Data visualization speeds review of potential adverse drug events in patients on multiple medications

ABiMed: An intelligent and visual clinical decision support system for medication reviews and polypharmacy management

BACKGROUND

Polypharmacy can be both a public health and an economic issue. Medication reviews are structured interviews of the patient by the pharmacist, aiming at optimizing the drug treatment and deprescribing potentially inappropriate medications. However, they remain difficult to perform and time-consuming. Several clinical decision support systems were developed for helping clinicians to reduce inappropriate polypharmacy. However, most were limited to the implementation of clinical practice guidelines. In this work, our objective is to design an innovative clinical decision support system for medication reviews and polypharmacy management, named ABiMed.

METHODS

ABiMed associates several approaches: guidelines implementation, but also the automatic extraction of patient data from the GP's electronic health record and its transfer to the pharmacist, and the visual presentation of contextualized drug knowledge using visual analytics. We performed an ergonomic assessment and qualitative evaluations involving pharmacists and GPs during focus groups and workshops.

RESULTS

We describe the proposed architecture, which allows a collaborative multi-user usage. We present the various screens of ABiMed for entering or verifying patient data, for accessing drug knowledge (posology, adverse effects, interactions), for viewing STOPP/START rules and for suggesting modification to the treatment. Qualitative evaluations showed that health professionals were highly interested in our approach, associating the automatic guidelines execution with the visual presentation of drug knowledge.

CONCLUSIONS

The association of guidelines implementation with visual presentation of knowledge is a promising approach for managing polypharmacy. Future works will focus on the improvement and the evaluation of ABiMed.

Oral Implications of Polypharmacy in Older Adults

Over the next several decades, rates of aged populations will increase rapidly. These populations are susceptible to multimorbidities and polypharmacy (concurrently, prescribed 5 or more medications). Many medications have side effects that manifest orally. Therefore, it essential to possess current pharmacologic knowledge to diagnose and treat oral implications of commonly prescribed medications. This article details common medication-induced oral lesions and patient assessment of risk factors for polypharmacy and provides a template to integrate medication reconciliation into dental clinical practice.

Comparing the Value of Data Visualization Methods for Communicating Harms in Clinical Trials

In clinical trials, harms (i.e., adverse events) are often reported by simply counting the number of people who experienced each event. Reporting only frequencies ignores other dimensions of the data that are important for stakeholders, including severity, seriousness, rate (recurrence), timing, and groups of related harms. Additionally, application of selection criteria to harms prevents most from being reported. Visualization of data could improve communication of multidimensional data. We replicated and compared the characteristics of 6 different approaches for visualizing harms: dot plot, stacked bar chart, volcano plot, heat map, treemap, and tendril plot. We considered binary events using individual participant data from a randomized trial of gabapentin for neuropathic pain. We assessed their value using a heuristic approach and a group of content experts. We produced all figures using R and share the open-source code on GitHub. Most original visualizations propose presenting individual harms (e.g., dizziness, somnolence) alone or alongside higher level (e.g., by body systems) summaries of harms, although they could be applied at either level. Visualizations can present different dimensions of all harms observed in trials. Except for the tendril plot, all other plots do not require individual participant data. The dot plot and volcano plot are favored as visualization approaches to present an overall summary of harms data. Our value assessment found the dot plot and volcano plot were favored by content experts. Using visualizations to report harms could improve communication. Trialists can use our provided code to easily implement these approaches.

Decision-support systems for managing polypharmacy in the elderly: A scoping review

Polypharmacy, the consuming of more than five drugs, is a public health problem. It can lead to many interactions and adverse drug reactions and is very expensive. Therapeutic guidelines for managing polypharmacy in the elderly have been issued, but are highly complex, limiting their use. Decision-support systems have therefore been developed to automate the execution of these guidelines, or to provide information about drugs adapted to the context of polypharmacy. These systems differ widely in terms of their technical design, knowledge sources and evaluation methods. We present here a scoping review of electronic systems for supporting the management, by healthcare providers, of polypharmacy in elderly patients. Most existing reviews have focused mainly on evaluation results, whereas the present review also describes the technical design of these systems and the methodologies for developing and evaluating them. A systematic bibliographic search identified 19 systems differing considerably in terms of their technical design (rule-based systems, documentary approach, mixed); outputs (textual report, alerts and/or visual approaches); and evaluations (impact on clinical practices, impact on patient outcomes, efficiency and/or user satisfaction). The evaluations performed are minimal (among all the systems identified, only one system has been evaluated according to all the criteria mentioned above) and no machine learning systems and/or conflict management systems were retrieved. This review highlights the need to develop new methodologies, combining various approaches for decision support system in polypharmacy.

