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.

[1]  Venkatarajan S Mathura,et al.  CliniProteus: A flexible clinical trials information management system , 2007, Bioinformation.

[2]  Mark B Horton,et al.  Special requirements for electronic health record systems in ophthalmology. , 2011, Ophthalmology.

[3]  Lee A Pyles,et al.  Development of a web-based database to manage American College of Emergency Physicians/American Academy of Pediatrics Emergency Information Forms. , 2005, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[4]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[5]  Peggy L. Peissig,et al.  Development of an optical character recognition pipeline for handwritten form fields from an electronic health record , 2012, J. Am. Medical Informatics Assoc..

[6]  Michael F Chiang,et al.  Adoption of electronic health records and preparations for demonstrating meaningful use: an American Academy of Ophthalmology survey. , 2013, Ophthalmology.

[7]  Wendy A. Wolf,et al.  The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies , 2011, BMC Medical Genomics.

[8]  Kayvan Najarian,et al.  Big Data Analytics in Healthcare , 2015, BioMed research international.

[9]  Jie Chen,et al.  Grand challenges for multimodal bio-medical systems , 2005 .

[10]  Sarah Clarke,et al.  Using Clinical Decision Support and Dashboard Technology to Improve Heart Team Efficiency and Accuracy in a Transcatheter Aortic Valve Implantation (TAVI) Program , 2016, Nursing Informatics.

[11]  Zekerijah Sabanovic,et al.  OPHTHALMOLOGY AND INFORMATION TECHNOLOGY IN TUZLA CANTON HEALTH CARE SYSTEM , 2012, Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH.

[12]  Chris F. Taylor,et al.  Pedro: a configurable data entry tool for XML , 2004, Bioinform..

[13]  Sarah Read-Brown,et al.  Electronic health record systems in ophthalmology: impact on clinical documentation. , 2013, Ophthalmology.

[14]  Christel Daniel-Le Bozec,et al.  The REUSE project: EHR as single datasource for biomedical research , 2010, MedInfo.

[15]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[16]  David W. Bates,et al.  Ten key considerations for the successful optimization of large-scale health information technology , 2017, J. Am. Medical Informatics Assoc..

[17]  Jakob Nielsen,et al.  Improving a human-computer dialogue , 1990, CACM.

[18]  Kai Lin,et al.  Application of Information Technology: Evaluation of an Online Platform for Cancer Patient Self-reporting of Chemotherapy Toxicities , 2007, J. Am. Medical Informatics Assoc..

[19]  A Safir Editorial: Computers in ophthalmology. , 1976, Investigative ophthalmology.

[20]  D. Blumenthal,et al.  The "meaningful use" regulation for electronic health records. , 2010, The New England journal of medicine.

[21]  Louise Locock,et al.  Collecting data on patient experience is not enough: they must be used to improve care , 2014, BMJ : British Medical Journal.

[22]  Michael A. Collins,et al.  Using Metadata to Generate Web-Based Electronic Data Capture Forms , 2006, AMIA.

[23]  A. Sheikh,et al.  Health information technology in hospitals: current issues and future trends , 2015, Future Hospital Journal.

[24]  P. Shekelle,et al.  Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care , 2006, Annals of Internal Medicine.