Purpose
Contemporary big data initiatives in health care will benefit from greater integration with nursing science and nursing practice; in turn, nursing science and nursing practice has much to ...gain from the data science initiatives. Big data arises secondary to scholarly inquiry (e.g., ‐omics) and everyday observations like cardiac flow sensors or Twitter feeds. Data science methods that are emerging ensure that these data be leveraged to improve patient care.
Organizing Construct
Big data encompasses data that exceed human comprehension, that exist at a volume unmanageable by standard computer systems, that arrive at a velocity not under the control of the investigator and possess a level of imprecision not found in traditional inquiry. Data science methods are emerging to manage and gain insights from big data.
Methods
The primary methods included investigation of emerging federal big data initiatives, and exploration of exemplars from nursing informatics research to benchmark where nursing is already poised to participate in the big data revolution. We provide observations and reflections on experiences in the emerging big data initiatives.
Conclusions
Existing approaches to large data set analysis provide a necessary but not sufficient foundation for nursing to participate in the big data revolution. Nursing's Social Policy Statement guides a principled, ethical perspective on big data and data science. There are implications for basic and advanced practice clinical nurses in practice, for the nurse scientist who collaborates with data scientists, and for the nurse data scientist.
Clinical Relevance
Big data and data science has the potential to provide greater richness in understanding patient phenomena and in tailoring interventional strategies that are personalized to the patient.
Abstract
Objective
Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science. We aim to synthesize the literature on ...the use of NLP to process or analyze symptom information documented in EHR free-text narratives.
Materials and Methods
Our search of 1964 records from PubMed and EMBASE was narrowed to 27 eligible articles. Data related to the purpose, free-text corpus, patients, symptoms, NLP methodology, evaluation metrics, and quality indicators were extracted for each study.
Results
Symptom-related information was presented as a primary outcome in 14 studies. EHR narratives represented various inpatient and outpatient clinical specialties, with general, cardiology, and mental health occurring most frequently. Studies encompassed a wide variety of symptoms, including shortness of breath, pain, nausea, dizziness, disturbed sleep, constipation, and depressed mood. NLP approaches included previously developed NLP tools, classification methods, and manually curated rule-based processing. Only one-third (n = 9) of studies reported patient demographic characteristics.
Discussion
NLP is used to extract information from EHR free-text narratives written by a variety of healthcare providers on an expansive range of symptoms across diverse clinical specialties. The current focus of this field is on the development of methods to extract symptom information and the use of symptom information for disease classification tasks rather than the examination of symptoms themselves.
Conclusion
Future NLP studies should concentrate on the investigation of symptoms and symptom documentation in EHR free-text narratives. Efforts should be undertaken to examine patient characteristics and make symptom-related NLP algorithms or pipelines and vocabularies openly available.
•Electronic patient-authored text (ePAT) is a critical component of understanding symptoms and experiences.•Natural language processing (NLP) and text mining aid in the characterization of ...sub-clinical symptoms and improved self-management.•This review synthesizes the literature on the use of NLP and text mining as they apply to symptom extraction and processing in ePAT.•Future applications integrate well with National Institutes of Health’s interest in data science research regarding symptom science.
In this systematic review, we aim to synthesize the literature on the use of natural language processing (NLP) and text mining as they apply to symptom extraction and processing in electronic patient-authored text (ePAT).
A comprehensive literature search of 1964 articles from PubMed and EMBASE was narrowed to 21 eligible articles. Data related to purpose, text source, number of users and/or posts, evaluation metrics, and quality indicators were recorded.
Pain (n = 18) and fatigue and sleep disturbance (n = 18) were the most frequently evaluated symptom clinical content categories. Studies accessed ePAT from sources such as Twitter and online community forums or patient portals focused on diseases, including diabetes, cancer, and depression. Fifteen studies used NLP as a primary methodology. Studies reported evaluation metrics including the precision, recall, and F-measure for symptom-specific research questions.
NLP and text mining have been used to extract and analyze patient-authored symptom data in a wide variety of online communities. Though there are computational challenges with accessing ePAT, the depth of information provided directly from patients offers new horizons for precision medicine, characterization of sub-clinical symptoms, and the creation of personal health libraries as outlined by the National Library of Medicine.
Future research should consider the needs of patients expressed through ePAT and its relevance to symptom science. Understanding the role that ePAT plays in health communication and real-time assessment of symptoms, through the use of NLP and text mining, is critical to a patient-centered health system.
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•Health self-monitoring technologies call for new theories of health behaviors.•The sensemaking perspective is adopted to chronic disease self-management.•We conduct meta-synthesis of ...qualitative studies of diabetes self-management.•The meta-synthesis provides ample empirical support for the framework.•We draw implications for the design of informatics interventions.