Harms in Systematic Reviews Paper 1: An introduction to research on harms

OBJECTIVE

Most systematic reviews of interventions focus on potential benefits. Common methods and assumptions that are appropriate for assessing benefits can be inappropriate for harms. This paper provides a primer on researching harms, particularly in systematic reviews.

STUDY DESIGN AND SETTING

Commentary describing challenges with assessing harm.

RESULTS

Investigators should be familiar with various terminologies used to describe, classify, and group harms. Published reports of clinical trials include limited information about harms, so systematic reviewers should not depend on these studies and journal articles to reach conclusions about harms. Visualizations might improve communication of multiple dimensions of harms such as severity, relatedness, and timing.

CONCLUSION

The terminology, classification, detection, collection, and reporting of harms create unique challenges that take time, expertise, and resources to navigate in both primary studies and evidence syntheses. Systematic reviewers might reach incorrect conclusions if they focus on evidence about harms found in published reports of randomized trials of a particular health problem. Systematic reviews could be improved through better identification and reporting of harms in primary studies and through better training and uptake of appropriate methods for synthesizing evidence about harms.

A Web Interface for Antibiotic Prescription Recommendations in Primary Care: User-Centered Design Approach

BACKGROUND

Antibiotic misuse is a serious public health problem worldwide. National health authorities release clinical practice guidelines (CPGs) to guide general practitioners (GPs) in their choice of antibiotics. However, despite the large-scale dissemination of CPGs, GPs continue to prescribe antibiotics that are not recommended as first-line treatments. This nonadherence to recommendations may be due to GPs misunderstanding the CPGs. A web interface displaying antibiotic prescription recommendations and their justifications could help to improve the comprehensibility and readability of CPGs, thereby increasing the adoption of recommendations regarding antibiotic treatment.

OBJECTIVE

This study aims to design and evaluate a web interface for antibiotic prescription displaying both the recommended antibiotics and their justifications in the form of antibiotic properties.

METHODS

A web interface was designed according to the same principles as e-commerce interfaces and was assessed by 117 GPs. These GPs were asked to answer 17 questions relating to the usefulness, user-friendliness, and comprehensibility and readability of the interface, and their satisfaction with it. Responses were recorded on a 4-point Likert scale (ranging from "absolutely disagree" to "absolutely agree"). At the end of the evaluation, the GPs were allowed to provide optional, additional free comments.

RESULTS

The antibiotic prescription web interface consists of three main sections: a clinical summary section, a filter section, and a recommended antibiotics section. The majority of GPs appreciated the clinical summary (90/117, 76.9%) and filter (98/117, 83.8%) sections, whereas 48.7% (57/117) of them reported difficulty reading some of the icons in the recommended antibiotics section. Overall, 82.9% (97/117) of GPs found the display of drug properties useful, and 65.8% (77/117) reported that the web interface improved their understanding of CPG recommendations.

CONCLUSIONS

The web interface displaying antibiotic recommendations and their properties can help doctors understand the rationale underlying CPG recommendations regarding antibiotic treatment, but further improvements are required before its implementation into a clinical decision support system.

Oral Implications of Polypharmacy in Older Adults

Over the next several decades, rates of aged populations will increase rapidly. These populations are susceptible to multimorbidities and polypharmacy (concurrently, prescribed 5 or more medications). Many medications have side effects that manifest orally. Therefore, it essential to possess current pharmacologic knowledge to diagnose and treat oral implications of commonly prescribed medications. This article details common medication-induced oral lesions and patient assessment of risk factors for polypharmacy and provides a template to integrate medication reconciliation into dental clinical practice.

Big data analysis techniques to address polypharmacy in patients - a scoping review

BACKGROUND

Polypharmacy is a key challenge in healthcare especially in older and multimorbid patients. The use of multiple medications increases the potential for drug interactions and for prescription of potentially inappropriate medications. eHealth solutions are increasingly recommended in healthcare, with big data analysis techniques as a major component. In the following we use the term analysis of big data as referring to the computational analysis of large data sets to find patterns, trends, and associations in large data sets collected from a wide range of sources in contrast to using classical statistics programs. It is hypothesized that big data analysis is able to reveal patterns in patient data that would not be identifiable using conventional methods of data analysis. The aim of this review was to evaluate whether there are existing big data analysis techniques that can help to identify patients consuming multiple drugs and to assist in the reduction of polypharmacy in patients.

