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Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Basel) 2024; 12:481. [PMID: 38391856 PMCID: PMC10887513 DOI: 10.3390/healthcare12040481] [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: 11/12/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.
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Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Haditya Behl
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Mili Shah
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
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Irtyuga O, Babakekhyan M, Kostareva A, Uspensky V, Gordeev M, Faggian G, Malashicheva A, Metsker O, Shlyakhto E, Kopanitsa G. Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods. J Pers Med 2023; 13:1588. [PMID: 38003903 PMCID: PMC10671965 DOI: 10.3390/jpm13111588] [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: 10/05/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
Aortic stenosis (AS) is the most commonly diagnosed valvular heart disease, and its prevalence increases with the aging of the general population. However, AS is often diagnosed at a severe stage, necessitating surgical treatment, due to its long asymptomatic period. The objective of this study was to analyze the frequency of AS in a population of cardiovascular patients using echocardiography (ECHO) and to identify clinical factors and features associated with these patient groups. We utilized machine learning methods to analyze 84,851 echocardiograms performed between 2010 and 2018 at the National Medical Research Center named after V.A. Almazov. The primary indications for ECHO were coronary artery disease (CAD) and hypertension (HP), accounting for 33.5% and 14.2% of the cases, respectively. The frequency of AS was found to be 13.26% among the patients (n = 11,252). Within our study, 1544 patients had a bicuspid aortic valve (BAV), while 83,316 patients had a tricuspid aortic valve (TAV). BAV patients were observed to be younger compared to TAV patients. AS was more prevalent in the BAV group (59%) compared to the TAV group (12%), with a p-value of <0.0001. By employing a machine learning algorithm, we randomly identified significant features present in AS patients, including age, hypertension (HP), aortic regurgitation (AR), ascending aortic dilatation (AscAD), and BAV. These findings could serve as additional indications for earlier observation and more frequent ECHO in specific patient groups for the earlier detection of developing AS.
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Affiliation(s)
- Olga Irtyuga
- Almazov National Medical Research Centre, 197341 Saint-Petersburg, Russia; (O.I.); (M.B.); (A.K.); (V.U.); (M.G.); (A.M.); (O.M.); (E.S.)
| | - Mary Babakekhyan
- Almazov National Medical Research Centre, 197341 Saint-Petersburg, Russia; (O.I.); (M.B.); (A.K.); (V.U.); (M.G.); (A.M.); (O.M.); (E.S.)
| | - Anna Kostareva
- Almazov National Medical Research Centre, 197341 Saint-Petersburg, Russia; (O.I.); (M.B.); (A.K.); (V.U.); (M.G.); (A.M.); (O.M.); (E.S.)
| | - Vladimir Uspensky
- Almazov National Medical Research Centre, 197341 Saint-Petersburg, Russia; (O.I.); (M.B.); (A.K.); (V.U.); (M.G.); (A.M.); (O.M.); (E.S.)
| | - Michail Gordeev
- Almazov National Medical Research Centre, 197341 Saint-Petersburg, Russia; (O.I.); (M.B.); (A.K.); (V.U.); (M.G.); (A.M.); (O.M.); (E.S.)
| | - Giuseppe Faggian
- Department of Cardiac Surgery, University of Verona Medical School, 37134 Verona, Italy;
| | - Anna Malashicheva
- Almazov National Medical Research Centre, 197341 Saint-Petersburg, Russia; (O.I.); (M.B.); (A.K.); (V.U.); (M.G.); (A.M.); (O.M.); (E.S.)
| | - Oleg Metsker
- Almazov National Medical Research Centre, 197341 Saint-Petersburg, Russia; (O.I.); (M.B.); (A.K.); (V.U.); (M.G.); (A.M.); (O.M.); (E.S.)
| | - Evgeny Shlyakhto
- Almazov National Medical Research Centre, 197341 Saint-Petersburg, Russia; (O.I.); (M.B.); (A.K.); (V.U.); (M.G.); (A.M.); (O.M.); (E.S.)
| | - Georgy Kopanitsa
- Almazov National Medical Research Centre, 197341 Saint-Petersburg, Russia; (O.I.); (M.B.); (A.K.); (V.U.); (M.G.); (A.M.); (O.M.); (E.S.)
