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Voices that matter:The impact of patient-reported outcome measures on clinical decision-making

World Journal of Methodology 2025年第2期15卷 54-61页

作者： Naveen Jeyaraman Madhan Jeyaraman Swaminathan Ramasubramanian Sangeetha Balaji Sathish Muthu Department of Orthopaedics ACS Medical College and HospitalDr MGR Educational and Research InstituteChennaiTN 600077India Department of Research Methods Orthopaedic Research GroupCoimbatoreTN 641045India Department of Orthopaedics Government Medical CollegeOmandurar Government EstateChennaiTN 600002India Department of Biotechnology Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatoreTN 641021India Department of Orthopaedics Government Medical CollegeKarurTN 639004India

The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific *** encapsulate a patient's health status directly from their perspective,encompassing various domains such as symptom severity,functional status,and overall quality of *** integrating PROMs into routine clinical practice and research,healthcare providers can achieve a more nuanced understanding of patient experiences and tailor treatments *** deployment of PROMs supports dynamic patient-provider interactions,fostering better patient engagement and adherence to tre-atment ***,PROMs are pivotal in clinical settings for monitoring disease progression and treatment efficacy,particularly in chronic and mental health ***,challenges in implementing PROMs include data collection and management,integration into existing health systems,and acceptance by patients and *** these barriers necessitates technological advancements,policy development,and continuous education to enhance the acceptability and effectiveness of *** paper concludes with recommendations for future research and policy-making aimed at optimizing the use and impact of PROMs across healthcare settings.

关键词： Patient-reported outcome measures Clinical decision-making Patient-centered care healthcare technology Data management Policy development

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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Computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model

Health Care Science

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Health Care Science 2023年第3期2卷 153-163页

作者： John Abisheganaden Kheng Hock Lee Lian Leng Low Eugene Shum Han Leong Goh Christine Gia Lee Ang Andy Wee An Ta Steven M.Miller Department of Respiratory and Critical Care Medicine Tan Tock Seng HospitalNational Healthcare GroupSingaporeSingapore National Working Group for the Hospital to Home Program SingaporeSingapore Department of Family Medicine and Continuing Care Singapore General HospitalSingHealth GroupSingaporeSingapore SingHealth Community Hospitals SingHealth GroupSingaporeSingapore Population Health and Integrated Care Office SingHealth GroupSingaporeSingapore Office of Community Development Changi General HospitalSingHealth GroupSingaporeSingapore Data Analytics and AI Department Integrated Health Information SystemsSingaporeSingapore School of Computing and Information Systems Singapore Management UniversitySingaporeSingapore

In a prior practice and policy article published in healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since *** this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 *** the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior healthcare Science publication is repeated at the beginning of this article.

关键词： hospital to home community care hospital to home lessons learned transitional care integrated care multiple readmissions AI prediction model machine learning in healthcare healthcare technology

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Artificial intelligence awareness and perceptions among pediatric orthopedic surgeons:A cross-sectional observational study

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World Journal of Orthopedics 2024年第11期15卷 1023-1035页

作者： Ammar K Alomran Mohammed F Alomar Ali A Akhdher Ali R Al Qanber Ahmad K Albik Arwa Alumran Ahmed H Abdulwahab Department of Orthopedic College of MedicineImam Abdulrahman Bin Faisal UniversityDammam 31441EasternSaudi Arabia College of Medicine Imam Abdulrahman Bin Faisal UniversityDammam 31441EasternSaudi Arabia Department of Health Information Management and technology Imam Abdulrahman Bin Faisal UniversityDammam 34212EasternSaudi Arabia

BACKGROUND Artificial intelligence(AI)is a branch of computer science that allows machines to analyze large datasets,learn from patterns,and perform tasks that would otherwise require human intelligence and *** is an emerging tool in pediatric orthopedic surgery,with various promising *** evaluation of the current awareness and perceptions among pediatric orthopedic surgeons is necessary to facilitate AI utilization and highlight possible areas of *** To assess the awareness and perceptions of AI among pediatric orthopedic *** This cross-sectional observational study was conducted using a structured questionnaire designed using QuestionPro online survey software to collect quantitative and qualitative *** hundred and twenty-eight pediatric orthopedic surgeons affiliated with two groups:Pediatric Orthopedic Chapter of Saudi Orthopedics Association and Middle East Pediatric Orthopedic Society in Gulf Cooperation Council Countries were *** The pediatric orthopedic surgeons surveyed had a low level of familiarity with AI,with more than 60%of respondents rating themselves as being slightly familiar or not at all *** most positively rated aspect of AI applications for pediatric orthopedic surgery was their ability to save time and enhance productivity,with 61.97%agreeing or strongly agreeing,and only 4.23%disagreeing or strongly *** participants also placed a high priority on patient privacy and data security,with over 90%rating them as quite important or highly *** bivariate analyses suggested that physicians with a higher awareness of AI also have a more positive *** Our study highlights a lack of familiarity among pediatric orthopedic surgeons towards AI,and suggests a need for enhanced education and regulatory frameworks to ensure the safe adoption of AI.

关键词： Artificial intelligence Pediatric orthopedics Surgeon awareness Data security Patient privacy healthcare technology Medical education Orthopedic surgery

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Using AI and Precision Nutrition to Support Brain Health during Aging

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Advances in Aging Research 2024年第5期13卷 85-106页

作者： Sabira Arefin Gideon Kipkoech Global healthcare Leadership Program Harvard University Boston USA Independent Researcher University of Embu Nairobi Kenya

Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they le

关键词： Artificial Intelligence (AI) Precision Nutrition Brain Health Aging Research Gerontology Cognitive Functions Temporal Reasoning Medication Adherence Electronic Health Records (EHRs) Machine Learning (ML) healthcare technology

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technology for Elderly with Memory Impairment and Wandering Risk

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E-Health Telecommunication Systems and Networks 2013年第1期2卷 13-22页

作者： Sami Perala Kari Makela Anna Salmenaho Rami Latvala South Ostrobothnia Health Care District SeinajokiFinland South Ostrobothnia Health technology Development Centre SeinajokiFinland Tampere University of technology SeinajokiFinland

Objective:The main objective of this project was to studyhow to secure the wandering of elderly persons diagnosed with dementia caused by Alzheimer’s disease by using healthcare technologies. Methods:The study was carried out during a three-year period (2008-2011) in the region of South Ostrobothnia, Finland. Thirty-two elderly persons living at home and diagnosed with Alzheimer’s disease participated in the study. The ages of the intervention group ranged from 66 to 90 years;the average age was 81 years. A total of 63 different home care devices including 24 location based technologies were tested during the *** choice of technology used was based on the individual needs of the elderly person. Results:Participants with mild stage memory impairment were able to use and benefit from the technology installed during the intervention to live more *** most useful devices were those that operated within the home. Nine of ten users of door alarm systems, five of nine users of GPS systems and all users of GSM systems were satisfied with the technologies. Conclusions:Location based alarm and access control technology can have a positive impact on the lives of elderly persons suffering from dementia. When chosen appropriately, technology can help to reduce or eliminate the wandering often associated with *** of the technology used, it should be installed when the elderly person is at the early stages of dementia;at later stages of the disease it is usually impossible for the elderly to adequately adopt the device.

关键词： healthcare technology Home Care Gerontechnology Wandering Dementia Alarm Systems

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