Machine learning-based donor permission extraction from informed consent documents.

BACKGROUND: With more clinical trials are offering optional participation in the collection of bio-specimens for biobanking comes the increasing complexity of requirements of informed consent forms. The aim of this study is to develop an automatic natural language processing (NLP) tool to annotate informed consent documents to promote biorepository data regulation, sharing, and decision support. We collected informed consent documents from several publicly available sources, then manually annotated them, covering sentences containing permission information about the sharing of either bio-specimens or donor data, or conducting genetic research or future research using bio-specimens or donor data. RESULTS: We evaluated a variety of machine learning algorithms including random forest (RF) and support vector machine (SVM) for the automatic identification of these sentences. 120 informed consent documents containing 29,204 sentences were annotated, of which 1250 sentences (4.28%) provide answers to a permission question. A support vector machine (SVM) model achieved a F-1 score of 0.95 on classifying the sentences when using a gold standard, which is a prefiltered corpus containing all relevant sentences. CONCLUSIONS: This study provides the feasibility of using machine learning tools to classify permission-related sentences in informed consent documents.

The case for expressing nursing theories using ontologies.

Nursing and informatics share a common strength in their use of structured representations of domains, specifically the underlying notion of 'things' (ie, concepts, constructs, or named entities) and the relationships among those things. Accurate representation of nursing knowledge in machine-interpretable formats is a necessary next step for leveraging contemporary technologies. Expressing validated nursing theories in ontologies, and in particular formal ontologies, would serve not only nursing, but also investigators from other domains, clinical information system developers, and the users of advanced technologies such as artificial intelligence that seek to learn from the real-world data and evidence generated by nurses and others. Such efforts will enable sharing knowledge and conceptualizations about phenomena across the domains of nursing and generating, testing, revising, and providing theoretically-based perspectives when leveraging contemporary technologies. Nursing is well situated for this work, leveraging intentional and focused collaborations among nurse informaticists, scientists, and theorists.

CRENO: An ontology to model concepts relating to culture, race, ethnicity, and nationality for health data.

Generating categories and classifications is a common function in life science research; however, categorizing the human population based on "race" remains controversial. There is an awareness and recognition of social-economic disparities with respect to health which are sometimes impacted by someone's ethnicity or race. This work describes an endeavor to develop a computable ontology model to represent a standardization of the concepts surrounding culture, race, ethnicity, and nationality - concepts misrepresented widely. We constructed an OWL ontology based on reliable resources with iterative human expert evaluations and aligned it to existing biomedical ontological models. The effort produced a preliminary ontology that expresses concepts related to classes of ethnic, racial, national, and cultural identities and showcases how health disparity data can be linked and expressed within our ontological framework. Future work will explore automated methods to expand the ontology and its utilization for clinical informatics.

Evaluating and Extending the Informed Consent Ontology for Representing Permissions from the Clinical Domain.

The purpose of this study was to evaluate, revise, and extend the Informed Consent Ontology (ICO) for expressing clinical permissions, including reuse of residual clinical biospecimens and health data. This study followed a formative evaluation design and used a bottom-up modeling approach. Data were collected from the literature on US federal regulations and a study of clinical consent forms. Eleven federal regulations and fifteen permission-sentences from clinical consent forms were iteratively modeled to identify entities and their relationships, followed by community reflection and negotiation based on a series of predetermined evaluation questions. ICO included fifty-two classes and twelve object properties necessary when modeling, demonstrating appropriateness of extending ICO for the clinical domain. Twenty-six additional classes were imported into ICO from other ontologies, and twelve new classes were recommended for development. This work addresses a critical gap in formally representing permissions clinical permissions, including reuse of residual clinical biospecimens and health data. It makes missing content available to the OBO Foundry, enabling use alongside other widely-adopted biomedical ontologies. ICO serves as a machine-interpretable and interoperable tool for responsible reuse of residual clinical biospecimens and health data at scale.

Tolerance to oral anticancer agent treatment in older adults with cancer: a secondary analysis of data from electronic health records and a pilot study of patient-reported outcomes.

