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Pereira AM, Jácome C, Jacinto T, Amaral R, Pereira M, Sá-Sousa A, Couto M, Vieira-Marques P, Martinho D, Vieira A, Almeida A, Martins C, Marreiros G, Freitas A, Almeida R, Fonseca JA. Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems. J Med Internet Res 2023; 25:e45364. [PMID: 38090790 PMCID: PMC10753423 DOI: 10.2196/45364] [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: 12/27/2022] [Revised: 04/25/2023] [Accepted: 10/11/2023] [Indexed: 12/18/2023] Open
Abstract
Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals' perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.
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Affiliation(s)
- Ana Margarida Pereira
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- Allergy Unit, Instituto and Hospital CUF-Porto, Porto, Portugal
- PaCeIT - Patient Centered Innovation and Technologies, Center for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Cristina Jácome
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - Tiago Jacinto
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- Allergy Unit, Instituto and Hospital CUF-Porto, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- Department of Cardiovascular and Respiratory Sciences, Porto Health School, Polytechnic Institute of Porto, Porto, Portugal
| | - Rita Amaral
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- Department of Cardiovascular and Respiratory Sciences, Porto Health School, Polytechnic Institute of Porto, Porto, Portugal
- Department of Women's and Children's Health, Pediatric Research, Uppsala University, Uppsala, Sweden
| | - Mariana Pereira
- Allergy Unit, Instituto and Hospital CUF-Porto, Porto, Portugal
- PaCeIT - Patient Centered Innovation and Technologies, Center for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
- MEDIDA - Medicina, Educação, Investigação, Desenvolvimento e Avaliação, Porto, Portugal
| | - Ana Sá-Sousa
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - Mariana Couto
- Allergy Unit, Instituto and Hospital CUF-Porto, Porto, Portugal
- Allergy Center, CUF Descobertas Hospital, Lisboa, Portugal
| | - Pedro Vieira-Marques
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - Diogo Martinho
- Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto, Porto, Portugal
| | - Ana Vieira
- Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto, Porto, Portugal
| | - Ana Almeida
- Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto, Porto, Portugal
| | - Constantino Martins
- Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto, Porto, Portugal
| | - Goreti Marreiros
- Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto, Porto, Portugal
| | - Alberto Freitas
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - Rute Almeida
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - João A Fonseca
- Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- Allergy Unit, Instituto and Hospital CUF-Porto, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- MEDIDA - Medicina, Educação, Investigação, Desenvolvimento e Avaliação, Porto, Portugal
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Statsenko Y, Smetanina D, Arora T, Östlundh L, Habuza T, Simiyu GL, Meribout S, Talako T, King FC, Makhnevych I, Gelovani JG, Das KM, Gorkom KNV, Almansoori TM, Al Zahmi F, Szólics M, Ismail F, Ljubisavljevic M. Multimodal diagnostics in multiple sclerosis: predicting disability and conversion from relapsing-remitting to secondary progressive disease course - protocol for systematic review and meta-analysis. BMJ Open 2023; 13:e068608. [PMID: 37451729 PMCID: PMC10351237 DOI: 10.1136/bmjopen-2022-068608] [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: 09/26/2022] [Accepted: 05/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND The number of patients diagnosed with multiple sclerosis (MS) has increased significantly over the last decade. The challenge is to identify the transition from relapsing-remitting to secondary progressive MS. Since available methods to examine patients with MS are limited, both the diagnostics and prognostication of disease progression would benefit from the multimodal approach. The latter combines the evidence obtained from disparate radiologic modalities, neurophysiological evaluation, cognitive assessment and molecular diagnostics. In this systematic review we will analyse the advantages of multimodal studies in predicting the risk of conversion to secondary progressive MS. METHODS AND ANALYSIS We will use peer-reviewed publications available in Web of Science, Medline/PubMed, Scopus, Embase and CINAHL databases. In vivo studies reporting the predictive value of diagnostic methods will be considered. Selected publications will be processed through Covidence software for automatic deduplication and blind screening. Two reviewers will use a predefined template to extract the data from eligible studies. We will analyse the performance metrics (1) for the classification models reflecting the risk of secondary progression: sensitivity, specificity, accuracy, area under the receiver operating characteristic curve, positive and negative predictive values; (2) for the regression models forecasting disability scores: the ratio of mean absolute error to the range of values. Then, we will create ranking charts representing performance of the algorithms for calculating disability level and MS progression. Finally, we will compare the predictive power of radiological and radiomical correlates of clinical disability and cognitive impairment in patients with MS. ETHICS AND DISSEMINATION The study does not require ethical approval because we will analyse publicly available literature. The project results will be published in a peer-review journal and presented at scientific conferences. PROSPERO REGISTRATION NUMBER CRD42022354179.
