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Finkelstein J, Gabriel A, Schmer S, Truong TT, Dunn A. Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System. J Med Syst 2024; 48:89. [PMID: 39292314 PMCID: PMC11410896 DOI: 10.1007/s10916-024-02104-9] [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: 05/23/2024] [Accepted: 08/29/2024] [Indexed: 09/19/2024]
Abstract
Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.
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Affiliation(s)
- Joseph Finkelstein
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Rm. 2028, Salt Lake City, UT, 84108, USA.
| | - Aileen Gabriel
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Rm. 2028, Salt Lake City, UT, 84108, USA
| | - Susanna Schmer
- Department of Case Management, Mount Sinai Health System, New York, NY, USA
| | - Tuyet-Trinh Truong
- Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew Dunn
- Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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2
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Gorham G, Abeyaratne A, Heard S, Moore L, George P, Kamler P, Majoni SW, Chen W, Balasubramanya B, Talukder MR, Pascoe S, Whitehead A, Sajiv C, Maple Brown L, Kangaharan N, Cass A. Developing an integrated clinical decision support system for the early identification and management of kidney disease-building cross-sectoral partnerships. BMC Med Inform Decis Mak 2024; 24:69. [PMID: 38459531 PMCID: PMC10924414 DOI: 10.1186/s12911-024-02471-w] [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: 08/31/2023] [Accepted: 02/26/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND The burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is complicated by a mobile population, high staff turnover, poor communication between health services and complex comorbid health conditions requiring multidisciplinary care. AIM This paper aims to describe the collaborative process between research, government and non-government health services to develop an integrated clinical decision support system to improve patient care. METHODS Building on established partnerships in the government and Aboriginal Community-Controlled Health Service (ACCHS) sectors, we developed a novel digital clinical decision support system for people at risk of developing kidney disease (due to hypertension, diabetes, cardiovascular disease) or with kidney disease. A cross-organisational and multidisciplinary Steering Committee has overseen the design, development and implementation stages. Further, the system's design and functionality were strongly informed by experts (Clinical Reference Group and Technical Working Group), health service providers, and end-user feedback through a formative evaluation. RESULTS We established data sharing agreements with 11 ACCHS to link patient level data with 56 government primary health services and six hospitals. Electronic Health Record (EHR) data, based on agreed criteria, is automatically and securely transferred from 15 existing EHR platforms. Through clinician-determined algorithms, the system assists clinicians to diagnose, monitor and provide guideline-based care for individuals, as well as service-level risk stratification and alerts for clinically significant events. CONCLUSION Disconnected health services and separate EHRs result in information gaps and a health and safety risk, particularly for patients who access multiple health services. However, barriers to clinical data sharing between health services still exist. In this first phase, we report how robust partnerships and effective governance processes can overcome these barriers to support clinical decision making and contribute to holistic care.
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Affiliation(s)
- Gillian Gorham
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Darwin, NT, 0810, Australia.
| | - Asanga Abeyaratne
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Darwin, NT, 0810, Australia
- Department of Nephrology, Royal Darwin Hospital, Northern Territory Health, Darwin, NT, Australia
| | - Sam Heard
- Central Australian Aboriginal Congress, Aboriginal Corporation, Alice Springs, NT, Australia
| | - Liz Moore
- Aboriginal Medical Services Alliance Northern Territory, Darwin, NT, Australia
| | - Pratish George
- Department of Nephrology, Alice Springs Hospital, Northern Territory Health, Alice Springs, NT, Australia
| | - Paul Kamler
- Department of Nephrology, Royal Darwin Hospital, Northern Territory Health, Darwin, NT, Australia
| | - Sandawana William Majoni
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Darwin, NT, 0810, Australia
- Department of Nephrology, Royal Darwin Hospital, Northern Territory Health, Darwin, NT, Australia
- Northern Territory Medical Program, Flinders University, Royal Darwin Hospital Campus, Darwin, NT, Australia
| | - Winnie Chen
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Darwin, NT, 0810, Australia
| | - Bhavya Balasubramanya
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Darwin, NT, 0810, Australia
| | - Mohammad Radwanur Talukder
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Darwin, NT, 0810, Australia
| | - Sophie Pascoe
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Darwin, NT, 0810, Australia
| | | | - Cherian Sajiv
- Department of Nephrology, Alice Springs Hospital, Northern Territory Health, Alice Springs, NT, Australia
- Northern Territory Medical Program, Flinders University, Royal Darwin Hospital Campus, Darwin, NT, Australia
| | - Louise Maple Brown
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Darwin, NT, 0810, Australia
- Department of Endocrinology, Royal Darwin Hospital Northern Territory Health, Darwin, NT, Australia
| | - Nadarajah Kangaharan
- Division of Medicine, Royal Darwin Hospital Northern Territory Health, Darwin, NT, Australia
| | - Alan Cass
- Wellbeing and Preventable Chronic Diseases Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Darwin, NT, 0810, Australia
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3
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Zhao G, Cheng W, Cai W, Zhang X, Liu J. Leveraging Interpretable Feature Representations for Advanced Differential Diagnosis in Computational Medicine. Bioengineering (Basel) 2023; 11:29. [PMID: 38247906 PMCID: PMC10813342 DOI: 10.3390/bioengineering11010029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/19/2023] [Accepted: 12/22/2023] [Indexed: 01/23/2024] Open
Abstract
Diagnostic errors represent a critical issue in clinical diagnosis and treatment. In China, the rate of misdiagnosis in clinical diagnostics is approximately 27.8%. By comparison, in the United States, which boasts the most developed medical resources globally, the average rate of misdiagnosis is estimated to be 11.1%. It is estimated that annually, approximately 795,000 Americans die or suffer permanent disabilities due to diagnostic errors, a significant portion of which can be attributed to physicians' failure to make accurate clinical diagnoses based on patients' clinical presentations. Differential diagnosis, as an indispensable step in the clinical diagnostic process, plays a crucial role. Accurately excluding differential diagnoses that are similar to the patient's clinical manifestations is key to ensuring correct diagnosis and treatment. Most current research focuses on assigning accurate diagnoses for specific diseases, but studies providing reasonable differential diagnostic assistance to physicians are scarce. This study introduces a novel solution specifically designed for this scenario, employing machine learning techniques distinct from conventional approaches. We develop a differential diagnosis recommendation computation method for clinical evidence-based medicine, based on interpretable representations and a visualized computational workflow. This method allows for the utilization of historical data in modeling and recommends differential diagnoses to be considered alongside the primary diagnosis for clinicians. This is achieved by inputting the patient's clinical manifestations and presenting the analysis results through an intuitive visualization. It can assist less experienced doctors and those in areas with limited medical resources during the clinical diagnostic process. Researchers discuss the effective experimental results obtained from a subset of general medical records collected at Shengjing Hospital under the premise of ensuring data quality, security, and privacy. This discussion highlights the importance of addressing these issues for successful implementation of data-driven differential diagnosis recommendations in clinical practice. This study is of significant value to researchers and practitioners seeking to improve the efficiency and accuracy of differential diagnoses in clinical diagnostics using data analysis.
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Affiliation(s)
- Genghong Zhao
- School of Computer Science, Engineering Northeastern University, No.195 Chuangxin Road Hunnan District, Shenyang 110169, China;
- Neusoft Research of Intelligent Healthcare Technology, Co., Ltd., No.175-2 Chuangxin Road Hunnan District, Shenyang 110167, China;
- The Liaoning Provincial Key Laboratory of Interdisciplinary Research on Gastrointestinal Tumor Combining Medicine with Engineering, Shenyang 110042, China
| | - Wen Cheng
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang 110004, China;
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co., Ltd., No.175-2 Chuangxin Road Hunnan District, Shenyang 110167, China;
- The Liaoning Provincial Key Laboratory of Interdisciplinary Research on Gastrointestinal Tumor Combining Medicine with Engineering, Shenyang 110042, China
| | - Xia Zhang
- School of Computer Science, Engineering Northeastern University, No.195 Chuangxin Road Hunnan District, Shenyang 110169, China;
- Neusoft Research of Intelligent Healthcare Technology, Co., Ltd., No.175-2 Chuangxin Road Hunnan District, Shenyang 110167, China;
- The Liaoning Provincial Key Laboratory of Interdisciplinary Research on Gastrointestinal Tumor Combining Medicine with Engineering, Shenyang 110042, China
| | - Jiren Liu
- School of Computer Science, Engineering Northeastern University, No.195 Chuangxin Road Hunnan District, Shenyang 110169, China;
- Neusoft Corporation, No.2 Xinxiu Road Hunnan District, Shenyang 110179, China
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Alghamdi SM. Content, Mechanism, and Outcome of Effective Telehealth Solutions for Management of Chronic Obstructive Pulmonary Diseases: A Narrative Review. Healthcare (Basel) 2023; 11:3164. [PMID: 38132054 PMCID: PMC10742533 DOI: 10.3390/healthcare11243164] [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: 10/18/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
Telehealth (TH) solutions for Chronic Obstructive Pulmonary Disease (COPD) are promising behavioral therapeutic interventions and can help individuals living with COPD to improve their health status. The linking content, mechanism, and outcome of TH interventions reported in the literature related to COPD care are unknown. This paper aims to summarize the existing literature about structured TH solutions in COPD care. We conducted an electronic search of the literature related to TH solutions for COPD management up to October 2023. Thirty papers presented TH solutions as an innovative treatment to manage COPD. TH and digital health solutions are used interchangeably in the literature, but both have the potential to improve care, accessibility, and quality of life. To date, current TH solutions in COPD care have a variety of content, mechanisms, and outcomes. TH solutions can enhance education as well as provide remote monitoring. The content of TH solutions can be summarized as symptom management, prompt physical activity, and psychological support. The mechanism of TH solutions is manipulated by factors such as content, mode of delivery, strategy, and intensity. The most common outcome measures with TH solutions were adherence to treatment, health status, and quality of life. Implementing effective TH with a COPD care bundle must consider important determinants such as patient's needs, familiarity with the technology, healthcare professional support, and data privacy. The development of effective TH solutions for COPD management also must consider patient engagement as a positive approach to optimizing implementation and effectiveness.