METHODS

A computerized search was conducted in February 2019 and updated in May 2020, using the PubMed, Web of Science and Cochrane Library databases. The search strategy was defined by the principles of a systematic search, using the PICO scheme. All studies evaluating big data analytics about patients consuming multiple drugs were considered. Two researchers assessed all search results independently to identify eligible studies. The data was then extracted into standardized tables.

RESULTS

A total of 327 studies were identified through the database search. After title and abstract screening, 302 items were removed. Only three studies were identified as addressing big data analysis techniques in patients with polypharmacy. One study extracted antipsychotic polypharmacy data, the second introduced a decision support system to evaluate side-effects in patients with polypharmacy and the third evaluated a decision support system to identify polypharmacy-related problems in individuals.

CONCLUSIONS

There are few studies to date which have used big data analysis techniques for identification and management of polypharmacy. There may be a need to further explore interdisciplinary collaboration between computer scientists and healthcare professionals, to develop and evaluate big data analysis techniques that can be implemented to manage polypharmacy.

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.

Information visualizations of symptom information for patients and providers: a systematic review

Objective

To systematically synthesize the literature on information visualizations of symptoms included as National Institute of Nursing Research common data elements and designed for use by patients and/or healthcare providers.

Methods

We searched CINAHL, Engineering Village, PsycINFO, PubMed, ACM Digital Library, and IEEE Explore Digital Library to identify peer-reviewed studies published between 2007 and 2017. We evaluated the studies using the Mixed Methods Appraisal Tool (MMAT) and a visualization quality score, and organized evaluation findings according to the Health Information Technology Usability Evaluation Model.

Results

Eighteen studies met inclusion criteria. Ten of these addressed all MMAT items; 13 addressed all visualization quality items. Symptom visualizations focused on pain, fatigue, and sleep and were represented as graphs (n = 14), icons (n = 4), and virtual body maps (n = 2). Studies evaluated perceived ease of use (n = 13), perceived usefulness (n = 12), efficiency (n = 9), effectiveness (n = 5), preference (n = 6), and intent to use (n = 3). Few studies reported race/ethnicity or education level.

Conclusion

The small number of studies for each type of information visualization limit generalizable conclusions about optimal visualization approaches. User-centered participatory approaches for information visualization design and more sophisticated evaluation designs are needed to assess which visualization elements work best for which populations in which contexts.

Designing a medication timeline for patients and physicians

Objective

Most electronic health records display historical medication information only in a data table or clinician notes. We designed a medication timeline visualization intended to improve ease of use, speed, and accuracy in the ambulatory care of chronic disease.

Materials and Methods

We identified information needs for understanding a patient medication history, then applied human factors and interaction design principles to support that process. After research and analysis of existing medication lists and timelines to guide initial requirements, we hosted design workshops with multidisciplinary stakeholders to expand on our initial concepts. Subsequent core team meetings used an iterative user-centered design approach to refine our prototype. Finally, a small pilot evaluation of the design was conducted with practicing physicians.

Results

We propose an open-source online prototype that incorporates user feedback from initial design workshops, and broad multidisciplinary audience feedback. We describe the applicable design principles associated with each of the prototype's key features. A pilot evaluation of the design showed improved physician performance in 5 common medication-related tasks, compared to tabular presentation of the same information.

Discussion

There is industry interest in developing medication timelines based on the example prototype concepts. An open, standards-based technology platform could enable developers to create a medication timeline that could be deployable across any compatible health IT application.

Conclusion

The design goal was to improve physician understanding of a patient's complex medication history, using a medication timeline visualization. Such a design could reduce temporal and cognitive load on physicians for improved and safer care.

Measuring Practicing Clinicians' Information Literacy. An Exploratory Analysis in the Context of Panel Management

BACKGROUND

As healthcare moves towards technology-driven population health management, clinicians must adopt complex digital platforms to access health information and document care.

OBJECTIVES

This study explored information literacy, a set of skills required to effectively navigate population health information systems, among primary care providers in one Veterans' Affairs (VA) medical center.

