Article

Clinicians’ Evaluation of Computer-Assisted Medication Summarization of Electronic Medical Records

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Abstract

Each year thousands of patients die of avoidable medication errors. When a patient is admitted to, transferred within, or discharged from a clinical facility, clinicians should review previous medication orders, current orders and future plans for care, and reconcile differences if there are any. If medication reconciliation is not accurate and systematic, medication errors such as omissions, duplications, dosing errors, or drug interactions may occur and cause harm. Computer-assisted medication applications showed promise as an intervention to reduce medication summarization inaccuracies and thus avoidable medication errors. In this study, a computer-assisted medication summarization application, designed to abstract and represent multi-source time-oriented medication data, was introduced to assist clinicians with their medication reconciliation processes. An evaluation study was carried out to assess clinical usefulness and analyze potential impact of such application. Both quantitative and qualitative methods were applied to measure clinicians' performance efficiency and inaccuracy in medication summarization process with and without the intervention of computer-assisted medication application. Clinicians' feedback indicated the feasibility of integrating such a medication summarization tool into clinical practice workflow as a complementary addition to existing electronic health record systems. The result of the study showed potential to improve efficiency and reduce inaccuracy in clinician performance of medication summarization, which could in turn improve care efficiency, quality of care, and patient safety.

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... In some of the studies, a foundation of the study was provided before the actual (further) development of the EHR. This included, for example, literature reviews [47,61,63,68,90], pilot-testing of the design [52], pilot-testing of the survey [81] or interview guide [47,51,68], a review of 12 different EHRs [57] as well as training with the software in advance [76,77,83,103,107], and the presentation of learning videos [91]. ...
... A common method of data collection and involvement of health care professionals was to test a prototype as a walkthrough using think-aloud technique [52,55,56,58,59,71,[74][75][76][77]83,89,90,92,93,[95][96][97][98][99][100][103][104][105][108][109][110][113][114][115]. As part of the walkthrough methodology, various programs (eg, Morae) have been used to record audio or screen displays, mouse clicks, and keyboard [52,54,[74][75][76]83,92,94,97,100,103,109,113,114]. ...
... A common method of data collection and involvement of health care professionals was to test a prototype as a walkthrough using think-aloud technique [52,55,56,58,59,71,[74][75][76][77]83,89,90,92,93,[95][96][97][98][99][100][103][104][105][108][109][110][113][114][115]. As part of the walkthrough methodology, various programs (eg, Morae) have been used to record audio or screen displays, mouse clicks, and keyboard [52,54,[74][75][76]83,92,94,97,100,103,109,113,114]. Eye-tracking software (eg, Tobii T120 eye tracker) was used [52,59,71,75]. ...
Article
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Background: Electronic health records (EHRs) are a promising approach to document and map (complex) health information gathered in health care worldwide. However, possible unintended consequences during use, which can occur owing to low usability or the lack of adaption to existing workflows (eg, high cognitive load), may pose a challenge. To prevent this, the involvement of users in the development of EHRs is crucial and growing. Overall, involvement is designed to be very multifaceted, for example, in terms of the timing, frequency, or even methods used to capture user preferences. Objective: Setting, users and their needs, and the context and practice of health care must be considered in the design and subsequent implementation of EHRs. Many different approaches to user involvement exist, each requiring a variety of methodological choices. The aim of the study was to provide an overview of the existing forms of user involvement and the circumstances they need and to provide support for the planning of new involvement processes. Methods: We conducted a scoping review to provide a database for future projects on which design of inclusion is worthwhile and to show the diversity of reporting. Using a very broad search string, we searched the PubMed, CINAHL, and Scopus databases. In addition, we searched Google Scholar. Hits were screened according to scoping review methodology and then examined, focusing on methods and materials, participants, frequency and design of the development, and competencies of the researchers involved. Results: In total, 70 articles were included in the final analysis. There was a wide range of methods of involvement. Physicians and nurses were the most frequently included groups and, in most cases, were involved only once in the process. The approach of involvement (eg, co-design) was not specified in most of the studies (44/70, 63%). Further qualitative deficiencies in the reporting were evident in the presentation of the competences of members of the research and development teams. Think-aloud sessions, interviews, and prototypes were frequently used. Conclusions: This review provides insights into the diversity of health care professionals' involvement in the development of EHRs. It provides an overview of the different approaches in various fields of health care. However, it also shows the necessity of considering quality standards in the development of EHRs together with future users and the need for reporting this in future studies.
