Browsing by Author "Liu, Gilbert C."

Now showing 1 - 8 of 8
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    Data visualization for truth maintenance in clinical decision support systems
    (2015-06-19) Liu, Gilbert C.; Odell, Jere D.; Whipple, Elizabeth C.; Ralston, Rick K.; Carroll, Aaron E.; Downs, Stephen M.
    Background and objectives The goal is to inform proactive initiatives to expand the knowledge base of clinical decision support systems. Design and setting We 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 methods We 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. Results We 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. Conclusions The 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.
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    Enhancing research on a clinical decision support and geographic information system: getting involved as informationists
    (Midwest Chapter, Medical Library Association, 2013-10-07) Whipple, Elizabeth C.; Ralston, Rick K.; Odell, Jere D.; Zimmerman, Carly; Liu, Gilbert C.
    In 2012, the National Library of Medicine (NLM) funded its first ever administrative supplement for informationists. The purpose of these grants is to enhance multidisciplinary basic and clinical research by integrating information specialists (informationists) on research teams in order to improve the capture, organization, and management of biomedical research data. Three informationists at the Indiana University School of Medicine were awarded one of these supplements to work on the Child Health Improvement through Computer Automation (CHICA) system. CHICA is a computer decision support system that interfaces with existing electronic medical record systems (EMRS) and delivers "just in time" patient-relevant guidelines to physicians during the clinical encounter. CHICA-GIS integrates a geographic information system (GIS) with CHICA to refer pediatricians and parents to relevant health services (as needed, for physical activity, dental care, or tutoring) near the patient's neighborhood. The informationists are enhancing the CHICA-GIS system 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. This paper describes the initial process for approaching and collaborating with researchers, writing the grant and getting funded, and progress on the project goals to date.
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    Mapping the rules: conceptual and logical relationships in a system for pediatric clinical decision support
    (2013-10-07) Ralston, Rick K.; Odell, Jere D.; Whipple, Elizabeth C.; Liu, Gilbert C.
    The Child Health Improvement through Computer Automation (CHICA) system uses evidence-based guidelines and information collected in the clinic and stored in an electronic medical record (EMR) to inform physician and patient decision making. CHICA helps physicians to identify and select relevant screenings and also provides personalized, just-in-time information for patients. This system relies on a database of Medical Logic Modules (MLMS) written in the Arden Rules syntax. These MLMs store observations (StorObs) during the clinical encounter which trigger potential screenings and preventive health interventions for discussion with the patient or for follow up at the next visit. This poster shows how informationists worked with the CHICA team to describe the MLMs using standard vocabularies, including Medical Subject Headings (MeSH) and Logical Observation Identifiers Names and Codes (LOINC). After assigning keywords to the database of MLMs, the informationists used visualization tools to generate maps. These maps show how rules are related by logic (shared StorObs) and by concept (shared vocabulary). The CHICA team will use these maps to identify gaps in the clinical decision support database and (if needed) to develop rules which bridge related but currently isolated concepts.
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    The obesity epidemic in children: Latino children are disproportionately affected at younger ages
    (Elsevier, 2015-03) Liu, Gilbert C.; Hannon, Tamara; Qi, Rong; Downs, Stephen M.; Marrero, David G.; Department of Medicine, IU School of Medicine
    Background and objectives National surveillance clearly illustrates that U.S. children are becoming increasingly overweight. However, the timing of the onset of childhood overweight has not been well-described. Patients and methods An accelerated failure time (AFT) model was used to describe the emergence of overweight based on a 12-year collection of height and weight data of over 40,000 children. Race, sex, insurance status and their interactions were specifically examined as predictors of earlier onset of overweight. The outcome of interest was an estimate of the age at which the model predicted that a subgroup would attain a 20% prevalence of overweight. Results The three-way interaction of race, sex, and insurance status was a significant predictor of onset of overweight. The model estimated that the publicly insured Latino male subgroup had the earliest onset of overweight, attaining a prevalence of 20% overweight by 4.3 years of age. The emergence of overweight in Latino subjects was significantly earlier than that for black or white subjects, irrespective of sex or insurance status. Conclusion Regardless of sex or insurance status, overweight emerges at significantly younger ages in Latino children when compared to black and white children. Substantial numbers of Latino male children are predicted to develop overweight at preschool ages. Obesity prevention may need to be directed toward parents or children well before children enter grade-school.
