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Tewfik G, Rivoli S, Methangkool E. The electronic health record: does it enhance or distract from patient safety? Curr Opin Anaesthesiol 2024:00001503-990000000-00229. [PMID: 39248015 DOI: 10.1097/aco.0000000000001429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2024]
Abstract
PURPOSE OF REVIEW The electronic health record (EHR) is an invaluable tool that may be used to improve patient safety. With a variety of different features, such as clinical decision support and computerized physician order entry, it has enabled improvement of patient care throughout medicine. EHR allows for built-in reminders for such items as antibiotic dosing and venous thromboembolism prophylaxis. RECENT FINDINGS In anesthesiology, EHR often improves patient safety by eliminating the need for reliance on manual documentation, by facilitating information transfer and incorporating predictive models for such items as postoperative nausea and vomiting. The use of EHR has been shown to improve patient safety in specific metrics such as using checklists or information transfer amongst clinicians; however, limited data supports that it reduces morbidity and mortality. SUMMARY There are numerous potential pitfalls associated with EHR use to improve patient safety, as well as great potential for future improvement.
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Affiliation(s)
| | - Steven Rivoli
- Mount Sinai School of Medicine: Icahn School of Medicine at Mount Sinai
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Lighterness A, Adcock M, Scanlon LA, Price G. Data Quality-Driven Improvement in Health Care: Systematic Literature Review. J Med Internet Res 2024; 26:e57615. [PMID: 39173155 PMCID: PMC11377907 DOI: 10.2196/57615] [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: 03/11/2024] [Revised: 05/10/2024] [Accepted: 05/30/2024] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND The promise of real-world evidence and the learning health care system primarily depends on access to high-quality data. Despite widespread awareness of the prevalence and potential impacts of poor data quality (DQ), best practices for its assessment and improvement are unknown. OBJECTIVE This review aims to investigate how existing research studies define, assess, and improve the quality of structured real-world health care data. METHODS A systematic literature search of studies in the English language was implemented in the Embase and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023. We standardized DQ concepts according to the Data Management Association (DAMA) DQ framework to enable comparison between studies. After screening and filtering by 2 independent authors, we identified 39 relevant articles reporting DQ improvement initiatives. RESULTS The studies were characterized by considerable heterogeneity in settings and approaches to DQ assessment and improvement. Affiliated institutions were from 18 different countries and 18 different health domains. DQ assessment methods were largely manual and targeted completeness and 1 other DQ dimension. Use of DQ frameworks was limited to the Weiskopf and Weng (3/6, 50%) or Kahn harmonized model (3/6, 50%). Use of standardized methodologies to design and implement quality improvement was lacking, but mainly included plan-do-study-act (PDSA) or define-measure-analyze-improve-control (DMAIC) cycles. Most studies reported DQ improvements using multiple interventions, which included either DQ reporting and personalized feedback (24/39, 61%), IT-related solutions (21/39, 54%), training (17/39, 44%), improvements in workflows (5/39, 13%), or data cleaning (3/39, 8%). Most studies reported improvements in DQ through a combination of these interventions. Statistical methods were used to determine significance of treatment effect (22/39, 56% times), but only 1 study implemented a randomized controlled study design. Variability in study designs, approaches to delivering interventions, and reporting DQ changes hindered a robust meta-analysis of treatment effects. CONCLUSIONS There is an urgent need for standardized guidelines in DQ improvement research to enable comparison and effective synthesis of lessons learned. Frameworks such as PDSA learning cycles and the DAMA DQ framework can facilitate this unmet need. In addition, DQ improvement studies can also benefit from prioritizing root cause analysis of DQ issues to ensure the most appropriate intervention is implemented, thereby ensuring long-term, sustainable improvement. Despite the rise in DQ improvement studies in the last decade, significant heterogeneity in methodologies and reporting remains a challenge. Adopting standardized frameworks for DQ assessment, analysis, and improvement can enhance the effectiveness, comparability, and generalizability of DQ improvement initiatives.