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Guo LL, Calligan M, Vettese E, Cook S, Gagnidze G, Han O, Inoue J, Lemmon J, Li J, Roshdi M, Sadovy B, Wallace S, Sung L. Development and validation of the SickKids Enterprise-wide Data in Azure Repository (SEDAR). Heliyon 2023; 9:e21586. [PMID: 38027579 PMCID: PMC10661187 DOI: 10.1016/j.heliyon.2023.e21586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 09/15/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Objectives To describe the processes developed by The Hospital for Sick Children (SickKids) to enable utilization of electronic health record (EHR) data by creating sequentially transformed schemas for use across multiple user types. Methods We used Microsoft Azure as the cloud service provider and named this effort the SickKids Enterprise-wide Data in Azure Repository (SEDAR). Epic Clarity data from on-premises was copied to a virtual network in Microsoft Azure. Three sequential schemas were developed. The Filtered Schema added a filter to retain only SickKids and valid patients. The Curated Schema created a data structure that was easier to navigate and query. Each table contained a logical unit such as patients, hospital encounters or laboratory tests. Data validation of randomly sampled observations in the Curated Schema was performed. The SK-OMOP Schema was designed to facilitate research and machine learning. Two individuals mapped medical elements to standard Observational Medical Outcomes Partnership (OMOP) concepts. Results A copy of Clarity data was transferred to Microsoft Azure and updated each night using log shipping. The Filtered Schema and Curated Schema were implemented as stored procedures and executed each night with incremental updates or full loads. Data validation required up to 16 iterations for each Curated Schema table. OMOP concept mapping achieved at least 80 % coverage for each SK-OMOP table. Conclusions We described our experience in creating three sequential schemas to address different EHR data access requirements. Future work should consider replicating this approach at other institutions to determine whether approaches are generalizable.
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Affiliation(s)
- Lin Lawrence Guo
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Maryann Calligan
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Emily Vettese
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Sadie Cook
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - George Gagnidze
- Information Management Technology, The Hospital for Sick Children, Toronto, Canada
| | - Oscar Han
- Information Management Technology, The Hospital for Sick Children, Toronto, Canada
| | - Jiro Inoue
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Joshua Lemmon
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
| | - Johnson Li
- Information Management Technology, The Hospital for Sick Children, Toronto, Canada
| | - Medhat Roshdi
- Information Management Technology, The Hospital for Sick Children, Toronto, Canada
| | - Bohdan Sadovy
- Information Management Technology, The Hospital for Sick Children, Toronto, Canada
| | - Steven Wallace
- Information Management Technology, The Hospital for Sick Children, Toronto, Canada
| | - Lillian Sung
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
- Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Canada
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Benis A, Min H, Gong Y, Biondich P, Robinson D, Law T, Nohr C, Faxvaag A, Rennert L, Hubig N, Gimbel R. Ontologies Applied in Clinical Decision Support System Rules: Systematic Review. JMIR Med Inform 2023; 11:e43053. [PMID: 36534739 PMCID: PMC9896360 DOI: 10.2196/43053] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/16/2022] [Accepted: 12/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Clinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused. OBJECTIVE Ontologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules. METHODS The literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively. RESULTS CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules. CONCLUSIONS Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules.