... Once CDSS scored significantly more exams as appropriate; better interface of one CDSS versus the other influenced provider willingness to use the CDS system Schneider et al [33] Improved safety Improved patient safety Improved accuracy and performance Accuracy improved: reduced inaccuracy Zhu and Cimino [34] Improved disease management A quality improvement initiative supported by CDS and workflow tools integrated in the EHR c improved recognition of eligibility and may have increased palivizumab administration rates; palivizumab-focused group performed significantly better than a comprehensive intervention More accurate prescribing Proportions of doses administered declined during the baseline seasons (from 72% to 62%) with partial recovery to 68% during the intervention season; palivizumab-focused group improved by 19.2 percentage points in the intervention season compared with the prior baseline season (P<.001), while the comprehensive intervention group only improved 5.5 percentage points (P=.29); difference in change between study groups was significant (P=.05) Utidjian et al [35] No difference reported No statistically significant difference: mortality 14% versus 15%, ICU d -free days 17 versus 19, vasopressor-free days 22.2 versus 22.6 No difference reported No statistically significant difference in performance (also low use of tool) Semler et al [36] Improved disease management Improved cardiovascular disease risk management; no difference in prescription rates Improved screening Patients more likely to receive screening with CDSS (63% vs 53%); no improvements in prescription of recommended medications at the end of the study Peiris et al [37] No difference reported Patients aged <65 years had greater mortality benefit (OR e 0.45, 95% CI 0.20-1.00; ...
... These themes are listed in Table 2 in order of occurrence first for positive effect followed by no difference and not discussed. As illustrated, 66% (25/38) of the occurrences of themes identified 10 positive indicators of practitioner performance [16][17][18]21,[23][24][25][28][29][30][31][33][34][35]37,38,40,44,45,[47][48][49][50]. Practitioner performance was reported as more accurate prescribing, improved screening of patients, improved overall performance, increased awareness of patient conditions, improved follow-up due to better communication with patients, improved accuracy of diagnosis, improved documentation, improved benchmarking, improved care plans, and improved buy-in of CDSSs. ...
... Practitioners using CDSSs experienced more accurate prescribing [16,24,28,35,38], improved screening [33,37,49,50], improved overall performance [21,29,34,44], improved care plans [17,25,30,47], improved documentation [31,40], overall improved buy-in for CDSSs [33,48], increased awareness of needs of patients [18], improved follow-up with patients due to enhanced communication channels enabled by the application [23], improved accuracy of diagnosis [34], and improved benchmarking [45]. ...