Self-monitoring is an integral component of many chronic diseases; however few theoretical frameworks address how individuals understand self-monitoring data and use it to guide self-management.
To articulate a theoretical framework of sensemaking in diabetes self-management that integrates existing scholarship with empirical data.
The proposed framework is grounded in theories of sensemaking adopted from organizational behavior, education, and human–computer interaction. To empirically validate the framework the researchers reviewed and analyzed reports on qualitative studies of diabetes self-management practices published in peer-reviewed journals from 2000 to 2015.
The proposed framework distinguishes between sensemaking and habitual modes of self-management and identifies three essential sensemaking activities: perception of new information related to health and wellness, development of inferences that inform selection of actions, and carrying out daily activities in response to new information. The analysis of qualitative findings from 50 published reports provided ample empirical evidence for the proposed framework; however, it also identified a number of barriers to engaging in sensemaking in diabetes self-management.
The proposed framework suggests new directions for research in diabetes self-management and for design of new informatics interventions for data-driven self-management.
How We Design Feasibility Studies Bowen, Deborah J., PhD; Kreuter, Matthew, PhD, MPH; Spring, Bonnie, PhD, ABPP ...
American journal of preventive medicine,
05/2009, Letnik:
36, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Abstract Public health is moving toward the goal of implementing evidence-based interventions. To accomplish this, there is a need to select, adapt, and evaluate intervention studies. Such selection ...relies, in part, on making judgments about the feasibility of possible interventions and determining whether comprehensive and multilevel evaluations are justified. There exist few published standards and guides to aid these judgments. This article describes the diverse types of feasibility studies conducted in the field of cancer prevention, using a group of recently funded grants from the National Cancer Institute. The grants were submitted in response to a request for applications proposing research to identify feasible interventions for increasing the utilization of the Cancer Information Service among underserved populations.
Abstract Purpose Low health literacy has been associated with poor health-related outcomes. The purposes are to report the development of a website for low-literate parents in the Neonatal Intensive ...Care Unit (NICU), and the findings of heuristic evaluation and a usability testing of this website. Methods To address low literacy of NICU parents, multimedia educational Website using visual aids (e.g., pictographs, photographs), voice-recorded text message in addition to a simplified text was developed. The text was created at the 5th grade readability level. The heuristic evaluation was conducted by three usability experts using 10 heuristics. End-users’ performance was measured by counting the time spent completing tasks and number of errors, as well as recording users’ perception of ease of use and usefulness (PEUU) in a sample of 10 NICU parents. Results Three evaluators identified 82 violations across the 10 heuristics. All violations, however, received scores <2, indicating minor usability problems. Participants’ time to complete task varies from 81.2 s (SD = 30.9) to 2.2 s (SD = 1.3). Participants rated the Website as easy to use and useful (PEUU mean = 4.52, SD = 0.53). Based on the participants’ comments, appropriate modifications were made. Discussion and conclusions Different types of visuals on the Website were well accepted by low-literate users and agreement of visuals with text improved understanding of the educational materials over that with text alone. The findings suggest that using concrete and realistic pictures and pictographs with clear captions would maximize the benefit of visuals. One emerging theme was “simplicity” in design (e.g., limited use of colors, one font type and size), content (e.g., avoid lengthy text), and technical features (e.g., limited use of pop-ups). The heuristic evaluation by usability experts and the usability test with actual users provided complementary expertise, which can give a richer assessment of a design for low literacy Website. These results facilitated design modification and implementation of solutions by categorizing and prioritizing the usability problems.
The current study examined the frequency and predictors of older adults' engagement with symptom reporting in COVIDWATCHER, a mobile health (mHealth) citizen science application.
is a type of ...participatory research that leverages information provided by community members. There were 1,028 COVIDWATCHER participants who engaged with symptom reporting between April 2020 and January 2021. Approximately 13.5% (
= 139) were adults aged ≥65 years. We used a Wilcoxon test to compare the mean frequency of engagement with symptom reporting by older adults (i.e., aged ≥65 years) to younger adults (i.e., aged ≤64 years) and multivariable linear regression to explore the predictors of engagement with symptom reporting. There was a significant difference in engagement with symptom reporting between adults aged ≥65 years compared to those aged ≤64 years (
< 0.001). In our final model, age (β = 26.0; 95% confidence interval 14.8, 34.2) was a significant predictor for engagement with symptom reporting. These results help further our understanding of older adult engagement with mHealth-enabled citizen science for symptom reporting.
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