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Affiliation(s)
- Anthony Lighterness
- Clinical Outcomes and Data Unit, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Michael Adcock
- Clinical Outcomes and Data Unit, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Lauren Abigail Scanlon
- Clinical Outcomes and Data Unit, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Gareth Price
- Radiotherapy Related Research Group, University of Manchester, Manchester, United Kingdom
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Zhou J, Hao J, Tang M, Sun H, Wang J, Li J, Qian Q. Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction. BMC Med Inform Decis Mak 2024; 24:178. [PMID: 38915008 PMCID: PMC11194906 DOI: 10.1186/s12911-024-02533-z] [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: 07/05/2023] [Accepted: 05/13/2024] [Indexed: 06/26/2024] Open
Abstract
OBJECTIVE This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS AND METHODS The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. RESULTS The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. DISCUSSION The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. CONCLUSION The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.
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Affiliation(s)
- Jiayin Zhou
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jie Hao
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Mingkun Tang
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Haixia Sun
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jiayang Wang
- Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jiao Li
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Qing Qian
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
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Razzaghi H, Goodwin Davies A, Boss S, Bunnell HT, Chen Y, Chrischilles EA, Dickinson K, Hanauer D, Huang Y, Ilunga KTS, Katsoufis C, Lehmann H, Lemas DJ, Matthews K, Mendonca EA, Morse K, Ranade D, Rosenman M, Taylor B, Walters K, Denburg MR, Forrest CB, Bailey LC. Systematic data quality assessment of electronic health record data to evaluate study-specific fitness: Report from the PRESERVE research study. PLOS DIGITAL HEALTH 2024; 3:e0000527. [PMID: 38935590 PMCID: PMC11210795 DOI: 10.1371/journal.pdig.0000527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 05/07/2024] [Indexed: 06/29/2024]
Abstract
Study-specific data quality testing is an essential part of minimizing analytic errors, particularly for studies making secondary use of clinical data. We applied a systematic and reproducible approach for study-specific data quality testing to the analysis plan for PRESERVE, a 15-site, EHR-based observational study of chronic kidney disease in children. This approach integrated widely adopted data quality concepts with healthcare-specific evaluation methods. We implemented two rounds of data quality assessment. The first produced high-level evaluation using aggregate results from a distributed query, focused on cohort identification and main analytic requirements. The second focused on extended testing of row-level data centralized for analysis. We systematized reporting and cataloguing of data quality issues, providing institutional teams with prioritized issues for resolution. We tracked improvements and documented anomalous data for consideration during analyses. The checks we developed identified 115 and 157 data quality issues in the two rounds, involving completeness, data model conformance, cross-variable concordance, consistency, and plausibility, extending traditional data quality approaches to address more complex stratification and temporal patterns. Resolution efforts focused on higher priority issues, given finite study resources. In many cases, institutional teams were able to correct data extraction errors or obtain additional data, avoiding exclusion of 2 institutions entirely and resolving 123 other gaps. Other results identified complexities in measures of kidney function, bearing on the study's outcome definition. Where limitations such as these are intrinsic to clinical data, the study team must account for them in conducting analyses. This study rigorously evaluated fitness of data for intended use. The framework is reusable and built on a strong theoretical underpinning. Significant data quality issues that would have otherwise delayed analyses or made data unusable were addressed. This study highlights the need for teams combining subject-matter and informatics expertise to address data quality when working with real world data.