Article
Full-text available
Background: Computerized decision support systems (CDSSs) are software programs that support the decision making of practitioners and other staff. Other reviews have analyzed the relationship between CDSSs, practitioner performance, and patient outcomes. These reviews reported positive practitioner performance in over half the articles analyzed, but very little information was found for patient outcomes. Objective: The purpose of this review was to analyze the relationship between CDSSs, practitioner performance, and patient medical outcomes. PubMed, CINAHL, Embase, Web of Science, and Cochrane databases were queried. Methods: Articles were chosen based on year published (last 10 years), high quality, peer-reviewed sources, and discussion of the relationship between the use of CDSS as an intervention and links to practitioner performance or patient outcomes. Reviewers used an Excel spreadsheet (Microsoft Corporation) to collect information on the relationship between CDSSs and practitioner performance or patient outcomes. Reviewers also collected observations of participants, intervention, comparison with control group, outcomes, and study design (PICOS) along with those showing implicit bias. Articles were analyzed by multiple reviewers following the Kruse protocol for systematic reviews. Data were organized into multiple tables for analysis and reporting. Results: Themes were identified for both practitioner performance (n=38) and medical outcomes (n=36). A total of 66% (25/38) of articles had occurrences of positive practitioner performance, 13% (5/38) found no difference in practitioner performance, and 21% (8/38) did not report or discuss practitioner performance. Zero articles reported negative practitioner performance. A total of 61% (22/36) of articles had occurrences of positive patient medical outcomes, 8% (3/36) found no statistically significant difference in medical outcomes between intervention and control groups, and 31% (11/36) did not report or discuss medical outcomes. Zero articles found negative patient medical outcomes attributed to using CDSSs. Conclusions: Results of this review are commensurate with previous reviews with similar objectives, but unlike these reviews we found a high level of reporting of positive effects on patient medical outcomes.
... Few of the researches are discussed in this section. Zhu and Cimino (2015) proposed a computer-assisted medication summarization application, which is designed to abstract and symbolize multi-source time-oriented medication data [35]. This is introduced to help clinicians in their medication processes and it recorded the existing health data of the patients. ...
... Few of the researches are discussed in this section. Zhu and Cimino (2015) proposed a computer-assisted medication summarization application, which is designed to abstract and symbolize multi-source time-oriented medication data [35]. This is introduced to help clinicians in their medication processes and it recorded the existing health data of the patients. ...
Preprint
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A rule based extractive text summarization system is proposed to obtain comprehensible summarized text of input Hindi and Punjabi text documents. The input document to be summarized can be any handwritten text or any text from internet saved in .rtf format. It works on Hindi and Punjabi documents and summarizes text of any length i.e. system summarizes text irrespective of size. The extensive rule based approach is applied consisting preprocessing and processing phases. Each phase consists of rules which are to be followed to get the desired concise summary. In the preprocessing phase, rules of Tokenization, Stop word removal and segmentation are applied whereas in processing phase rules such as Monetary values, Measurement Values, Special Symbols, Equations, Figure Number in sentences are applied. Also, other features such as frequently occurring words and maximum length word are added to make the system output comprehendible. The system is made user friendly by proposing a graphical user interface (GUI). The generated summary by the proposed system is compared with the summary of documents generated by human experts and is evaluated in terms of parameters such as Compression Ratio and Execution Time. Further, accuracy is calculated based on Precision, Recall and F-score. It has been observed that the compression ratio obtained lies in the range of 39–50% for 200, 300 and 400 words paragraphs. The execution time is quite less which do varies with the size of the paragraph entered as the summary size varies. In general, it has been observed that the execution time for 200 words paragraph takes 1519.147 milliseconds, for 300 words paragraph the time is 3022.825 milliseconds whereas for 400 words size paragraph, the time taken is 4023.123 milliseconds. The time is quite less as compared to human generated summary. The accuracy obtained in each case is above 95% which is high as compared to humans and other state of art methods. This shows that the system is robust enough and provides definitive results.
... These themes are listed in Table 2 and are listed in order of occurrence rst for positive effect followed by no difference and not discussed. Table 2: Summary of themes identi ed for practitioner performance As illustrated, 14 of 27 (52%) of articles analyzed identi ed ten unique positive indicators of practitioner performance (15,18,19,23,25,28,29,31,35,36,38,39,40,41). Practitioner performance was reported as more accurate prescribing, improved screening of patients, improved overall performance, increased awareness of patients' conditions, improved follow-up due to better communication with patients, improved accuracy of diagnosis, improved documentation, improved benchmarking, improved care plans, and improved buy-in of CDSS. ...
