanghai, China
| | - Ranran Li
- Department of Critical Care Medicine, Shanghai Jiaotong University School of Medicine, Ruijin Hospital, Shanghai, China
| | - ChihChe Lin
- Department of Intelligent Medical Products, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China
| | - Chiming Lai
- Department of Intelligent Medical Products, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China
| | - Yaling Huang
- Department of Intelligent Medical Products, Shanghai Electric Group Co., Ltd. Central Academe, Shanghai, China
| | - Wenlian Lu
- Division of Applied Mathematics, Fudan University, Shanghai, China
| | - Lei Li
- Department of Critical Care Medicine, Shanghai Jiaotong University School of Medicine, Ruijin Hospital, Shanghai, China
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4
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Tennant R, Graham J, Mercer K, Ansermino JM, Burns CM. Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol. BMJ Open 2022; 12:e065429. [PMID: 36414283 PMCID: PMC9685233 DOI: 10.1136/bmjopen-2022-065429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION While there have been several literature reviews on the performance of digital sepsis prediction technologies and clinical decision-support algorithms for adults, there remains a knowledge gap in examining the development of automated technologies for sepsis prediction in children. This scoping review will critically analyse the current evidence on the design and performance of automated digital technologies to predict paediatric sepsis, to advance their development and integration within clinical settings. METHODS AND ANALYSIS This scoping review will follow Arksey and O'Malley's framework, conducted between February and December 2022. We will further develop the protocol using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. We plan to search the following databases: Association of Computing Machinery (ACM) Digital Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Google Scholar, Institute of Electric and Electronic Engineers (IEEE), PubMed, Scopus and Web of Science. Studies will be included on children >90 days postnatal to <21 years old, predicted to have or be at risk of developing sepsis by a digitalised model or algorithm designed for a clinical setting. Two independent reviewers will complete the abstract and full-text screening and the data extraction. Thematic analysis will be used to develop overarching concepts and present the narrative findings with quantitative results and descriptive statistics displayed in data tables. ETHICS AND DISSEMINATION Ethics approval for this scoping review study of the available literature is not required. We anticipate that the scoping review will identify the current evidence and design characteristics of digital prediction technologies for the timely and accurate prediction of paediatric sepsis and factors influencing clinical integration. We plan to disseminate the preliminary findings from this review at national and international research conferences in global and digital health, gathering critical feedback from multidisciplinary stakeholders. SCOPING REVIEW REGISTRATION: https://osf.io/veqha/?view_only=f560d4892d7c459ea4cff6dcdfacb086.
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Affiliation(s)
- Ryan Tennant
- Department of Systems Design Engineering, University of Waterloo Faculty of Engineering, Waterloo, Ontario, Canada
| | - Jennifer Graham
- Department of Psychology, University of Waterloo Faculty of Arts, Waterloo, Ontario, Canada
| | - Kate Mercer
- Department of Systems Design Engineering, University of Waterloo Faculty of Engineering, Waterloo, Ontario, Canada
- Library, University of Waterloo, Waterloo, Ontario, Canada
| | - J Mark Ansermino
- Department of Anesthesiology, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Catherine M Burns
- Department of Systems Design Engineering, University of Waterloo Faculty of Engineering, Waterloo, Ontario, Canada
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Analysis of the Relevant Vital Signs and Infection of Sepsis Patients and to Explore the Influencing Factors of Acute Lung Injury/Acute Respiratory Distress Syndrome. CONTRAST MEDIA & MOLECULAR IMAGING 2022. [DOI: 10.1155/2022/7718248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In order to analyze the relevant vital signs and infection of sepsis patients, the influencing factors of acute lung injury/acute respiratory distress syndrome (ALI/ARDS) are explored. A total of 142 sepsis patients admitted to our hospital from January 2019 to January 2022 are divided into an ALI/ARDS group and a non-ALI/ARDS group according to the incidence of ALI/ARDS. Logistic analysis showed that pulmonary/abdominal infection, fungal origin of infection, Acinetobacter baumannii, low oxygenation index, high blood lactic acid value, APACHE II score, SOFA score, and LIPS score are the risk factors for sepsis-induced ALI/ARDS. The results indicate that pulmonary/abdominal infection, fungal origin of infection, Acinetobacter baumannii, low oxygenation index, high blood lactic acid, APACHE II score, SOFA score, and LIPS score are the risk factors for sepsis induced ALI/ARDS.
