OBJECTIVE: To conduct a meta-ethnographic synthesis summarizing the overarching themes of the qualitative literature on nurse interaction with medication administration technologies (MAT) comprising electronic medication administration record (eMAR) and bar-coded medication administration (BCMA). MATERIALS AND METHODS: We searched scientific databases from their inception until September 23, 2024, resulting in 2270 unique articles, and extracted data from 27 articles. Scientific rigor was assessed by the Standards for Reporting Qualitative Research (SRQR) checklist. Noblit and Hare's methodology was used to conduct a meta-ethnography to identify and interpret emergent themes. RESULTS: SRQR revealed low-to-medium methodological quality and theoretical framing of the literature. We found 6 overarching themes connecting MAT with nursing work: (1) View of the MAT system as mechanistic and invariant vs living and co-evolving with its users drives the research approach; (2) MAT limits nurse autonomy; (3) MAT unnaturally splits the medication administration workflow; (4) Nurses must manage MAT challenges at the sharp end; (5) MAT does not align with social dependencies of nursing work; and (6) MAT increases perceived safety but can also result in new types of errors. DISCUSSION: MAT does not support key cognitive work that nurses must perform to maintain safety. Additionally, MAT can impede problem solving during medication management and limit nursing autonomy that is essential for safe medication administration. CONCLUSION: Recommendations based in human factors engineering recognizing how MAT design impacts nursing work and workload are essential in improving the fit of MAT to nurse cognitive workflows.

OBJECTIVES: We sought to (1) characterize the process of diagnosing pneumonia in an emergency department (ED) and (2) examine clinician reactions to a clinician-facing diagnostic discordance feedback tool. MATERIALS AND METHODS: We designed a diagnostic feedback tool, using electronic health record data from ED clinicians' patients to establish concordance or discordance between ED diagnosis, radiology reports, and hospital discharge diagnosis for pneumonia. We conducted semistructured interviews with 11 ED clinicians about pneumonia diagnosis and reactions to the feedback tool. We administered surveys measuring individual differences in mindset beliefs, comfort with feedback, and feedback tool usability. We qualitatively analyzed interview transcripts and descriptively analyzed survey data. RESULTS: Thematic results revealed: (1) the diagnostic process for pneumonia in the ED is characterized by diagnostic uncertainty and may be secondary to goals to treat and dispose the patient; (2) clinician diagnostic self-evaluation is a fragmented, inconsistent process of case review and follow-up that a feedback tool could fill; (3) the feedback tool was described favorably, with task and normative feedback harnessing clinician values of high-quality patient care and personal excellence; and (4) strong reactions to diagnostic feedback varied from implicit trust to profound skepticism about the validity of the concordance metric. Survey results suggested a relationship between clinicians' individual differences in learning and failure beliefs, feedback experience, and usability ratings. DISCUSSION AND CONCLUSION: Clinicians value feedback on pneumonia diagnoses. Our results highlight the importance of feedback about diagnostic performance and suggest directions for considering individual differences in feedback tool design and implementation.

OBJECTIVES: We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2). MATERIALS AND METHODS: A mixed-method analysis was conducted at 2 pilot sites. Interviews were conducted with 12 of 27 enrolled patients and with 13 participating clinicians. iPARIHS constructs were used for interview construction to identify workflow, communication patterns, and clinician's beliefs. Interviews were transcribed and analyzed using inductive coding protocols to identify key themes. Behavioral response data from the AI-generated notifications were collected. RESULTS: Clinicians responded to notifications within 24 hours in 95% of instances, with 26.7% resulting in clinical action. Four implementation themes emerged: (1) High anticipatory expectations for reliable patient communications, reduced patient burden, and less proactive provider monitoring. (2) The AI notifications required a differential and tailored balance of trust and action advice related to role. (3) Clinic experience with other home-based programs influenced utilization. (4) Responding to notifications involved significant effort, including electronic health record (EHR) review, patient contact, and consultation with other clinicians. DISCUSSION: Clinician's use of AI data is a function of beliefs regarding the trustworthiness and usefulness of the data, the degree of autonomy in professional roles, and the cognitive effort involved. CONCLUSION: The implementation planning analysis guided development of strategies that addressed communication technology, patient education, and EHR integration to reduce clinician and patient burden in the subsequent main randomized phase of the trial. Our results provide important insights into the unique implications of implementing AI analytics into clinical workflow.