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Affiliation(s)
- Saeed Mardy Alghamdi
- Respiratory Care Program, Clinical Technology Department, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah 21961, Saudi Arabia
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5
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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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Chen Z, Liang N, Zhang H, Li H, Yang Y, Zong X, Chen Y, Wang Y, Shi N. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart 2023; 10:e002432. [PMID: 38016787 PMCID: PMC10685930 DOI: 10.1136/openhrt-2023-002432] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/31/2023] [Indexed: 11/30/2023] Open
Abstract
Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.
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Affiliation(s)
- Zhao Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Liang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haili Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Huizhen Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yijiu Yang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xingyu Zong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yaxin Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Nannan Shi
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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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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Román-Villarán E, Alvarez-Romero C, Martínez-García A, Escobar-Rodríguez GA, García-Lozano MJ, Barón-Franco B, Moreno-Gaviño L, Moreno-Conde J, Rivas-González JA, Parra-Calderón CL. A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study. JMIR Form Res 2022; 6:e27990. [PMID: 35916719 PMCID: PMC9382545 DOI: 10.2196/27990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/24/2021] [Accepted: 03/29/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Due to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits. OBJECTIVE The aim of this study is to design, develop, and validate an ontology-based CDSS that provides personalized recommendations related to drug prescription. The target population is older adult patients with chronic diseases and polypharmacy, and the goal is to reduce complications related to these types of conditions while offering integrated care. METHODS A study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, including CPGs, PROFUND index, LESS/CHRON criteria, and STOPP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT; International Health Terminology Standards Development Organisation). Once the CDSS was developed, validation was carried out by using a retrospective case study. RESULTS This project was funded in January 2015 and approved by the Virgen del Rocio University Hospital ethics committee on November 24, 2015. Two different tasks were carried out to test the functioning of the tool. First, retrospective data from a real patient who met the inclusion criteria were used. Second, the analysis of an adoption model was performed through the study of the requirements and characteristics that a CDSS must meet in order to be well accepted and used by health professionals. The results are favorable and allow the proposed research to continue to the next phase. CONCLUSIONS An ontology-based CDSS was successfully designed, developed, and validated. However, in future work, validation in a real environment should be performed to ensure the tool is usable and reliable.
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Affiliation(s)
- Esther Román-Villarán
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - Celia Alvarez-Romero
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - Alicia Martínez-García
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - German Antonio Escobar-Rodríguez
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | | | - Bosco Barón-Franco
- Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain
| | | | - Jesús Moreno-Conde
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - José Antonio Rivas-González
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - Carlos Luis Parra-Calderón
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
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9
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Improving Pharmacists’ Awareness of Inadequate Antibiotic Use for URTIs through an Educational Intervention: A Pilot Study. Healthcare (Basel) 2022; 10:healthcare10081385. [PMID: 35893207 PMCID: PMC9394361 DOI: 10.3390/healthcare10081385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
The inadequate use of antibiotics led to the development of multi-resistant bacteria that are now causing millions of deaths worldwide. Since most antibiotics are prescribed/dispensed to treat respiratory tract infections, it is important to raise awareness among health professionals to optimize antibiotic use, especially within the primary care context. Thus, this pilot study aimed to evaluate pharmacists’ feedback about the eHealthResp platform, composed by an online course and a mobile application (app) to help in the management of upper respiratory tract infections (URTIs). Ten community pharmacists were invited to participate in this study, exploring the contents of the eHealthResp platforms and answering a content validation questionnaire composed by eight qualitative and thirty-five quantitative questions about the online course and mobile app. The eHealthResp platform is a comprehensive, consistent, and high-quality e-learning tool. Median scores of 5.00 were attributed to the course contents’ and clinical cases’ adequacy and correction. Most qualitative feedback was about completeness and objectivity of the course, and its usefulness for clinical practice. This study showed that eHealthResp has great potential as an e-health tool for the management of URTIs’ symptoms, which may ultimately aid in reducing inappropriate antibiotic use.
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10
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Papadopoulos P, Soflano M, Chaudy Y, Adejo W, Connolly TM. A systematic review of technologies and standards used in the development of rule-based clinical decision support systems. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00672-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
AbstractA Clinical Decision Support System (CDSS) is a technology platform that uses medical knowledge with clinical data to provide customised advice for an individual patient's care. CDSSs use rules to encapsulate expert knowledge and rules engines to infer logic by evaluating rules according to a patient's specific information and related medical facts. However, CDSSs are by nature complex with a plethora of different technologies, standards and methods used to implement them and it can be difficult for practitioners to determine an appropriate solution for a specific scenario. This study's main goal is to provide a better understanding of different technical aspects of a CDSS, identify gaps in CDSS development and ultimately provide some guidelines to assist their translation into practice. We focus on issues related to knowledge representation including use of clinical ontologies, interoperability with EHRs, technology standards, CDSS architecture and mobile/cloud access.This study performs a systematic literature review of rule-based CDSSs that discuss the underlying technologies used and have evaluated clinical outcomes. From a search that yielded an initial set of 1731 papers, only 15 included an evaluation of clinical outcomes. This study has found that a large majority of papers did not include any form of evaluation and, for many that did include an evaluation, the methodology was not sufficiently rigorous to provide statistically significant results. From the 15 papers shortlisted, there were no RCT or quasi-experimental studies, only 6 used ontologies to represent domain knowledge, only 2 integrated with an EHR system, only 5 supported mobile use and only 3 used recognised healthcare technology standards (and all these were HL7 standards). Based on these findings, the paper provides some recommendations for future CDSS development.
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11
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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12
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Estrela M, Magalhães Silva T, Pisco Almeida AM, Regueira C, Zapata-Cachafeiro M, Figueiras A, Roque F, Herdeiro MT. A roadmap for the development and evaluation of the eHealthResp online course. Digit Health 2022; 8:20552076221089088. [PMID: 35360007 PMCID: PMC8961349 DOI: 10.1177/20552076221089088] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 03/06/2022] [Indexed: 11/29/2022] Open
Abstract
Background Inappropriate antibiotic use constitutes one of the most concerning public
health issues, being one of the main causes of antibiotic resistance. Hence,
to tackle this issue, it is important to encourage the development of
educational interventions for health practitioners, namely by using digital
health tools. This study focuses on the description of the development and
validation process of the eHealthResp online course, a web platform directed
to physicians and pharmacists, with the overall goal of improving antibiotic
use for respiratory tract infections, along with the assessment of its
usability. Methods The eHealthResp platform and the courses, developed with a user-centered
design and based on Wordpress and MySQL, were based on a previously
developed online course. A questionnaire to assess the usability was
distributed among physicians (n = 6) and pharmacists (n = 6). Based on the
obtained results, statistical analyses were conducted to calculate the
usability score and appraise the design of the online course, as well as to
compare the overall scores attributed by both groups. Further qualitative
comments provided by the participants have also been analyzed. Results The eHealthResp contains two online courses directed to physicians and
pharmacists aiming to aid in the management of respiratory tract infections.
The average usability score of the eHealthResp online courses for physicians
and pharmacists was of 78.33 (±11.57, 95%CI), and 83.75 (±15.90, 95%CI),
respectively. Qualitative feedback emphasized the usefulness of the course,
including overall positive reviews regarding user-friendliness and
consistency. Conclusions This study led us to conclude that the eHealthResp online course is not
recognized as a complex web platform, as both qualitative and quantitative
feedback obtained were globally positive.