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Affiliation(s)
| | - Hua Min
- College of Public Health, George Mason University, Fairfax, VA, United States
| | - Yang Gong
- School of Biomedical Informatics, The University of Texas Health Sciences Center at Houston, Houston, TX, United States
| | - Paul Biondich
- Clem McDonald Biomedical Informatics Center, Regenstrief Institute, Indianapolis, IN, United States
| | | | - Timothy Law
- Ohio Musculoskeletal and Neurologic Institute, Ohio University, Athens, OH, United States
| | - Christian Nohr
- Department of Planning, Aalborg University, Aalborg, Denmark
| | - Arild Faxvaag
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lior Rennert
- Department of Public Health Sciences, Clemson University, Clemson, SC, United States
| | - Nina Hubig
- School of Computing, Clemson University, Clemson, SC, United States
| | - Ronald Gimbel
- Department of Public Health Sciences, Clemson University, Clemson, SC, United States
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Tachkov K, Zemplenyi A, Kamusheva M, Dimitrova M, Siirtola P, Pontén J, Nemeth B, Kalo Z, Petrova G. Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries. Front Public Health 2022; 10:921226. [PMID: 35910914 PMCID: PMC9330148 DOI: 10.3389/fpubh.2022.921226] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 06/20/2022] [Indexed: 12/05/2022] Open
Abstract
The aim of this paper is to identify the barriers that are specifically relevant to the use of Artificial Intelligence (AI)-based evidence in Central and Eastern European (CEE) Health Technology Assessment (HTA) systems. The study relied on two main parallel sources to identify barriers to use AI methodologies in HTA in CEE, including a scoping literature review and iterative focus group meetings with HTx team members. Most of the other selected articles discussed AI from a clinical perspective (n = 25), and the rest are from regulatory perspective (n = 13), and transfer of knowledge point of view (n = 3). Clinical areas studied are quite diverse—from pediatric, diabetes, diagnostic radiology, gynecology, oncology, surgery, psychiatry, cardiology, infection diseases, and oncology. Out of all 38 articles, 25 (66%) describe the AI method and the rest are more focused on the utilization barriers of different health care services and programs. The potential barriers could be classified as data related, methodological, technological, regulatory and policy related, and human factor related. Some of the barriers are quite similar, especially concerning the technologies. Studies focusing on the AI usage for HTA decision making are scarce. AI and augmented decision making tools are a novel science, and we are in the process of adapting it to existing needs. HTA as a process requires multiple steps, multiple evaluations which rely on heterogenous data. Therefore, the observed range of barriers come as a no surprise, and experts in the field need to give their opinion on the most important barriers in order to develop recommendations to overcome them and to disseminate the practical application of these tools.
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Affiliation(s)
| | - Antal Zemplenyi
- Syreon Research Institute, Budapest, Hungary
- Center for Health Technology Assessment and Pharmacoeconomic Research, University of Pecs, Pecs, Hungary
| | - Maria Kamusheva
- Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Maria Dimitrova
- Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
| | - Pekka Siirtola
- Biomimetics and Intelligent Systems Group, University of Oulu, Oulu, Finland
| | - Johan Pontén
- Dental and Pharmaceutical Benefits Agency, Stockholm, Sweden
| | | | - Zoltan Kalo
- Syreon Research Institute, Budapest, Hungary
- Centre for Health Technology Assessment, Semmelweis University, Budapest, Hungary
| | - Guenka Petrova
- Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
- *Correspondence: Guenka Petrova
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Irtyuga O, Kopanitsa G, Kostareva A, Metsker O, Uspensky V, Mikhail G, Faggian G, Sefieva G, Derevitskii I, Malashicheva A, Shlyakhto E. Application of Machine Learning Methods to Analyze Occurrence and Clinical Features of Ascending Aortic Dilatation in Patients with and without Bicuspid Aortic Valve. J Pers Med 2022; 12:jpm12050794. [PMID: 35629216 PMCID: PMC9146498 DOI: 10.3390/jpm12050794] [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: 04/17/2022] [Revised: 05/06/2022] [Accepted: 05/08/2022] [Indexed: 01/25/2023] Open
Abstract
Aortic aneurysm (AA) rapture is one of the leading causes of death worldwide. Unfortunately, the diagnosis of AA is often verified after the onset of complications, in most cases after aortic rupture. The aim of this study was to evaluate the frequency of ascending aortic aneurysm (AscAA) and aortic dilatation (AD) in patients with cardiovascular diseases undergoing echocardiography, and to identify the main risk factors depending on the morphology of the aortic valve. We processed 84,851 echocardiographic (ECHO) records of 13,050 patients with aortic dilatation (AD) in the Almazov National Medical Research Centre from 2010 to 2018, using machine learning methodologies. Despite a high prevalence of AD, the main reason for the performed ECHO was coronary artery disease (CAD) and hypertension (HP) in 33.5% and 14.2% of the patient groups, respectively. The prevalence of ascending AD (>40 mm) was 15.4% (13,050 patients; 78.3% (10,212 patients) in men and 21.7% (2838 patients) in women). Only 1.6% (n = 212) of the 13,050 patients with AD knew about AD before undergoing ECHO in our center. Among all the patients who underwent ECHO, we identified 1544 (1.8%) with bicuspid aortic valve (BAV) and 635 with BAV had AD (only 4.8% of all AD patients). According to the results of the random forest feature importance analysis, we identified the eight main factors of AD: age, male sex, vmax aortic valve (AV), aortic stenosis (AS), blood pressure, aortic regurgitation (AR), diabetes mellitus, and heart failure (HF). The known factors of AD-like HP, CAD, hyperlipidemia, BAV, and obesity, were also AD risk factors, but were not as important. Our study showed a high frequency of AscAA and dilation. Standard risk factors of AscAA such as HP, hyperlipidemia, or obesity are significantly more common in patients with AD, but the main factors in the formation of AD are age, male sex, vmax AV, blood pressure, AS, AR, HF, and diabetes mellitus. In males with BAV, AD incidence did not differ significantly, but the presence of congenital heart disease was one of the 12 main risk factors for the formation of AD and association with more significant aortic dilatation in AscAA groups.