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Affiliation(s)
- Hanieh Razzaghi
- Applied Clinical Research Center, Departments of Pediatrics and Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Amy Goodwin Davies
- Applied Clinical Research Center, Departments of Pediatrics and Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Samuel Boss
- Applied Clinical Research Center, Departments of Pediatrics and Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - H. Timothy Bunnell
- Biomedical Research Informatics Center, Nemours Children’s Hospital, Wilmington, Delaware, United States of America
| | - Yong Chen
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Elizabeth A. Chrischilles
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, United States of America
| | - Kimberley Dickinson
- Applied Clinical Research Center, Departments of Pediatrics and Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - David Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Yungui Huang
- IT Research and Innovation, Nationwide Children’s Hospital, Columbus, Ohio, United States of America
| | - K. T. Sandra Ilunga
- Applied Clinical Research Center, Departments of Pediatrics and Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Chryso Katsoufis
- Division of Pediatric Nephrology, University of Miami Miller School of Medicine, Miami, Florida United States of America
| | - Harold Lehmann
- Biomedical Informatics & Data Science Section, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Dominick J. Lemas
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FLorida, United States of America
| | - Kevin Matthews
- Analytics Research Center, Children’s Hospital of Colorado, Aurora, Colorado, United States of America
| | - Eneida A. Mendonca
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
| | - Keith Morse
- Division of Pediatric Hospital Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Daksha Ranade
- Biostatistics, Epidemiology, and Analytics in Research (BEAR), Seattle Children’s Hospital, Seattle, Washington, United States of America
| | - Marc Rosenman
- Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital, Chicago, Illinois, United States of America
| | - Bradley Taylor
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Kellie Walters
- Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michelle R. Denburg
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Division of Nephrology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Christopher B. Forrest
- Applied Clinical Research Center, Departments of Pediatrics and Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - L. Charles Bailey
- Applied Clinical Research Center, Departments of Pediatrics and Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Shaikh N, Kais A, Dewey J, Jaffal H. Effect of perioperative ketorolac on postoperative bleeding after pediatric tonsillectomy. Int J Pediatr Otorhinolaryngol 2024; 180:111953. [PMID: 38653108 DOI: 10.1016/j.ijporl.2024.111953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 03/28/2024] [Accepted: 04/11/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Ketorolac is a frequently used anesthetic pain agent which is traditionally avoided during tonsillectomy due to concern for postoperative hemorrhage. Our goal was to assess the degree of risk associated with the use of Ketorolac following pediatric tonsillectomy. METHODS The TriNetX electronic health records research database was queried in January 2024 for patients undergoing tonsillectomy with or without adenoidectomy under the age of 18 years and without a diagnosed bleeding disorder. Patients were separated into two cohorts either having received or not having received ketorolac the same day as surgery. Propensity score matching was performed for age at the time of surgery, sex, race, ethnicity, and preoperative diagnoses. The outcomes assessed were postoperative hemorrhage requiring operative control within the first day (primary hemorrhage) and within the first month after surgery (secondary hemorrhage). RESULTS 17,434 patients were identified who had undergone pediatric tonsillectomy with or without adenoidectomy and had received ketorolac the same day as surgery. 290,373 patients were identified who had undergone pediatric tonsillectomy with or without adenoidectomy and had not received ketorolac the same day as surgery. 1:1 propensity score matching resulted in 17,434 patients within each cohort. Receipt of ketorolac the same day as surgery resulted in an increased risk of primary hemorrhage OR 2.158 (95 % CI 1.354, 3.437) and secondary hemorrhage OR 1.374 (95 % CI 1.057, 1.787) requiring operative control. CONCLUSION Ketorolac use during pediatric tonsillectomy with or without adenoidectomy was associated with an increased risk of postoperative primary and secondary bleeding requiring surgery.
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Affiliation(s)
- Noah Shaikh
- Department of Otolaryngology-Head and Neck Surgery, West Virginia University, Morgantown, WV, USA
| | - Amani Kais
- Department of Otolaryngology-Head and Neck Surgery, West Virginia University, Morgantown, WV, USA
| | - John Dewey
- Department of Otolaryngology-Head and Neck Surgery, West Virginia University, Morgantown, WV, USA
| | - Hussein Jaffal
- Department of Otolaryngology-Head and Neck Surgery, West Virginia University, Morgantown, WV, USA.
