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Yigit D, Acikgoz A. Evaluation of future nurses' knowledge, attitudes and anxiety levels about artificial intelligence applications. J Eval Clin Pract 2024. [PMID: 38894635 DOI: 10.1111/jep.14062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/19/2024] [Accepted: 06/05/2024] [Indexed: 06/21/2024]
Abstract
RATIONALE Evaluating future nurses' perspectives on artificial intelligence, determining their missing or incorrect information on the subject and determining their anxiety levels are of great importance in terms of providing science and technology-based health services in the future. AIMS This research was conducted to evaluate the knowledge, attitude and anxiety levels of future nurses about artificial intelligence applications. METHOD The research was a descriptive type, conducted with 552 nursing students. In collecting data, 'Data collection form' and 'Artificial Intelligence Anxiety Scale' (AIAS) were used. Analysis of data was performed with descriptive statistics, Kolmogorov-Smirnov, Shapiro-Wilk, Spearman, Mann-Whitney U and Kruskal-Wallis tests. In the study, p < 0.05 value was considered statistically significant. RESULTS It was determined that the students' average AIAS score was 51.68 ± 12.32. It was determined that 95.3% of the students did not receive training on artificial intelligence, and 94.0% did not have artificial intelligence-related subjects in their school courses. It was determined that 79.2% of the students wanted artificial intelligence-related subjects to be included in school courses. CONCLUSION In the study, it was determined that the artificial intelligence anxiety levels of nursing students were high. It has been determined that students with negative feelings about artificial intelligence have higher artificial intelligence anxiety levels. Our suggestion; adding courses or subjects related to artificial intelligence to the university curriculum and starting to include nurses in the working processes during their student years.
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Affiliation(s)
- Deniz Yigit
- Department of Pediatric Nursing, Faculty of Health Sciences, Kütahya University of Health Sciences, Kütahya, Turkey
| | - Ayfer Acikgoz
- Department of Pediatric Nursing, Faculty of Health Sciences, Eskisehir Osmangazi University, Eskişehir, Turkey
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Toma M, Bönisch C, Löhnhardt B, Kelm M, Bohnenberger H, Winkelmann S, Ströbel P, Kesztyüs T. Research collaboration data platform ensuring general data protection. Sci Rep 2024; 14:11887. [PMID: 38789442 PMCID: PMC11126409 DOI: 10.1038/s41598-024-61912-8] [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: 10/10/2023] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Translational data is of paramount importance for medical research and clinical innovation. It has the potential to benefit individuals and organizations, however, the protection of personal data must be guaranteed. Collecting diverse omics data and electronic health records (EHR), re-using the minimized data, as well as providing a reliable data transfer between different institutions are mandatory steps for the development of the promising field of big data and artificial intelligence in medical research. This is made possible within the proposed data platform in this research project. The established data platform enables the collaboration between public and commercial organizations by data transfer from various clinical systems into a cloud for supporting multi-site research while ensuring compliant data governance.
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Affiliation(s)
| | - Caroline Bönisch
- Medical Data Integration Center, Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany.
- Faculty of Electrical Engineering and Computer Science, University of Applied Sciences Stralsund, Stralsund, Germany.
| | - Benjamin Löhnhardt
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | | | | | - Sven Winkelmann
- Siemens Healthineers AG, Erlangen, Germany
- Nuremberg Institute of Technology, Nuremberg, Germany
| | - Philipp Ströbel
- Institute of Pathology, University Medical Center Göttingen, Göttingen, Germany
| | - Tibor Kesztyüs
- Medical Data Integration Center, Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
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Maheshwari K, Cywinski JB, Papay F, Khanna AK, Mathur P. Artificial Intelligence for Perioperative Medicine: Perioperative Intelligence. Anesth Analg 2023; 136:637-645. [PMID: 35203086 DOI: 10.1213/ane.0000000000005952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The anesthesiologist's role has expanded beyond the operating room, and anesthesiologist-led care teams can deliver coordinated care that spans the entire surgical experience, from preoperative optimization to long-term recovery of surgical patients. This expanded role can help reduce postoperative morbidity and mortality, which are regrettably common, unlike rare intraoperative mortality. Postoperative mortality, if considered a disease category, will be the third leading cause of death just after heart disease and cancer. Rapid advances in technologies like artificial intelligence provide an opportunity to build safe perioperative practices. Artificial intelligence helps by analyzing complex data across disparate systems and producing actionable information. Using artificial intelligence technologies, we can critically examine every aspect of perioperative medicine and devise innovative value-based solutions that can potentially improve patient safety and care delivery, while optimizing cost of care. In this narrative review, we discuss specific applications of artificial intelligence that may help advance all aspects of perioperative medicine, including clinical care, education, quality improvement, and research. We also discuss potential limitations of technology and provide our recommendations for successful adoption.