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Affiliation(s)
- Marta Estrela
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Tânia Magalhães Silva
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | | | - Carlos Regueira
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, 15702 Santiago de Compostela, Spain.,Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiology and Public Health - CIBERESP), Santiago de Compostela, Spain
| | - Maruxa Zapata-Cachafeiro
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, 15702 Santiago de Compostela, Spain.,Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiology and Public Health - CIBERESP), Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Adolfo Figueiras
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, 15702 Santiago de Compostela, Spain.,Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiology and Public Health - CIBERESP), Santiago de Compostela, Spain.,Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Fátima Roque
- Research Unit for Inland Development, Guarda Polytechnic Institute (UDI-IPG), Guarda, Portugal.,Health Sciences Research Center, University of Beira Interior (CICS-UBI), Covilhã, Portugal
| | - Maria Teresa Herdeiro
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
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13
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Mlakar I, Smrke U, Flis V, Bergauer A, Kobilica N, Kampič T, Horvat S, Vidovič D, Musil B, Plohl N. A randomized controlled trial for evaluating the impact of integrating a computerized clinical decision support system and a socially assistive humanoid robot into grand rounds during pre/post-operative care. Digit Health 2022; 8:20552076221129068. [PMID: 36185391 PMCID: PMC9515524 DOI: 10.1177/20552076221129068] [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: 03/16/2022] [Accepted: 09/10/2022] [Indexed: 11/17/2022] Open
Abstract
Although clinical decision support systems (CDSSs) are increasingly emphasized as
one of the possible levers for improving care, they are still not widely used
due to different barriers, such as doubts about systems’ performance, their
complexity and poor design, practitioners’ lack of time to use them, poor
computer skills, reluctance to use them in front of patients, and deficient
integration into existing workflows. While several studies on CDSS exist, there
is a need for additional high-quality studies using large samples and examining
the differences between outcomes following a decision based on CDSS support and
those following decisions without this kind of information. Even less is known
about the effectiveness of a CDSS that is delivered during a grand round routine
and with the help of socially assistive humanoid robots (SAHRs). In this study,
200 patients will be randomized into a Control Group (i.e. standard care) and an
Intervention Group (i.e. standard care and novel CDSS delivered via a SAHR).
Health care quality and Quality of Life measures will be compared between the
two groups. Additionally, approximately 22 clinicians, who are also active
researchers at the University Clinical Center Maribor, will evaluate the
acceptability and clinical usability of the system. The results of the proposed
study will provide high-quality evidence on the effectiveness of CDSS systems
and SAHR in the grand round routine.
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Affiliation(s)
- Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Urška Smrke
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Vojko Flis
- University Clinical Centre Maribor, Maribor, Slovenia
| | | | - Nina Kobilica
- University Clinical Centre Maribor, Maribor, Slovenia
| | - Tadej Kampič
- University Clinical Centre Maribor, Maribor, Slovenia
| | - Samo Horvat
- University Clinical Centre Maribor, Maribor, Slovenia
| | | | - Bojan Musil
- Faculty of Arts, Department of Psychology, University of Maribor, Maribor, Slovenia
| | - Nejc Plohl
- Faculty of Arts, Department of Psychology, University of Maribor, Maribor, Slovenia
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14
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De Ramón Fernández A, Ruiz Fernández D, Gilart Iglesias V, Marcos Jorquera D. Analyzing the use of artificial intelligence for the management of chronic obstructive pulmonary disease (COPD). Int J Med Inform 2021; 158:104640. [PMID: 34890934 DOI: 10.1016/j.ijmedinf.2021.104640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/21/2021] [Accepted: 11/03/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Chronic obstructive pulmonary disease (COPD) is a disease that causes airflow limitation to the lungs and has a high morbidity around the world. The objective of this study was to evaluate how artificial intelligence (AI) is being applied for the management of the disease, analyzing the objectives that are raised, the algorithms that are used and what results they offer. METHODS We conducted a scoping review following the Arksey and O'Malley (2005) and Levac et al. (2010) guidelines. Two reviewers independently searched, analyzed and extracted data from papers of five databases: Web of Science, PubMed, Scopus, Cinahl and Cochrane. To be included, the studies had to apply some AI techniques for the management of at least one stage of the COPD clinical process. In the event of any discrepancy between both reviewers, the criterion of a third reviewer prevailed. RESULTS 380 papers were identified through database searches. After applying the exclusion criteria, 67 papers were included in the study. The studies were of a different nature and pursued a wide range of objectives, highlighting mainly those focused on the identification, classification and prevention of the disease. Neural nets, support vector machines and decision trees were the AI algorithms most commonly used. The mean and median values of all the performance metrics evaluated were between 80% and 90%. CONCLUSIONS The results obtained show a growing interest in the development of medical applications that manage the different phases of the COPD clinical process, especially predictive models. According to the performance shown, these models could be a useful complementary tool in the decision-making by health specialists, although more high-quality ML studies are needed to endorse the findings of this study.
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15
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Mariani S, Metting E, Lahr MMH, Vargiu E, Zambonelli F. Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service. INT J INTELL SYST 2021. [DOI: 10.1002/int.22568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Stefano Mariani
- Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy
| | - Esther Metting
- Health Technology Assessment, Department of Epidemiology, University of Groningen University Medical Center Groningen The Netherlands
| | - Maarten M. H. Lahr
- Health Technology Assessment, Department of Epidemiology, University of Groningen University Medical Center Groningen The Netherlands
| | - Eloisa Vargiu
- EURECAT Technology Centre Digital Health Unit Barcelona Spain
| | - Franco Zambonelli
- Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy
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16
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Sloots J, Bakker M, van der Palen J, Eijsvogel M, van der Valk P, Linssen G, van Ommeren C, Grinovero M, Tabak M, Effing T, Lenferink A. Adherence to an eHealth Self-Management Intervention for Patients with Both COPD and Heart Failure: Results of a Pilot Study. Int J Chron Obstruct Pulmon Dis 2021; 16:2089-2103. [PMID: 34290502 PMCID: PMC8289298 DOI: 10.2147/copd.s299598] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 04/19/2021] [Indexed: 01/02/2023] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) and chronic heart failure (CHF) often coexist and share periods of symptom deterioration. Electronic health (eHealth) might play an important role in adherence to interventions for the self-management of COPD and CHF symptoms by facilitating and supporting home-based care. Methods In this pilot study, an eHealth self-management intervention was developed based on paper versions of multi-morbid exacerbation action plans and evaluated in patients with both COPD and CHF. Self-reporting of increased symptoms in diaries was linked to an automated decision support system that generated self-management actions, which was communicated via an eHealth application on a tablet. After participating in self-management training sessions, patients used the intervention for a maximum of four months. Adherence to daily symptom diary completion and follow-up of actions were analyzed. An add-on sensorized (Respiro®) inhaler was used to analyze inhaled medication adherence and inhalation technique. Results In total, 1148 (91%) of the daily diaries were completed on the same day by 11 participating patients (mean age 66.8 ± 2.9 years; moderate (55%) to severe (45%) COPD; 46% midrange left ventricular function (LVF) and 27% reduced LVF). Seven patients received a total of 24 advised actions because of increased symptoms of which 11 (46%) were followed-up. Of the 13 (54%) unperformed advised actions, six were “call the case manager”. Adherence to inhaled medication was 98.4%, but 51.9% of inhalations were performed incorrectly, with “inhaling too shortly” (<1.25 s) being the most frequent error (79.6%). Discussion Whereas adherence to completing daily diaries was high, advised actions were inadequately followed-up, particularly the action “call the case manager”. Inhaled medication adherence was high, but inhalations were poorly performed. Future research is needed to identify adherence barriers, further tailor the intervention to the individual patient and analyse the intervention effects on health outcomes.
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Affiliation(s)
- Joanne Sloots
- Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Mirthe Bakker
- Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Job van der Palen
- Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands.,Department of Research Methodology, Measurement & Data Analysis, University of Twente, Enschede, the Netherlands
| | - Michiel Eijsvogel
- Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Paul van der Valk
- Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Gerard Linssen
- Department of Cardiology, Hospital Group Twente, Almelo and Hengelo, the Netherlands
| | - Clara van Ommeren
- Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | | | - Monique Tabak
- eHealth Group, Roessingh Research and Development, Enschede, the Netherlands.,Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, the Netherlands
| | - Tanja Effing
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Anke Lenferink
- Department of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands.,Department of Health Technology and Services Research, Faculty of Behavioural, Management and Social sciences, Technical Medical Centre, University of Twente, Enschede, the Netherlands
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17
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Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11115088] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.