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Thompson YT, Li Y, Silovsky J. From Scientific Research to Practical Implementations: Applications to Improve Data Quality in Child Welfare. J Behav Health Serv Res 2024; 51:289-301. [PMID: 38153681 DOI: 10.1007/s11414-023-09875-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2023] [Indexed: 12/29/2023]
Abstract
Child welfare decisions have life-impacting consequences which, often times, are underpinned by limited or inadequate data and poor quality. Though research of data quality has gained popularity and made advancements in various practical areas, it has not made significant inroads for child welfare fields or data systems. Poor data quality can hinder service decision-making, impacting child behavioral health and well-being as well as increasing unnecessary expenditure of time and resources. Poor data quality can also undermine the validity of research and slow policymaking processes. The purpose of this commentary is to summarize the data quality research base in other fields, describe obstacles and uniqueness to improve data quality in child welfare, and propose necessary steps to scientific research and practical implementation that enables researchers and practitioners to improve the quality of child welfare services based on the enhanced quality of data.
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Affiliation(s)
- Yutian T Thompson
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
| | - Yaqi Li
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
| | - Jane Silovsky
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
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Declerck J, Kalra D, Vander Stichele R, Coorevits P. Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews. JMIR Med Inform 2024; 12:e51560. [PMID: 38446534 PMCID: PMC10955383 DOI: 10.2196/51560] [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: 08/03/2023] [Revised: 11/07/2023] [Accepted: 01/09/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data. OBJECTIVE This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework. METHODS A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English. RESULTS A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions. CONCLUSIONS The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.
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Affiliation(s)
- Jens Declerck
- Department of Public Health and Primary Care, Unit of Medical Informatics and Statistics, Ghent University, Ghent, Belgium
- The European Institute for Innovation through Health Data, Ghent, Belgium
| | - Dipak Kalra
- Department of Public Health and Primary Care, Unit of Medical Informatics and Statistics, Ghent University, Ghent, Belgium
- The European Institute for Innovation through Health Data, Ghent, Belgium
| | - Robert Vander Stichele
- Faculty of Medicine and Health Sciences, Heymans Institute of Pharmacology, Ghent, Belgium
| | - Pascal Coorevits
- Department of Public Health and Primary Care, Unit of Medical Informatics and Statistics, Ghent University, Ghent, Belgium
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Ramakrishnaiah Y, Macesic N, Webb GI, Peleg AY, Tyagi S. EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes. J Biomed Inform 2023; 147:104509. [PMID: 37827477 DOI: 10.1016/j.jbi.2023.104509] [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: 06/02/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023]
Abstract
The adoption of electronic health records (EHRs) has created opportunities to analyse historical data for predicting clinical outcomes and improving patient care. However, non-standardised data representations and anomalies pose major challenges to the use of EHRs in digital health research. To address these challenges, we have developed EHR-QC, a tool comprising two modules: the data standardisation module and the preprocessing module. The data standardisation module migrates source EHR data to a standard format using advanced concept mapping techniques, surpassing expert curation in benchmarking analysis. The preprocessing module includes several functions designed specifically to handle healthcare data subtleties. We provide automated detection of data anomalies and solutions to handle those anomalies. We believe that the development and adoption of tools like EHR-QC is critical for advancing digital health. Our ultimate goal is to accelerate clinical research by enabling rapid experimentation with data-driven observational research to generate robust, generalisable biomedical knowledge.
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Affiliation(s)
- Yashpal Ramakrishnaiah
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia
| | - Nenad Macesic
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne 3000, VIC, Australia
| | - Geoffrey I Webb
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne 3000, VIC, Australia
| | - Anton Y Peleg
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne 3000, VIC, Australia.
| | - Sonika Tyagi
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3000, VIC, Australia; School of Computing Technologies, RMIT University, Melbourne 3000, VIC, Australia.
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