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Affiliation(s)
| | | | | | - Ashish K Khanna
- Department of Anesthesiology, Section on Critical Care Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
- Outcomes Research Consortium, Cleveland, Ohio
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Fasterholdt I, Naghavi-Behzad M, Rasmussen BSB, Kjølhede T, Skjøth MM, Hildebrandt MG, Kidholm K. Value assessment of artificial intelligence in medical imaging: a scoping review. BMC Med Imaging 2022; 22:187. [PMID: 36316665 PMCID: PMC9620604 DOI: 10.1186/s12880-022-00918-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/22/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. METHODS We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. RESULTS Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. CONCLUSIONS This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.
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Affiliation(s)
- Iben Fasterholdt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mohammad Naghavi-Behzad
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Benjamin S. B. Rasmussen
- grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Radiology, Odense University Hospital, Odense, Denmark ,grid.7143.10000 0004 0512 5013CAI-X – Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark
| | - Tue Kjølhede
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mette Maria Skjøth
- grid.7143.10000 0004 0512 5013Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | - Malene Grubbe Hildebrandt
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark ,grid.10825.3e0000 0001 0728 0170Department of Clinical Research, University of Southern Denmark, Odense, Denmark ,grid.7143.10000 0004 0512 5013Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Kristian Kidholm
- grid.7143.10000 0004 0512 5013CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
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Alasnag M, Hanfi Y, Alasnag M. Artificial intelligence and machine learning: the resurgence of the industrial revolution by robots. Open Heart 2022; 9:openhrt-2021-001883. [PMID: 35046123 PMCID: PMC8772451 DOI: 10.1136/openhrt-2021-001883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/31/2021] [Indexed: 12/26/2022] Open
Affiliation(s)
- Manal Alasnag
- Evelina London Children's Hospital, St Thomas' Hospital, London, UK
| | - Yasmin Hanfi
- Cardiology, Dallah Hospital, Riyadh, Saudi Arabia
| | - Mirvat Alasnag
- Cardiac Center, King Fahd Armed Forces Hospital, Jeddah, Saudi Arabia
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Affiliation(s)
- Samir Kendale
- Department of Anesthesia, Critical Care & Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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Charow R, Jeyakumar T, Younus S, Dolatabadi E, Salhia M, Al-Mouaswas D, Anderson M, Balakumar S, Clare M, Dhalla A, Gillan C, Haghzare S, Jackson E, Lalani N, Mattson J, Peteanu W, Tripp T, Waldorf J, Williams S, Tavares W, Wiljer D. Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review. JMIR MEDICAL EDUCATION 2021; 7:e31043. [PMID: 34898458 PMCID: PMC8713099 DOI: 10.2196/31043] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 05/12/2023]
Abstract
BACKGROUND As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education. OBJECTIVE With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness. METHODS After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career). RESULTS Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective. CONCLUSIONS This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.