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Estrela M, Roque F, Silva TM, Zapata-Cachafeiro M, Figueiras A, Herdeiro MT. Validation of the eHealthResp online course for pharmacists and physicians: A Delphi method approach. Biomed Pharmacother 2021; 140:111739. [PMID: 34020245 DOI: 10.1016/j.biopha.2021.111739] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/07/2021] [Accepted: 05/11/2021] [Indexed: 11/30/2022] Open
Abstract
FRAMEWORK The inappropriate use of antibiotics for respiratory tract infections is dispersed worldwide, thus being a strong contributor to antibiotic resistances. As the use of educational interventions among health practitioners is shown to have an impact on judicious antibiotic use, an online course (eHealthResp) has been developed, especially targeted to pharmacists and physicians. Thus, the main goal of this study is to validate the contents of the online course eHealthResp. METHODS This two-round Delphi study involved the recruitment of a multidisciplinary panel (n = 19), to which the questionnaires of the first round were sent. After the first round, a report summing up the results has been forwarded to the panel, along with a new, reformulated version of the questionnaire. RESULTS After the two rounds of the Delphi process, consensus was evaluated. Six clinical cases and fifty-one treatments obtained minor consensus [60-75%] or full consensus (≥75%). The question on antibiotic practice has obtained a consensus >90% on both rounds. CONCLUSIONS The validation of the contents based on experts' consensus has been an essential approach to improve eHealthResp's online course, as valuable feedback has been provided by the panel on both rounds.
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Affiliation(s)
- Marta Estrela
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal.
| | - Fátima Roque
- Research Unit for Inland Development, Guarda Polytechnic Institute (UDI-IPG), Guarda, Portugal; Health Sciences Research Center, University of Beira Interior (CICS-UBI), Covilhã, Portugal
| | - Tânia Magalhães Silva
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Maruxa Zapata-Cachafeiro
- Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Spain; Department of Preventive Medicine and Public Health, University of Santiago de Compostela, 15702 Santiago de Compostela, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiology and Public Health - CIBERESP), Madrid, Spain
| | - Adolfo Figueiras
- Health Research Institute of Santiago de Compostela (IDIS), University of Santiago de Compostela, Spain; Department of Preventive Medicine and Public Health, University of Santiago de Compostela, 15702 Santiago de Compostela, Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiology and Public Health - CIBERESP), Madrid, Spain
| | - Maria Teresa Herdeiro
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
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El-Rashidy N, El-Sappagh S, Islam SMR, M. El-Bakry H, Abdelrazek S. Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges. Diagnostics (Basel) 2021; 11:diagnostics11040607. [PMID: 33805471 PMCID: PMC8067150 DOI: 10.3390/diagnostics11040607] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/17/2021] [Accepted: 03/05/2021] [Indexed: 02/07/2023] Open
Abstract
Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.
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Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt;
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
- Correspondence: (S.E.-S.); (S.M.R.I.)
| | - S. M. Riazul Islam
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
- Correspondence: (S.E.-S.); (S.M.R.I.)
| | - Hazem M. El-Bakry
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura 13518, Egypt; (H.M.E.-B.); (S.A.)
| | - Samir Abdelrazek
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura 13518, Egypt; (H.M.E.-B.); (S.A.)
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Martinez-Garcia A, Naranjo-Saucedo AB, Rivas JA, Romero Tabares A, Marín Cassinello A, Andrés-Martín A, Sánchez Laguna FJ, Villegas R, Pérez León FDP, Moreno Conde J, Parra Calderón CL. A Clinical Decision Support System (KNOWBED) to Integrate Scientific Knowledge at the Bedside: Development and Evaluation Study. JMIR Med Inform 2021; 9:e13182. [PMID: 33709932 PMCID: PMC7991993 DOI: 10.2196/13182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 12/18/2020] [Accepted: 01/23/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The evidence-based medicine (EBM) paradigm requires the development of health care professionals' skills in the efficient search of evidence in the literature, and in the application of formal rules to evaluate this evidence. Incorporating this methodology into the decision-making routine of clinical practice will improve the patients' health care, increase patient safety, and optimize resources use. OBJECTIVE The aim of this study is to develop and evaluate a new tool (KNOWBED system) as a clinical decision support system to support scientific knowledge, enabling health care professionals to quickly carry out decision-making processes based on EBM during their routine clinical practice. METHODS Two components integrate the KNOWBED system: a web-based knowledge station and a mobile app. A use case (bronchiolitis pathology) was selected to validate the KNOWBED system in the context of the Paediatrics Unit of the Virgen Macarena University Hospital (Seville, Spain). The validation was covered in a 3-month pilot using 2 indicators: usability and efficacy. RESULTS The KNOWBED system has been designed, developed, and validated to support clinical decision making in mobility based on standards that have been incorporated into the routine clinical practice of health care professionals. Using this tool, health care professionals can consult existing scientific knowledge at the bedside, and access recommendations of clinical protocols established based on EBM. During the pilot project, 15 health care professionals participated and accessed the system for a total of 59 times. CONCLUSIONS The KNOWBED system is a useful and innovative tool for health care professionals. The usability surveys filled in by the system users highlight that it is easy to access the knowledge base. This paper also sets out some improvements to be made in the future.
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Affiliation(s)
- Alicia Martinez-Garcia
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | - Ana Belén Naranjo-Saucedo
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | - Jose Antonio Rivas
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | - Antonio Romero Tabares
- Publications Department, Andalusian Institute of Emergencies and Public Safety, Seville, Spain
| | | | | | | | - Roman Villegas
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | - Francisco De Paula Pérez León
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | - Jesús Moreno Conde
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | - Carlos Luis Parra Calderón
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain.,Department of Innovation Technology, Virgen del Rocío University Hospital, Seville, Spain
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End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring. ELECTRONICS 2020. [DOI: 10.3390/electronics9091439] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems. The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring. By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions. The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app. In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission. In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning. The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85%). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses.
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Carvalho É, Estrela M, Zapata-Cachafeiro M, Figueiras A, Roque F, Herdeiro MT. E-Health Tools to Improve Antibiotic Use and Resistances: A Systematic Review. Antibiotics (Basel) 2020; 9:antibiotics9080505. [PMID: 32806583 PMCID: PMC7460242 DOI: 10.3390/antibiotics9080505] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/10/2020] [Accepted: 08/11/2020] [Indexed: 12/04/2022] Open
Abstract
(1) Background: e-Health tools, especially in the form of clinical decision support systems (CDSSs), have been emerging more quickly than ever before. The main objective of this systematic review is to assess the influence of these tools on antibiotic use for respiratory tract infections. (2) Methods: The scientific databases, MEDLINE-PubMed and EMBASE, were searched. The search was conducted by two independent researchers. The search strategy was mainly designed to identify relevant studies on the effectiveness of CDSSs in improving antibiotic use, as a primary outcome, and on the acceptability and usability of CDSSs, as a secondary outcome. (3) Results: After the selection, 22 articles were included. The outcomes were grouped either into antibiotics prescription practices or adherence to guidelines concerning antibiotics prescription. Overall, 15 out of the 22 studies had statistically significant outcomes related to the interventions. (4) Conclusions: Overall, the results show a positive impact on the prescription and conscientious use of antibiotics for respiratory tract infections, both with respect to patients and prescribing healthcare professionals. CDSSs have been shown to have great potential as powerful tools for improving both clinical care and patient outcomes.
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Affiliation(s)
- Érico Carvalho
- iBiMED–Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, 3800 Aveiro, Portugal; (É.C.); (M.E.)
| | - Marta Estrela
- iBiMED–Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, 3800 Aveiro, Portugal; (É.C.); (M.E.)
| | - Maruxa Zapata-Cachafeiro
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, 15702 Santiago de Compostela, Spain; (M.Z.-C.); (A.F.)
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiology and Public Health-CIBERESP), 28001 Madrid, Spain
| | - Adolfo Figueiras
- Department of Preventive Medicine and Public Health, University of Santiago de Compostela, 15702 Santiago de Compostela, Spain; (M.Z.-C.); (A.F.)
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiology and Public Health-CIBERESP), 28001 Madrid, Spain
- Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain
| | - Fátima Roque
- Research Unit for Inland Development-Polytechnic of Guarda (UDI-IPG), 6300 Guarda, Portugal;
- Health Sciences Research Centre, University of Beira Interior (CICS-UBI), 6200 Covilhã, Portugal
| | - Maria Teresa Herdeiro
- iBiMED–Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, 3800 Aveiro, Portugal; (É.C.); (M.E.)
