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Linking medications with their indications is important for clinical care and research. We have recently developed a freely-available, computable medication-indication resource, called MEDI, which links RxNorm medications to indications mapped to ICD9 codes. In this paper, we identified the medications and diagnoses for 1.3 million individuals at Vanderbilt University Medical Center to evaluate the medication coverage of MEDI and then to calculate the prevalence for each indication for each medication. Our results demonstrated MEDI covered 97.3% of medications recorded in medical records. The "high precision subset" of MEDI covered 93.8% of recorded medications. No significant prescription drugs were missed by MEDI. Manual physician review of random patient records for four example medications found that the MEDI covered the observed indications, and confirmed the estimated prevalence of these medications using practice information. Indication prevalence information for each medication, previously unavailable in other public resources, may improve the clinical usability of MEDI. We believe MEDI will be useful for both clinical informatics and to aid in recognition of phenotypes for electronic medical record-based research.
Developing effective methods to enable the practice of personalized medicine is a national priority for translational science. By leveraging modern genotyping technology and health information technologies, prescribing therapies based on genotype becomes an achievable goal. Within this manuscript, we describe the development, implementation, and piloting of a surveillance tool to assure the quality of clinical decision making in the context of new pharmacogenetic information. The surveillance tool allows a quality assurance (QA) team to review significant genotyping results and deliver focused educational interventions to providers. We report on the first eight patients undergoing genotyping to support antiplatelet therapy selection after drug-eluting stent placement. The collected pilot data supports an informatics approach to QA process management, as our tool delivered actionable patient information. It also enabled providers to tailor antiplatelet therapy to individual patients' genotypes. Our expectation is to continue collecting surveillance reports to perform an in-depth analysis of our tool.
PURPOSE - Computerized clinical decision support systems (CDSSs) for intensive insulin therapy (IIT) are increasingly common. However, recent studies question IIT's safety and mortality benefit. Researchers have identified factors influencing IIT performance, but little is known about how workflow affects computer-based IIT. We used ethnographic methods to evaluate IIT CDSS with respect to other clinical information systems and care processes.
METHODS - We conducted direct observation of and unstructured interviews with nurses using IIT CDSS in the surgical and trauma intensive care units at an academic medical center. We observed 49h of intensive care unit workflow including 49 instances of nurses using IIT CDSS embedded in a provider order entry system. Observations focused on the interaction of people, process, and technology. By analyzing qualitative field note data through an inductive approach, we identified barriers and facilitators to IIT CDSS use.
RESULTS - Barriers included (1) workload tradeoffs between computer system use and direct patient care, especially related to electronic nursing documentation, (2) lack of IIT CDSS protocol reminders, (3) inaccurate user interface design assumptions, and (4) potential for error in operating medical devices. Facilitators included (1) nurse trust in IIT CDSS combined with clinical judgment, (2) nurse resilience, and (3) paper serving as an intermediary between patient bedside and IIT CDSS.
CONCLUSION - This analysis revealed sociotechnical interactions affecting IIT CDSS that previous studies have not addressed. These issues may influence protocol performance at other institutions. Findings have implications for IIT CDSS user interface design and alerts, and may contribute to nascent general CDSS theory.
2011 Elsevier Ireland Ltd. All rights reserved.
OBJECTIVE - To evaluate the performance of a system that extracts medication information and administration-related actions from patient short message service (SMS) messages.
DESIGN - Mobile technologies provide a platform for electronic patient-centered medication management. MyMediHealth (MMH) is a medication management system that includes a medication scheduler, a medication administration record, and a reminder engine that sends text messages to cell phones. The object of this work was to extend MMH to allow two-way interaction using mobile phone-based SMS technology. Unprompted text-message communication with patients using natural language could engage patients in their healthcare, but presents unique natural language processing challenges. The authors developed a new functional component of MMH, the Patient-centered Automated SMS Tagging Engine (PASTE). The PASTE web service uses natural language processing methods, custom lexicons, and existing knowledge sources to extract and tag medication information from patient text messages.
MEASUREMENTS - A pilot evaluation of PASTE was completed using 130 medication messages anonymously submitted by 16 volunteers via a website. System output was compared with manually tagged messages.