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Affiliation(s)
- Rebecca Charow
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
| | | | | | - Elham Dolatabadi
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Mohammad Salhia
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Dalia Al-Mouaswas
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Sarmini Balakumar
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Megan Clare
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Caitlin Gillan
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Shabnam Haghzare
- University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | | | | | - Jane Mattson
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Wanda Peteanu
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Tim Tripp
- University Health Network, Toronto, ON, Canada
| | - Jacqueline Waldorf
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Walter Tavares
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Wilson Centre, Toronto, ON, Canada
| | - David Wiljer
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- CAMH Education, Centre for Addictions and Mental Health (CAMH), Toronto, ON, Canada
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Leite ML, de Loiola Costa LS, Cunha VA, Kreniski V, de Oliveira Braga Filho M, da Cunha NB, Costa FF. Artificial intelligence and the future of life sciences. Drug Discov Today 2021; 26:2515-2526. [PMID: 34245910 DOI: 10.1016/j.drudis.2021.07.002] [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: 12/01/2020] [Revised: 05/12/2021] [Accepted: 07/01/2021] [Indexed: 12/23/2022]
Abstract
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.
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Affiliation(s)
- Michel L Leite
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Department of Molecular Biology, Biological Sciences Institute, University of Brasília, Campus Darcy Ribeiro, Block K, 70.790-900, Brasilia, Federal District, Brazil
| | - Lorena S de Loiola Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor A Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor Kreniski
- Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil
| | | | - Nicolau B da Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Fabricio F Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil; Cancer Biology and Epigenomics Program, Ann & Robert H Lurie Children's Hospital of Chicago Research Center and Northwestern University's Feinberg School of Medicine, 2430 N. Halsted St, Box 220, Chicago, IL 60614, USA; MATTER Chicago, 222 W. Merchandise Mart Plaza, Suite 12th Floor, Chicago, IL 60654, USA; Genomic Enterprise, San Diego, CA 92008, USA; Genomic Enterprise, New York, NY 11581, USA.
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Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis. Artif Intell Med 2020; 112:102003. [PMID: 33581824 DOI: 10.1016/j.artmed.2020.102003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 11/16/2020] [Accepted: 12/08/2020] [Indexed: 11/23/2022]
Abstract
INTRODUCTION In recent years, reinforcement learning (RL) has gained traction in the healthcare domain. In particular, RL methods have been explored for haemodynamic optimization of septic patients in the Intensive Care Unit. Most hospitals however, lack the data and expertise for model development, necessitating transfer of models developed using external datasets. This approach assumes model generalizability across different patient populations, the validity of which has not previously been tested. In addition, there is limited knowledge on safety and reliability. These challenges need to be addressed to further facilitate implementation of RL models in clinical practice. METHOD We developed and validated a new reinforcement learning model for hemodynamic optimization in sepsis on the MIMIC intensive care database from the USA using a dueling double deep Q network. We then transferred this model to the European AmsterdamUMCdb intensive care database. T-Distributed Stochastic Neighbor Embedding and Sequential Organ Failure Assessment scores were used to explore the differences between the patient populations. We apply off-policy policy evaluation methods to quantify model performance. In addition, we introduce and apply a novel deep policy inspection to analyse how the optimal policy relates to the different phases of sepsis and sepsis treatment to provide interpretable insight in order to assess model safety and reliability. RESULTS The off-policy evaluation revealed that the optimal policy outperformed the physician policy on both datasets despite marked differences between the two patient populations and physician's policies. Our novel deep policy inspection method showed insightful results and unveiled that the model could initiate therapy adequately and adjust therapy intensity to illness severity and disease progression which indicated safe and reliable model behaviour. Compared to current physician behavior, the developed policy prefers a more liberal use of vasopressors with a more restrained use of fluid therapy in line with previous work. CONCLUSION We created a reinforcement learning model for optimal bedside hemodynamic management and demonstrated model transferability between populations from the USA and Europe for the first time. We proposed new methods for deep policy inspection integrating expert domain knowledge. This is expected to facilitate progression to bedside clinical decision support for the treatment of critically ill patients.