... These themes are listed in Table 3 by order of greatest occurrence for positive effect followed by no difference and not discussed. Table 3: Summary of themes identi ed for patient medical outcomes As illustrated, 15 of 27 (56%) articles analyzed identi ed positive patient medical outcomes as a result of using CDSS (18,22,(24)(25)(26)28,30,32,34,35-37-39,41). Patient medical outcomes were reported as improved symptoms, improved e cacy of treatment, improved disease management, improved safety, improved mortality, improved screening ability, and improved feedback between patient and provider. ...
Preprint
Full-text available
Background Computerized decision support systems (CDSS) are software programs that support the decision making of practitioners and other staff. Other reviews have analyzed the relationship between CDSS, practitioner performance, and patient outcomes. These reviews reported positive practitioner performance in over half the articles analyzed, but very little information was found for patient outcomes. The purpose of this review was to analyze the relationship between CDSS, practitioner performance, and patient medical outcomes. PubMed, CINAHL, and Cochrane databases were queried.Methods 27 articles were chosen based on year published (last ten years), high quality source, and discussion of the relationship between the use of CDSS as an intervention and links to practitioner performance or patient outcomes. Reviewers used an Excel spreadsheet to collect information on the relationship between CDSS and practitioner performance or patient outcomes. Reviewers also collected observations of participants, intervention, comparison with control group, and outcomes (PICO) along with those showing implicit bias. Articles were analyzed by multiple reviewers following the Kruse Protocol for systematic reviews. Data were organized into multiple tables for analysis and reporting.Results Fourteen articles (52%) discussed positive practitioner performance, three articles (11%) found no difference in practitioner performance, ten articles (37%) did not discuss practitioner performance. Zero articles reported negative practitioner performance. Fifteen articles (56%) discussed positive patient medical outcomes, two articles (7%) found no statistically significant difference in medical outcomes between intervention and control groups, and ten articles (37%) did not discuss medical outcomes. Zero articles found negative patient medical outcomes.Conclusions Results of this review are commensurate with previous reviews with similar objectives, but unlike these reviews we found significant positive reporting of a positive effect on patient medical outcomes. Our findings support adoption of decision support systems.
... As a consequence, current practice seldom assesses whether more user-facing goals of summarization (e.g., the actual reduction of labor) are attained. Task-based evaluation, where summaries are assessed based on how they help humans perform a particular task (Lloret et al., 2018), is not a foreign concept in automatic summarization (Van Labeke et al., 2013;Zhu and Cimino, 2015;Jimeno-Yepes et al., 2013) and could be adopted by the community to better suit certain research goals. ...
... Popular approaches for visual extractive summaries have been small visuals and patient data temporal views. [39][40][41][42][43][44][45][46][47][48][49][50][51][52]A few examples also exist of visualizations of abstractive summaries or reorganization of selected patient data. [53][54][55][56][57][58][59][60][61][62][63] Machine learning used for CAO systems have largely focused on generating abstractive summaries of the patient rather than extractive summaries. ...
Preprint
Full-text available
Deep learning, an area of machine learning, is set to revolutionize patient care. But it is not yet part of standard of care, especially when it comes to individual patient care. In fact, it is unclear to what extent data-driven techniques are being used to support clinical decision making (CDS). Heretofore, there has not been a review of ways in which research in machine learning and other types of data-driven techniques can contribute effectively to clinical care and the types of support they can bring to clinicians. In this paper, we consider ways in which two data driven domains - machine learning and data visualizations - can contribute to the next generation of clinical decision support systems. We review the literature regarding the ways heuristic knowledge, machine learning, and visualization are - and can be - applied to three types of CDS. There has been substantial research into the use of predictive modeling for alerts, however current CDS systems are not utilizing these methods. Approaches that leverage interactive visualizations and machine-learning inferences to organize and review patient data are gaining popularity but are still at the prototype stage and are not yet in use. CDS systems that could benefit from prescriptive machine learning (e.g., treatment recommendations for specific patients) have not yet been developed. We discuss potential reasons for the lack of deployment of data-driven methods in CDS and directions for future research.