RESULTS - Verified medication names, medication terms, and action terms reached high F-measures of 91.3%, 94.7%, and 90.4%, respectively. The overall medication name F-measure was 79.8%, and the medication action term F-measure was 90%.
CONCLUSION - Other studies have demonstrated systems that successfully extract medication information from clinical documents using semantic tagging, regular expression-based approaches, or a combination of both approaches. This evaluation demonstrates the feasibility of extracting medication information from patient-generated medication messages.
Diffusion-weighted magnetic resonance imaging data obtained early in the course of therapy can be used to estimate tumor proliferation rates, and the estimated rates can be used to predict tumor cellularity at the conclusion of therapy. Six patients underwent diffusion-weighted magnetic resonance imaging immediately before, after one cycle, and after all cycles of neoadjuvant chemotherapy. Apparent diffusion coefficient values were calculated for each voxel and for a whole tumor region of interest. Proliferation rates were estimated using the apparent diffusion coefficient data from the first two time points and then used with the logistic model of tumor growth to predict cellularity after therapy. The predicted number of tumor cells was then correlated to the corresponding experimental data. Pearson's correlation coefficient for the region of interest analysis yielded 0.95 (P = 0.004), and, after applying a 3 × 3 mean filter to the apparent diffusion coefficient data, the voxel-by-voxel analysis yielded a Pearson correlation coefficient of 0.70 ± 0.10 (P < 0.05).
Copyright © 2011 Wiley Periodicals, Inc.
OBJECTIVE - Alerting systems, a type of clinical decision support, are increasingly prevalent in healthcare, yet few studies have concurrently measured the appropriateness of alerts with provider responses to alerts. Recent reports of suboptimal alert system design and implementation highlight the need for better evaluation to inform future designs. The authors present a comprehensive framework for evaluating the clinical appropriateness of synchronous, interruptive medication safety alerts.
METHODS - Through literature review and iterative testing, metrics were developed that describe successes, justifiable overrides, provider non-adherence, and unintended adverse consequences of clinical decision support alerts. The framework was validated by applying it to a medication alerting system for patients with acute kidney injury (AKI).
RESULTS - Through expert review, the framework assesses each alert episode for appropriateness of the alert display and the necessity and urgency of a clinical response. Primary outcomes of the framework include the false positive alert rate, alert override rate, provider non-adherence rate, and rate of provider response appropriateness. Application of the framework to evaluate an existing AKI medication alerting system provided a more complete understanding of the process outcomes measured in the AKI medication alerting system. The authors confirmed that previous alerts and provider responses were most often appropriate.
CONCLUSION - The new evaluation model offers a potentially effective method for assessing the clinical appropriateness of synchronous interruptive medication alerts prior to evaluating patient outcomes in a comparative trial. More work can determine the generalizability of the framework for use in other settings and other alert types.
BACKGROUND - Although pediatric electronic prescribing systems are increasingly being used in pediatric care, many of these systems lack the clinical decision-support infrastructure needed to calculate a safe and effective rounded medication dose. This infrastructure is required to facilitate tailoring of established dosing guidance while maintaining the medication's therapeutic intent.
OBJECTIVE - The goal of this project was to establish best practices for generating an appropriate medication dose and to create an interoperable rounding knowledge base combining best practices and dose-rounding information.
METHODS - We interviewed 19 pediatric health care and pediatric pharmacy experts and conducted a literature review. After using these data to construct initial rounding tolerances, we used a Delphi process to achieve consensus about the rounding tolerance for each commonly prescribed medication.
RESULTS - Three categories for medication-rounding philosophy emerged from our literature review: (1) medications for which rounding is used judiciously to retain the intended effect; (2) medications that are rounded with attention to potential unintended effects; and (3) medications that are rarely rounded because of the potential for toxicity. We assigned a small subset of medications to a fourth category-inadequate data-for which there was insufficient information to provide rounding recommendations. For all 102 medications, we were able to arrive at a consensus recommendation for rounding a given calculated dose.
CONCLUSIONS - Results of this study provide the pediatric information technology community with a primary set of recommended rounding tolerances for commonly prescribed drugs. The interoperable knowledge base developed here can be integrated with existing and developing electronic prescribing systems, potentially improving prescribing safety and reducing cognitive workload.
