Purpose: Decision aids increase patient participation in decision making and reduce decision conflict. The goal of this study was to evaluate the effect of a decision aid prior to the appointment, upon decisional conflict measured immediately after the visit relative to usual care. We also evaluated other effects of the decision aid over time.
Methods: In this randomized controlled trial, we included 90 patients seeking the care of a hand surgeon for trapeziometacarpal (TMC) arthritis for the first time. Patients were randomly assigned to receive either usual care (an informational brochure) or an interactive Web-based decision aid. At enrollment, consult duration was recorded, and patients completed the following measures: (1) Decisional Conflict Scale; (2) Quick Disabilities of Arm, Shoulder, and Hand (QuickDASH); (3) pain intensity; (4) Physical Health Questionnaire (PHQ-2); (5) satisfaction with the visit; and (6) Consultation And Relational Empathy (CARE) scale. At 6 weeks and 6 months, patients completed: (1) pain intensity measure; (2) Decision Regret Scale; and (3) satisfaction with treatment. We also recorded changes in treatment and provider.
Results: Patients who reviewed the interactive decision aid prior to visiting their hand surgeon had less decisional conflict at the end of the visit. Other outcomes were not affected.
Conclusions: Use of a decision aid prior to a first-time visit for TMC led to a measurable reduction in decision conflict. Decision aids make people seeking care for TMC arthritis more comfortable with their decision making. Future research might address the ability of decision aids to reduce surgeon-to-surgeon variation, resource utilization, and dissatisfaction with care CLINICAL RELEVANCE: Surgeons should consider the routine use of decision aids to reduce decision conflict.
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http://dx.doi.org/10.1016/j.jhsa.2018.06.004 | DOI Listing |
Infect Dis Now
January 2025
Department of Medical Microbiology and Virology. NHS Grampian, Aberdeen, UK. Electronic address:
Context: Recent advances in the development of rapid SARS-CoV-2 point of care (POC) testing provided an opportunity to aid clinical decision making in front-line healthcare settings. Perspectives of POC COVID-19 screening of pregnant women are under-researched.
Objective: To assess the impact of a SARS-CoV-2 POC testing platform implemented in a busy maternity hospital, with limited isolation capacity, during the third wave of the COVID-19 pandemic.
Gynecol Obstet Invest
January 2025
Background: Endometriosis is a chronic disease characterized by endometrial-like tissue outside the uterus. Superficial endometriosis (SE) is the most prevalent form, yet it remains underdiagnosed due to subtle clinical and imaging presentations. Traditionally, diagnosis relies on laparoscopy, which is relatively invasive and often contributes to diagnostic delay.
View Article and Find Full Text PDFPurpose: Heart failure (HF) is a disease that leads to approximately 300,000 fatalities annually in Europe and 250,000 deaths each year in the United States. Type 2 Diabetes Mellitus (T2DM) is a significant risk factor for HF, and testing for N-terminal (NT)-pro hormone BNP (NT-proBNP) can aid in early detection of HF in T2DM patients. We therefore developed and validated the HFriskT2DM-HScore, an algorithm to predict the risk of HF in T2DM patients, so guiding NT-proBNP investigation in a primary care setting.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Department of Industrial Engineering, Dalhousie University, PO Box 15000, Halifax, B3H 4R2, NS, Canada.
Background: The growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital overcrowding. Predicting these patients at admission allows for better resource planning, reducing bottlenecks, and improving flow.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Orthopaedics, Traditional Chinese Medical Hospital of Gansu Province, Qilihe District, Guazhou Street 418, Lanzhou, 730050,, Gansu, China.
Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio.
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