... There is also a number of applications where the integration or the use of automatic summaries have been shown to be appropriate, so therefore, they are evaluated in the context of these applications, such as information retrieval (Tombros and Sanderson 1998;Perea-Ortega et al. 2013;Alhindi et al. 2013), question answering tasks (Teufel 2001;Wu et al. 2004;Lloret et al. 2011;Jimeno-Yepes et al. 2013), report generation or synthesis tasks (Amigo et al. 2004;McKeown et al. 2005), or more recently, to obtain formative feedback (Labeke et al. 2013b, a;Field et al. 2013), or to manage clinical information (Zhu and Cimino 2013). Summaries applied to information retrieval have been normally used from a double perspective. ...
Article
Full-text available
Evaluation is crucial in the research and development of automatic summarization applications, in order to determine the appropriateness of a summary based on different criteria, such as the content it contains, and the way it is presented. To perform an adequate evaluation is of great relevance to ensure that automatic summaries can be useful for the context and/or application they are generated for. To this end, researchers must be aware of the evaluation metrics, approaches, and datasets that are available, in order to decide which of them would be the most suitable to use, or to be able to propose new ones, overcoming the possible limitations that existing methods may present. In this article, a critical and historical analysis of evaluation metrics, methods, and datasets for automatic summarization systems is presented, where the strengths and weaknesses of evaluation efforts are discussed and the major challenges to solve are identified. Therefore, a clear up-to-date overview of the evolution and progress of summarization evaluation is provided, giving the reader useful insights into the past, present and latest trends in the automatic evaluation of summaries.
... The 'wildcard' addition to the issue is a paper on digital medicines reconciliation by Zhu and Cimino [14], which is somewhat out with the topic, but addresses an important issue for patient safety that is also being discussed in the context of PHR. The reader may wish to reflect on the role of patients themselves as agents of medication reconciliation, which will be possible if they are given the right information and tools to be able to achieve this [15]. ...
Article
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Background Information overflow, a common problem in the present clinical environment, can be mitigated by summarizing clinical data. Although there are several solutions for clinical summarization, there is a lack of a complete overview of the research relevant to this field. Objective This study aims to identify state-of-the-art solutions for clinical summarization, to analyze their capabilities, and to identify their properties. Methods A scoping review of articles published between 2005 and 2022 was conducted. With a clinical focus, PubMed and Web of Science were queried to find an initial set of reports, later extended by articles found through a chain of citations. The included reports were analyzed to answer the questions of where, what, and how medical information is summarized; whether summarization conserves temporality, uncertainty, and medical pertinence; and how the propositions are evaluated and deployed. To answer how information is summarized, methods were compared through a new framework “collect—synthesize—communicate” referring to information gathering from data, its synthesis, and communication to the end user. Results Overall, 128 articles were included, representing various medical fields. Exclusively structured data were used as input in 46.1% (59/128) of papers, text in 41.4% (53/128) of articles, and both in 10.2% (13/128) of papers. Using the proposed framework, 42.2% (54/128) of the records contributed to information collection, 27.3% (35/128) contributed to information synthesis, and 46.1% (59/128) presented solutions for summary communication. Numerous summarization approaches have been presented, including extractive (n=13) and abstractive summarization (n=19); topic modeling (n=5); summary specification (n=11); concept and relation extraction (n=30); visual design considerations (n=59); and complete pipelines (n=7) using information extraction, synthesis, and communication. Graphical displays (n=53), short texts (n=41), static reports (n=7), and problem-oriented views (n=7) were the most common types in terms of summary communication. Although temporality and uncertainty information were usually not conserved in most studies (74/128, 57.8% and 113/128, 88.3%, respectively), some studies presented solutions to treat this information. Overall, 115 (89.8%) articles showed results of an evaluation, and methods included evaluations with human participants (median 15, IQR 24 participants): measurements in experiments with human participants (n=31), real situations (n=8), and usability studies (n=28). Methods without human involvement included intrinsic evaluation (n=24), performance on a proxy (n=10), or domain-specific tasks (n=11). Overall, 11 (8.6%) reports described a system deployed in clinical settings. Conclusions The scientific literature contains many propositions for summarizing patient information but reports very few comparisons of these proposals. This work proposes to compare these algorithms through how they conserve essential aspects of clinical information and through the “collect—synthesize—communicate” framework. We found that current propositions usually address these 3 steps only partially. Moreover, they conserve and use temporality, uncertainty, and pertinent medical aspects to varying extents, and solutions are often preliminary.