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Khurrum M, Asmar S, Joseph B. Telemedicine in the ICU: Innovation in the Critical Care Process. J Intensive Care Med 2020; 36:1377-1384. [PMID: 33111599 DOI: 10.1177/0885066620968518] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Tele-ICU is a technology-based model designed to deliver effective critical care in the intensive care unit (ICU). The tele-ICU system has been developed to address the increasing demand for intensive care services and the shortage of intensivists. A finite number of intensivists from remote locations provide real-time services to multiple ICUs and assist in the treatment of critically ill patients. Risk prediction algorithms, smart alarm systems, and machine learning tools augment conventional coverage and can potentially improve the quality of care. Tele-ICU is associated with substantial improvements in mortality, reduced hospital and ICU length of stay, and decreased health care costs. Although multiple studies show improved outcomes following the implementation of tele-ICU, results are not consistent. Several factors, including the heterogeneity of tele-ICU infrastructure deployed in different facilities and the reluctance of health care workers to accept tele-ICU, could be associated with these varied results. Considerably high installation and ongoing operational costs might also be limiting the widespread utilization of this innovative service. While we believe that the implementation of tele-ICU offers potential advantages and makes critical care delivery more efficient, further research on the impact of this technology in critical care settings is warranted.
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Affiliation(s)
- Muhammad Khurrum
- Division of Trauma, Critical Care, Emergency Surgery, and Burns, Department of Surgery, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Samer Asmar
- Division of Trauma, Critical Care, Emergency Surgery, and Burns, Department of Surgery, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Bellal Joseph
- Division of Trauma, Critical Care, Emergency Surgery, and Burns, Department of Surgery, College of Medicine, University of Arizona, Tucson, AZ, USA
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Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning. Anesthesiology 2020; 132:738-749. [PMID: 32028374 DOI: 10.1097/aln.0000000000003150] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures. METHODS Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard. RESULTS Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text. CONCLUSIONS Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes.
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Lal A, Pinevich Y, Gajic O, Herasevich V, Pickering B. Artificial intelligence and computer simulation models in critical illness. World J Crit Care Med 2020; 9:13-19. [PMID: 32577412 PMCID: PMC7298588 DOI: 10.5492/wjccm.v9.i2.13] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 04/21/2020] [Accepted: 05/12/2020] [Indexed: 02/06/2023] Open
Abstract
Widespread implementation of electronic health records has led to the increased use of artificial intelligence (AI) and computer modeling in clinical medicine. The early recognition and treatment of critical illness are central to good outcomes but are made difficult by, among other things, the complexity of the environment and the often non-specific nature of the clinical presentation. Increasingly, AI applications are being proposed as decision supports for busy or distracted clinicians, to address this challenge. Data driven "associative" AI models are built from retrospective data registries with missing data and imprecise timing. Associative AI models lack transparency, often ignore causal mechanisms, and, while potentially useful in improved prognostication, have thus far had limited clinical applicability. To be clinically useful, AI tools need to provide bedside clinicians with actionable knowledge. Explicitly addressing causal mechanisms not only increases validity and replicability of the model, but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.
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Affiliation(s)
- Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Rochester, Mayo Clinic, MN 55905, United States
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
| | - Yuliya Pinevich
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Ognjen Gajic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Rochester, Mayo Clinic, MN 55905, United States
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
| | - Vitaly Herasevich
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Brian Pickering
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, United States
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13
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Liu Y. Artificial Intelligence-Based Neural Network for the Diagnosis of Diabetes: Model Development. JMIR Med Inform 2020; 8:e18682. [PMID: 32459183 PMCID: PMC7287715 DOI: 10.2196/18682] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 03/21/2020] [Accepted: 03/22/2020] [Indexed: 01/02/2023] Open
Abstract
Background The incidence of diabetes is increasing in China, and its impact on national health cannot be ignored. Smart medicine is a medical model that uses technology to assist the diagnosis and treatment of disease. Objective The aim of this paper was to apply artificial intelligence (AI) in the diagnosis of diabetes. Methods We established an AI diagnostic model in the MATLAB software platform based on a backpropagation neural network by collecting data for the cases of integration and extraction and selecting an input feature vector. Based on this diagnostic model, using an intelligent combination of the LabVIEW development platform and the MATLAB software-designed diabetes diagnosis system with user data, we called the neural network diagnostic module to correctly diagnose diabetes. Results Compared to conventional diagnostic procedures, the system can effectively improve diagnostic efficiency and save time for physicians. Conclusions The development of AI applications has utility to aid diabetes diagnosis.
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Affiliation(s)
- Yue Liu
- The First People's Hospital of Fuyang, Hangzhou, Zhejiang Province China, Hangzhou, China
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