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Affiliation(s)
- Olga Irtyuga
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (G.K.); (A.K.); (O.M.); (V.U.); (G.M.); (G.S.); (A.M.); (E.S.)
- Correspondence:
| | - Georgy Kopanitsa
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (G.K.); (A.K.); (O.M.); (V.U.); (G.M.); (G.S.); (A.M.); (E.S.)
- Department of Cardiac Surgery, Medical School, ITMO University, 49 Kronverskiy Prospect, 197101 Saint Petersburg, Russia;
| | - Anna Kostareva
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (G.K.); (A.K.); (O.M.); (V.U.); (G.M.); (G.S.); (A.M.); (E.S.)
| | - Oleg Metsker
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (G.K.); (A.K.); (O.M.); (V.U.); (G.M.); (G.S.); (A.M.); (E.S.)
| | - Vladimir Uspensky
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (G.K.); (A.K.); (O.M.); (V.U.); (G.M.); (G.S.); (A.M.); (E.S.)
| | - Gordeev Mikhail
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (G.K.); (A.K.); (O.M.); (V.U.); (G.M.); (G.S.); (A.M.); (E.S.)
| | - Giuseppe Faggian
- Department of Cardiac Surgery, Medical School, University of Verona, 37126 Verona, Italy;
| | - Giunai Sefieva
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (G.K.); (A.K.); (O.M.); (V.U.); (G.M.); (G.S.); (A.M.); (E.S.)
| | - Ilia Derevitskii
- Department of Cardiac Surgery, Medical School, ITMO University, 49 Kronverskiy Prospect, 197101 Saint Petersburg, Russia;
| | - Anna Malashicheva
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (G.K.); (A.K.); (O.M.); (V.U.); (G.M.); (G.S.); (A.M.); (E.S.)
| | - Evgeny Shlyakhto
- Almazov National Medical Research Centre, 197341 Saint Petersburg, Russia; (G.K.); (A.K.); (O.M.); (V.U.); (G.M.); (G.S.); (A.M.); (E.S.)
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de Mello BH, Rigo SJ, da Costa CA, da Rosa Righi R, Donida B, Bez MR, Schunke LC. Semantic interoperability in health records standards: a systematic literature review. HEALTH AND TECHNOLOGY 2022; 12:255-272. [PMID: 35103230 PMCID: PMC8791650 DOI: 10.1007/s12553-022-00639-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/07/2022] [Indexed: 01/03/2023]
Abstract
The integration and exchange of information among health organizations and system providers are currently regarded as a challenge. Each organization usually has an internal ecosystem and a proprietary way to store electronic health records of the patient’s history. Recent research explores the advantages of an integrated ecosystem by exchanging information between the different inpatient care actors. Many efforts seek quality in health care, economy, and sustainability in process management. Some examples are reducing medical errors, disease control and monitoring, individualized patient care, and avoiding duplicate and fragmented entries in the electronic medical record. Likewise, some studies showed technologies to achieve this goal effectively and efficiently, with the ability to interoperate data, allowing the interpretation and use of health information. To that end, semantic interoperability aims to share data among all the sectors in the organization, clinicians, nurses, lab, the entire hospital. Therefore, avoiding data silos and keep data regardless of vendors, to exchange the information across organizational boundaries. This study presents a comprehensive systematic literature review of semantic interoperability in electronic health records. We searched seven databases of articles published between 2010 to September 2020. We showed the most chosen scenarios, technologies, and tools employed to solve interoperability problems, and we propose a taxonomy around semantic interoperability in health records. Also, we presented the main approaches to solve the exchange problem of legacy and heterogeneous data across healthcare organizations.