Article
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Background: Despite the unprecedented performance of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces hinders the adoption of these AI systems in practice. Objective: This study aims to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered manner. Methods: Our study is based on a user-centered design framework for developing explanations in a CDSS that identifies why explanations are needed, what information should be contained in explanations, and how explanations can be provided in the CDSS. We conducted user interviews, user observation sessions, and an iterative design process to identify three key aspects for designing explanations in the CDSS. After constructing the CDSS, the tool was evaluated to investigate how the CDSS explanations helped technicians. We measured the accuracy of sleep staging and interrater reliability with macro-F1 and Cohen κ scores to assess quantitative improvements after our tool was adopted. We assessed qualitative improvements through participant interviews that established how participants perceived and used the tool. Results: The user study revealed that technicians desire explanations that are relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of AI predictions. Here, technicians wanted explanations that could be used to evaluate whether the AI models properly locate and use these patterns during prediction. On the basis of this, information that is closely related to sleep EEG patterns was formulated for the AI models. In the iterative design phase, we developed a different visualization strategy for each pattern based on how technicians interpreted the EEG recordings with these patterns during their workflows. Our evaluation study on 9 polysomnographic technicians quantitatively and qualitatively investigated the helpfulness of the tool. For technicians with <5 years of work experience, their quantitative sleep staging performance improved significantly from 56.75 to 60.59 with a P value of .05. Qualitatively, participants reported that the information provided effectively supported them, and they could develop notable adoption strategies for the tool. Conclusions: Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.
Article
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Background Technological support may be crucial in optimizing healthcare professional practice and improving patient outcomes. A focus on electronic health records has left other technological supports relatively neglected. Additionally, there has been no comparison between different types of technology-based interventions, and the importance of delivery setting on the implementation of technology-based interventions to change professional practice. Consequently, there is a need to synthesise and examine intervention characteristics using a methodology suited to identifying important features of effective interventions, and the barriers and facilitators to implementation. Three aims were addressed: to identify interventions with a technological component that are successful at changing professional practice, to determine if and how such interventions are theory-based, and to examine barriers and facilitators to successful implementation. Methods A literature review informed by realist review methods was conducted involving a systematic search of studies reporting either: (1) behavior change interventions that included technology to support professional practice change; or (2) barriers and facilitators to implementation of technological interventions. Extracted data was quantitative and qualitative, and included setting, target professionals, and use of Behaviour Change Techniques (BCTs). The primary outcome was a change in professional practice. A thematic analysis was conducted on studies reporting barriers and facilitators of implementation. Results Sixty-nine studies met the inclusion criteria; 48 (27 randomized controlled trials) reported behavior change interventions and 21 reported practicalities of implementation. The most successful technological intervention was decision support providing healthcare professionals with knowledge and/or person-specific information to assist with patient management. Successful technologies were more likely to operationalise BCTs, particularly “instruction on how to perform the behavior”. Facilitators of implementation included aligning studies with organisational initiatives, ensuring senior peer endorsement, and integration into clinical workload. Barriers included organisational challenges, and design, content and technical issues of technology-based interventions. Conclusions Technological interventions must focus on providing decision support for clinical practice using recognized behavior change techniques. Interventions must consider organizational context, clinical workload, and have clearly defined benefits for improving practice and patient outcomes. Electronic supplementary material The online version of this article (10.1186/s12911-018-0661-3) contains supplementary material, which is available to authorized users.