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Lin XY, Lin YZ, Lin SH, Lian JJ. Effect of procalcitonin on the severity and prognostic value of elderly patients with a severe infection of oral and maxillofacial space. Medicine (Baltimore) 2022; 101:e30158. [PMID: 36042587 PMCID: PMC9410655 DOI: 10.1097/md.0000000000030158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
This study aimed to investigate the effect on the severity and prognostic value of serum procalcitonin for elderly patients with oral and maxillofacial infections. We divided 163 elderly patients with severe oral and maxillofacial infection into survival and death groups according to the prognosis between June 2015 and May 2021, measured serum procalcitonin by enzyme-linked immunosorbent assay on the 1st, 2nd, 3rd, 5th, and 7th day after admission for the dynamic changes of serum procalcitonin level, collected the general physiological and biochemical indexes for the scores of acute physiology and general chronic condition, compared the correlation between serum procalcitonin, mean platelet count and APACHE score, analyzed the prognostic value of serum procalcitonin levels at different time after admission by ROC curve. The serum procalcitonin level increased significantly in both groups after admission, sharply increased at first and then rapidly decreased in the survival group, and continued to rise or declined slowly with fluctuation of high level in the death group. There was a negative correlation between serum procalcitonin level and mean platelet count (r = -0.698, P < .05) and a positive correlation between serum procalcitonin and APACHE II (R = 0.803, P < .05). The ROC curve showed that the serum procalcitonin level had little value on the first day and great value on the third day in predicting the prognosis of elderly patients with severe oral and maxillofacial infection (PCT1d = 0.539, PCT3d = 0.875, P < .05). The serum procalcitonin level is correlated with the severity of the disease in elderly patients with severe oral and maxillofacial space infection. Dynamic observation of it is helpful for the prognosis judgment of patients. After admission, serum procalcitonin level on the third day has a great value for the prognosis judgment of elderly patients with severe oral and maxillofacial space infection.
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Affiliation(s)
- Xin-yan Lin
- Department of Stomatology, Rongcheng Hospital Affiliated to Shandong First Medical University, Rongcheng, P.R. China
| | - Yu-zhao Lin
- Department of Stomatology, Rongcheng Hospital Affiliated to Shandong First Medical University, Rongcheng, P.R. China
| | - Shao-hua Lin
- Department of Infectious Disease, Rongcheng Hospital Affiliated to Shandong First Medical University, Rongcheng, P.R. China
| | - Jun-Jie Lian
- Respiratory and critical illness Department, Rongcheng Hospital Affiliated to Shandong First Medical University, Rongcheng, P.R. China
- *Correspondence: Jun-Jie Lian, Respiratory and critical illness Department, Rongcheng, Hospital Affiliated to Shandong First Medical University, Rongcheng, 264300, P.R. China (e-mail: )
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Catchpoole J, Nanda G, Vallmuur K, Nand G, Lehto M. Application of a Machine Learning-based Decision Support Tool to Improve an Injury Surveillance System Workflow. Appl Clin Inform 2022; 13:700-710. [PMID: 35644141 DOI: 10.1055/a-1863-7176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
Abstract
Background
Emergency department (ED)-based injury surveillance systems across many countries face resourcing challenges related to manual validation and coding of data.
Objective
This paper describes the evaluation of a machine learning-based Decision Support Tool (DST) to assist injury surveillance departments in the validation, coding and use of their data, comparing outcomes in coding time and accuracy pre- and post-implementation.
Methods
Manually coded injury surveillance data has been used to develop, train and iteratively refine a machine learning-based classifier to enable semi-automated coding of injury narrative data. This paper describes a trial implementation of the machine learning-based DST in the Queensland Injury Surveillance Unit (QISU) workflow using a major pediatric hospital's emergency department data comparing outcomes in coding time and accuracy pre- and post-implementation.
Results
The study found a 10% reduction in manual coding time after the DST was introduced. The Kappa statistics analysis in both DST-assisted and unassisted data shows increases in accuracy across three data fields; injury intent (85.4% unassisted vs. 94.5% assisted), external cause (88.8% unassisted vs. 91.8% assisted) and injury factor (89.3% unassisted vs. 92.9% assisted). The classifier was also used to produce a timely report monitoring injury patterns during the COVID-19 pandemic. Hence, it has the potential for near real-time surveillance of emerging hazards to inform public health responses.
Conclusions
The integration of the DST into the injury surveillance workflow shows benefits as it facilitates timely reporting and acts as a DST in the manual coding process.