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Affiliation(s)
- Yauhen Statsenko
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
- Medical Imaging Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain, Abu Dhabi Emirate, UAE
- Big Data Analytics Center, United Arab Emirates University, Al Ain, Abu Dhabi Emirate, UAE
| | - Darya Smetanina
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
- Medical Imaging Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain, Abu Dhabi Emirate, UAE
| | - Teresa Arora
- Psychology Department, College of Natural and Health Sciences, Zayed University, Abu Dhabi, Abu Dhabi Emirate, UAE
| | - Linda Östlundh
- National Medical Library, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, Abu Dhabi Emirate, UAE
- Library, Örebro University, Örebro, Sweden
| | - Tetiana Habuza
- Big Data Analytics Center, United Arab Emirates University, Al Ain, Abu Dhabi Emirate, UAE
- Department of Computer Science, College of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi Emirate, UAE
| | - Gillian Lylian Simiyu
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
- Medical Imaging Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain, Abu Dhabi Emirate, UAE
| | - Sarah Meribout
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
- Medical Imaging Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain, Abu Dhabi Emirate, UAE
- Internal Medicine Department, Maimonides Medical Center, New York, New York, USA
| | - Tatsiana Talako
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
- Department of Oncohematology, Minsk Scientific and Practical Center for Surgery, Transplantology and Hematology, Minsk, Belarus
| | - Fransina Christina King
- Physiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
- Neuroscience Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain, Abu Dhabi Emirate, UAE
| | - Iryna Makhnevych
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
| | - Juri George Gelovani
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
- Biomedical Engineering Department, Wayne State University, College of Engineering, Detroit, Michigan, USA
- Radiology Department, Siriraj Hospital, Faculty of Medicine, Mahidol University, Bangkok, Thailand
- Provost Office, United Arab Emirates University, Al Ain, Abu Dhabi Emirate, UAE
| | - Karuna M Das
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
| | - Klaus Neidl-Van Gorkom
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
| | - Taleb M Almansoori
- Radiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
| | - Fatmah Al Zahmi
- Neurology Department, Mediclinic Parkview Hospital, Dubai, Dubai Emirate, UAE
- Neurology Department, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, Dubai Emirate, UAE
| | - Miklós Szólics
- Internal Medicine Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
- Division of Neurology, Department of Medicine, Tawam Hospital, Al Ain, Abu Dhabi Emirate, UAE
| | - Fatima Ismail
- Pediatrics Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi, UAE
| | - Milos Ljubisavljevic
- Physiology Department, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, Abu Dhabi Emirate, UAE
- Neuroscience Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain, Abu Dhabi Emirate, UAE
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Gholamzadeh M, Abtahi H, Safdari R. The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:8550905. [PMID: 37284487 PMCID: PMC10241579 DOI: 10.1155/2023/8550905] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 02/07/2023] [Accepted: 02/19/2023] [Indexed: 06/08/2023]
Abstract
Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.