BACKGROUND - Adverse drug events are a major safety issue in ambulatory care. Improving medication self-management could reduce these adverse events. Researchers have developed medication applications for tethered personal health records (PHRs), but little has been reported about medication applications for interoperable PHRs.
OBJECTIVE - Our objective was to develop two complementary personal health applications on a common PHR platform: one to assist children with complex health needs (MyMediHealth), and one to assist older adults in care transitions (Colorado Care Tablet).
METHODS - The applications were developed using a user-centered design approach. The two applications shared a common PHR platform based on a service-oriented architecture. MyMediHealth employed Web and mobile phone user interfaces. Colorado Care Tablet employed a Web interface customized for a tablet PC.
RESULTS - We created complementary medication management applications tailored to the needs of distinctly different user groups using common components. Challenges were addressed in multiple areas, including how to encode medication identities, how to incorporate knowledge bases for medication images and consumer health information, how to include supplementary dosing information, how to simplify user interfaces for older adults, and how to support mobile devices for children.
CONCLUSIONS - These prototypes demonstrate the utility of abstracting PHR data and services (the PHR platform) from applications that can be tailored to meet the needs of diverse patients. Based on the challenges we faced, we provide recommendations on the structure of publicly available knowledge resources and the use of mobile messaging systems for PHR applications.
OBJECTIVE - To determine characteristics and effects of nurse dosing over-rides of a clinical decision support system (CDSS) for intensive insulin therapy (IIT) in critical care units.
DESIGN - Retrospective analysis of patient database records and ethnographic study of nurses using IIT CDSS.
MEASUREMENTS - The authors determined the frequency, direction-greater than recommended (GTR) and less than recommended (LTR)- and magnitude of over-rides, and then compared recommended and over-ride doses' blood glucose (BG) variability and insulin resistance, two measures of IIT CDSS associated with mortality. The authors hypothesized that rates of hypoglycemia and hyperglycemia would be greater for recommended than over-ride doses. Finally, the authors observed and interviewed nurse users.
RESULTS - 5.1% (9075) of 179,452 IIT CDSS doses were over-rides. 83.4% of over-ride doses were LTR, and 45.5% of these were ≥ 50% lower than recommended. In contrast, 78.9% of GTR doses were ≤ 25% higher than recommended. When recommended doses were administered, the rate of hypoglycemia was higher than the rate for GTR (p = 0.257) and LTR (p = 0.033) doses. When recommended doses were administered, the rate of hyperglycemia was lower than the rate for GTR (p = 0.003) and LTR (p < 0.001) doses. Estimates of patients' insulin requirements were higher for LTR doses than recommended and GTR doses. Nurses reported trusting IIT CDSS overall but appeared concerned about recommendations when administering LTR doses.
CONCLUSION - When over-riding IIT CDSS recommendations, nurses overwhelmingly administered LTR doses, which emphasized prevention of hypoglycemia but interfered with hyperglycemia control, especially when BG was >150 mg/dl. Nurses appeared to consider the amount of a recommended insulin dose, not a patient's trend of insulin resistance, when administering LTR doses overall. Over-rides affected IIT CDSS protocol performance.
A number of methods have been developed to assist surgeons at various stages of deep brain stimulation (DBS) therapy. These include construction of anatomical atlases, functional databases, and electrophysiological atlases and maps. But, a complete system that can be integrated into the clinical workflow has not been developed. In this paper we present a system designed to assist physicians in pre-operative target planning, intra-operative target refinement and implantation, and post-operative DBS lead programming. The purpose of this system is to centralize the data acquired a the various stages of the procedure, reduce the amount of time needed at each stage of the therapy, and maximize the efficiency of the entire process. The system consists of a central repository (CranialVault), of a suite of software modules called CRAnialVault Explorer (CRAVE) that permit data entry and data visualization at each stage of the therapy, and of a series of algorithms that permit the automatic processing of the data. The central repository contains image data for more than 400 patients with the related pre-operative plans and position of the final implants and about 10,550 electrophysiological data points (micro-electrode recordings or responses to stimulations) recorded from 222 of these patients. The system has reached the stage of a clinical prototype that is being evaluated clinically at our institution. A preliminary quantitative validation of the planning component of the system performed on 80 patients who underwent the procedure between January 2009 and December 2009 shows that the system provides both timely and valuable information.
Copyright Â© 2010 Elsevier B.V. All rights reserved.