Aim: To describe our methods to compare patient-reported symptoms of acute myeloid leukemia and the corresponding documentation by healthcare providers in the electronic health record.
Background: Patients with acute myeloid leukemia experience many distressing symptoms, particularly related to chemotherapy. The timely recognition and provision of evidence-based interventions to manage these symptoms can improve outcomes. However, lack of standardized formatting for symptom documentation within electronic health records leads to challenges for clinicians when accessing and comprehending patients' symptom information, as it primarily exists in narrative forms in various parts of the electronic health record. This variability raises concerns about over- or under-reporting of symptoms. Consistency between patient-reported symptoms and clinician's symptom documentation is important for patient-centered symptom management, but little is known about the degree of agreement between patient reports and their documentation. This is a detailed description of the study's methodology, procedures and design to determine how patient-reported symptoms are similar or different from symptoms documented in electronic health records by clinicians.
Design: Exploratory, descriptive study.
Methods: Forty symptoms will be assessed as patient-reported outcomes using the modified version of the Memorial Symptom Assessment Scale. The research team will annotate symptoms from the electronic health record (clinical notes and flowsheets) corresponding to the 40 symptoms. The degree of agreement between patient reports and electronic health record documentation will be analyzed using positive and negative agreement, kappa statistics and McNemar's test.
Conclusion: We present innovative methods to comprehensively compare the symptoms reported by acute myeloid leukemia patients with all available electronic health record documentation, including clinical notes and flowsheets, providing insights into symptom reporting in clinical practice.
Impact: Findings from this study will provide foundational understanding and compelling evidence, suggesting the need for more thorough efforts to assess patients' symptoms. Methods presented in this paper are applicable to other symptom-intensive diseases.
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http://dx.doi.org/10.1111/jan.16320 | DOI Listing |
Appl Health Econ Health Policy
December 2024
Centre for Health Economics Research and Evaluation, University of Technology Sydney, Level 5, Building 20, 100 Broadway, Chippendale, Sydney, NSW, 2008, Australia.
Objective: This article reviews the assessment pathways that have been implemented worldwide to facilitate access to drugs for patients with rare diseases.
Methods: The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to conduct a systematic literature review. The Ovid (Embase/MEDLINE), Cochrane, Web of Science, Econlit, National Institute of Health Research, Centre for Reviews and Dissemination, and International Network of Agencies for Health Technology Assessment databases were searched.
ACS Appl Mater Interfaces
December 2024
Center for Optics Research and Engineering, State Key Laboratory of Crystal Materials, Shandong University, Qingdao 266237, China.
Shear mode ultrasonic waves are in high demand for structural health monitoring (SHM) applications owing to their nondispersive characteristics, singular mode, and minimal energy loss, especially in harsh environments. However, the generation and detection of a pure shear wave using conventional piezoelectric materials present substantial challenges because of their complex piezoelectric response, involving multiple modes. Herein, we introduce a high-quality piezoelectric crystal BiSiO (BSO), exhibiting a robust piezoelectric response ( = 45.
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
AI for Health Institute, Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.
View Article and Find Full Text PDFGenet Med
December 2024
Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA; The Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA; Harvard Medical School, Boston, MA.
Purpose: Genomic sequencing of newborns (NBSeq) can initiate disease surveillance and therapy for children, and may identify at-risk relatives through reverse cascade testing. We explored genetic risk communication and reverse cascade testing among families of newborns who underwent exome sequencing and had a risk for autosomal dominant disease identified.
Methods: We conducted semi-structured interviews with parents of newborns enrolled in the BabySeq Project who had a pathogenic or likely-pathogenic (P/LP) variant associated with an autosomal dominant (AD) childhood- and/or adult-onset disease returned.
Clin Pharmacol Ther
December 2024
Flatiron Health, New York, NY, USA.
Clinical research has historically failed to include representative levels of historically underrepresented populations and these inequities continue to persist. Ensuring representativeness in clinical trials is crucial for patients to receive clinically appropriate treatment and have equitable access to novel therapies; enhancing the generalizability of study results; and reducing the need for post-marketing commitments focused on underrepresented groups. As demonstrated by recent legislation and guidance documents, regulatory agencies have shown an increased interest in understanding how novel therapies will impact the patient population that will receive them.
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