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Affiliation(s)
- Marsa Gholamzadeh
- Medical Informatics, Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
- Thoracic Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Abtahi
- Pulmonary and Critical Care Department, Thoracic Research Center, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Safdari
- Health Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
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Abtahi H, Amini S, Gholamzadeh M, Gharabaghi MA. Development and evaluation of a mobile-based asthma clinical decision support system to enhance evidence-based patient management in primary care. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
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5
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Ed‐driouch C, Chéneau F, Simon F, Pasquier G, Combès B, Kerbrat A, Le Page E, Limou S, Vince N, Laplaud D, Mars F, Dumas C, Edan G, Gourraud P. Multiple sclerosis clinical decision support system based on projection to reference datasets. Ann Clin Transl Neurol 2022; 9:1863-1873. [PMID: 36412095 PMCID: PMC9735373 DOI: 10.1002/acn3.51649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/08/2022] [Accepted: 07/27/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Multiple sclerosis (MS) is a multifactorial disease with increasingly complicated management. Our objective is to use on-demand computational power to address the challenges of dynamically managing MS. METHODS A phase 3 clinical trial data (NCT00906399) were used to contextualize the medication efficacy of peg-interferon beta-1a vs placebo on patients with relapsing-remitting MS (RRMS). Using a set of reference patients (PORs), selected based on adequate features similar to those of an individual patient, we visualize disease activity by measuring the percentage of relapses, accumulation of new T2 lesions on MRI, and worsening EDSS during the clinical trial. RESULTS We developed MS Vista, a functional prototype of clinical decision support system (CDSS), with a user-centered design and distributed infrastructure. MS Vista shows the medication efficacy of peginterferon beta-1a versus placebo for each individual patient with RRMS. In addition, MS Vista initiated the integration of a longitudinal magnetic resonance imaging (MRI) viewer and interactive dual physician-patient data display to facilitate communication. INTERPRETATION The pioneer use of PORs for each individual patient enables personalized analytics sustaining the dialog between neurologists, patients and caregivers with quantified evidence.
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Affiliation(s)
- Chadia Ed‐driouch
- Nantes Université, INSERM, CR2TI ‐ Center for Research in Transplantation and Translational ImmunologyF‐44000NantesFrance,Département Automatique, Productique et Informatique, IMT AtlantiqueCNRS, LS2N, UMR CNRS6004NantesFrance
| | - Florent Chéneau
- Département Automatique, Productique et Informatique, IMT AtlantiqueCNRS, LS2N, UMR CNRS6004NantesFrance
| | - Françoise Simon
- Nantes Université, INSERM, CR2TI ‐ Center for Research in Transplantation and Translational ImmunologyF‐44000NantesFrance,Mount Sinai School of Medicine and Columbia UniversityNew YorkNYUSA
| | | | - Benoit Combès
- Université de Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228F‐35000RennesFrance
| | - Anne Kerbrat
- Université de Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228F‐35000RennesFrance,CRC‐SEP, CICP 1414 INSERM, CHU Pontchaillou RennesRennesFrance
| | | | - Sophie Limou
- Nantes Université, INSERM, CR2TI ‐ Center for Research in Transplantation and Translational ImmunologyF‐44000NantesFrance,Ecole Centrale Nantes, Department of MathematicsComputer Sciences and BiologyF-44000NantesFrance
| | - Nicolas Vince
- Nantes Université, INSERM, CR2TI ‐ Center for Research in Transplantation and Translational ImmunologyF‐44000NantesFrance
| | - David‐Axel Laplaud
- Nantes Université, CRC‐SEP, CHU Nantes, CIC 1413, Centre de Recherche en Transplantation et Immunologie UMR 1064, INSERMNantesFrance
| | - Franck Mars
- Nantes Université, Centrale NantesCNRS, LS2N, UMR 6004F‐44000NantesFrance
| | - Cédric Dumas
- Département Automatique, Productique et Informatique, IMT AtlantiqueCNRS, LS2N, UMR CNRS6004NantesFrance
| | - Gilles Edan
- Université de Rennes, Inria, CNRS, Inserm IRISA UMR 6074, Empenn ERL U 1228F‐35000RennesFrance,CRC‐SEP, CICP 1414 INSERM, CHU Pontchaillou RennesRennesFrance
| | - Pierre‐Antoine Gourraud
- Nantes Université, CHU Nantes, Pôle Hospitalo‐Universitaire 11: Santé Publique, Clinique des données, INSERM CIC 1413F‐44000NantesFrance
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