BACKGROUND: More than 60% of cancer cases occur in older adults, and many are treated with oral anticancer agents. Yet, the treatment tolerability in older adults has not been fully understood due to their underrepresentation in oncology clinical trials, creating challenges for treatment decision-making and symptom management. The objective of this study was to investigate the tolerance of capecitabine, an example of oral chemotherapy, among older adults with cancer and explore factors associated with capecitabine-related side effects and treatment changes, to enhance supportive care. METHODS: A secondary analysis used combined data from electronic health records and a pilot study of patient-reported outcomes, with a total of 97 adult patients taking capecitabine during 2016-2017, including older adult patients aged 65 years or older (n = 43). The data extracted included patient socio-demographics, capecitabine information, side effects, and capecitabine treatment changes (dose reductions and dose interruptions). Bivariate correlations, negative binomial regression, and multiple linear regression were conducted for data analysis. RESULTS: Older adults were more likely to experience fatigue (86% vs. 51%, p = .001) and experienced more severe fatigue (ß = 0.44, p = 0.03) and hand-foot syndrome (HFS) (ß = 1.15, p = 0.004) than younger adults. The severity of fatigue and HFS were associated with the number of outpatient medications (ß = 0.06, p = 0.006) and the duration of treatment (ß = 0.50, p = 0.009), respectively. Correlations among side effects presented different patterns between younger and older adults. Although more older adults experienced dose reductions (21% vs. 13%) and dose interruptions (33% vs. 28%) than younger adults, the differences were not statistically different. Female sex, breast cancer diagnosis, capecitabine monotherapy, and severe HFS were found to be associated with dose reductions (p-values < 0.05). CONCLUSIONS: Older adults were less likely to tolerate capecitabine treatment and had different co-occurring side effects compared to younger adults. While dose reductions are common among older adults, age 65 years or older may not be an independent factor of treatment changes. Other socio-demographic and clinical factors may be more likely to be associated. Future studies can be conducted to further explore older adults' tolerance to a variety of oral anticancer agents to generate more evidence to support optimal treatment decision-making and symptom management.

A Concept Analysis of Oral Anticancer Agent Self-management.

BACKGROUND: The rapid development and adoption of oral anticancer agents (OAAs) for cancer management have shifted patients' roles from recipient to owner of their care delivery, assuming their responsibilities for self-managing their OAA treatments at home, while the concept of oral anticancer agent self-management (OAA-SM) has not been well clarified and defined. OBJECTIVE: This study was to clarify the concept of OAA-SM and identify major components, influential factors, and consequences of OAA-SM, as well as propose a representative conceptual model of OAA-SM. METHODS: A literature review was conducted concerning the concept and application of OAA-SM. The Walker and Avant method for concept analysis was utilized to guide the examination of OAA-SM. RESULTS: OAA-SM is a multifaceted and dynamic process that requires continuous adaptation by patients as multiple self-management challenges can emerge throughout OAA treatments. The defining attributes of OAA-SM include OAA adherence, adverse-effect self-management, patient-provider communication, and OAA safe storage, handling, and administration practices. Oral anticancer agent-SM is potentially influenced by a variety of patient-related, OAA-related, and healthcare system factors. Effective OAA-SM is associated with better patient and healthcare outcomes. CONCLUSIONS: The clarification of the concept of OAA-SM and the identification of attributes of OAA-SM and their interrelationships contribute to the body of knowledge in OAA-SM. IMPLICATIONS FOR PRACTICE: This concept analysis provides the foundation to increase healthcare providers' understanding of patients' needs for OAA-SM support and guides the development of patient-centered interventions to empower and engage patients and their families in effective OAA-SM, and improve patients' quality of life and care.

Regulations and Norms for Reuse of Residual Clinical Biospecimens and Health Data.

Nurse scientists are increasingly interested in conducting secondary research using real world collections of biospecimens and health data. The purposes of this scoping review are to (a) identify federal regulations and norms that bear authority or give guidance over reuse of residual clinical biospecimens and health data, (b) summarize domain experts' interpretations of permissions of such reuse, and (c) summarize key issues for interpreting regulations and norms. Final analysis included 25 manuscripts and 23 regulations and norms. This review illustrates contextual complexity for reusing residual clinical biospecimens and health data, and explores issues such as privacy, confidentiality, and deriving genetic information from biospecimens. Inconsistencies make it difficult to interpret, which regulations or norms apply, or if applicable regulations or norms are congruent. Tools are necessary to support consistent, expert-informed consent processes and downstream reuse of residual clinical biospecimens and health data by nurse scientists.

Infrastructuring an organizational node for a federated research and data network: A case study from a sociotechnical perspective.