- Correspondence:
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De Ramón Fernández A, Ruiz Fernández D, Marcos-Jorquera D, Gilart Iglesias V. Support System for Early Diagnosis of Chronic Obstructive Pulmonary Disease Based on the Service-Oriented Architecture Paradigm and Business Process Management Strategy: Development and Usability Survey Among Patients and Health Care Providers. J Med Internet Res 2020; 22:e17161. [PMID: 32181744 PMCID: PMC7109614 DOI: 10.2196/17161] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/06/2020] [Accepted: 01/26/2020] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease with a high global prevalence. The main scientific societies dedicated to the management of this disease have published clinical practice guidelines for quality practice. However, at present, there are important weaknesses in COPD diagnosis criteria that often lead to underdiagnosis or misdiagnosis. OBJECTIVE We sought to develop a new support system for COPD diagnosis. The system was designed to overcome the weaknesses detected in current guidelines with the goals of enabling early diagnosis, and improving the diagnostic accuracy and quality of care provided. METHODS We first analyzed the main clinical guidelines for COPD to detect weaknesses that exist in the current diagnostic process, and then proposed a redesign based on a business process management (BPM) strategy for its optimization. The BPM system acts as a backbone throughout the process of COPD diagnosis in this proposed approach. The newly developed support system was integrated into a health information system for validation of its use in a hospital environment. The system was qualitatively evaluated by experts (n=12) and patients (n=36). RESULTS Among the 12 experts, 10 (83%) positively evaluated our system with respect to increasing the speed for making the diagnosis, helping in interpreting results, and encouraging opportunistic diagnosis. With an overall rating of 4.29 on a 5-point scale, 27/36 (75%) of patients considered that the system was very useful in providing a warning about possible cases of COPD. The overall assessment of the system was 4.53 on a 5-point Likert scale with agreement to extend its use to all primary care centers. CONCLUSIONS The proposed system provides a functional method to overcome the weaknesses detected in the current diagnostic process for COPD, which can help foster early diagnosis, while improving the diagnostic accuracy and quality of care provided.
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Affiliation(s)
| | - Daniel Ruiz Fernández
- Department of Computer Technology, University of Alicante, San Vicente del Raspeig, Alicante, Spain
| | - Diego Marcos-Jorquera
- Department of Computer Technology, University of Alicante, San Vicente del Raspeig, Alicante, Spain
| | - Virgilio Gilart Iglesias
- Department of Computer Technology, University of Alicante, San Vicente del Raspeig, Alicante, Spain
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KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services. PLoS One 2019; 14:e0223010. [PMID: 31581246 PMCID: PMC6776354 DOI: 10.1371/journal.pone.0223010] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 09/11/2019] [Indexed: 11/19/2022] Open
Abstract
Background and objective To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results We evaluated the platform by establishing and deploying an analysis and end user application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end user application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR).
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Clim A, Zota R, Constantinescu R, Ilie-Nemedi I. Health services in smart cities: Choosing the big data mining based decision support. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2019. [DOI: 10.1080/20479700.2019.1650478] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Antonio Clim
- The Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Bucharest, Romania
| | - Răzvan Zota
- The Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Bucharest, Romania
| | - Radu Constantinescu
- The Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Bucharest, Romania
| | - Iulian Ilie-Nemedi
- The Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Bucharest, Romania
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Franssen FME, Alter P, Bar N, Benedikter BJ, Iurato S, Maier D, Maxheim M, Roessler FK, Spruit MA, Vogelmeier CF, Wouters EFM, Schmeck B. Personalized medicine for patients with COPD: where are we? Int J Chron Obstruct Pulmon Dis 2019; 14:1465-1484. [PMID: 31371934 PMCID: PMC6636434 DOI: 10.2147/copd.s175706] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Accepted: 06/05/2019] [Indexed: 12/19/2022] Open
Abstract
Chronic airflow limitation is the common denominator of patients with chronic obstructive pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of individual patients based on the degree of lung function impairment, nor does the degree of airflow limitation allow guidance regarding therapies. Over the last decades, understanding of the factors contributing to the heterogeneity of disease trajectories, clinical presentation, and response to existing therapies has greatly advanced. Indeed, diagnostic assessment and treatment algorithms for COPD have become more personalized. In addition to the pulmonary abnormalities and inhaler therapies, extra-pulmonary features and comorbidities have been studied and are considered essential components of comprehensive disease management, including lifestyle interventions. Despite these advances, predicting and/or modifying the course of the disease remains currently impossible, and selection of patients with a beneficial response to specific interventions is unsatisfactory. Consequently, non-response to pharmacologic and non-pharmacologic treatments is common, and many patients have refractory symptoms. Thus, there is an ongoing urgency for a more targeted and holistic management of the disease, incorporating the basic principles of P4 medicine (predictive, preventive, personalized, and participatory). This review describes the current status and unmet needs regarding personalized medicine for patients with COPD. Also, it proposes a systems medicine approach, integrating genetic, environmental, (micro)biological, and clinical factors in experimental and computational models in order to decipher the multilevel complexity of COPD. Ultimately, the acquired insights will enable the development of clinical decision support systems and advance personalized medicine for patients with COPD.
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Affiliation(s)
- Frits ME Franssen
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
| | - Peter Alter
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Nadav Bar
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Birke J Benedikter
- Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
- Department of Medical Microbiology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
| | | | | | - Michael Maxheim
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Fabienne K Roessler
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Martijn A Spruit
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
- REVAL - Rehabilitation Research Center, BIOMED - Biomedical Research Institute, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Emiel FM Wouters
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
| | - Bernd Schmeck
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
- Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
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Palmiotti GA, Lacedonia D, Liotino V, Schino P, Satriano F, Di Napoli PL, Sabato E, Mastrosimone V, Scoditti A, Carone M, Costantino E, Resta E, Attolini E, Foschino Barbaro MP. Adherence to GOLD guidelines in real-life COPD management in the Puglia region of Italy. Int J Chron Obstruct Pulmon Dis 2018; 13:2455-2462. [PMID: 30147311 PMCID: PMC6101739 DOI: 10.2147/copd.s157779] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Background COPD is a disease associated with significant economic burden. It was reported that Global initiative for chronic Obstructive Lung Disease (GOLD) guideline-oriented pharmacotherapy improves airflow limitation and reduces health care costs. However, several studies showed a significant dissociation between international recommendations and clinicians’ practices. The consequent reduced diagnostic and therapeutic inappropriateness has proved to be associated with an increase in costs and a waste of economic resources in the health sector. The aim of the study was to evaluate COPD management in the Puglia region. The study was performed in collaboration with the pulmonology centers and the Regional Health Agency (AReS Puglia). Methods An IT platform allowed the pulmonologists to enter data via the Internet. All COPD patients who visited a pneumological outpatient clinic for the first time or for regular follow-ups or were admitted to a pneumological department for an exacerbation were considered eligible for the study. COPD’s diagnosis was confirmed by a pulmonologist at the moment of the visit. The project lasted 18 months and involved 17 centers located in the Puglia region. Results Six hundred ninety-three patients were enrolled, evenly distributed throughout the region. The mean age was 71±9 years, and 85% of them were males. Approximately 23% were current smokers, 63% former smokers and 13.5% never smokers. The mean post-bronchodilator forced expiratory volume in 1 second was 59%±20% predicted. The platform allowed the classification of patients according to the GOLD guidelines (Group A: 20.6%, Group B: 32.3%, Group C: 5.9% and Group D: 39.2%), assessed the presence and severity of exacerbations (20% of the patients had an exacerbation defined as mild [13%], moderate [37%] and severe [49%]) and evaluated the appropriateness of inhalation therapy at the time of the visit. Forty-nine percent of Group A patients were following inappropriate therapy; in Group B, 45.8% were following a therapy in contrast with the guidelines. Among Group C patients, 41.46% resulted in triple combination therapy, whilê14% of Group D patients did not have a therapy or were following an inappropriate therapy. In conclusion, 30% of all patients evaluated had been following an inadequate therapy. Subsequently, an online survey was developed to inquire about the reasons for the results obtained. In particular, we investigated the reasons why 30% of our population did not follow the therapy suggested by the GOLD guidelines: 1) why was there an excessive use of inhaled corticosteroids, 2) why a significantly high percentage was inappropriately treated with triple therapy and 3) why a consistent percentage (11%) of Group D patients were not treated at all. Conclusion The data provides an overview on the management of COPD in the region of Puglia (Italy) and represents a resource in order to improve appropriateness and reduce the waste of health resources.
