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Gonzalez R, Saha A, Campbell CJ, Nejat P, Lokker C, Norgan AP. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. J Pathol Inform 2024; 15:100347. [PMID: 38162950 PMCID: PMC10755052 DOI: 10.1016/j.jpi.2023.100347] [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: 08/21/2023] [Revised: 10/06/2023] [Accepted: 11/01/2023] [Indexed: 01/03/2024] Open
Abstract
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.
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Affiliation(s)
- Ricardo Gonzalez
- DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada
- Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Ashirbani Saha
- Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Clinton J.V. Campbell
- William Osler Health System, Brampton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Peyman Nejat
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Andrew P. Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
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Wynn M. Perceptions and digitalisation of outbreak management in UK health services: A cross-sectional survey. J Infect Prev 2024; 25:134-141. [PMID: 39055676 PMCID: PMC11268242 DOI: 10.1177/17571774241239221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 02/28/2024] [Indexed: 07/27/2024] Open
Abstract
Background Global challenges arise from infectious diseases which represent significant challenges to the provision of healthcare, requiring efficient management procedures to limit transmission. Evaluating current outbreak management processes within UK healthcare services is essential for identifying strengths, weaknesses, and potential improvements. Objectives This study aimed to assess infection prevention and control (IPC) practitioners' access to outbreak management (OM) data. Secondary objectives involved determining IPC practitioners' perceptions of outbreak management processes and the state of digitalisation of OM in the UK. Methods National cross-sectional survey data were collected to evaluate current outbreak management approaches. To supplement this, information requests were sent to the 10 largest teaching and research NHS hospital trusts in England. Findings The survey received 55 responses with 53 considered for analysis. Out of 10 NHS trusts, nine provided completed FOI responses, while one was unable to provide data. Discussion The study offers unique insights into prevailing outbreak management practices within UK health services. Although positive perceptions surround key outbreak management stages, concerns arise, including varying confidence levels in surveillance processes' robustness, efficacy of management interventions, and communication effectiveness. Conclusions The study highlights challenges with OM processes in the UK, including issues like poor surveillance and delayed outbreak detection. Positive practitioner perceptions contrast with concerns over data collection, follow-up, and limited digitalisation, relying on basic tools like Excel and Word, hindering retrospective learning.
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Affiliation(s)
- Matthew Wynn
- School of Health and Society, University of Salford, Salford, UK
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Horgan D, Hamdi Y, Lal JA, Nyawira T, Meyer S, Kondji D, Francisco NM, De Guzman R, Paul A, Nallamalla KR, Park WY, Triapthi V, Tripathi R, Johns A, Singh MP, Phipps ME, Dube F, Abu Rasheed HM, Kozaric M, Pinto JA, Stefani SD, Aponte Rueda ME, Alarcon RF, Barrera-Saldana HA. Empowering quality data - the gordian knot of bringing real innovation into healthcare system. Diagnosis (Berl) 2022; 10:140-157. [PMID: 36548810 DOI: 10.1515/dx-2022-0115] [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: 10/19/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES The introduction of Personalised Medicine (PM) into healthcare systems could benefit from a clearer understanding of the distinct national and regional frameworks around the world. Recent engagement by international regulators on maximising the use of real-world evidence (RWE) has highlighted the scope for improving the exploitation of the treasure-trove of health data that is currently largely neglected in many countries. The European Alliance for Personalised Medicine (EAPM) led an international study aimed at identifying the current status of conditions. METHODS A literature review examined how far such frameworks exist, with a view to identifying conducive factors - and crucial gaps. This extensive review of key factors across 22 countries and 5 regions revealed a wide variety of attitudes, approaches, provisions and conditions, and permitted the construction of a comprehensive overview of the current status of PM. Based on seven key pillars identified from the literature review and expert panels, the data was quantified, and on the basis of further analysis, an index was developed to allow comparison country by country and region by region. RESULTS The results show that United States of America is leading according to overall outcome whereas Kenya scored the least in the overall outcome. CONCLUSIONS Still, common approaches exist that could help accelerate take-up of opportunities even in the less prosperous parts of the world.