BACKGROUND: Local nodes on federated research and data networks (FR&DNs) provide enabling infrastructure for collaborative clinical and translational research. Studies in other fields note that infrastructuring, that is, work to identify and negotiate relationships among people, technologies, and organizations, is invisible, unplanned, and undervalued. This may explain the limited literature on nodes in FR&DNs in health care. METHODS: A retrospective case study of one PCORnet® node explored 3 questions: (1) how were components of infrastructure assembled; (2) what specific work was required; and (3) what theoretically grounded, pragmatic questions should be considered when infrastructuring a node for sustainability. Artifacts, work efforts, and interviews generated during node development and implementation were reviewed. A sociotechnical lens was applied to the analysis. Validity was established with internal and external partners. RESULTS: Resources, services, and expertise needed to establish the node existed within the organization, but were scattered across work units. Aligning, mediating, and institutionalizing for sustainability among network and organizational teams, governance, and priorities consumed more work efforts than deploying technical aspects of the node. A theoretically based set of questions relevant to infrastructuring a node was developed and organized within a framework of infrastructuring emphasizing enacting technology, organizing work, and institutionalizing; validity was established with internal and external partners. CONCLUSIONS: FR&DNs are expanding; we provide a sociotechnical perspective on infrastructuring a node. Future research should evaluate the applicability of the framework and questions to other node and network configurations, and more broadly the infrastructuring required to enable and support federated clinical and translational science.

Lessons Learned for Identifying and Annotating Permissions in Clinical Consent Forms.

BACKGROUND: The lack of machine-interpretable representations of consent permissions precludes development of tools that act upon permissions across information ecosystems, at scale. OBJECTIVES: To report the process, results, and lessons learned while annotating permissions in clinical consent forms. METHODS: We conducted a retrospective analysis of clinical consent forms. We developed an annotation scheme following the MAMA (Model-Annotate-Model-Annotate) cycle and evaluated interannotator agreement (IAA) using observed agreement (A o), weighted kappa (κw ), and Krippendorff's α. RESULTS: The final dataset included 6,399 sentences from 134 clinical consent forms. Complete agreement was achieved for 5,871 sentences, including 211 positively identified and 5,660 negatively identified as permission-sentences across all three annotators (A o = 0.944, Krippendorff's α = 0.599). These values reflect moderate to substantial IAA. Although permission-sentences contain a set of common words and structure, disagreements between annotators are largely explained by lexical variability and ambiguity in sentence meaning. CONCLUSION: Our findings point to the complexity of identifying permission-sentences within the clinical consent forms. We present our results in light of lessons learned, which may serve as a launching point for developing tools for automated permission extraction.

Expressing and Executing Informed Consent Permissions Using SWRL: The All of Us Use Case.

The informed consent process is a complicated procedure involving permissions as well a variety of entities and actions. In this paper, we discuss the use of Semantic Web Rule Language (SWRL) to further extend the Informed Consent Ontology (ICO) to allow for semantic machine-based reasoning to manage and generate important permission-based information that can later be viewed by stakeholders. We present four use cases of permissions from the All of Us informed consent document and translate these permissions into SWRL expressions to extend and operationalize ICO. Our efforts show how SWRL is able to infer some of the implicit information based on the defined rules, and demonstrate the utility of ICO through the use of SWRL extensions. Future work will include developing formal and generalized rules and expressing permissions from the entire document, as well as working towards integrating ICO into software systems to enhance the semantic representation of informed consent for biomedical research.

Side Effects, Self-Management Activities, and Adherence to Oral Anticancer Agents.

