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Tan SF, Cher B, Berian JR. Improving Surgical Outcomes for Older Adults with Adoption of Technological Advances in Comprehensive Geriatric Assessment. SEMINARS IN COLON AND RECTAL SURGERY 2024; 35:101060. [PMID: 39669478 PMCID: PMC11633772 DOI: 10.1016/j.scrs.2024.101060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2024]
Abstract
Frailty is a well-recognized predictor of poor surgical outcomes for older adults, yet effective measurements and interventions remain limited. Technological advances offer an opportunity to address this gap and improve surgical care for older adults. This paper reviews the background of frailty and comprehensive geriatric assessments in surgery, and how technological innovations can advance frailty measurement and intervention in surgical settings. We review two broad areas of technological advancement as applied to frailty in surgery: 1) Innovation in the use of electronic health records (EHR) using Artificial Intelligence (AI) and Machine Learning (ML), and 2) Novel uses for wearable sensors and mobile health (mHealth) applications. We explore the integration of AI and ML with EHR systems, which can surpass traditional comorbidity indices by providing comprehensive health assessments and enhancing prediction models. Innovations like the electronic Frailty Index (eFI) show promise in expanding the reach of frailty assessments and facilitating real-time screening. Additionally, wearable devices and mobile health (mHealth) applications offer new ways to monitor and improve physical activity, nutrition, and psychological well-being, supporting perioperative rehabilitation. While these technologies present challenges, such as the need for infrastructure, training, and data interoperability, they offer promising strategies to facilitate the assessment and management of frailty among surgical patients. Continued research and tailored implementation strategies will be essential to fully realize the potential of these advancements in improving surgical outcomes for frail older adults.
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Affiliation(s)
- Sydney F Tan
- Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Benjamin Cher
- Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Julia R Berian
- Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI
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Osman M, Cooper R, Sayer AA, Witham MD. The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review. Age Ageing 2024; 53:afae135. [PMID: 38970549 PMCID: PMC11227113 DOI: 10.1093/ageing/afae135] [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: 11/29/2023] [Indexed: 07/08/2024] Open
Abstract
BACKGROUND Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear. METHODS We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines. RESULTS From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%-100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%-100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms. CONCLUSIONS Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported.
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Affiliation(s)
- Mo Osman
- AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne NHS Foundation Trust, Cumbria Northumberland Tyne and Wear NHS Foundation Trust and Newcastle University, Newcastle upon Tyne, UK
| | - Rachel Cooper
- AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne NHS Foundation Trust, Cumbria Northumberland Tyne and Wear NHS Foundation Trust and Newcastle University, Newcastle upon Tyne, UK
| | - Avan A Sayer
- AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne NHS Foundation Trust, Cumbria Northumberland Tyne and Wear NHS Foundation Trust and Newcastle University, Newcastle upon Tyne, UK
| | - Miles D Witham
- AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne NHS Foundation Trust, Cumbria Northumberland Tyne and Wear NHS Foundation Trust and Newcastle University, Newcastle upon Tyne, UK
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Wang J, Ji M, Han Y, Wu Y. Development and Usability Testing of a Mobile App-Based Clinical Decision Support System for Delirium: Randomized Crossover Trial. JMIR Aging 2024; 7:e51264. [PMID: 38298029 PMCID: PMC10850851 DOI: 10.2196/51264] [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: 07/26/2023] [Accepted: 01/02/2024] [Indexed: 02/02/2024] Open
Abstract
Background The 3-Minute Diagnostic Interview for Confusion Assessment Method-Defined Delirium (3D-CAM) is an instrument specially developed for the assessment of delirium in general wards, with high reported sensitivity and specificity. However, the use of the 3D-CAM by bedside nurses in routine practice showed relatively poor usability, with multiple human errors during assessment. Objective This study aimed to develop a mobile app-based delirium assessment tool based on the 3D-CAM and evaluate its usability among older patients by bedside nurses. Methods The Delirium Assessment Tool With Decision Support Based on the 3D-CAM (3D-DST) was developed to address existing issues of the 3D-CAM and optimize the assessment process. Following a randomized crossover design, questionnaires were used to evaluate the usability of the 3D-DST among older adults by bedside nurses. Meanwhile, the performances of both the 3D-DST and the 3D-CAM paper version, including the assessment completion rate, time required for completing the assessment, and the number of human errors made by nurses during assessment, were recorded, and their differences were compared. Results The 3D-DST included 3 assessment modules, 9 evaluation interfaces, and 16 results interfaces, with built-in reminders to guide nurses in completing the delirium assessment. In the usability testing, a total of 432 delirium assessments (216 pairs) on 148 older adults were performed by 72 bedside nurses with the 3D-CAM paper version and the 3D-DST. Compared to the 3D-CAM paper version, the mean usability score was significantly higher when using the 3D-DST (4.35 vs 3.40; P<.001). The median scores of the 6 domains of the satisfactory evaluation questionnaire for nurses using the 3D-CAM paper version and the 3D-DST were above 2.83 and 4.33 points, respectively (P<.001). The average time for completing the assessment reduced by 2.1 minutes (4.4 vs 2.3 min; P<.001) when the 3D-DST was used. Conclusions This study demonstrated that the 3D-DST significantly improved the efficiency of delirium assessment and was considered highly acceptable by bedside nurses.