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Affiliation(s)
- Jesani Catchpoole
- Jamieson Trauma Institute, Metro North Hospital and Health Service, Herston, Australia
- Queensland Injury Surveillance Unit, Metro North Hospital and Health Service, Herston, Australia
- Queensland University of Technology, Kelvin Grove, Australia
| | - Gaurav Nanda
- School of Engineering Technology, Purdue University, West Lafayette, United States
| | - Kirsten Vallmuur
- Australian Centre for Health Services Innovation, Queensland University of Technology, Kelvin Grove, Australia
- Jamieson Trauma Institute, Metro North Hospital and Health Service, Herston, Australia
| | - Goshad Nand
- Queensland Injury Surveillance Unit, Metro North Hospital and Health Service, Herston, Australia
| | - Mark Lehto
- Industrial Engineering, Purdue University, West Lafayette, 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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Desai MD, Tootooni MS, Bobay KL. Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches. Appl Clin Inform 2022; 13:189-202. [PMID: 35108741 PMCID: PMC8810268 DOI: 10.1055/s-0042-1742369] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Sepsis is associated with high mortality, especially during the novel coronavirus disease 2019 (COVID-19) pandemic. Along with high monetary health care costs for sepsis treatment, there is a lasting impact on lives of sepsis survivors and their caregivers. Early identification is necessary to reduce the negative impact of sepsis and to improve patient outcomes. Prehospital data are among the earliest information collected by health care systems. Using these untapped sources of data in machine learning (ML)-based approaches can identify patients with sepsis earlier in emergency department (ED). OBJECTIVES This integrative literature review aims to discuss the importance of utilizing prehospital data elements in ED, summarize their current use in developing ML-based prediction models, and specifically identify those data elements that can potentially contribute to early identification of sepsis in ED when used in ML-based approaches. METHOD Literature search strategy includes following two separate searches: (1) use of prehospital data in ML models in ED; and (2) ML models that are developed specifically to predict/detect sepsis in ED. In total, 24 articles are used in this review. RESULTS A summary of prehospital data used to identify time-sensitive conditions earlier in ED is provided. Literature related to use of ML models for early identification of sepsis in ED is limited and no studies were found related to ML models using prehospital data in prediction/early identification of sepsis in ED. Among those using ED data, ML models outperform traditional statistical models. In addition, the use of the free-text elements and natural language processing (NLP) methods could result in better prediction of sepsis in ED. CONCLUSION This study reviews the use of prehospital data in early decision-making in ED and suggests that researchers utilize such data elements for prediction/early identification of sepsis in ML-based approaches.
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Affiliation(s)
- Manushi D. Desai
- Marcella Niehoff School of Nursing, Loyola University Chicago, Maywood, Illinois, United States
| | - Mohammad S. Tootooni
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, Illinois, United States
| | - Kathleen L. Bobay
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, Illinois, United States,Address for correspondence Kathleen L. Bobay, PhD, RN, FAAN Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Marcella Niehoff School of Nursing, Loyola University Chicago2160 South First Avenue, Maywood, IL 60153United States
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10
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Yan MY, Gustad LT, Nytrø Ø. Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review. J Am Med Inform Assoc 2022; 29:559-575. [PMID: 34897469 PMCID: PMC8800516 DOI: 10.1093/jamia/ocab236] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 09/11/2021] [Accepted: 10/11/2021] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis. MATERIALS AND METHODS PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted. RESULTS The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies. DISCUSSION Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units. CONCLUSIONS Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.
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Affiliation(s)
- Melissa Y Yan
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lise Tuset Gustad
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Medicine, Levanger Hospital, Clinic of Medicine and Rehabilitation, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Øystein Nytrø
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway
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Bastarache L, Brown JS, Cimino JJ, Dorr DA, Embi PJ, Payne PR, Wilcox AB, Weiner MG. Developing real-world evidence from real-world data: Transforming raw data into analytical datasets. Learn Health Syst 2022; 6:e10293. [PMID: 35036557 PMCID: PMC8753316 DOI: 10.1002/lrh2.10293] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/10/2021] [Accepted: 09/21/2021] [Indexed: 11/25/2022] Open
Abstract
Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.
