Managing information for safe care, especially in the new health IT environment, continues to be cognitively challenging work for care providers. Adequate cognitive support to providers when building health IT systems can increase provider effectiveness in their work by reducing workarounds and medical errors. To model information support, cognitive engineers traditionally have modeled the task-technology-people triad. But, preliminary work from the investigator suggests that low-level information really binds care tasks and how providers perform their tasks with technologies. The researcher has piloted an information-based cognitive work design technique for modeling low-level healthcare information providers create, use and share in their cognitive work. The technique, when extended to medical error modeling, will improve patient safety. However, for the extension to succeed, the researcher needs to enhance her skills in health informatics, and medical error modeling in addition to acquiring a comprehensive understanding of health outcomes modeling. This Pathway to Independence (K99/R00) grant application describes a training and career development plan that will allow the candidate, a Post Doctoral Research Fellow in the Quality and Safety Research Group at Johns Hopkins School of Medicine to achieve these objectives. The training component will be carried out under the mentorship of Dr. H. Lehmann (JHU SOM, Health Informatics). Drs. Peter Pronovost (JHU SOM, Patient Safety), Ayse Gurses (JHU SOM, Patient Safety) and Ann Bisantz (SUNY, Buffalo, Cognitive Systems Engineering) will provide additional mentoring in their areas of expertise. The long-term goal of this Pathway to Independence (K99/R00) project is to design health IT systems for provider work based on low-level information they create and use for care. During the award period, research will be focused on the following specific aims: (1) Map provider process workflows corresponding to patient state changes; (2) Identify low-level information attributes providers create and use in care processes, and relate them to potential medical errors at each major care process step; and (3) demonstrate utility of low-level information attributes as design bases for health IT support systems.
(Public Health Relevance) Project aims are to understand and model how health care providers create, use and transform health information in their work. When health IT support systems are designed based on information providers create or use in their work, workarounds will be avoided, so medical errors from poor health IT support systems can be reduced.