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Affiliation(s)
- Giuseppe Antonio Palmiotti
- Department of Medical and Surgical Sciences, Institute of Respiratory Diseases, University of Foggia, Foggia, Italy,
| | - Donato Lacedonia
- Department of Medical and Surgical Sciences, Institute of Respiratory Diseases, University of Foggia, Foggia, Italy,
| | - Vito Liotino
- Department of Cardiac, Thoracic, and Vascular Science, Institute of Respiratory Diseases, School of Medicine, University of Bari, Bari, Italy
| | - Pietro Schino
- Physiopathology Respiratory Unit, F Miulli General Hospital, Acquaviva delle Fonti, Bari, Italy
| | | | - Pier Luigi Di Napoli
- Physiopathology Respiratory Unit, F Miulli General Hospital, Acquaviva delle Fonti, Bari, Italy
| | - Eugenio Sabato
- UOC of Pneumology, "N Melli" Hospital, San Pietro Vernotico, Italy
| | - Vincenzo Mastrosimone
- Division of Pulmonary Disease, Medical Center of Rehabilitation, Foundation Salvatore Maugeri, IRCCS, Marina di Ginosa, Italy
| | - Alfredo Scoditti
- Department of Respiratory Diseases, San Camillo Clinic, Taranto, Italy
| | - Mauro Carone
- Division of Pulmonary Disease, Medical Center of Rehabilitation, Foundation Salvatore Maugeri, IRCCS, Cassano delle Murge, Italy
| | - Elio Costantino
- UOC of Pneumology, Hospital "Madonna delle Grazie", Matera, Italy
| | - Emanuela Resta
- Department of Cardiac, Thoracic, and Vascular Science, Institute of Respiratory Diseases, School of Medicine, University of Bari, Bari, Italy
| | | | - Maria Pia Foschino Barbaro
- Department of Medical and Surgical Sciences, Institute of Respiratory Diseases, University of Foggia, Foggia, Italy,
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Marina N, López de Santa María E, Gáldiz JB. Telemedicina, una oportunidad para la espirometría. Arch Bronconeumol 2018; 54:306-307. [DOI: 10.1016/j.arbres.2017.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 12/19/2017] [Accepted: 12/20/2017] [Indexed: 10/18/2022]
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Braido F, Santus P, Corsico AG, Di Marco F, Melioli G, Scichilone N, Solidoro P. Chronic obstructive lung disease "expert system": validation of a predictive tool for assisting diagnosis. Int J Chron Obstruct Pulmon Dis 2018; 13:1747-1753. [PMID: 29881264 PMCID: PMC5978461 DOI: 10.2147/copd.s165533] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Purpose The purposes of this study were development and validation of an expert system (ES) aimed at supporting the diagnosis of chronic obstructive lung disease (COLD). Methods A questionnaire and a WebFlex code were developed and validated in silico. An expert panel pilot validation on 60 cases and a clinical validation on 241 cases were performed. Results The developed questionnaire and code validated in silico resulted in a suitable tool to support the medical diagnosis. The clinical validation of the ES was performed in an academic setting that included six different reference centers for respiratory diseases. The results of the ES expressed as a score associated with the risk of suffering from COLD were matched and compared with the final clinical diagnoses. A set of 60 patients were evaluated by a pilot expert panel validation with the aim of calculating the sample size for the clinical validation study. The concordance analysis between these preliminary ES scores and diagnoses performed by the experts indicated that the accuracy was 94.7% when both experts and the system confirmed the COLD diagnosis and 86.3% when COLD was excluded. Based on these results, the sample size of the validation set was established in 240 patients. The clinical validation, performed on 241 patients, resulted in ES accuracy of 97.5%, with confirmed COLD diagnosis in 53.6% of the cases and excluded COLD diagnosis in 32% of the cases. In 11.2% of cases, a diagnosis of COLD was made by the experts, although the imaging results showed a potential concomitant disorder. Conclusion The ES presented here (COLDES) is a safe and robust supporting tool for COLD diagnosis in primary care settings.
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Affiliation(s)
- Fulvio Braido
- Department of Internal Medicine, IRCCS San Martino di Genova University Hospital, Genoa, Italy
| | - Pierachille Santus
- Department of Biomedical and Clinical Sciences, University of Milan, Division of Respiratory Diseases, "L. Sacco" University Hospital, ASST Fatebenefratelli-Sacco, Milan, Italy
| | - Angelo Guido Corsico
- Department of Internal Medicine and Therapeutics, Division of Respiratory Diseases, IRCCS Policlinico San Matteo Foundation, University of Pavia, Italy
| | - Fabiano Di Marco
- Department of Health Sciences, University of Milan, San Paolo Hospital, Milan, Italy
| | - Giovanni Melioli
- Center for Precision Medicine, Asthma, and Allergy, Humanitas University, Milan, Italy
| | - Nicola Scichilone
- Department of Internal Medicine, University of Palermo, Palermo, Italy
| | - Paolo Solidoro
- Unit of Pulmonology, Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, Torino, Italy
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Barken TL, Thygesen E, Söderhamn U. Advancing beyond the system: telemedicine nurses' clinical reasoning using a computerised decision support system for patients with COPD - an ethnographic study. BMC Med Inform Decis Mak 2017; 17:181. [PMID: 29282068 PMCID: PMC5745905 DOI: 10.1186/s12911-017-0573-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 12/11/2017] [Indexed: 12/19/2022] Open
Abstract
Background Telemedicine is changing traditional nursing care, and entails nurses performing advanced and complex care within a new clinical environment, and monitoring patients at a distance. Telemedicine practice requires complex disease management, advocating that the nurses’ reasoning and decision-making processes are supported. Computerised decision support systems are being used increasingly to assist reasoning and decision-making in different situations. However, little research has focused on the clinical reasoning of nurses using a computerised decision support system in a telemedicine setting. Therefore, the objective of the study is to explore the process of telemedicine nurses’ clinical reasoning when using a computerised decision support system for the management of patients with chronic obstructive pulmonary disease. The factors influencing the reasoning and decision-making processes were investigated. Methods In this ethnographic study, a combination of data collection methods, including participatory observations, the think-aloud technique, and a focus group interview was employed. Collected data were analysed using qualitative content analysis. Results When telemedicine nurses used a computerised decision support system for the management of patients with complex, unstable chronic obstructive pulmonary disease, two categories emerged: “the process of telemedicine nurses’ reasoning to assess health change” and “the influence of the telemedicine setting on nurses’ reasoning and decision-making processes”. An overall theme, termed “advancing beyond the system”, represented the connection between the reasoning processes and the telemedicine work and setting, where being familiar with the patient functioned as a foundation for the nurses’ clinical reasoning process. Conclusion In the telemedicine setting, when supported by a computerised decision support system, nurses’ reasoning was enabled by the continuous flow of digital clinical data, regular video-mediated contact and shared decision-making with the patient. These factors fostered an in-depth knowledge of the patients and acted as a foundation for the nurses’ reasoning process. Nurses’ reasoning frequently advanced beyond the computerised decision support system recommendations. Future studies are warranted to develop more accurate algorithms, increase system maturity, and improve the integration of the digital clinical information with clinical experiences, to support telemedicine nurses’ reasoning process.
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Affiliation(s)
- Tina Lien Barken
- Centre for eHealth, Centre for Care Research, Southern Norway, Department of Health and Nursing Sciences, Faculty of Health and Sport Sciences, University of Agder, Post box 422, 4604, Kristiansand, Norway. .,Centre for Care Research, Southern Norway, Department of Health and Nursing Sciences, Faculty of Health and Sport Sciences, University of Agder, Post box 422, 4604, Kristiansand, Norway.
| | - Elin Thygesen
- Centre for eHealth, Centre for Care Research, Southern Norway, Department of Health and Nursing Sciences, Faculty of Health and Sport Sciences, University of Agder, Post box 422, 4604, Kristiansand, Norway
| | - Ulrika Söderhamn
- Centre for Care Research, Southern Norway, Department of Health and Nursing Sciences, Faculty of Health and Sport Sciences, University of Agder, Post box 422, 4604, Kristiansand, Norway
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Bright P, Hambly K. What Is the Proportion of Studies Reporting Patient and Practitioner Satisfaction with Software Support Tools Used in the Management of Knee Pain and Is This Related to Sample Size, Effect Size, and Journal Impact Factor? Telemed J E Health 2017; 24:562-576. [PMID: 29265954 DOI: 10.1089/tmj.2017.0207] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION E-health software tools have been deployed in managing knee conditions. Reporting of patient and practitioner satisfaction in studies regarding e-health usage is not widely explored. The objective of this review was to identify studies describing patient and practitioner satisfaction with software use concerning knee pain. MATERIALS AND METHODS A computerized search was undertaken: four electronic databases were searched from January 2007 until January 2017. Keywords were decision dashboard, clinical decision, Web-based resource, evidence support, and knee. Full texts were scanned for effect of size reporting and satisfaction scales from participants and practitioners. Binary regression was run; impact factor and sample size were predictors with indicators for satisfaction and effect size reporting as dependent variables. RESULTS Seventy-seven articles were retrieved; 37 studies were included in final analysis. Ten studies reported patient satisfaction ratings (27.8%): a single study reported both patient and practitioner satisfaction (2.8%). Randomized control trials were the most common design (35%) and knee osteoarthritis the most prevalent condition (38%). Electronic patient-reported outcome measures and Web-based training were the most common interventions. No significant dependency was found within the regression models (p > 0.05). DISCUSSION AND CONCLUSIONS The proportion of reporting of patient satisfaction was low; practitioner satisfaction was poorly represented. There may be implications for the suitability of administering e-health, a medium for capturing further meta-evidence needs to be established and used as best practice for implicated studies in future. This is the first review of its kind to address patient and practitioner satisfaction with knee e-health.