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Affiliation(s)
- Lisa Bastarache
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jeffrey S. Brown
- Department of Population MedicineHarvard Medical School and Harvard Pilgrim Health Care InstituteBostonMassachusettsUSA
| | - James J. Cimino
- Informatics Institute, University of Alabama at BirminghamBirminghamAlabamaUSA
| | - David A. Dorr
- Department of Medical Informatics and Clinical EpidemiologyOregon Health Sciences UniversityPortlandOregonUSA
| | - Peter J. Embi
- Center for Biomedical InformaticsRegenstrief InstituteIndianapolisIndianaUSA
| | - Philip R.O. Payne
- Institute for Informatics, Washington University in St. LouisSt. LouisMissouriUSA
| | - Adam B. Wilcox
- Institute for InformaticsWashington University in St. Louis School of MedicineSt. LouisMissouriUSA
| | - Mark G. Weiner
- Department of Population Health SciencesWeill Cornell MedicineNew YorkNew YorkUSA
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Bai E, Song SL, Fraser HSF, Ranney ML. A Graphical Toolkit for Longitudinal Dataset Maintenance and Predictive Model Training in Health Care. Appl Clin Inform 2022; 13:56-66. [PMID: 35172371 PMCID: PMC8850007 DOI: 10.1055/s-0041-1740923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/09/2021] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Predictive analytic models, including machine learning (ML) models, are increasingly integrated into electronic health record (EHR)-based decision support tools for clinicians. These models have the potential to improve care, but are challenging to internally validate, implement, and maintain over the long term. Principles of ML operations (MLOps) may inform development of infrastructure to support the entire ML lifecycle, from feature selection to long-term model deployment and retraining. OBJECTIVES This study aimed to present the conceptual prototypes for a novel predictive model management system and to evaluate the acceptability of the system among three groups of end users. METHODS Based on principles of user-centered software design, human-computer interaction, and ethical design, we created graphical prototypes of a web-based MLOps interface to support the construction, deployment, and maintenance of models using EHR data. To assess the acceptability of the interface, we conducted semistructured user interviews with three groups of users (health informaticians, clinical and data stakeholders, chief information officers) and evaluated preliminary usability using the System Usability Scale (SUS). We subsequently revised prototypes based on user input and developed user case studies. RESULTS Our prototypes include design frameworks for feature selection, model training, deployment, long-term maintenance, visualization over time, and cross-functional collaboration. Users were able to complete 71% of prompted tasks without assistance. The average SUS score of the initial prototype was 75.8 out of 100, translating to a percentile range of 70 to 79, a letter grade of B, and an adjective rating of "good." We reviewed persona-based case studies that illustrate functionalities of this novel prototype. CONCLUSION The initial graphical prototypes of this MLOps system are preliminarily usable and demonstrate an unmet need within the clinical informatics landscape.
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Affiliation(s)
- Eric Bai
- Warren Alpert Medical School, Brown University, Providence, Rhode Island, United States
| | - Sophia L. Song
- Warren Alpert Medical School, Brown University, Providence, Rhode Island, United States
| | - Hamish S. F. Fraser
- Brown University Center for Biomedical Informatics, Providence, Rhode Island, United States
| | - Megan L. Ranney
- Brown-Lifespan Center for Digital Health, Providence, Rhode Island, United States
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14
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Ronzio L, Cabitza F, Barbaro A, Banfi G. Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine. Diagnostics (Basel) 2021; 11:372. [PMID: 33671623 PMCID: PMC7926482 DOI: 10.3390/diagnostics11020372] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/08/2021] [Accepted: 02/18/2021] [Indexed: 02/08/2023] Open
Abstract
This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time.
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Affiliation(s)
- Luca Ronzio
- Department of Informatics, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Federico Cabitza
- Department of Informatics, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Alessandro Barbaro
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161 Milan, Italy; (A.B.); (G.B.)
| | - Giuseppe Banfi
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161 Milan, Italy; (A.B.); (G.B.)
- School of Medicine, University Vita-Salute San Raffaele, Via Olgettina, 58, 20132 Milan, Italy
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Giacobbe DR, Signori A, Del Puente F, Mora S, Carmisciano L, Briano F, Vena A, Ball L, Robba C, Pelosi P, Giacomini M, Bassetti M. Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective. Front Med (Lausanne) 2021; 8:617486. [PMID: 33644097 PMCID: PMC7906970 DOI: 10.3389/fmed.2021.617486] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/19/2021] [Indexed: 12/15/2022] Open
Abstract
Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Alessio Signori
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Filippo Del Puente
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Sara Mora
- Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Luca Carmisciano
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Federica Briano
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Antonio Vena
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
| | - Lorenzo Ball
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Chiara Robba
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Paolo Pelosi
- Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
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16
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Martin N, De Weerdt J, Fernández-Llatas C, Gal A, Gatta R, Ibáñez G, Johnson O, Mannhardt F, Marco-Ruiz L, Mertens S, Munoz-Gama J, Seoane F, Vanthienen J, Wynn MT, Boilève DB, Bergs J, Joosten-Melis M, Schretlen S, Van Acker B. Recommendations for enhancing the usability and understandability of process mining in healthcare. Artif Intell Med 2020; 109:101962. [PMID: 34756220 DOI: 10.1016/j.artmed.2020.101962] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 07/19/2020] [Accepted: 09/22/2020] [Indexed: 11/28/2022]
Abstract
Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining.
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Affiliation(s)
- Niels Martin
- Research Foundation Flanders (FWO), Belgium; Hasselt University, Belgium; Vrije Universiteit Brussel, Belgium.
| | | | | | - Avigdor Gal
- Technion - Israel Institute of Technology, Israel.
| | - Roberto Gatta
- Centre Hopitalier Universitaire de Vaudois, Switzerland; Università degli Studi di Brescia, Italy.
| | | | | | | | | | | | | | - Fernando Seoane
- Karolinska Institutet, Sweden; Karolinska University Hospital, Sweden; University of Borås, Sweden.
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