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Teufel A, Binder H. Clinical Decision Support Systems. Visc Med 2021; 37:491-498. [PMID: 35087899 PMCID: PMC8738909 DOI: 10.1159/000519420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 09/03/2021] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge. SUMMARY AND KEY MESSAGES Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.
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Affiliation(s)
- Andreas Teufel
- Department of Medicine II, Section of Hepatology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health Baden-Württemberg (CPDBW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
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Schaaf J, Sedlmayr M, Schaefer J, Storf H. Diagnosis of Rare Diseases: a scoping review of clinical decision support systems. Orphanet J Rare Dis 2020; 15:263. [PMID: 32972444 PMCID: PMC7513302 DOI: 10.1186/s13023-020-01536-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 09/07/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. METHODS We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items "Objective and background of the publication/project", "System or project name", "Functionality", "Type of clinical data", "Rare Diseases covered", "Development status", "System availability", "Data entry and integration", "Last software update" and "Clinical usage". RESULTS The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: "Analysis or comparison of genetic and phenotypic data," "machine learning" and "information retrieval". Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. CONCLUSIONS Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.
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Affiliation(s)
- Jannik Schaaf
- Medical Informatics Group (MIG), University Hospital Frankfurt, Frankfurt, Germany.
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine Technische Universität Dresden, Dresden, Germany
| | - Johanna Schaefer
- Medical Informatics Group (MIG), University Hospital Frankfurt, Frankfurt, Germany
| | - Holger Storf
- Medical Informatics Group (MIG), University Hospital Frankfurt, Frankfurt, Germany
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Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:ijerph17010073. [PMID: 31861851 PMCID: PMC6981801 DOI: 10.3390/ijerph17010073] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/13/2019] [Accepted: 12/10/2019] [Indexed: 01/17/2023]
Abstract
This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.
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Sarrouti M, Ouatik El Alaoui S. SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions. Artif Intell Med 2019; 102:101767. [PMID: 31980104 DOI: 10.1016/j.artmed.2019.101767] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 11/19/2019] [Accepted: 11/19/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND OBJECTIVE Question answering (QA), the identification of short accurate answers to users questions written in natural language expressions, is a longstanding issue widely studied over the last decades in the open-domain. However, it still remains a real challenge in the biomedical domain as the most of the existing systems support a limited amount of question and answer types as well as still require further efforts in order to improve their performance in terms of precision for the supported questions. Here, we present a semantic biomedical QA system named SemBioNLQA which has the ability to handle the kinds of yes/no, factoid, list, and summary natural language questions. METHODS This paper describes the system architecture and an evaluation of the developed end-to-end biomedical QA system named SemBioNLQA, which consists of question classification, document retrieval, passage retrieval and answer extraction modules. It takes natural language questions as input, and outputs both short precise answers and summaries as results. The SemBioNLQA system, dealing with four types of questions, is based on (1) handcrafted lexico-syntactic patterns and a machine learning algorithm for question classification, (2) PubMed search engine and UMLS similarity for document retrieval, (3) the BM25 model, stemmed words and UMLS concepts for passage retrieval, and (4) UMLS metathesaurus, BioPortal synonyms, sentiment analysis and term frequency metric for answer extraction. RESULTS AND CONCLUSION Compared with the current state-of-the-art biomedical QA systems, SemBioNLQA, a fully automated system, has the potential to deal with a large amount of question and answer types. SemBioNLQA retrieves quickly users' information needs by returning exact answers (e.g., "yes", "no", a biomedical entity name, etc.) and ideal answers (i.e., paragraph-sized summaries of relevant information) for yes/no, factoid and list questions, whereas it provides only the ideal answers for summary questions. Moreover, experimental evaluations performed on biomedical questions and answers provided by the BioASQ challenge especially in 2015, 2016 and 2017 (as part of our participation), show that SemBioNLQA achieves good performances compared with the most current state-of-the-art systems and allows a practical and competitive alternative to help information seekers find exact and ideal answers to their biomedical questions. The SemBioNLQA source code is publicly available at https://github.com/sarrouti/sembionlqa.