PURPOSE: There are growing concerns about patients' adherence to oral anticancer agents (OAAs), and the need for patients to engage in self-management of OAA-related side effects. We assessed associations among adherence, severity of side effects, and effectiveness of self-management of side effects in patients taking capecitabine. METHODS: Adherence to capecitabine at 6 weeks was measured by the Medication Event Monitoring System among 50 patients with gastrointestinal cancers. Severity of side effects related to capecitabine and effectiveness of self-management of side effects were captured using the Modified Self-Care Diary at the time of enrollment and weekly for 6 weeks. Spearman's correlation, Mann-Whitney U-tests, and multiple linear regression were conducted, p<0.05. RESULTS: Overall mean adherence rate was 85.4±14.1%. Adherence rate was not significantly correlated to the mean severity of total side effects at any time point and was correlated with the mean effectiveness of self-management of total side effects only at week 2 (rho=0.29, p=0.04). However, adherence rate was associated with the mean severity of one specific side effect, diarrhea, at 6 weeks (rho=0.36, p=0.01) and marginally correlated to the mean effectiveness of self-management of diarrhea at 6 weeks (rho=0.28, p=0.05). Mean severity of diarrhea at 6 weeks was an independent predictor of adherence rate (b=4.97, p=0.01), with the control of age (b=0.52, p=0.002), number of outpatient medications (b=1.12, p=0.007), health literacy (b=2.53, p=0.04), diagnosis of colorectal cancer (b=11.6, p=0.03), and capecitabine in combination with other chemotherapies (b=16.8, p=0.001) in the model. CONCLUSION: This pilot study suggests ongoing examination of both severity and effectiveness of self-management of side effects in future studies of adherence to OAAs is merited. There is a need for future studies with larger sample sizes that explore the complex relationships among adherence, severity of side effects, and effectiveness of self-management of side effects in OAA therapy.

Acceptance and Use of eHealth/mHealth Applications for Self-Management Among Cancer Survivors.

Cancer survivors' acceptance and use of eHealth/mHealth applications for self-management can be unique and are not fully understood. We used data from the Health Information National Trends Survey 4 Cycle 4 to examine cancer survivors' acceptance and use of eHealth/mHea applications for key self-management processes, and conducted logistic regression and Rao-Scott design-adjusted Chi-square tests to assess bivariate associations between potential predictors and actual use. Potential factors were selected based on the Individual and Family Self-Management theory. High acceptance of eHealth applictions was identified, and adoption of mHealth was relatively low. Younger, higher educated, married, employed, and higher income survivors tended to use eHealth/mHealth applications for self-management. Survivors who were newly diagnosed or still on treatment were more likely to look for cancer information online or communicate with health providers electronically. BMI and rural residency were associated with use of mHealth apps to achieve a health-related goal and treatment decision-making.

Expressing Biomedical Ontologies in Natural Language for Expert Evaluation.

We report on a study of our custom Hootation software for the purposes of assessing its ability to produce clear and accurate natural language phrases from axioms embedded in three biomedical ontologies. Using multiple domain experts and three discrete rating scales, we evaluated the tool on clarity of the natural language produced, fidelity of the natural language produced from the ontology to the axiom, and the fidelity of the domain knowledge represented by the axioms. Results show that Hootation provided relatively clear natural language equivalents for a select set of OWL axioms, although the clarity of statements hinges on the accuracy and representation of axioms in the ontology.

The Ontology of Biological and Clinical Statistics (OBCS) for standardized and reproducible statistical analysis.

BACKGROUND: Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem. RESULTS: The terms in OBCS including 'data collection', 'data transformation in statistics', 'data visualization', 'statistical data analysis', and 'drawing a conclusion based on data', cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. Currently, OBCS comprehends 878 terms, representing 20 BFO classes, 403 OBI classes, 229 OBCS specific classes, and 122 classes imported from ten other OBO ontologies. We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. Other ongoing projects using OBCS for statistical data processing are also discussed. The OBCS source code and documentation are available at: https://github.com/obcs/obcs . CONCLUSIONS: The Ontology of Biological and Clinical Statistics (OBCS) is a community-based open source ontology in the domain of biological and clinical statistics. OBCS is a timely ontology that represents statistics-related terms and their relations in a rigorous fashion, facilitates standard data analysis and integration, and supports reproducible biological and clinical research.

Harmonizing and extending standards from a domain-specific and bottom-up approach: an example from development through use in clinical applications.

OBJECTIVE: Currently, the processes for harmonizing and extending standards by leveraging the knowledge within local documentation artifacts are not well described. We describe a collaborative project to develop common information models, terminology bindings, and term definitions based on nursing documentation systems, and carry the findings through to the adoption in standards development organizations (SDOs) and technical implementations in clinical applications. MATERIALS AND METHODS: Nursing flowsheet documents from six large organizations were analyzed to generate a common information model and terminologies that fully expressed documentation across all systems, and were sufficient for evidence-based decision support, reporting, and analysis. RESULTS: Significant gaps in existing standards were identified. The models and terminologies were submitted to and incorporated by SDOs, are published, implemented, and now serving as a foundation for an eMeasure. DISCUSSION: There are few examples in the literature of success working through the standards development process from a bottom-up perspective. Subsequently, standards do not yet fully address the need for detailed clinical data that enables, for example, decision support as well as a range of reporting and analytic requirements. Recommendations from this project include transparent processes within SDOs, registries that make models and associated terminologies freely available, and coordinated governance processes. CONCLUSION: We demonstrated the feasibility of using documentation artifacts in a bottom-up approach to develop common models and sets of terms that are complete from the perspective of clinical implementation. Importantly, we demonstrated a process by which a community of practice can contribute to closing gaps in existing standards using SDO processes.

