Browsing by Subject "clinical decision support"

Now showing 1 - 6 of 6
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    Examining the Relationship between Clinical Decision Support and Performance Measurement
    (2009-11) Haggstrom, David A.; Militello, Laura G.; Arbuckle, Nicole; Flanagan, Mindy; Doebbeling, Bradley N.
    In concept and practice, clinical decision support (CDS) and performance measurement represent distinct approaches to organizational change, yet these two organizational processes are interrelated. We set out to better understand how the relationship between the two is perceived, as well as how they jointly influence clinical practice. To understand the use of CDS at benchmark institutions, we conducted semistructured interviews with key managers, information technology personnel, and clinical leaders during a qualitative field study. Improved performance was frequently cited as a rationale for the use of clinical reminders. Pay-for-performance efforts also appeared to provide motivation for the use of clinical reminders. Shared performance measures were associated with shared clinical reminders. The close link between clinical reminders and performance measurement causes these tools to have many of the same implementation challenges.
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    Integrating Clinical Decision Support into Workflow
    (2011) Doebbeling, Bradley N.; Saleem, Jason; Haggstrom, David; Militello, Laura; Flanagan, Mindy; Arbuckle, Nicole; Kiess, Chris; Hoke, Shawn; Dexter, Paul; Linder, Jeff; Sarbah, Steedman; Burgo, Lucille
    Purpose: The aims were to (1) identify barriers and facilitators related to integration of clinical decision support (CDS) into workflow and (2) develop and test CDS design alternatives. Scope: To better understand CDS integration, we studied its use in practice, focusing on CDS for colorectal cancer (CRC) screening and followup. Phase 1 involved outpatient clinics of four different systems—120 clinic staff and providers and 118 patients were observed. In Phase 2, prototyped design enhancements to the Veterans Administration’s CRC screening reminder were compared against its current reminder in a simulation experiment. Twelve providers participated. Methods: Phase 1 was a qualitative project, using key informant interviews, direct observation, opportunistic interviews, and focus groups. All data were analyzed using a coding template, based on the sociotechnical systems theory, which was modified as coding proceeded and themes emerged. Phase 2 consisted of rapid prototyping of CDS design alternatives based on Phase 1 findings and a simulation experiment to test these design changes in a within-subject comparison. Results: Very different CDS types existed across sites, yet there are common barriers: (1) lack of coordination of “outside” results and between primary and specialty care; (2) suboptimal data organization and presentation; (3) needed provider and patient education; (4) needed interface flexibility; (5) needed technological enhancements; (6) unclear role assignments; (7) organizational issues; and (8) disconnect with quality reporting. Design enhancements positively impacted usability and workflow integration but not workload. Conclusions: Effective CDS design and integration requires: (1) organizational and workflow integration; (2) integrating outside results; (3) improving data organization and presentation in a flexible interface; and (4) providing just-in time education, cognitive support, and quality reporting.
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    Lessons Learned When Introducing Pharmacogenomic Panel Testing into Clinical Practice
    (Elsevier, 2017-01-01) Rosenman, Marc B.; Decker, Brian; Levy, Kenneth D.; Holmes, Ann M.; Pratt, Victoria M.; Eadon, Michael T.; Medicine, School of Medicine
    Objectives: Implementing new programs to support precision medicine in clinical settings is a complex endeavor. We describe challenges and potential solutions based on the Indiana GENomics Implementation: an Opportunity for the Underserved (INGenious) program at Eskenazi Health-one of six sites supported by the Implementing GeNomics In pracTicE network grant of the National Institutes of Health/National Human Genome Research Institute. INGenious is an implementation of a panel of genomic tests. Methods: We conducted a descriptive case study of the implementation of this pharmacogenomics program, which has a wide scope (14 genes, 27 medications) and a diverse population (patients who often have multiple chronic illnesses, in a large urban safety-net hospital and its outpatient clinics). Challenges: We placed the clinical pharmacogenomics implementation challenges into six categories: patient education and engagement in care decision making; clinician education and changes in standards of care; integration of technology into electronic health record systems; translational and implementation sciences in real-world clinical environments; regulatory and reimbursement considerations, and challenges in measuring outcomes. A cross-cutting theme was the need for careful attention to workflow. Our clinical setting, a safety-net health care system, presented some distinctive challenges. Patients often had multiple chronic illnesses and sometimes were taking more than one pharmacogenomics-relevant medication. Reaching patients for recruitment or follow-up was another challenge. Conclusions: New, large-scale endeavors in health care are challenging. A description of the challenges that we encountered and the approaches that we adopted to address them may provide insights for those who implement and study innovations in other health care systems.