Article
Decision support for the guideline-based management of patients with multimorbidity is a challenge since it relies on the combination of single-disease clinical practice guidelines (CPGs). The aim of this work is to present a framework to check, at the modelling level, whether two CPGs overlap and are potentially inconsistent, thus requiring further reconciliation. The method relies on an ontological comparison of the patient profiles covered by CPGs and the recommended actions attached. It was applied to check the consistency of CPGs for the management of arterial hypertension and for the management of type 2 diabetes. Results showed that the two CPGs had only one common patient profile, although more profiles were impacted through profile subsumption. In this specific case, recommended actions were not found inconsistent since antihypertensive and anti-diabetic drugs could be combined in an additive way.
Conference Paper
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Objective: To explore approaches for integrating and visualizing time-oriented medication data in narrative and structured formats and to address related issues on handling temporal abstraction, granularity, and uncertainty. The ultimate goal is to improve medication reconciliation by providing clinicians with more accurate medication information in patient care. Methods: An event taxonomy was generated to capture different combinations of clinical and temporal uncertainties. A prototype of a temporal visualization system was implemented using an open source software package called Timeline. Medications were parsed and mapped to the event taxonomy, and then represented in Timelines. Seventy-five medications from narrative discharge summary reports and seventy-nine medications from structured orders were used as data input for temporal visualization. Five physicians served as domain experts and answered ten proof-of-concept survey questions. Results: Overall positive feedback from experts suggested the potential value of the proposed timeline visualization method. Challenges were also identified, and future work will include reconciliation of medications from various sources based on temporal attributes and medication classification.
Article
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We present a method that extracts medication information from discharge summaries. The program relies on parsing rules written as a set of regular expressions and on a user-configurable drug lexicon. Our evaluation shows a precision of 94% and recall of 83% in the extraction of medication information. We use a broader definition of medication information than previous studies, including drug names appearing with and without dosage information, misspelled drug names, and contextual information.
Chapter
After reading this chapter, you should know the answers to these questions: How can cognitive science theory meaningfully inform and shape design, development and assessment of health care information systems? What are some of the ways in which cognitive science differs from behavioral science? What are some of the ways in which we can characterize the structure of knowledge? What are the basic components of a cognitive architecture? What are some of the dimensions of difference between experts and novices? Describe some of the attributes of system usability. What are the gulfs of execution and evaluation? What role do these considerations play in system design? What is the difference between a textbase and a situation model? How can we use cognitive methods to develop and implement clinical practice guidelines for different kinds of clinicians?
Chapter
There is some evidence to demonstrate how a medication reconciliation process is effective at preventing adverse drug events. Few studies have been published demonstrating how to do the process effectively or outlining the costs associated with design and implementation of programs. Nonetheless, an effective medication reconciliation process across care settings—where medications a patient is taking are compared to what is being ordered—is believed to reduce errors. Comparing what is being taken in one setting with what is being prescribed in another will avoid errors of omission, drug-drug interactions, drug-disease interactions, and other discrepancies. Medication reconciliation is a major component of safe patient care in any environment.
Article
WebCIS is a Web-based clinical information system. It sits atop the existing Columbia University clinical information system architecture, which includes a clinical repository, the Medical Entities Dictionary, an HL7 interface engine, and an Arden Syntax based clinical event monitor. WebCIS security features include authentication with secure tokens, authorization maintained in an LDAP server, SSL encryption, permanent audit logs, and application time outs. WebCIS is currently used by 810 physicians at the Columbia-Presbyterian center of New York Presbyterian Healthcare to review and enter data into the electronic medical record. Current deployment challenges include maintaining adequate database performance despite complex queries, replacing large numbers of computers that cannot run modern Web browsers, and training users that have never logged onto the Web. Although the raised expectations and higher goals have increased deployment costs, the end result is a far more functional, far more available system.
Computer Applications in Health Care and Biomedicine
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