Collaborative software development for a Brazilian telehealth program.

Telehealth services in the State of Pernambuco, Brazil are led by the Telehealth Center (RedeNUTES) and based on HealthNet 2.0 software. Among the tele-assistance services, health professionals have clinical discussions focused on second opinions. This paper reports the experience in a PhD study through mixed-methods, to evaluate the telehealth services, planning and modeling a new tool to improve a telehealth system. We described the nurse's role in each phase of this study. The method of User-Centered Design was explored in three phases as Identification of work process, User's perception and collaborative modeling, Observational usability study. The main frame was based on collaborative techniques as Collaborative Prototype Design Process, cognitive walkthrough, and thinking-aloud. The users also identified all usability problems identified by the evaluators. The methods were useful in identifying usability problems, and easy to employ using standard equipment and software thus a relatively low cost approach to usability testing.

Leveraging EHR data for outcomes and comparative effectiveness research in oncology.

Along with the increasing adoption of electronic health records (EHRs) are expectations that data collected within EHRs will be readily available for outcomes and comparative effectiveness research. Yet the ability to effectively share and reuse data depends on implementing and configuring EHRs with these goals in mind from the beginning. Data sharing and integration must be planned both locally as well as nationally. The rich data transmission and semantic infrastructure developed by the National Cancer Institute (NCI) for research provides an excellent example of moving beyond paper-based paradigms and exploiting the power of semantically robust, network-based systems, and engaging both domain and informatics expertise. Similar efforts are required to address current challenges in sharing EHR data.

Nurses' ratings of their health and professional work environments.

Interactions between nursing work environments and nurses' health are of growing significance, given the aging work force, nursing shortage, and workplace health risks. This study examined relationships among nurses' ratings of health behaviors, health status, and professional work environments. Registered nurses (N = 3,132) from five multi-state settings completed an electronic survey. Participants' general health ratings were good, yet stress levels remained the one consistent predictor of poorer health ratings and work environment ratings in regression models. Additionally, more than half of the participants reported being overweight, only 50% met physical activity standards, more than two thirds reported a history of back or needlestick injuries, and 44% and 62% reported experiencing verbal abuse by colleagues and patients, respectively. Contrary to other studies, professional work environment as measured in this study did not predict nurses' health ratings. Further study of the impact of stress on long-term health outcomes and work force retention, as well as on worksite health strategies, is needed.

Representation of Clinical Nursing Protocols Using GEM II & GEM Cutter.

The machineable representation and execution of clinical guidelines has been the focus of research efforts for some time, however there is less examination of whether the methods and techniques for guidelines are sufficient for clinical protocols. The objective of this study was to test the feasibility of using the Guideline Elements Model II (GEM II) and GEM Cutter for the representation of clinical protocols, specifically clinical protocols commonly used by nurses. After downloading the GEM Cutter 2.5, we decomposed a set of clinical protocols and analyzed the completeness in which elemental protocol data was represented. One of the most complicated of these protocols (extravasations of infused medication) is presented as an example. While GEM II adequately represents core elements of clinical protocols at the high level, it was not possible to adequately represent sequence and associated role based permissions via use of conditional criteria at branching and procedural levels. Functionality of the tool would also be enhanced with more robust terminology management and support for multi-authoring.

Implementing a health system-wide evidence-based practice educational program to reach nurses with various levels of experience and educational preparation.

This article describes a system-wide evidence-based practice (EBP) educational initiative implemented with a geographically, educationally, and clinically diverse group of nurses with the intent of increasing their EBP skill set and efficacy as local change agents and leaders. The overall scope of the larger National Quality Forum Scholar Program is described, and then the focus is narrowed to describe the EBP components of the initiative with case examples and lessons learned.