OBJECTIVES: (1) Characterize persistent hazards and inefficiencies in inpatient medication administration; (2) Explore cognitive attributes of medication administration tasks; and (3) Discuss strategies to reduce medication administration technology-related hazards. MATERIALS AND METHODS: Interviews were conducted with 32 nurses practicing at 2 urban, eastern and western US health systems. Qualitative analysis using inductive and deductive coding included consensus discussion, iterative review, and coding structure revision. We abstracted hazards and inefficiencies through the lens of risks to patient safety and the cognitive perception-action cycle (PAC). RESULTS: Persistent safety hazards and inefficiencies related to MAT organized around the PAC cycle included: (1) Compatibility constraints create information silos; (2) Missing action cues; (3) Intermittent communication flow between safety monitoring systems and nurses; (4) Occlusion of important alerts by other, less helpful alerts; (5) Dispersed information: Information required for tasks is not collocated; (6) Inconsistent data organization: Mismatch of the display and the user's mental model; (7) Hidden medication administration technologies (MAT) limitations: Inaccurate beliefs about MAT functionality contribute to overreliance on the technology; (8) Software rigidity causes workarounds; (9) Cumbersome dependencies between technology and the physical environment; and (10) Technology breakdowns require adaptive actions. DISCUSSION: Errors might persist in medication administration despite successful Bar Code Medication Administration and Electronic Medication Administration Record deployment for reducing errors. Opportunities to improve MAT require a deeper understanding of high-level reasoning in medication administration, including control over the information space, collaboration tools, and decision support. CONCLUSION: Future medication administration technology should consider a deeper understanding of nursing knowledge work for medication administration.

OBJECTIVE: To evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user's view. MATERIALS AND METHODS: We identified features (eg, patient and provider characteristics) proposed to modulate user responses to medication alerts through the literature; these features were then refined through expert review. Models were developed using rule-based and machine learning techniques (logistic regression, random forest, support vector machine, neural network, and LightGBM). We collected log data on alerts shown to users throughout 2019 at University of Utah Health. We sought to maximize precision while maintaining a false-negative rate <0.01, a threshold predefined through discussion with physicians and pharmacists. We developed models while maintaining a sensitivity of 0.99. Two null hypotheses were developed: H1-there is no difference in precision among prediction models; and H2-the removal of any feature category does not change precision. RESULTS: A total of 3,481,634 medication alerts with 751 features were evaluated. With sensitivity fixed at 0.99, LightGBM achieved the highest precision of 0.192 and less than 0.01 for the pre-defined maximal false-negative rate by subject-matter experts (H1) (P < 0.001). This model could reduce alert volume by 54.1%. We removed different combinations of features (H2) and found that not all features significantly contributed to precision. Removing medication order features (eg, dosage) most significantly decreased precision (-0.147, P = 0.001). CONCLUSIONS: Machine learning potentially enables the intelligent filtering of medication alerts.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

OBJECTIVE: Our objective was to develop an evaluation framework for electronic health record (EHR)-integrated innovations to support evaluation activities at each of four information technology (IT) life cycle phases: planning, development, implementation, and operation. METHODS: The evaluation framework was developed based on a review of existing evaluation frameworks from health informatics and other domains (human factors engineering, software engineering, and social sciences); expert consensus; and real-world testing in multiple EHR-integrated innovation studies. RESULTS: The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers four IT life cycle phases and three measure levels (society, user, and IT). The ELICIT framework recommends 12 evaluation steps: (1) business case assessment; (2) stakeholder requirements gathering; (3) technical requirements gathering; (4) technical acceptability assessment; (5) user acceptability assessment; (6) social acceptability assessment; (7) social implementation assessment; (8) initial user satisfaction assessment; (9) technical implementation assessment; (10) technical portability assessment; (11) long-term user satisfaction assessment; and (12) social outcomes assessment. DISCUSSION: Effective evaluation requires a shared understanding and collaboration across disciplines throughout the entire IT life cycle. In contrast with previous evaluation frameworks, the ELICIT framework focuses on all phases of the IT life cycle across the society, user, and IT levels. Institutions seeking to establish evaluation programs for EHR-integrated innovations could use our framework to create such shared understanding and justify the need to invest in evaluation. CONCLUSION: As health care undergoes a digital transformation, it will be critical for EHR-integrated innovations to be systematically evaluated. The ELICIT framework can facilitate these evaluations.

OBJECTIVE: The purpose of the study was to explore the theoretical underpinnings of effective clinical decision support (CDS) factors using the comparative effectiveness results. MATERIALS AND METHODS: We leveraged search results from a previous systematic literature review and updated the search to screen articles published from January 2017 to January 2020. We included randomized controlled trials and cluster randomized controlled trials that compared a CDS intervention with and without specific factors. We used random effects meta-regression procedures to analyze clinician behavior for the aggregate effects. The theoretical model was the Unified Theory of Acceptance and Use of Technology (UTAUT) model with motivational control. RESULTS: Thirty-four studies were included. The meta-regression models identified the importance of effort expectancy (estimated coefficient = -0.162; P = .0003); facilitating conditions (estimated coefficient = 0.094; P = .013); and performance expectancy with motivational control (estimated coefficient = 1.029; P = .022). Each of these factors created a significant impact on clinician behavior. The meta-regression model with the multivariate analysis explained a large amount of the heterogeneity across studies (R2 = 88.32%). DISCUSSION: Three positive factors were identified: low effort to use, low controllability, and providing more infrastructure and implementation strategies to support the CDS. The multivariate analysis suggests that passive CDS could be effective if users believe the CDS is useful and/or social expectations to use the CDS intervention exist. CONCLUSIONS: Overall, a modified UTAUT model that includes motivational control is an appropriate model to understand psychological factors associated with CDS effectiveness and to guide CDS design, implementation, and optimization.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Clinicians often attribute much of their burnout experience to use of the electronic health record, the adoption of which was greatly accelerated by the Health Information Technology for Economic and Clinical Health Act of 2009. That same year, AMIA's Policy Meeting focused on possible unintended consequences associated with rapid implementation of electronic health records, generating 17 potential consequences and 15 recommendations to address them. At the 2020 annual meeting of the American College of Medical Informatics (ACMI), ACMI fellows participated in a modified Delphi process to assess the accuracy of the 2009 predictions and the response to the recommendations. Among the findings, the fellows concluded that the degree of clinician burnout and its contributing factors, such as increased documentation requirements, were significantly underestimated. Conversely, problems related to identify theft and fraud were overestimated. Only 3 of the 15 recommendations were adjudged more than half-addressed.