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Affiliation(s)
- Jiamin Wang
- School of Nursing, Beijing University of Chinese Medicine, Beijing, China
- School of Nursing, Capital Medical University, Beijing, China
| | - Meihua Ji
- School of Nursing, Capital Medical University, Beijing, China
| | - Yuan Han
- Peking University First Hospital, Beijing, China
| | - Ying Wu
- School of Nursing, Capital Medical University, Beijing, China
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Miyazawa Y, Katsuta N, Nara T, Nojiri S, Naito T, Hiki M, Ichikawa M, Takeshita Y, Kato T, Okumura M, Tobita M. Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records. PLoS One 2024; 19:e0296760. [PMID: 38241284 PMCID: PMC10798448 DOI: 10.1371/journal.pone.0296760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
COVID-19 has a range of complications, from no symptoms to severe pneumonia. It can also affect multiple organs including the nervous system. COVID-19 affects the brain, leading to neurological symptoms such as delirium. Delirium, a sudden change in consciousness, can increase the risk of death and prolong the hospital stay. However, research on delirium prediction in patients with COVID-19 is insufficient. This study aimed to identify new risk factors that could predict the onset of delirium in patients with COVID-19 using machine learning (ML) applied to nursing records. This retrospective cohort study used natural language processing and ML to develop a model for classifying the nursing records of patients with delirium. We extracted the features of each word from the model and grouped similar words. To evaluate the usefulness of word groups in predicting the occurrence of delirium in patients with COVID-19, we analyzed the temporal changes in the frequency of occurrence of these word groups before and after the onset of delirium. Moreover, the sensitivity, specificity, and odds ratios were calculated. We identified (1) elimination-related behaviors and conditions and (2) abnormal patient behavior and conditions as risk factors for delirium. Group 1 had the highest sensitivity (0.603), whereas group 2 had the highest specificity and odds ratio (0.938 and 6.903, respectively). These results suggest that these parameters may be useful in predicting delirium in these patients. The risk factors for COVID-19-associated delirium identified in this study were more specific but less sensitive than the ICDSC (Intensive Care Delirium Screening Checklist) and CAM-ICU (Confusion Assessment Method for the Intensive Care Unit). However, they are superior to the ICDSC and CAM-ICU because they can predict delirium without medical staff and at no cost.
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Affiliation(s)
- Yusuke Miyazawa
- Department of Healthcare Innovation, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Narimasa Katsuta
- Department of Psychiatry, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Tamaki Nara
- Department of Healthcare Innovation, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
- Clinical Research and Trial Center, Juntendo University, Tokyo, Japan
| | - Shuko Nojiri
- Department of Healthcare Innovation, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
- Clinical Research and Trial Center, Juntendo University, Tokyo, Japan
| | - Toshio Naito
- Department of General Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Makoto Hiki
- Department of Emergency and Disaster Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan
- Department of Cardiovascular Biology and Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Masako Ichikawa
- Department of Emergency and Disaster Medicine, Juntendo University Faculty of Medicine, Tokyo, Japan
- Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yoshihide Takeshita
- Department of Psychiatry, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Tadafumi Kato
- Department of Psychiatry, Juntendo University Faculty of Medicine, Tokyo, Japan
| | | | - Morikuni Tobita
- Department of Healthcare Innovation, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Medical Technology Innovation Center, Juntendo University, Tokyo, Japan
- Clinical Research and Trial Center, Juntendo University, Tokyo, Japan
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Bednorz A, Mak JKL, Jylhävä J, Religa D. Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future. Clin Interv Aging 2023; 18:2171-2183. [PMID: 38152074 PMCID: PMC10752027 DOI: 10.2147/cia.s400887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/05/2023] [Indexed: 12/29/2023] Open
Abstract
Electronic medical records (EMRs) have many benefits in clinical research in gerontology, enabling data analysis, development of prognostic tools and disease risk prediction. EMRs also offer a range of advantages in clinical practice, such as comprehensive medical records, streamlined communication with healthcare providers, remote data access, and rapid retrieval of test results, ultimately leading to increased efficiency, enhanced patient safety, and improved quality of care in gerontology, which includes benefits like reduced medication use and better patient history taking and physical examination assessments. The use of artificial intelligence (AI) and machine learning (ML) approaches on EMRs can further improve disease diagnosis, symptom classification, and support clinical decision-making. However, there are also challenges related to data quality, data entry errors, as well as the ethics and safety of using AI in healthcare. This article discusses the future of EMRs in gerontology and the application of AI and ML in clinical research. Ethical and legal issues surrounding data sharing and the need for healthcare professionals to critically evaluate and integrate these technologies are also emphasized. The article concludes by discussing the challenges related to the use of EMRs in research as well as in their primary intended use, the daily clinical practice.
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Affiliation(s)
- Adam Bednorz
- John Paul II Geriatric Hospital, Katowice, Poland
- Institute of Psychology, Humanitas Academy, Sosnowiec, Poland
| | - Jonathan K L Mak
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Juulia Jylhävä
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), University of Tampere, Tampere, Finland
| | - Dorota Religa
- Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden
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Sun X, Yin Y, Yang Q, Huo T. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res 2023; 28:242. [PMID: 37475050 PMCID: PMC10360247 DOI: 10.1186/s40001-023-01065-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 07/22/2023] Open
Abstract
Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.
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Affiliation(s)
- Xiaoyu Sun
- National Institute of Hospital Administration, National Health Commission, Beijing, China
| | - Yuzhe Yin
- The Sixth Clinical Medical School, Capital Medical University, Beijing, China
| | - Qiwei Yang
- Department of Thorax, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Tianqi Huo
- National Institute of Hospital Administration, National Health Commission, Beijing, China.
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