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    The role of informationists in delivering geospatial intelligence to health care professionals
    (National Network of Libraries of Medicine, 2013-10-11) Ralston, Rick K.; Whipple, Elizabeth C.; Odell, Jere D.; Liu, Gilbert C.
    Three informationists at the Indiana University School of Medicine were awarded NLM supplement to work on the Child Health Improvement through Computer Automation (CHICA) system. CHICA is a computer decision support system that interfaces with existing electronic medical record systems (EMRS) and delivers "just in time" patient-relevant guidelines to physicians during the clinical encounter. CHICA-GIS integrates a geographic information system (GIS) with CHICA to refer pediatricians and parents to relevant health services (as needed, for physical activity, dental care, or tutoring) near the patient's neighborhood. The informationists are enhancing the CHICA-GIS system 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. This presentation describes the initial process for approaching and collaborating with researchers, writing the grant and getting funded, and progress on the project goals to date.
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    A Spatial Analysis of Obesogenic Environments for Children
    (2002) Liu, Gilbert C.; Cunningham, Cynthia; Downs, Stephen M.; Marrero, David G.; Fineberg, Naomi
    In this study, we use spatial analysis techniques to explore environmental and social predictors of obesity in children. We constructed a merged database, incorporating clinical data from an electronic medical record system, the Regenstrief Medical Record System (RMRS) and societal & environmental data from a geographical information system, the Social Assets and Vulnerabilities Indicators (SAVI) Project. We used the RMRS to identify cohorts of children that were normal weight, overweight, or obese. The RMRS records were geocoded and merged into the SAVI database. Using the merged databases, we analyzed the relationships between markers of socioeconomic status and obesity outcomes in children. Our preliminary analyses show that markers of low socioeconomic status at the census tract level correlate with both overweight and obese outcomes in our study population. Utilization of geographic information systems (GIS) for the study of health epidemiology is discussed.
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    Using GPS-enabled cell phones to track the travel patterns of adolescents
    (2009-05) Wiehe, Sarah E.; Carroll, Aaron E.; Liu, Gilbert C.; Haberkorn, Kelly L.; Hoch, Shawn C.; Wilson, Jeffrey S.; Fortenberry, J. Dennis
    Background Few tools exist to directly measure the microsocial and physical environments of adolescents in circumstances where participatory observation is not practical or ethical. Yet measuring these environments is important as they are significantly associated with adolescent health-risk. For example, health-related behaviors such as cigarette smoking often occur in specific places where smoking may be relatively surreptitious. Results We assessed the feasibility of using GPS-enabled cell phones to track adolescent travel patterns and gather daily diary data. We enrolled 15 adolescent women from a clinic-based setting and asked them to carry the phones for 1 week. We found that these phones can accurately and reliably track participant locations, as well as record diary information on adolescent behaviors. Participants had variable paths extending beyond their immediate neighborhoods, and denied that GPS-tracking influenced their activity. Conclusion GPS-enabled cell phones offer a feasible and, in many ways, ideal modality of monitoring the location and travel patterns of adolescents. In addition, cell phones allow space- and time-specific interaction, probing, and intervention which significantly extends both research and health promotion beyond a clinical setting. Future studies can employ GPS-enabled cell phones to better understand adolescent environments, how they are associated with health-risk behaviors, and perhaps intervene to change health behavior.
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    When Informationists Get Involved: The CHICA-GIS Project
    (http://escholarship.umassmed.edu/jeslib/vol2/iss1/10/, 2013-05-02) Whipple, Elizabeth C.; Odell, Jere D.; Ralston, Rick K.; Liu, Gilbert C.
    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" patientrelevant 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 (CHICAGIS)”(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.