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Affiliation(s)
- Mourad Sarrouti
- Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, U.S. National Institutes of Health, Bethesda, MD.
| | - Said Ouatik El Alaoui
- National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco; Laboratory of Informatics and Modeling, FSDM, Sidi Mohammed Ben Abdellah University, Fez, Morocco
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Vezertzis K, Lambrou GI, Koutsouris D. Development of Patient Databases for Endocrinological Clinical and Pharmaceutical Trials: A Survey. Rev Recent Clin Trials 2019; 15:5-21. [PMID: 31744453 DOI: 10.2174/1574887114666191118122714] [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: 09/05/2019] [Revised: 10/22/2019] [Accepted: 11/05/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND According to European legislation, a clinical trial is a research involving patients, which also includes a research end-product. The main objective of the clinical trial is to prove that the research product, i.e. a proposed medication or treatment, is effective and safe for patients. The implementation, development, and operation of a patient database, which will function as a matrix of samples with the appropriate parameterization, may provide appropriate tools to generate samples for clinical trials. AIMS The aim of the present work is to review the literature with respect to the up-to-date progress on the development of databases for clinical trials and patient recruitment using free and open-source software in the field of endocrinology. METHODS An electronic literature search was conducted by the authors from 1984 to June 2019. Original articles and systematic reviews selected, and the titles and abstracts of papers screened to determine whether they met the eligibility criteria, and full texts of the selected articles were retrieved. RESULTS The present review has indicated that the electronic health records are related with both the patient recruitment and the decision support systems in the domain of endocrinology. The free and open-source software provides integrated solutions concerning electronic health records, patient recruitment, and the decision support systems. CONCLUSION The patient recruitment relates closely to the electronic health record. There is maturity at the academic and research level, which may lead to good practices for the deployment of the electronic health record in selecting the right patients for clinical trials.
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Affiliation(s)
- Konstantinos Vezertzis
- School of Electrical and Computer Engineering, Biomedical Engineering Laboratory, National Technical University of Athens, Heroon Polytecniou 9, Athens, 15780, Athens, Greece
| | - George I Lambrou
- School of Electrical and Computer Engineering, Biomedical Engineering Laboratory, National Technical University of Athens, Heroon Polytecniou 9, Athens, 15780, Athens, Greece.,First Department of Pediatrics, Choremeio Research Laboratory, National and Kapodistrian University of Athens, Thivon & Levadeias 8, 11527, Goudi, Athens, Greece
| | - Dimitrios Koutsouris
- School of Electrical and Computer Engineering, Biomedical Engineering Laboratory, National Technical University of Athens, Heroon Polytecniou 9, Athens, 15780, Athens, Greece
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Vest TA, Gazda NP, Schenkat DH, Eckel SF. Practice-enhancing publications about the medication use process in 2017. Am J Health Syst Pharm 2019; 76:667-676. [DOI: 10.1093/ajhp/zxz028] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Tyler A Vest
- Wake Forest Baptist Medical Center, Winston Salem, NC
- University of North Carolina at Chapel Hill Eshelman School of Pharmacy, Chapel Hill, NC
| | | | | | - Stephen F Eckel
- University of North Carolina Medical Center, and University of North Carolina at Chapel Hill Eshelman School of Pharmacy, Chapel Hill, NC
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Wang Z. Data integration of electronic medical record under administrative decentralization of medical insurance and healthcare in China: a case study. Isr J Health Policy Res 2019; 8:24. [PMID: 30929644 PMCID: PMC6442402 DOI: 10.1186/s13584-019-0293-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 01/24/2019] [Indexed: 12/01/2022] Open
Abstract
In most regions of China, Electronic Medical Record (EMR) systems in hospitals are developed in an uncoordinated manner. Medical Insurance and Healthcare Administration are localised and organizations gather data from a functional management viewpoint without consideration of wider information sharing. Discontinuity of data resources is serious. Despite the government’s repeated emphasis on EMR data integration, little progress has been made, causing inconvenience to patients, but also significantly hindering data mining. This exploratory investigation used a case study to identify bottlenecks of data integration and proposes countermeasures. Interviews were carried out with 27 practitioners from central and provincial governments, hospitals, and related enterprises in China. This research shows that EMR data collection without patients’ authorization poses a major hazard to data integration. In addition, non-uniform information standards and hospitals’ unwillingness to share data are also significant obstacles to integration. Moreover, friction caused by the administrative decentralization, as well as unsustainability of public finance investment, also hinders the integration of data resources. To solve these problems, first, a protocol should be adopted for multi-stakeholder participation in data collection. Administrative authorities should then co-establish information standards and a data audit mechanism. Finally, measures are proposed for expanding data integration for multiplying effectiveness and adopting the Public-Private Partnerships model.
