Landis Lewis Z, Mello-Thoms C, Gadabu OJ, Gillespie EM, Douglas GP, Crowley RS. The Feasibility of Automating Audit and Feedback for ART Guideline Adherence in Malawi. Accepted to J Am Med Inform Assoc, (JAMIA) April 19, 2011.
ABSTRACT Objective: To determine the feasibility of using electronic medical record (EMR) data to provide audit and feedback of anti-retroviral therapy (ART) clinical guideline adherence to healthcare workers (HCWs) in Malawi. Materials and methods: We evaluated recommendations from Malawi’s ART guidelines using GuideLine Implementability Appraisal criteria. Recommendations that passed selected criteria were converted into ratio-based performance measures. We queried representative EMR data to determine the feasibility of generating feedback for each performance measure, summed clinical encounters representing each performance measure’s denominator, and then measured the distribution of encounter frequency for individual HCWs across nurse and clinical officer groups. Results: We analyzed 423 831 encounters in the EMR data and generated automated feedback for 21 recommendations (12%) from Malawi’s ART guidelines. We identified 11 nurse recommendations and eight clinical officer recommendations. Individual nurses and clinical officers had an average of 45 and 59 encounters per month, per recommendation, respectively. Another 37 recommendations (21%) would support audit and feedback if additional routine EMR data are captured and temporal constraints are modeled. Discussion: It appears feasible to implement automated guideline adherence feedback that could potentially improve HCW performance and supervision. Feedback reports may support workplace learning by increasing HCWs’ opportunities to reflect on their performance. Conclusion: A moderate number of recommendations from Malawi’s ART guidelines can be used to generate automated guideline adherence feedback using existing EMR data. Further study is needed to determine the receptivity of HCWs to peer comparison feedback and barriers to implementation of automated audit and feedback in low-resource settings.
Boyd LB, Hunike-Smith SP, Stafford GA, Freund ET, Elhman M, Chandran U, Dennis R, Fernandez AT, Goldstein S, Steffen D, Tycko B, Klemm JD. The caBIG(R) Life Science Business Architecture Model. Bioinformatics. 2011 May 15;27(10):1429:35. doi: 10.1093/bioinformatics/btr141. Epub 2011 Mar 29. PMID: 21450709. PMCID: PMC3087952.
Motivation: Business Architecture Models (BAMs) describe what a business does, who performs the activities, where and when activities are performed, how activities are accomplished, and which data are present. The purpose of a BAM is to provide a common resource for understanding business functions and requirements and to guide software development. The cancer Biomedical Informatics Grid (caBIG®) Life Science BAM (LS BAM) provides a shared understanding of the vocabulary, goals and processes that are common in the business of LS research. Results: Results: LS BAM 1.1 includes 90 goals and 61 people and groups within Use Case and Activity Unified Modeling Language (UML) Diagrams. Here we report on the model's current release, LS BAM 1.1, its utility and usage, and plans for future use and continuing development for future releases.
The FaceBase Consortium: A Comprehensive Program to Facilitate Craniofacial Research. Harry Hochheiser, Bruce J. Aronow, Kristin Artinger, Terri H.Beaty, James F. Brinkley, Yang Chai, David Clouthier, Michael L. Cunningham, Michael Dixon, Leah Rae Donahue, Scott E. Fraser, Junichi Iwata, Mary L. Marazita11, Jeffrey C. Murray, Stephen Murray, John Postlethwait, Steven Potter, Linda Shapiro, Richard Spritz, Axel Visel, Seth M. Weinberg and Paul A. Trainor*, for the FaceBase Consortium.
Translational bioinformatics is an emerging area in informatics focused on “the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data in particular, into proactive, predictive, preventive, and participatory health” (from the American Medical Informatics Association Strategic Plan http://www.amia.org/inside/stratplan/). Within DBMI, Xinghua Lu, MD, PhD, Vanathi Gopalakr
Clinical care is complex and often fast paced. Preventable medical errors can and do occur, as has been well documented in recent years. Clinical guidelines and rule-based alerts provide clinical decision support that is intended to reduce medical errors. These methods are driven by expert knowledge. As such, they tend to focus on high impact areas in which medical errors are either prevalent, serious, or both. However, the coverage of such methods is relatively narrow.
Clinical care involves making many predictions under uncertainty, including risk assessment, diagnosis, prognosis and therapeutic management. The better those predictions can be made, the better clinical care is likely to be. The increasing availability and richness of electronic health records (EHRs) are increasing the opportunities for developing and deploying computer-based clinical prediction methods. Such methods can serve as key components of computer-based decision support systems. The data in EHRs can be used to construct prediction models using machine learning methods.
Gerald Douglas, PhD is leading research efforts that will contribute to the elimination of health disparities, improve health care quality, encourage the adoption of personal wellness strategies, and provide support for the development and advancement of underserved communities. Dr. Douglas implemented several innovative approaches to using technology in health care, including the use of a low-power, robust and inexpensive touch screen workstation for clinicians at the point of care in Malawi, Africa.
Feasibility of Using a Telemedicine Medication Delivery Unit for Older Adults that Require Medication Assistance During Transition from Hospital to Home
Care transition interventions have been successful in reducing medication-related problems and associated rehospitalization primarily by focusing on medication reconciliation conducted by trained healthcare professionals. The goal of this project is to assess the feasibility of using a telemedicine medication delivery unit rather than trained healthcare professionals for frail older adults that require medication assistance in their home immediately following hospitalization.
The Center for Informatics in Oral Health Translational Research (CIOHTR) at the School of Dental Medicine, University of Pittsburgh, is affiliated with DBMI. Dr. Heiko Spallek, Executive Director, and Dr. Tanja Bekhuis, Director of Oral Health Translational Methods, are members of the DBMI Training Program Faculty. They encourage multidisciplinary collaboration among trainees and faculty interested in dental informatics and translational research.