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Affiliation(s)
- Denis Horgan
- European Alliance for Personalised Medicine, Brussels, Belgium
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering Sam Higginbottom University of Agriculture, Technology and Sciences Prayagraj, India
| | - Yosr Hamdi
- Laboratory of Biomedical Genomics and Oncogenetics, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Laboratory of Human and Experimental Pathology, Institut Pasteur de Tunis, Tunis, Tunisia
| | - Jonathan A Lal
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering Sam Higginbottom University of Agriculture, Technology and Sciences Prayagraj, India
- Department of Genetics and Cell Biology, GROW School of Oncology and Developmental Biology, Faculty of Health, Medicine and Life Sciences, Institute for Public Health Genomics, Maastricht University, Maastricht, Netherlands
| | - Teresia Nyawira
- National Commission for Science, Technology and Innovation in Kenya (NACOSTI), Nairobi Kenya, Kenya
| | | | - Dominique Kondji
- Health & Development Communication, Building Capacity for Better Health in Africa Building Capacities for Better Health in AFRICA, Yaounde, Cameroun
| | - Ngiambudulu M Francisco
- Grupo de Investigação Microbiana e Imunológica, Instituto Nacional de Investigação em Saúde (National Institute for Health Research), Luanda, Angola
| | - Roselle De Guzman
- Oncology and Pain Management Section, Manila Central University-Filemon D. Tanchoco Medical Foundation Hospital, Caloocan City, Philippines
| | - Anupriya Paul
- Department of Mathematics and Statistics, Faculty of Science, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India
| | | | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University, Seoul, Korea
| | - Vijay Triapthi
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering Sam Higginbottom University of Agriculture, Technology and Sciences Prayagraj, India
| | - Ravikant Tripathi
- Department Health Govt of India, Ministry of labor, New Delhi, India
| | - Amber Johns
- Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Sydney, Australia
| | - Mohan P Singh
- Center of Biotechnology, University of Allahabad, Allahabad, India
| | - Maude E Phipps
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, Malaysia
| | - France Dube
- Astra Zeneca, Concord Pike, Wilmington, DE, USA
| | | | - Marta Kozaric
- European Alliance for Personalised Medicine, Brussels, Belgium
| | - Joseph A Pinto
- Center for Basic and Translational Research, Auna Ideas, Lima, Peru
| | | | | | - Ricardo Fujita Alarcon
- Centro de Genética y Biología Molecular, Universidad de San Martín de Porres, Lima, Perú
| | - Hugo A Barrera-Saldana
- Innbiogem SC/Vitagenesis SA at National Laboratory for Services of Research, Development, and Innovation for the Pharma and Biotech Industries (LANSEIDI) of CONACyT Vitaxentrum Group, Monterrey, Mexico
- Schools of Medicine and Biology, Autonomous University of Nuevo Leon, Monterrey, Mexico
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Zhang H, Lyu T, Yin P, Bost S, He X, Guo Y, Prosperi M, Hogan WR, Bian J. A scoping review of semantic integration of health data and information. Int J Med Inform 2022; 165:104834. [PMID: 35863206 DOI: 10.1016/j.ijmedinf.2022.104834] [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: 03/21/2022] [Revised: 07/06/2022] [Accepted: 07/13/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains. MATERIALS AND METHODS We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review. RESULTS The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications. CONCLUSION SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.
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Affiliation(s)
- Hansi Zhang
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Tianchen Lyu
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Pengfei Yin
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Sarah Bost
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Xing He
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Yi Guo
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Willian R Hogan
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jiang Bian
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.
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Abstract
Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.
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Affiliation(s)
- Youcef Derbal
- Ted Rogers School of Information Technology Management, 7984Ryerson University, Toronto, ON, Canada
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Drake A, Sassoon I, Balatsoukas P, Porat T, Ashworth M, Wright E, Curcin V, Chapman M, Kokciyan N, Modgil S, Sklar E, Parsons S. The relationship of socio-demographic factors and patient attitudes to connected health technologies: A survey of stroke survivors. Health Informatics J 2022; 28:14604582221102373. [PMID: 35726817 DOI: 10.1177/14604582221102373] [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: 11/16/2022]
Abstract
More evidence is needed on technology implementation for remote monitoring and self-management across the various settings relevant to chronic conditions. This paper describes the findings of a survey designed to explore the relevance of socio-demographic factors to attitudes towards connected health technologies in a community of patients. Stroke survivors living in the UK were invited to answer questions about themselves and about their attitudes to a prototype remote monitoring and self-management app developed around their preferences. Eighty (80) responses were received and analysed, with limitations and results presented in full. Socio-demographic factors were not found to be associated with variations in participants' willingness to use the system and attitudes to data sharing. Individuals' levels of interest in relevant technology was suggested as a more important determinant of attitudes. These observations run against the grain of most relevant literature to date, and tend to underline the importance of prioritising patient-centred participatory research in efforts to advance connected health technologies.
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Affiliation(s)
- Archie Drake
- University of Lincoln, UK.,4616King's College London, UK
| | | | | | | | | | | | | | | | - Nadin Kokciyan
- 151022The University of Edinburgh School of Informatics, UK
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Azzi S, Michalowski W, Iglewski M. Developing a pneumonia diagnosis ontology from multiple knowledge sources. Health Informatics J 2022; 28:14604582221083850. [PMID: 35377253 DOI: 10.1177/14604582221083850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Pneumonia is difficult to differentiate from other pulmonary diseases because it shares many symptoms with these diseases. Diagnosing pneumonia in clinical practice would benefit from having access to a codified representation of clinical knowledge. An ontology represents a well-established paradigm for such codification. Objectives: The goal of this research is to create Pneumonia Diagnosis Ontology (PNADO) that brings together the medical knowledge dispersed among multiple medical knowledge sources. Material and Methods: We used several clinical practice guidelines (CPGs) describing the pneumonia diagnostic process as a starting point in developing PNADO. Preliminary version of PNADO was subsequently expanded to cover a broader range of the concepts by reusing ontologies from Open Biological and Biomedical Ontology (OBO) Foundry and BioPortal. PNADO was evaluated by examining relevant concepts from the pneumonia-specific systematic reviews, using patient data from the MIMIC-III clinical dataset, and by clinical domain experts. Results: PNADO is a comprehensive ontology and has a rich set of classes and properties that cover different types of pneumonia, pathogens, symptoms, clinical signs, laboratory tests and imaging, clinical findings, complications, and diagnoses. Conclusion: PNADO unifies pneumonia diagnostic concepts from multiple knowledge sources. It is available in the BioPortal repository.