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Affiliation(s)
- Zhong Wang
- Economic Institute, Beijing Academy of Social Sciences, No. 33, North Fourth Ring Road, Chaoyang District, Beijing, 100101, China.
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Wulff A, Haarbrandt B, Tute E, Marschollek M, Beerbaum P, Jack T. An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR. Artif Intell Med 2018; 89:10-23. [PMID: 29753616 DOI: 10.1016/j.artmed.2018.04.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2017] [Revised: 04/26/2018] [Accepted: 04/30/2018] [Indexed: 12/27/2022]
Abstract
BACKGROUND Clinical decision-support systems (CDSS) are designed to solve knowledge-intensive tasks for supporting decision-making processes. Although many approaches for designing CDSS have been proposed, due to high implementation costs, as well as the lack of interoperability features, current solutions are not well-established across different institutions. Recently, the use of standardized formalisms for knowledge representation as terminologies as well as the integration of semantically enriched clinical information models, as openEHR Archetypes, and their reuse within CDSS are theoretically considered as key factors for reusable CDSS. OBJECTIVE We aim at developing and evaluating an openEHR based approach to achieve interoperability in CDSS by designing and implementing an exemplary system for automated systemic inflammatory response syndrome (SIRS) detection in pediatric intensive care. METHODS We designed an interoperable concept, which enables an easy integration of the CDSS across different institutions, by using openEHR Archetypes, terminology bindings and the Archetype Query Language (AQL). The practicability of the approach was tested by (1) implementing a prototype, which is based on an openEHR based data repository of the Hannover Medical School (HaMSTR), and (2) conducting a first pilot study. RESULTS We successfully designed and implemented a CDSS with interoperable knowledge bases and interfaces by reusing internationally agreed-upon Archetypes, incorporating LOINC terminology and creating AQL queries, which allowed retrieving dynamic facts in a standardized and unambiguous form. The technical capabilities of the system were evaluated by testing the prototype on 16 randomly selected patients with 129 days of stay, and comparing the results with the assessment of clinical experts (leading to a sensitivity of 1.00, a specificity of 0.94 and a Cohen's kappa of 0.92). CONCLUSIONS We found the use of openEHR Archetypes and AQL a feasible approach to bridge the interoperability gap between local infrastructures and CDSS. The designed concept was successfully transferred into a clinically evaluated openEHR based CDSS. To the authors' knowledge, this is the first openEHR based CDSS, which is technically reliable and capable in a real context, and facilitates clinical decision-support for a complex task. Further activities will comprise enrichments of the knowledge base, the reasoning processes and cross-institutional evaluations.
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Affiliation(s)
- Antje Wulff
- Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Hannover, Germany.
| | - Birger Haarbrandt
- Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Hannover, Germany
| | - Erik Tute
- Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Hannover, Germany
| | - Michael Marschollek
- Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Hannover, Germany
| | - Philipp Beerbaum
- Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Germany
| | - Thomas Jack
- Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Germany
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