METHODS

Information literacy was assessed during an 8-month randomized trial that tested a population health (panel) management intervention. Providers were asked about their use and comfort with two VA digital tools for panel management at baseline, 16 weeks, and post-intervention. An 8-item scale (range 0-40) was used to measure information literacy (Cronbach's α=0.84). Scores between study arms and provider types were compared using paired t-tests and ANOVAs. Associations between self-reported digital tool use and information literacy were measured via Pearson's correlations.

RESULTS

Providers showed moderate levels of information literacy (M= 27.4, SD 6.5). There were no significant differences in mean information literacy between physicians (M=26.4, SD 6.7) and nurses (M=30.5, SD 5.2, p=0.57 for difference), or between intervention (M=28.4, SD 6.5) and control groups (M=25.1, SD 6.2, p=0.12 for difference). Information literacy was correlated with higher rates of self-reported information system usage (r=0.547, p=0.001). Clinicians identified data access, accuracy, and interpretability as potential information literacy barriers.

CONCLUSIONS

While exploratory in nature, cautioning generalizability, the study suggests that measuring and improving clinicians' information literacy may play a significant role in the implementation and use of digital information tools, as these tools are rapidly being deployed to enhance communication among care teams, improve health care outcomes, and reduce overall costs.

Visualization approaches to support healthy aging: A systematic review

Background

Informatics tools have the potential to support the growing number of older adults who are aging in place. Many tools include visualizations (data visualizations and visualizations of physical representations). However, the role of visualizations in supporting aging in place remains largely unexplored.

Objective

To synthesize and identify gaps in the literature evaluating visualizations (data visualizations and visualizations of physical representations) for informatics tools to support healthy aging.

Methods

We conducted a search in CINAHL, Embase, Engineering Village, PsycINFO, PubMed, and Web of Science using a priori defined terms for publications in English describing community-based studies evaluating visualizations used by adults aged ≥ 65 years.

Results

Six out of the identified 251 publications were eligible. Most studies described in the publications were user studies and all varied methodological quality. Three publications described visualizations of virtual representations supported performing at-home exercises. Participants found visual representations either (1) helpful, motivational, and supported their understanding of their health behaviours or (2) not an improvement over alternatives. Three publications described data visualizations that aimed to support understanding of one’s health. Participants were able to interpret data visualizations that used precise data and encodings that were more concrete better than those that did not provide precision or were abstract. Participants found data visualizations helpful in understanding their overall health and granular data.

Conclusions

Few studies were identified that used and evaluated visualizations for older adults to promote engagement in exercises or understanding of their health. While visualizations demonstrated some promise to support older adult users in these activities, the studies had various methodological limitations. More research is needed, including research that overcomes methodological limitations of studies we identified, to develop visualizations that older adults could use with ease and accuracy to support their health behaviours and decision making.

R.A.P.I.D. (Root Aggregated Prioritized Information Display): A single screen display for efficient digital triaging of medical reports

OBJECTIVE

The timely acknowledgement of critical patient clinical reports is vital for the delivery of safe patient care. With current EHR systems, critical reports reside on different screens. This leads to treatment delays and inefficient work flows. As a remedy, the R.A.P.I.D. (Root Aggregated Prioritized Information Display) system represents all data on a single screen, and its simple and intuitive "button" array structure allows triaged sign-off/sign-out of critical and non-critical reports.

MATERIALS AND METHODS

With 100 hematology and chemistry reports from each of two EHR systems Meditech (Westwood, MA) and Orchard Labs, Inc. (Carmel, IN), we generated files of the reports in their individual standard display formats (enhanced Meditech-EM and enhanced Orchard-EO). We also displayed the same 200 reports in the R.A.P.I.D.

FORMAT

We then conducted a randomized trial to compare the time and accuracy of acknowledgement of critical and non-critical results.

RESULTS

The sign-off times for reviewing the results for physician and non-physician providers, respectively, in seconds (with 95% confidence intervals) were for EM 1.78 (1.40-2.26) and 1.99 (1.72-2.30), for EO 2.69 (2.12-3.42) and 2.78 (2.40-3.21), and for R.A.P.I.D. 0.83 (0.70-0.98) and 1.58 (1.43-1.76). Non-physician providers reassigned system-defined non-critical results as critical with a frequency of 15.2% for EM, 18.4% for EO, and 7.83% for R.A.P.I.D., and critical results as non-critical with a frequency of 14.7%, 5.6%, and 5.8% respectively.

DISCUSSION

The new display system was superior to two standard EHR systems that were significantly enhanced by first collecting the reports from their usual distributed locations and then by creating for each of the two standard EHRs a single file of reports for acknowledgement.

