The overwhelming amount, production speed, multidimensionality, and potential value of data currently available-often simplified and referred to as big data -exceed the limits of understanding of the human brain. At the same time, developments in data analytics and computational power provide the opportunity to obtain new insights and transfer data-provided added value to clinical practice in real time. What is the role of the health care professional in collaboration with the data scientist in the changing landscape of modern care? We discuss how health care professionals should provide expert knowledge in each of the stages of clinical decision support design: data level, algorithm level, and decision support level. Including various ethical considerations, we advocate for health care professionals to responsibly initiate and guide interprofessional teams, including patients, and embrace novel analytic technologies to translate big data into patient benefit driven by human(e) values.
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http://dx.doi.org/10.2196/11732 | DOI Listing |
Prostate
January 2025
Research Department, School of Medicine, Autonomous University of Sinaloa, Culiacan, México.
Introduction: Prostate cancer (PCa) is the second most common cancer in men worldwide, with significant incidence and mortality, particularly in Mexico, where diagnosis at advanced stages is common. Early detection through screening methods such as digital rectal examination and prostate-specific antigen testing is essential to improve outcomes. Despite current efforts, compliance with prostate screening (PS) remains low due to several barriers.
View Article and Find Full Text PDFKnee Surg Relat Res
January 2025
Bioengineering Laboratory, Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Background: Unplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Higher Institute of Medical Technology, Yaoundé, Cameroon.
Background: In Cameroon, like in many other resource-limited countries, data generated by health settings including morbidity and mortality parameters are not always uniform. In the absence of a national guideline necessary for the standardization and harmonization of data, precision of data required for effective decision-making is therefore not guaranteed. The objective of the present study was to assess the reporting style of morbidity and mortality data in healthcare settings.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Community Nursing, School of Nursing, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning Province, China.
Background: Examining urban-rural disparity in Chinese adults' advance care planning (ACP) attitudes is crucial for healthcare decision-making. A comprehensive understanding of contributing factors, especially through decomposition and comparative analysis, remains limited.
Methods: Data were derived from Psychology and Behavior Investigation of Chinese Residents (PBICR) including 19,738 participants, representative of Chinese adults.
BMC Cancer
January 2025
The Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
Background And Objectives: Accurate classification of lymphadenopathy is essential for determining the pathological nature of lymph nodes (LNs), which plays a crucial role in treatment selection. The biopsy method is invasive and carries the risk of sampling failure, while the utilization of non-invasive approaches such as ultrasound can minimize the probability of iatrogenic injury and infection. With the advancement of artificial intelligence (AI) and machine learning, the diagnostic efficiency of LNs is further enhanced.
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