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Affiliation(s)
- Philip Bright
- 1 Research Department, European School of Osteopathy , Kent, United Kingdom
- 2 School of Sports and Exercise Sciences, University of Kent at Medway , Kent, United Kingdom
| | - Karen Hambly
- 2 School of Sports and Exercise Sciences, University of Kent at Medway , Kent, United Kingdom
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Nan S, Van Gorp P, Lu X, Kaymak U, Korsten H, Vdovjak R, Duan H. A meta-model for computer executable dynamic clinical safety checklists. BMC Med Inform Decis Mak 2017; 17:170. [PMID: 29233155 PMCID: PMC5727863 DOI: 10.1186/s12911-017-0551-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 11/19/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Safety checklist is a type of cognitive tool enforcing short term memory of medical workers with the purpose of reducing medical errors caused by overlook and ignorance. To facilitate the daily use of safety checklists, computerized systems embedded in the clinical workflow and adapted to patient-context are increasingly developed. However, the current hard-coded approach of implementing checklists in these systems increase the cognitive efforts of clinical experts and coding efforts for informaticists. This is due to the lack of a formal representation format that is both understandable by clinical experts and executable by computer programs. METHODS We developed a dynamic checklist meta-model with a three-step approach. Dynamic checklist modeling requirements were extracted by performing a domain analysis. Then, existing modeling approaches and tools were investigated with the purpose of reusing these languages. Finally, the meta-model was developed by eliciting domain concepts and their hierarchies. The feasibility of using the meta-model was validated by two case studies. The meta-model was mapped to specific modeling languages according to the requirements of hospitals. RESULTS Using the proposed meta-model, a comprehensive coronary artery bypass graft peri-operative checklist set and a percutaneous coronary intervention peri-operative checklist set have been developed in a Dutch hospital and a Chinese hospital, respectively. The result shows that it is feasible to use the meta-model to facilitate the modeling and execution of dynamic checklists. CONCLUSIONS We proposed a novel meta-model for the dynamic checklist with the purpose of facilitating creating dynamic checklists. The meta-model is a framework of reusing existing modeling languages and tools to model dynamic checklists. The feasibility of using the meta-model is validated by implementing a use case in the system.
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Affiliation(s)
- Shan Nan
- School of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.,School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Pieter Van Gorp
- School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Xudong Lu
- School of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China. .,School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Uzay Kaymak
- School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Hendrikus Korsten
- School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Anesthesiology and Intensive Care, Catharina Ziekenhuis in Eindhoven, Eindhoven, The Netherlands
| | | | - Huilong Duan
- School of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
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Swaminathan S, Qirko K, Smith T, Corcoran E, Wysham NG, Bazaz G, Kappel G, Gerber AN. A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLoS One 2017; 12:e0188532. [PMID: 29166411 PMCID: PMC5699810 DOI: 10.1371/journal.pone.0188532] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2017] [Accepted: 11/08/2017] [Indexed: 02/07/2023] Open
Abstract
COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised prediction algorithm. The accuracy of the model is assessed against a panel of physicians each triaging identical cases in a representative patient validation set. Our results show that algorithm accuracy and safety indicators surpass all individual pulmonologists in both identifying exacerbations and predicting the consensus triage in a 101 case validation set. The algorithm is also the top performer in sensitivity, specificity, and ppv when predicting a patient's need for emergency care.
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Affiliation(s)
- Sumanth Swaminathan
- Revon Systems Inc, Louisville, KY, United States of America, 40014
- Department of Mathematics, University of Delaware, Newark, DE, United States of America, 19716
| | - Klajdi Qirko
- Revon Systems Inc, Louisville, KY, United States of America, 40014
- Department of Mathematics, University of Delaware, Newark, DE, United States of America, 19716
| | - Ted Smith
- Revon Systems Inc, Louisville, KY, United States of America, 40014
| | - Ethan Corcoran
- Department of Pulmonology, Kaiser Permanente, Clackamas, OR, United States of America, 97015
| | - Nicholas G. Wysham
- Vancouver Clinic Division of Pulmonology & Critical Care, Vancouver, WA, United States of America, 98664
- Washington State University School of Medicine, Spokane, WA, United States of America, 99210
| | - Gaurav Bazaz
- Revon Systems Inc, Louisville, KY, United States of America, 40014
| | - George Kappel
- Revon Systems Inc, Louisville, KY, United States of America, 40014
| | - Anthony N. Gerber
- Department of Medicine, National Jewish Health, Denver, CO, United States of America, 80206
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Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice. J Med Syst 2017; 41:178. [PMID: 28956226 DOI: 10.1007/s10916-017-0823-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 09/18/2017] [Indexed: 10/18/2022]
Abstract
As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.
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35
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Modeling the Construct of an Expert Evidence-Adaptive Knowledge Base for a Pressure Injury Clinical Decision Support System. INFORMATICS 2017. [DOI: 10.3390/informatics4030020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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Takahashi R, Kajikawa Y. Computer-aided diagnosis: A survey with bibliometric analysis. Int J Med Inform 2017; 101:58-67. [DOI: 10.1016/j.ijmedinf.2017.02.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 01/28/2017] [Accepted: 02/04/2017] [Indexed: 12/18/2022]
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Vihinen M. How to Define Pathogenicity, Health, and Disease? Hum Mutat 2016; 38:129-136. [PMID: 27862583 DOI: 10.1002/humu.23144] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 10/13/2016] [Accepted: 11/03/2016] [Indexed: 11/07/2022]
Abstract
Scientific and clinical communities produce ever increasing amounts of data and details about health and disease. Our ability to understand and utilize this information is limited because of imprecise language and lack of well-defined concepts. This problem involves also the principal concepts of health, disease, and pathogenicity. Here, a systematic model is presented for pathogenicity, as well as for health and disease. It has three components: extent, modulation, and severity, which jointly define the continuum of pathogenicity. The model is population based, and once implemented, it can be used for numerous purposes such as diagnosis, patient stratification, prognosis, finding phenotype-genotype correlations, or explaining adverse drug reactions. The new model has several benefits including health economy by allowing evidence-based personalized/precision medicine.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, Lund University, BMC B13, Lund, SE-22184, Sweden
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38
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Roca J, Cano I, Gomez-Cabrero D, Tegnér J. From Systems Understanding to Personalized Medicine: Lessons and Recommendations Based on a Multidisciplinary and Translational Analysis of COPD. Methods Mol Biol 2016; 1386:283-303. [PMID: 26677188 DOI: 10.1007/978-1-4939-3283-2_13] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Systems medicine, using and adapting methods and approaches as developed within systems biology, promises to be essential in ongoing efforts of realizing and implementing personalized medicine in clinical practice and research. Here we review and critically assess these opportunities and challenges using our work on COPD as a case study. We find that there are significant unresolved biomedical challenges in how to unravel complex multifactorial components in disease initiation and progression producing different clinical phenotypes. Yet, while such a systems understanding of COPD is necessary, there are other auxiliary challenges that need to be addressed in concert with a systems analysis of COPD. These include information and communication technology (ICT)-related issues such as data harmonization, systematic handling of knowledge, computational modeling, and importantly their translation and support of clinical practice. For example, clinical decision-support systems need a seamless integration with new models and knowledge as systems analysis of COPD continues to develop. Our experience with clinical implementation of systems medicine targeting COPD highlights the need for a change of management including design of appropriate business models and adoption of ICT providing and supporting organizational interoperability among professional teams across healthcare tiers, working around the patient. In conclusion, in our hands the scope and efforts of systems medicine need to concurrently consider these aspects of clinical implementation, which inherently drives the selection of the most relevant and urgent issues and methods that need further development in a systems analysis of disease.
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Affiliation(s)
- Josep Roca
- IDIBAPS, Hospital Clínic, CIBERES, Universitat de Barcelona, Villarroel, 170, Barcelona, Catalunya, 08036, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands.
| | - Isaac Cano
- IDIBAPS, Hospital Clínic, CIBERES, Universitat de Barcelona, Villarroel, 170, Barcelona, Catalunya, 08036, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden. .,L8:05 Karolinska University Hospital, Stockholm, 17176, Sweden.
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Khalilia M, Choi M, Henderson A, Iyengar S, Braunstein M, Sun J. Clinical Predictive Modeling Development and Deployment through FHIR Web Services. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2015; 2015:717-726. [PMID: 26958207 PMCID: PMC4765683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.
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Affiliation(s)
| | - Myung Choi
- Georgia Institute of Technology, Atlanta, Georgia
| | | | | | | | - Jimeng Sun
- Georgia Institute of Technology, Atlanta, Georgia
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Gomez-Cabrero D, Menche J, Cano I, Abugessaisa I, Huertas-Migueláñez M, Tenyi A, Marin de Mas I, Kiani NA, Marabita F, Falciani F, Burrowes K, Maier D, Wagner P, Selivanov V, Cascante M, Roca J, Barabási AL, Tegnér J. Systems Medicine: from molecular features and models to the clinic in COPD. J Transl Med 2014; 12 Suppl 2:S4. [PMID: 25471042 PMCID: PMC4255907 DOI: 10.1186/1479-5876-12-s2-s4] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Background and hypothesis Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice. Objective and method Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework. Results In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice. Conclusions The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.