CONCLUSIONS

From a single screen display of all reports, the new display system enables timely acknowledgement of critical reports for patient safety and non-critical report triage for improved provider work flows.

[Bariatric surgery, stomas and other digestive tract reductions: Insufficient data and recommendations to adapt medicines regimens in therapeutic practice]

Surgery modifying digestive tract may alter drugs pharmacokinetics. To maintain concentrations of active substance in their therapeutic ranges, a dosage adjustment or change of drug may be necessary. This is particularly important when no pharmacological or pharmacodynamic parameter reflecting the medication effectiveness is easily measurable. Our objective was to gather the information and documentary tools that can guide prescription in these patients with rearranged digestive tract. We searched information on the documentary portals of French agencies, on gray literature, on MEDLINE and in the summaries product characteristics. No information was found on the website of French agencies, sparse data were identified in gray literature. Some document are discordant, most are imprecise. One hundred and ten studies or case reports referenced on MEDLINE describe 79 medications pharmacokinetics after gastrointestinal surgery. Four are not available in France. Six literature reviews were found. Four summaries of product characteristics provided information related to drug absorption. No documentary tool adapted to clinical routine exists. This unsatisfactory situation is a barrier to optimal patients care. Information is available. It is however necessary to gather under an ergonomic shape adapted to clinical routine, bringing the surgery type, pharmacokinetic changes induced and what to do about the dose adjustment.

Design and evaluation of a software for the objective and easy-to-read presentation of new drug properties to physicians

BACKGROUND

When new pharmaceutical products appear on the market, physicians need to know whether they are likely to be useful in their practices. Physicians currently obtain most of their information about the market release and properties of new drugs from pharmaceutical industry representatives. However, the official information contained in the summary of product characteristics (SPCs) and evaluation reports from health agencies, provide a more complete view of the potential value of new drugs, although they can be long and difficult to read. The main objective of this work was to design a prototype computer program to facilitate the objective appraisal of the potential value of a new pharmaceutical product by physicians. This prototype is based on the modeling of pharmaceutical innovations described in a previous paper.

METHODS

The interface was designed to allow physicians to develop a rapid understanding of the value of a new drug for their practices. We selected five new pharmaceutical products, to illustrate the function of this prototype. We considered only the texts supplied by national or international drug agencies at the time of market release. The perceived usability of the prototype was evaluated qualitatively, except for the System Usability Scale (SUS) score evaluation, by 10 physicians differing in age and medical background.

RESULTS

The display is based on the various axes of the conceptual model of pharmaceutical innovations. The user can select three levels of detail when consulting this information (highly synthetic, synthetic and detailed). Tables provide a comparison of the properties of the new pharmaceutical product with those of existing drugs, if available for the same indication, in terms of efficacy, safety and ease of use. The interface was highly appreciated by evaluators, who found it easy to understand and suggested no other additions of important, internationally valid information. The mean System Usability Scale score for the 10 physicians was 82, corresponding to a "good" user interface.

CONCLUSIONS

This work led us to propose the selection, grouping, and mode of presentation for various types of knowledge on pharmaceutical innovations in a way that was appreciated by evaluators. It provides physicians with readily accessible objective information about new drugs.

Mining severe drug-drug interaction adverse events using Semantic Web technologies: a case study

Background

Drug-drug interactions (DDIs) are a major contributing factor for unexpected adverse drug events (ADEs). However, few of knowledge resources cover the severity information of ADEs that is critical for prioritizing the medical need. The objective of the study is to develop and evaluate a Semantic Web-based approach for mining severe DDI-induced ADEs.

Methods

We utilized a normalized FDA Adverse Event Report System (AERS) dataset and performed a case study of three frequently prescribed cardiovascular drugs: Warfarin, Clopidogrel and Simvastatin. We extracted putative DDI-ADE pairs and their associated outcome codes. We developed a pipeline to filter the associations using ADE datasets from SIDER and PharmGKB. We also performed a signal enrichment using electronic medical records (EMR) data. We leveraged the Common Terminology Criteria for Adverse Event (CTCAE) grading system and classified the DDI-induced ADEs into the CTCAE in the Web Ontology Language (OWL).

Results

We identified 601 DDI-ADE pairs for the three drugs using the filtering pipeline, of which 61 pairs are in Grade 5, 56 pairs in Grade 4 and 484 pairs in Grade 3. Among 601 pairs, the signals of 59 DDI-ADE pairs were identified from the EMR data.

