Building a specialized lexicon for breast cancer clinical trial subject eligibility analysis.

A natural language processing (NLP) application requires sophisticated lexical resources to support its processing goals. Different solutions, such as dictionary lookup and MetaMap, have been proposed in the healthcare informatics literature to identify disease terms with more than one word (multi-gram disease named entities). Although a lot of work has been done in the identification of protein- and gene-named entities in the biomedical field, not much research has been done on the recognition and resolution of terminologies in the clinical trial subject eligibility analysis. In this study, we develop a specialized lexicon for improving NLP and text mining analysis in the breast cancer domain, and evaluate it by comparing it with the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). We use a hybrid methodology, which combines the knowledge of domain experts, terms from multiple online dictionaries, and the mining of text from sample clinical trials. Use of our methodology introduces 4243 unique lexicon items, which increase bigram entity match by 38.6% and trigram entity match by 41%. Our lexicon, which adds a significant number of new terms, is very useful for matching patients to clinical trials automatically based on eligibility matching. Beyond clinical trial matching, the specialized lexicon developed in this study could serve as a foundation for future healthcare text mining applications.

An ontological model of the practice transformation process.

Patient-centered medical home is defined as an approach for providing comprehensive primary care that facilitates partnerships between individual patients and their personal providers. The current state of the practice transformation process is ad hoc and no methodological basis exists for transforming a practice into a patient-centered medical home. Practices and hospitals somehow accomplish the transformation and send the transformation information to a certification agency, such as the National Committee for Quality Assurance, completely ignoring the development and maintenance of the processes that keep the medical home concept alive. Many recent studies point out that such a transformation is hard as it requires an ambitious whole-practice reengineering and redesign. As a result, the practices suffer change fatigue in getting the transformation done. In this paper, we focus on the complexities of the practice transformation process and present a robust ontological model for practice transformation. The objective of the model is to create an understanding of the practice transformation process in terms of key process areas and their activities. We describe how our ontology captures the knowledge of the practice transformation process, elicited from domain experts, and also discuss how, in the future, that knowledge could be diffused across stakeholders in a healthcare organization. Our research is the first effort in practice transformation process modeling. To build an ontological model for practice transformation, we adopt the Methontology approach. Based on the literature, we first identify the key process areas essential for a practice transformation process to achieve certification status. Next, we develop the practice transformation ontology by creating key activities and precedence relationships among the key process areas using process maturity concepts. At each step, we employ a panel of domain experts to verify the intermediate representations of the ontology. Finally, we implement a prototype of the practice transformation ontology using Protégé.