Agreement between patient-reported and clinically documented symptoms of acute myeloid leukemia: Study protocol.

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.

[Descriptive Review of Patents in Healthcare and Nursing: Based on Network Analysis].

PURPOSE: The significance of the healthcare industry has grown exponentially in recent years due to the impact of the fourth industrial revolution and the ongoing pandemic. Accordingly, this study aimed to examine domestic healthcare-related patents comprehensively. Big data analysis was used to present the trend and status of patents filed in nursing. METHODS: The descriptive review was conducted based on Grant and Booth's descriptive review framework. Patents related to nursing was searched in the Korea Intellectual Property Rights Information Service between January 2016 to December 2020. Data analysis included descriptive statistics, phi-coefficient for correlations, and network analysis using the R program (version 4.2.2). RESULTS: Among 37,824 patents initially searched, 1,574 were selected based on the inclusion criteria. Nursing-related patents did not specify subjects, and many patents (41.4%) were related to treatment in the healthcare delivery phase. Furthermore, most patents (56.1%) were designed to increase effectiveness. The words frequently used in the titles of nursing-related patents were, in order, "artificial intelligence," "health management," and "medical information," and the main terms with high connection centrality were "artificial intelligence" and "therapeutic system." CONCLUSION: The industrialization of nursing is the best solution for developing the healthcare industry and national health promotion. Collaborations in education, research, and policy will help the nursing industry become a healthcare industry of the future. This will prime the enhancement of the national economy and public health.

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review.

BACKGROUND: People with cancer frequently experience severe and distressing symptoms associated with cancer and its treatments. Predicting symptoms in patients with cancer continues to be a significant challenge for both clinicians and researchers. The rapid evolution of machine learning (ML) highlights the need for a current systematic review to improve cancer symptom prediction. OBJECTIVE: This systematic review aims to synthesize the literature that has used ML algorithms to predict the development of cancer symptoms and to identify the predictors of these symptoms. This is essential for integrating new developments and identifying gaps in existing literature. METHODS: We conducted this systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. We conducted a systematic search of CINAHL, Embase, and PubMed for English records published from 1984 to August 11, 2023, using the following search terms: cancer, neoplasm, specific symptoms, neural networks, machine learning, specific algorithm names, and deep learning. All records that met the eligibility criteria were individually reviewed by 2 coauthors, and key findings were extracted and synthesized. We focused on studies using ML algorithms to predict cancer symptoms, excluding nonhuman research, technical reports, reviews, book chapters, conference proceedings, and inaccessible full texts. RESULTS: A total of 42 studies were included, the majority of which were published after 2017. Most studies were conducted in North America (18/42, 43%) and Asia (16/42, 38%). The sample sizes in most studies (27/42, 64%) typically ranged from 100 to 1000 participants. The most prevalent category of algorithms was supervised ML, accounting for 39 (93%) of the 42 studies. Each of the methods-deep learning, ensemble classifiers, and unsupervised ML-constituted 3 (3%) of the 42 studies. The ML algorithms with the best performance were logistic regression (9/42, 17%), random forest (7/42, 13%), artificial neural networks (5/42, 9%), and decision trees (5/42, 9%). The most commonly included primary cancer sites were the head and neck (9/42, 22%) and breast (8/42, 19%), with 17 (41%) of the 42 studies not specifying the site. The most frequently studied symptoms were xerostomia (9/42, 14%), depression (8/42, 13%), pain (8/42, 13%), and fatigue (6/42, 10%). The significant predictors were age, gender, treatment type, treatment number, cancer site, cancer stage, chemotherapy, radiotherapy, chronic diseases, comorbidities, physical factors, and psychological factors. CONCLUSIONS: This review outlines the algorithms used for predicting symptoms in individuals with cancer. Given the diversity of symptoms people with cancer experience, analytic approaches that can handle complex and nonlinear relationships are critical. This knowledge can pave the way for crafting algorithms tailored to a specific symptom. In addition, to improve prediction precision, future research should compare cutting-edge ML strategies such as deep learning and ensemble methods with traditional statistical models.

Social Determinants of Health and Cancer Pain in the US: Scoping Review.

Social determinants of health (SDOH) are structural factors that yield health inequities. Within the context of cancer, these inequities include screening rates and survival rates, as well as higher symptom burden during and after treatment. While pain is one of the most frequently reported symptoms, the relationship between SDOHs and cancer pain is not well understood. The purpose of this study is to describe and synthesize the published research that has evaluated the relationships between SDOH and cancer pain. A systematic search of PubMed, CINAHL, and Embase was conducted to identify studies in which cancer pain and SDOH were described. In all, 20 studies met the inclusion criteria. In total, 14 studies reported a primary aim related to SDOH and cancer pain. Demographic variables including education or income were used most frequently. Six specific measurements were utilized to measure SDOH, such as the acculturation scale, the composite measure of zip codes for poverty level and blight prevalence, or the segregation index. Among the five domains of SDOH based on Healthy People 2030, social and community was the most studied, followed by economic stability, and education access and quality. The neighborhood and built environment domain was the least studied. Despite increasing attention to SDOH, the majority of published studies use single-dimension variables derived from demographic data to evaluate the relationships between SDOH and cancer pain. Future research is needed to explore the intersectionality of SDOH domains and their impact on cancer pain. Additionally, intervention studies should be conducted to address existing disparities and to reduce the incidence and impact of cancer pain.

What are the late effects of older gastric cancer survivors? A scoping review.

Objective: Older gastric cancer survivors account for a high proportion of cancer survivors. To improve their quality of life, a cancer survivorship care plan with a consideration of the late effects is required. This study aimed to understand the extent and type of evidence in relation to the late effects in older gastric cancer survivors. Methods: We conducted a scoping review based on the JBI scoping review framework. We explored articles in the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Medical Literature Analysis and Retrieval System Online (MEDLINE), SCOPUS, Web of science, Excerpta Medica dataBASE (EMBASE), Research InformationSharing Service (RISS), Korean Medical dataBASE(KMBase), and National Digital Science Library (NDSL) databases published from January 1, 2012, to December 31, 2021. The keywords used for search are "gastric cancer", "aged", "survivors", and "late effect or long-term effect or late symptom or time factors". While 439 records were initially identified, 14 articles were eventually selected based on the inclusion criteria. Results: Most studies were conducted in 2019 (4 studies, 28.6%), and more than half (8 studies, 57.1%) were conducted in Asia. In total, six definitions of cancer survivors were found in the studies. The most common age range in the studies was 60-64 years (7 studies, 50.0%). The second primary cancer risk was the most common late effects (5 studies, 20.8%). Among 14 studies, there was only one study of intervention study (7.1%). Conclusions: It is time to shift the focus from survival to care that improve the quality of life after treatment. We suggest future studies to define cancer survivors, set the age criteria and characterize the late effects in older gastric cancer survivors.