The Application of a Falls Risk Index

This study examined the prospective use of a falls risk index. The research question was: To what extent do the intrinsic factors identified in the Tinetti et al. falls risk index predict which patients are likely to experience a fall. Hogue\u27s ecological model of falls in late life provided a conceptual framework for the study. Direct observation was used to collect baseline data from a convenience/purposive sample of 26 male patients in a midwest nursing home care unit with a rehabilitation focus. Patients were then assigned to one of three risk groups: yes-fall, 30% chance of fall, no-fall. Reports of patient falls were reviewed during the following four months. Data were analyzed by discriminant analysis and frequency tables. Actual occurrences were demonstrated to be consistent with predicted occurrences in the frequency tabulation, and 23/26 Participants were classified correctly by discriminant analysis. There are several considerations in the interpretation of this data: (1) over half the sample was in the predicted middle—risk group (30% chance of falls) which has limited clinical usefulness, (2) the discriminant analysis equation was developed from study data, and (3) no variable contributed significantly to risk of falling in the stepwise entrance of variables analysis. Nonetheless, predictability of the extremes (yes-fall or no-fall) using reproducible scales to evaluate risk factors was demonstrated, and may be useful clinically as well as in other studies of patient falls

OBCS: The Ontology of Biological and Clinical Statistics

Statistics play a critical role in biological and clinical research. To promote logically consistent representation and classification of statistical entities, we have developed the Ontology of Biological and Clinical Statistics (OBCS). OBCS extends the Ontology of Biomedical Investigations (OBI), an OBO Foundry ontology supported by some 20 communities. Currently, OBCS contains 686 terms, including 381 classes imported from OBI and 147 classes specific to OBCS. The goal of this paper is to present OBCS for community critique and to describe a number of use cases designed to illustrate its potential applications. The OBCS project and source code are available at http://obcs.googlecode.com

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

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. 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. 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. By representing statistics-related terms and their relations in a rigorous fashion, OBCS 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

Appendices for Article: "Evaluating and extending the Informed Consent Ontology for representing permissions from the clinical domain"

These appendices contain supporting material for the study reported in the article, "Evaluating and Extending the Informed Consent Ontology for Permissions from the Clinical Domain."EU was supported in part by the Robert Wood Johnson Foundation Future of Nursing Scholar’s Program predoctoral training program. The study was further supported by the National Human Genome Research Institute of the National Institutes of Health under award number U01HG009454, the Rackham Graduate Student Research Grant, and the University of Michigan Institute for Data Science. EU is presently funded as a Postdoctoral Research Fellow in Public & Population Health Informatics at Fairbanks School of Public Health and Regenstrief Institute, supported by the National Library of Medicine of the National Institutes of Health under award number T15LM012502. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the Robert Wood Johnson Foundation, the National Institutes of Health, the University of Michigan, Indiana University, or Regenstrief Institute.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170913/2/Umberfield_Applied Ontology_Appendices_Final Submission.pdfDescription of Umberfield_Applied Ontology_Appendices_Final Submission.pdf : Full appendicesSEL

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