Since 2002, library faculty at the Indiana University School of Medicine have taught third-year medical students how to retrieve the best evidence from MEDLINE to address their clinical questions. In preparation for their Neurology, Medicine, and Psychiatry clerkships, students attended a review of evidence-based medicine principles and techniques for searching the literature. The session was team-taught by two faculty members, one from the Internal Medicine department and the other from the Library. The librarian reviewed important MEDLINE principles for constructing a good subject search and applying appropriate evidence-based filters. During the clerkships, students were asked to generate clinical questions arising from their patient encounters, searched MEDLINE for the best evidence, critiqued the results, and then applied them back to their patients' care. Library faculty provided individualized feedback on the student searches. A follow-up session two months later reinforced MEDLINE principles, used student searches as examples, and extended the discussion to other evidence-based, point-of-care resources. To add to the interactivity of the follow-up sessions, librarians used an audience response system to measure students' understanding of literature retrieval techniques and to gauge student preferences for information seeking on clinical topics. Overall, the sessions have been well-received by the students.

No abstract

The Oregon Medicaid program legislatively separates the administration of physical health and mental health services, even though behavioral and physical health conditions significantly impact each other. To overcome this barrier and enhance integrated care, CareOregon, a large Medicaid only health plan partnered with two of its largest provider groups to pilot two different models of integration. In one, an "ownership" model, behavioral health specialists were employed by Federally Qualified Health Center primary care clinics and functioned in a common care model with other providers. In the other, a "loaned" model, behavioral specialists were placed in primary care clinics by community mental health centers and continued to function in a specialty mental health model. The qualitative effects of these two models are discussed.

Background and objectivesThe goal is to inform proactive initiatives to expand the knowledge base of clinical decision support systems.Design and settingWe describe an initiative in which research informationists and health services researchers employ visualization tools to map logic models for clinical decision support within an electronic health record.Materials and methodsWe mapped relationships using software for social network analysis: NodeXL and CMAP. We defined relationships by shared observations, such as two Arden rules within medical logic modules that consider the same clinical observation, or by the presence of common keywords that were used to label rules according to standardized vocabularies.ResultsWe studied the Child Health Improvement through Computer Automation (CHICA) system, an electronic medical record that contains 170 unique variables representing discrete clinical observations. These variables were used in 300 medical logic modules (MLM's) that prompted health care providers to deliver preventive counseling or otherwise served as clinical decision support. Using data visualization tools, we generated maps that illustrate connections, or lack thereof, between clinical topics within CHICA's MLMs.ConclusionsThe development of such maps may allow multiple disciplines commonly interacting over EMR platforms, and various perspectives (clinicians, programmers, informationists) to work more effectively as teams to refine the EMR by programming logic routines to address co-morbidities or other instances where domains of medical knowledge should be connected.

Child Health Improvement through Computer Automation (CHICA) is a computer decision support system (CDSS) that interfaces with existing electronic medical record systems (EMRS) and delivers “just-in-time” patient-relevant guidelines to physicians during the clinical encounter and accurately captures structured data from all who interact with the system. “Delivering Geospatial Intelligence to Health Care Professionals (CHICA-GIS)” (1R01LM010923-01) expands the medical application of Geographic Information Systems (GIS) by integrating a geographic information system with CHICA. To provide knowledge management support for CHICA-GIS, three informationists at the Indiana University School of Medicine were awarded a supplement from the National Library Medicine. The informationists will enhance CHICA-GIS by: improving the accuracy and accessibility of information, managing and mapping the knowledge which undergirds the CHICA-GIS decision support tool, supporting community engagement and consumer health information outreach, and facilitating the dissemination of new CHICA-GIS research results and services.