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Cano I, Tényi Á, Schueller C, Wolff M, Huertas Migueláñez MM, Gomez-Cabrero D, Antczak P, Roca J, Cascante M, Falciani F, Maier D. The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research. J Transl Med 2014; 12 Suppl 2:S6. [PMID: 25471253 PMCID: PMC4255911 DOI: 10.1186/1479-5876-12-s2-s6] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Background Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data. Results The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice. Conclusions The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.
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Miralles F, Gomez-Cabrero D, Lluch-Ariet M, Tegnér J, Cascante M, Roca J. Predictive medicine: outcomes, challenges and opportunities in the Synergy-COPD project. J Transl Med 2014; 12 Suppl 2:S12. [PMID: 25472742 PMCID: PMC4255885 DOI: 10.1186/1479-5876-12-s2-s12] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Chronic Obstructive Pulmonary Disease (COPD) is a major challenge for healthcare. Heterogeneities in clinical manifestations and in disease progression are relevant traits in COPD with impact on patient management and prognosis. It is hypothesized that COPD heterogeneity results from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering. OBJECTIVES To assess the potential of systems medicine to better understand non-pulmonary determinants of COPD heterogeneity. To transfer acquired knowledge to healthcare enhancing subject-specific health risk assessment and stratification to improve management of chronic patients. METHOD Underlying mechanisms of skeletal muscle dysfunction and of co-morbidity clustering in COPD patients were explored with strategies combining deterministic modelling and network medicine analyses using the Biobridge dataset. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was done (ICD9-CM data from Medicare, 13 million people). A targeted network analysis using the two studies: skeletal muscle dysfunction and co-morbidity clustering explored shared pathways between them. RESULTS (1) Evidence of abnormal regulation of pivotal skeletal muscle biological pathways and increased risk for co-morbidity clustering was observed in COPD; (2) shared abnormal pathway regulation between skeletal muscle dysfunction and co-morbidity clustering; and, (3) technological achievements of the projects were: (i) COPD Knowledge Base; (ii) novel modelling approaches; (iii) Simulation Environment; and, (iv) three layers of Clinical Decision Support Systems. CONCLUSIONS The project demonstrated the high potential of a systems medicine approach to address COPD heterogeneity. Limiting factors for the project development were identified. They were relevant to shape strategies fostering 4P Medicine for chronic patients. The concept of Digital Health Framework and the proposed roadmap for its deployment constituted relevant project outcomes.
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Gomez-Cabrero D, Lluch-Ariet M, Tegnér J, Cascante M, Miralles F, Roca J. Synergy-COPD: a systems approach for understanding and managing chronic diseases. J Transl Med 2014; 12 Suppl 2:S2. [PMID: 25472826 PMCID: PMC4255903 DOI: 10.1186/1479-5876-12-s2-s2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Chronic diseases (CD) are generating a dramatic societal burden worldwide that is expected to persist over the next decades. The challenges posed by the epidemics of CD have triggered a novel health paradigm with major consequences on the traditional concept of disease and with a profound impact on key aspects of healthcare systems. We hypothesized that the development of a systems approach to understand CD together with the generation of an ecosystem to transfer the acquired knowledge into the novel healthcare scenario may contribute to a cost-effective enhancement of health outcomes. To this end, we designed the Synergy-COPD project wherein the heterogeneity of chronic obstructive pulmonary disease (COPD) was addressed as a use case representative of CD. The current manuscript describes main features of the project design and the strategies put in place for its development, as well the expected outcomes during the project life-span. Moreover, the manuscript serves as introductory and unifying chapter of the different papers associated to the Supplement describing the characteristics, tools and the objectives of Synergy-COPD.
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Affiliation(s)
- David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Magi Lluch-Ariet
- Department of eHealth, Barcelona Digital, 08017 Barcelona, Catalunya, Spain
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Marta Cascante
- Hospital Clinic - Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS). Universitat de Barcelona, 08036 Barcelona, Spain
- Departament de Bioquimica i Biologia Molecular i IBUB, Facultat de Biologia, Universitat de Barcelona, 08028 Barcelona, Spain
| | - Felip Miralles
- Department of eHealth, Barcelona Digital, 08017 Barcelona, Catalunya, Spain
| | - Josep Roca
- Hospital Clinic - Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS). Universitat de Barcelona, 08036 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands
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Cano I, Lluch-Ariet M, Gomez-Cabrero D, Maier D, Kalko S, Cascante M, Tegnér J, Miralles F, Herrera D, Roca J. Biomedical research in a Digital Health Framework. J Transl Med 2014; 12 Suppl 2:S10. [PMID: 25472554 PMCID: PMC4255881 DOI: 10.1186/1479-5876-12-s2-s10] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
This article describes a Digital Health Framework (DHF), benefitting from the lessons learnt during the three-year life span of the FP7 Synergy-COPD project. The DHF aims to embrace the emerging requirements--data and tools--of applying systems medicine into healthcare with a three-tier strategy articulating formal healthcare, informal care and biomedical research. Accordingly, it has been constructed based on three key building blocks, namely, novel integrated care services with the support of information and communication technologies, a personal health folder (PHF) and a biomedical research environment (DHF-research). Details on the functional requirements and necessary components of the DHF-research are extensively presented. Finally, the specifics of the building blocks strategy for deployment of the DHF, as well as the steps toward adoption are analyzed. The proposed architectural solutions and implementation steps constitute a pivotal strategy to foster and enable 4P medicine (Predictive, Preventive, Personalized and Participatory) in practice and should provide a head start to any community and institution currently considering to implement a biomedical research platform.
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Affiliation(s)
- Isaac Cano
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
| | - Magí Lluch-Ariet
- Department of eHealth, Barcelona Digital, Roc Boronat 117, 08017 Barcelona, Catalunya, Spain
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Dieter Maier
- Biomax Informatics AG, Robert-Koch-Str. 2, Planegg, Germany
| | - Susana Kalko
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
| | - Marta Cascante
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
- Departament de Bioquimica i Biologia Molecular i IBUB, Facultat de Biologia, Universitat de Barcelona, 08028 Barcelona, Spain
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Felip Miralles
- Department of eHealth, Barcelona Digital, Roc Boronat 117, 08017 Barcelona, Catalunya, Spain
| | - Diego Herrera
- Departament de Bioquimica i Biologia Molecular i IBUB, Facultat de Biologia, Universitat de Barcelona, 08028 Barcelona, Spain
- Almirall R&D, 08980 Sant Feliu de Llobregat, Barcelona, Spain
| | - Josep Roca
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
- Centro de Investigacíon Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands
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Roca J, Vargas C, Cano I, Selivanov V, Barreiro E, Maier D, Falciani F, Wagner P, Cascante M, Garcia-Aymerich J, Kalko S, De Mas I, Tegnér J, Escarrabill J, Agustí A, Gomez-Cabrero D. Chronic Obstructive Pulmonary Disease heterogeneity: challenges for health risk assessment, stratification and management. J Transl Med 2014; 12 Suppl 2:S3. [PMID: 25472887 PMCID: PMC4255905 DOI: 10.1186/1479-5876-12-s2-s3] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Background and hypothesis Heterogeneity in clinical manifestations and disease progression in Chronic Obstructive Pulmonary Disease (COPD) lead to consequences for patient health risk assessment, stratification and management. Implicit with the classical "spill over" hypothesis is that COPD heterogeneity is driven by the pulmonary events of the disease. Alternatively, we hypothesized that COPD heterogeneities result from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering, each of them with their own dynamics. Objective and method To explore the potential of a systems analysis of COPD heterogeneity focused on skeletal muscle dysfunction and on co-morbidity clustering aiming at generating predictive modeling with impact on patient management. To this end, strategies combining deterministic modeling and network medicine analyses of the Biobridge dataset were used to investigate the mechanisms of skeletal muscle dysfunction. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was performed using a large dataset (ICD9-CM data from Medicare, 13 million people). Finally, a targeted network analysis using the outcomes of the two approaches (skeletal muscle dysfunction and co-morbidity clustering) explored shared pathways between these phenomena. Results (1) Evidence of abnormal regulation of skeletal muscle bioenergetics and skeletal muscle remodeling showing a significant association with nitroso-redox disequilibrium was observed in COPD; (2) COPD patients presented higher risk for co-morbidity clustering than non-COPD patients increasing with ageing; and, (3) the on-going targeted network analyses suggests shared pathways between skeletal muscle dysfunction and co-morbidity clustering. Conclusions The results indicate the high potential of a systems approach to address COPD heterogeneity. Significant knowledge gaps were identified that are relevant to shape strategies aiming at fostering 4P Medicine for patients with COPD.
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