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    Patient-tailored prioritization for a pediatric care decision support system through machine learning
    (Oxford University Press, 2013-12-01) Klann, Jeffrey G.; Anand, Vibha; Downs, Stephen M.; Department of Pediatrics, IU School of Medicine
    Objective Over 8 years, we have developed an innovative computer decision support system that improves appropriate delivery of pediatric screening and care. This system employs a guidelines evaluation engine using data from the electronic health record (EHR) and input from patients and caregivers. Because guideline recommendations typically exceed the scope of one visit, the engine uses a static prioritization scheme to select recommendations. Here we extend an earlier idea to create patient-tailored prioritization. Materials and methods We used Bayesian structure learning to build networks of association among previously collected data from our decision support system. Using area under the receiver-operating characteristic curve (AUC) as a measure of discriminability (a sine qua non for expected value calculations needed for prioritization), we performed a structural analysis of variables with high AUC on a test set. Our source data included 177 variables for 29 402 patients. Results The method produced a network model containing 78 screening questions and anticipatory guidance (107 variables total). Average AUC was 0.65, which is sufficient for prioritization depending on factors such as population prevalence. Structure analysis of seven highly predictive variables reveals both face-validity (related nodes are connected) and non-intuitive relationships. Discussion We demonstrate the ability of a Bayesian structure learning method to ‘phenotype the population’ seen in our primary care pediatric clinics. The resulting network can be used to produce patient-tailored posterior probabilities that can be used to prioritize content based on the patient's current circumstances. Conclusions This study demonstrates the feasibility of EHR-driven population phenotyping for patient-tailored prioritization of pediatric preventive care services.
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    Pediatricians’ Responses to Printed Clinical Reminders: Does Highlighting Prompts Improve Responsiveness?
    (Elsevier, 2015-03) Hendrix, Kristin S.; Downs, Stephen M.; Carroll, Aaron E.; Department of Pediatrics, IU School of Medicine
    Objective Physicians typically respond to roughly half of the clinical decision support prompts they receive. This study was designed to test the hypothesis that selectively highlighting prompts in yellow would improve physicians' responsiveness. Methods We conducted a randomized controlled trial using the Child Health Improvement Through Computer Automation clinical decision support system in 4 urban primary care pediatric clinics. Half of a set of electronic prompts of interest was highlighted in yellow when presented to physicians in 2 clinics. The other half of the prompts was highlighted when presented to physicians in the other 2 clinics. Analyses compared physician responsiveness to the 2 randomized sets of prompts: highlighted versus not highlighted. Additionally, several prompts deemed high priority were highlighted during the entire study period in all clinics. Physician response rates to the high-priority highlighted prompts were compared to response rates for those prompts from the year before the study period, when they were not highlighted. Results Physicians did not respond to prompts that were highlighted at higher rates than prompts that were not highlighted (62% and 61%, respectively; odds ratio 1.056, P = .259, NS). Similarly, physicians were no more likely to respond to high-priority prompts that were highlighted compared to the year before, when the prompts were not highlighted (59% and 59%, respectively, χ2 = 0.067, P = .796, NS). Conclusions Highlighting reminder prompts did not increase physicians' responsiveness. We provide possible explanations why highlighting did not improve responsiveness and offer alternative strategies to increasing physician responsiveness to prompts.
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    A Service Oriented Architecture Approach to Achieve Interoperability between Immunization Information Systems in Iran
    (2014) Hosseini, Masoud; Ahmadi, Maryam; Dixon, Brian E.; Department of Biohealth Informatics, School of Informatics and Computing
    Clinical decision support (CDS) systems can support vaccine forecasting and immunization reminders; however, immunization decision-making requires data from fragmented, independent systems. Interoperability and accurate data exchange between immunization information systems (IIS) is an essential factor to utilize Immunization CDS systems. Service oriented architecture (SOA) and Health Level 7 (HL7) are dominant standards for web-based exchange of clinical information. We implemented a system based on SOA and HL7 v3 to support immunization CDS in Iran. We evaluated system performance by exchanging 1500 immunization records for roughly 400 infants between two IISs. System turnaround time is less than a minute for synchronous operation calls and the retrieved immunization history of infants were always identical in different systems. CDS generated reports were accordant to immunization guidelines and the calculations for next visit times were accurate. Interoperability is rare or nonexistent between IIS. Since inter-state data exchange is rare in United States, this approach could be a good prototype to achieve interoperability of immunization information.