Conclusions

The approach developed could be generalized to detect the signals of putative severe ADEs induced by DDIs in other drug domains and would be useful for supporting translational and pharmacovigilance study of severe ADEs.

Adoption of clinical decision support in multimorbidity: a systematic review

Background

Patients with multiple conditions have complex needs and are increasing in number as populations age. This multimorbidity is one of the greatest challenges facing health care. Having more than 1 condition generates (1) interactions between pathologies, (2) duplication of tests, (3) difficulties in adhering to often conflicting clinical practice guidelines, (4) obstacles in the continuity of care, (5) confusing self-management information, and (6) medication errors. In this context, clinical decision support (CDS) systems need to be able to handle realistic complexity and minimize iatrogenic risks.

Objective

The aim of this review was to identify to what extent CDS is adopted in multimorbidity.

Methods

This review followed PRISMA guidance and adopted a multidisciplinary approach. Scopus and PubMed searches were performed by combining terms from 3 different thesauri containing synonyms for (1) multimorbidity and comorbidity, (2) polypharmacy, and (3) CDS. The relevant articles were identified by examining the titles and abstracts. The full text of selected/relevant articles was analyzed in-depth. For articles appropriate for this review, data were collected on clinical tasks, diseases, decision maker, methods, data input context, user interface considerations, and evaluation of effectiveness.

Results

A total of 50 articles were selected for the full in-depth analysis and 20 studies were included in the final review. Medication (n=10) and clinical guidance (n=8) were the predominant clinical tasks. Four studies focused on merging concurrent clinical practice guidelines. A total of 17 articles reported their CDS systems were knowledge-based. Most articles reviewed considered patients’ clinical records (n=19), clinical practice guidelines (n=12), and clinicians’ knowledge (n=10) as contextual input data. The most frequent diseases mentioned were cardiovascular (n=9) and diabetes mellitus (n=5). In all, 12 articles mentioned generalist doctor(s) as the decision maker(s). For articles reviewed, there were no studies referring to the active involvement of the patient in the decision-making process or to patient self-management. None of the articles reviewed adopted mobile technologies. There were no rigorous evaluations of usability or effectiveness of the CDS systems reported.

Conclusions

This review shows that multimorbidity is underinvestigated in the informatics of supporting clinical decisions. CDS interventions that systematize clinical practice guidelines without considering the interactions of different conditions and care processes may lead to unhelpful or harmful clinical actions. To improve patient safety in multimorbidity, there is a need for more evidence about how both conditions and care processes interact. The data needed to build this evidence base exist in many electronic health record systems and are underused.

Sim•TwentyFive: an interactive visualization system for data-driven decision support

Clinicians at the bedside are increasingly overwhelmed by an inundation of information and must rely largely on pattern recognition and professional experience to comprehend complex clinical data and treat their patients in a timely manner. Traditional decision support systems are based on rules and predictive models and often fail to take advantage of increasingly large digital clinical data stores available in real-time. We propose an alternative approach to delivering data-driven decision support based on an interactive system for exploring and visualizing a context of physiologically similar patients from a database. Here we present Sim•TwentyFive, a highly flexible, responsive, intuitive prototype with a comprehensive set of interaction techniques that effectively reduces the cognitive burden of querying, exploring, analyzing and comparing similar past patient episodes. Quantitative performance tests and anonymous summative evaluations from PICU physicians indicated that Sim•TwentyFive is an efficient, intuitive and clinically-useful tool.

ADESSA: A Real-Time Decision Support Service for Delivery of Semantically Coded Adverse Drug Event Data

Evaluating medications for potential adverse events is a time-consuming process, typically involving manual lookup of information by physicians. This process can be expedited by CDS systems that support dynamic retrieval and filtering of adverse drug events (ADE's), but such systems require a source of semantically-coded ADE data. We created a two-component system that addresses this need. First we created a natural language processing application which extracts adverse events from Structured Product Labels and generates a standardized ADE knowledge base. We then built a decision support service that consumes a Continuity of Care Document and returns a list of patient-specific ADE's. Our database currently contains 534,125 ADE's from 5602 product labels. An NLP evaluation of 9529 ADE's showed recall of 93% and precision of 95%. On a trial set of 30 CCD's, the system provided adverse event data for 88% of drugs and returned these results in an average of 620ms.