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Paré G, Raymond L, Castonguay A, Grenier Ouimet A, Trudel MC. Assimilation of Medical Appointment Scheduling Systems and Their Impact on the Accessibility of Primary Care: Mixed Methods Study. JMIR Med Inform 2021; 9:e30485. [PMID: 34783670 PMCID: PMC8663712 DOI: 10.2196/30485] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/14/2021] [Accepted: 10/09/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has prompted the adoption of digital health technologies to maximize the accessibility of medical care in primary care settings. Medical appointment scheduling (MAS) systems are among the most essential technologies. Prior studies on MAS systems have taken either a user-oriented perspective, focusing on perceived outcomes such as patient satisfaction, or a technical perspective, focusing on optimizing medical scheduling algorithms. Less attention has been given to the extent to which family medicine practices have assimilated these systems into their daily operations and achieved impacts. OBJECTIVE This study aimed to fill this gap and provide answers to the following questions: (1) to what extent have primary care practices assimilated MAS systems into their daily operations? (2) what are the impacts of assimilating MAS systems on the accessibility and availability of primary care? and (3) what are the organizational and managerial factors associated with greater assimilation of MAS systems in family medicine clinics? METHODS A survey study targeting all family medicine clinics in Quebec, Canada, was conducted. The questionnaire was addressed to the individual responsible for managing medical schedules and appointments at these clinics. Following basic descriptive statistics, component-based structural equation modeling was used to empirically explore the causal paths implied in the conceptual framework. A cluster analysis was also performed to complement the causal analysis. As a final step, 6 experts in MAS systems were interviewed. Qualitative data were then coded and extracted using standard content analysis methods. RESULTS A total of 70 valid questionnaires were collected and analyzed. A large majority of the surveyed clinics had implemented MAS systems, with an average use of 1 or 2 functionalities, mainly "automated appointment confirmation and reminders" and "online appointment confirmation, modification, or cancellation by the patient." More extensive use of MAS systems appears to contribute to improved availability of medical care in these clinics, notwithstanding the effect of their application of advanced access principles. Also, greater integration of MAS systems into the clinic's electronic medical record system led to more extensive use. Our study further indicated that smaller clinics were less likely to undertake such integration and therefore showed less availability of medical care for their patients. Finally, our findings indicated that those clinics that showed a greater adoption rate and that used the provincial MAS system tended to be the highest-performing ones in terms of accessibility and availability of care. CONCLUSIONS The main contribution of this study lies in the empirical demonstration that greater integration and assimilation of MAS systems in family medicine clinics lead to greater accessibility and availability of care for their patients and the general population. Valuable insight has also been provided on how to identify the clinics that would benefit most from such digital health solutions.
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Affiliation(s)
- Guy Paré
- Department of Information Technologies, HEC Montréal, Montréal, QC, Canada
| | - Louis Raymond
- École de gestion, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada
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Clay I, Angelopoulos C, Bailey AL, Blocker A, Carini S, Carvajal R, Drummond D, McManus KF, Oakley-Girvan I, Patel KB, Szepietowski P, Goldsack JC. Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices. J Med Internet Res 2021; 23:e34493. [PMID: 34751656 PMCID: PMC8663457 DOI: 10.2196/34493] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 01/16/2023] Open
Abstract
Data integration, the processes by which data are aggregated, combined, and made available for use, has been key to the development and growth of many technological solutions. In health care, we are experiencing a revolution in the use of sensors to collect data on patient behaviors and experiences. Yet, the potential of this data to transform health outcomes is being held back. Deficits in standards, lexicons, data rights, permissioning, and security have been well documented, less so the cultural adoption of sensor data integration as a priority for large-scale deployment and impact on patient lives. The use and reuse of trustworthy data to make better and faster decisions across drug development and care delivery will require an understanding of all stakeholder needs and best practices to ensure these needs are met. The Digital Medicine Society is launching a new multistakeholder Sensor Data Integration Tour of Duty to address these challenges and more, providing a clear direction on how sensor data can fulfill its potential to enhance patient lives.
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Affiliation(s)
- Ieuan Clay
- Digital Medicine Society (DiMe), Boston, MA, United States
| | | | | | | | - Simona Carini
- University of California San Francisco, San Francisco, CA, United States
| | - Rodrigo Carvajal
- H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | | | | | | | - Krupal B Patel
- H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
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