Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application.

BACKGROUND: The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. OBJECTIVE: The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. METHODS: Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. RESULTS: The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. CONCLUSIONS: This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities.

WHATIF: An open-source desktop application for extraction and management of the incidental findings from next-generation sequencing variant data.

BACKGROUND: Identification and evaluation of incidental findings in patients following whole exome (WGS) or whole genome sequencing (WGS) is challenging for both practicing physicians and researchers. The American College of Medical Genetics and Genomics (ACMG) recently recommended a list of reportable incidental genetic findings. However, no informatics tools are currently available to support evaluation of incidental findings in next-generation sequencing data. METHODS: The Wisconsin Hierarchical Analysis Tool for Incidental Findings (WHATIF), was developed as a stand-alone Windows-based desktop executable, to support the interactive analysis of incidental findings in the context of the ACMG recommendations. WHATIF integrates the European Bioinformatics Institute Variant Effect Predictor (VEP) tool for biological interpretation and the National Center for Biotechnology Information ClinVar tool for clinical interpretation. RESULTS: An open-source desktop program was created to annotate incidental findings and present the results with a user-friendly interface. Further, a meaningful index (WHATIF Index) was devised for each gene to facilitate ranking of the relative importance of the variants and estimate the potential workload associated with further evaluation of the variants. Our WHATIF application is available at: http://tinyurl.com/WHATIF-SOFTWARE CONCLUSIONS: The WHATIF application offers a user-friendly interface and allows users to investigate the extracted variant information efficiently and intuitively while always accessing the up to date information on variants via application programming interfaces (API) connections. WHATIF׳s highly flexible design and straightforward implementation aids users in customizing the source code to meet their own special needs.

Farm Mapping to Assist, Protect, and Prepare Emergency Responders: Farm MAPPER.

Responders such as firefighters and emergency medical technicians who respond to farm emergencies often face complex and unknown environments. They may encounter hazards such as fuels, solvents, pesticides, caustics, and exploding gas storage cylinders. Responders may be unaware of dirt roads within the farm that can expedite their arrival at critical sites or snow-covered manure pits that act as hidden hazards. A response to a farm, unless guided by someone familiar with the operation, may present a risk to responders and post a challenge in locating the victim. This project explored the use of a Web-based farm-mapping application optimized for tablets and accessible via easily accessible on-site matrix barcodes, or quick response codes (QR codes), to provide emergency responders with hazard and resource information to agricultural operations. Secured portals were developed for both farmers and responders, allowing both parties to populate and customize farm maps with icons. Data were stored online and linked to QR codes attached to mailbox posts where emergency responders may read them with a mobile device. Mock responses were conducted on dairy farms to test QR code linking efficacy, Web site security, and field usability. Findings from farmer usability tests showed willingness to enter data as well as ease of Web site navigation and data entry even with farmers who had limited computer knowledge. Usability tests with emergency responders showed ease of QR code connectivity to the farm maps and ease of Web site navigation. Further research is needed to improve data security as well as assess the program's applicability to nonfarm environments and integration with existing emergency response systems. The next phases of this project will expand the program for regional and national use, develop QR code-linked, Web-based extrication guidance for farm machinery for victim entrapment rescue, and create QR code-linked online training videos and materials for limited English proficient immigrant farm workers.

A fuzzy-match search engine for physician directories.

BACKGROUND: A search engine to find physicians' information is a basic but crucial function of a health care provider's website. Inefficient search engines, which return no results or incorrect results, can lead to patient frustration and potential customer loss. A search engine that can handle misspellings and spelling variations of names is needed, as the United States (US) has culturally, racially, and ethnically diverse names. OBJECTIVE: The Marshfield Clinic website provides a search engine for users to search for physicians' names. The current search engine provides an auto-completion function, but it requires an exact match. We observed that 26% of all searches yielded no results. The goal was to design a fuzzy-match algorithm to aid users in finding physicians easier and faster. METHODS: Instead of an exact match search, we used a fuzzy algorithm to find similar matches for searched terms. In the algorithm, we solved three types of search engine failures: "Typographic", "Phonetic spelling variation", and "Nickname". To solve these mismatches, we used a customized Levenshtein distance calculation that incorporated Soundex coding and a lookup table of nicknames derived from US census data. RESULTS: Using the "Challenge Data Set of Marshfield Physician Names," we evaluated the accuracy of fuzzy-match engine-top ten (90%) and compared it with exact match (0%), Soundex (24%), Levenshtein distance (59%), and fuzzy-match engine-top one (71%). CONCLUSIONS: We designed, created a reference implementation, and evaluated a fuzzy-match search engine for physician directories. The open-source code is available at the codeplex website and a reference implementation is available for demonstration at the datamarsh website.