The psychology of motivation can help us understand the impact of electronic health records (EHRs) on clinician burnout both directly and indirectly. Informatics approaches to EHR usability tend to focus on the extrinsic motivation associated with successful completion of clearly defined tasks in clinical workflows. Intrinsic motivation, which includes the need for autonomy, sense-making, creativity, connectedness, and mastery is not well supported by current designs and workflows. This piece examines existing research on the importance of 3 psychological drives in relation to healthcare technology: goal-based decision-making, sense-making, and agency/autonomy. Because these motives are ubiquitous, foundational to human functioning, automatic, and unconscious, they may be overlooked in technological interventions. The results are increased cognitive load, emotional distress, and unfulfilling workplace environments. Ultimately, we hope to stimulate new research on EHR design focused on expanding functionality to support intrinsic motivation, which, in turn, would decrease burnout and improve care.

OBJECTIVE: The study sought to evaluate a novel electronic health record (EHR) add-on application for chronic disease management that uses an integrated display to decrease user cognitive load, improve efficiency, and support clinical decision making. MATERIALS AND METHODS: We designed a chronic disease management application using the technology framework known as SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources). We used mixed methods to obtain user feedback on a prototype to support ambulatory providers managing chronic obstructive pulmonary disease. Each participant managed 2 patient scenarios using the regular EHR with and without access to our prototype in block-randomized order. The primary outcome was the percentage of expert-recommended ideal care tasks completed. Timing, keyboard and mouse use, and participant surveys were also collected. User experiences were captured using a retrospective think-aloud interview analyzed by concept coding. RESULTS: With our prototype, the 13 participants completed more recommended care (81% vs 48%; P < .001) and recommended tasks per minute (0.8 vs 0.6; P = .03) over longer sessions (7.0 minutes vs 5.4 minutes; P = .006). Keystrokes per task were lower with the prototype (6 vs 18; P < .001). Qualitative themes elicited included the desire for reliable presentation of information which matches participants' mental models of disease and for intuitive navigation in order to decrease cognitive load. DISCUSSION: Participants completed more recommended care by taking more time when using our prototype. Interviews identified a tension between using the inefficient but familiar EHR vs learning to use our novel prototype. Concept coding of user feedback generated actionable insights. CONCLUSIONS: Mixed methods can support the design and evaluation of SMART on FHIR EHR add-on applications by enhancing understanding of the user experience.

OBJECTIVE: To determine the impact of a graphical information display on diagnosing circulatory shock. MATERIALS AND METHODS: This was an experimental study comparing integrated and conventional information displays. Participants were intensivists or critical care fellows (experts) and first-year medical residents (novices). RESULTS: The integrated display was associated with higher performance (87% vs 82%; P < .001), less time (2.9 vs 3.5 min; P = .008), and more accurate etiology (67% vs 54%; P = .048) compared to the conventional display. When stratified by experience, novice physicians using the integrated display had higher performance (86% vs 69%; P < .001), less time (2.9 vs 3.7 min; P = .03), and more accurate etiology (65% vs 42%; P = .02); expert physicians using the integrated display had nonsignificantly improved performance (87% vs 82%; P = .09), time (2.9 vs 3.3; P = .28), and etiology (69% vs 67%; P = .81). DISCUSSION: The integrated display appeared to support efficient information processing, which resulted in more rapid and accurate circulatory shock diagnosis. Evidence more strongly supported a difference for novices, suggesting that graphical displays may help reduce expert-novice performance gaps.

The adoption of electronic health records (EHRs) across the United States has impacted the methods by which health care professionals care for their patients. It is not always recognized, however, that pharmacists also actively use advanced functionality within the EHR. As critical members of the health care team, pharmacists utilize many different features of the EHR. The literature focuses on 3 main roles: documentation, medication reconciliation, and patient evaluation and monitoring. As health information technology proliferates, it is imperative that pharmacists' workflow and information needs are met within the EHR to optimize medication therapy quality, team communication, and patient outcomes.

OBJECTIVES: Identify and describe information needs and associated goals of physicians, care coordinators, and families related to coordinating care for medically complex children and youth with special health care needs (CYSHCN). MATERIALS AND METHODS: We conducted 19 in-depth interviews with physicians, care coordinators, and parents of CYSHCN following the Critical Decision Method technique. We analyzed the interviews for information needs posed as questions using a systematic content analysis approach and categorized the questions into information need goal types and subtypes. RESULTS: The Critical Decision Method interviews resulted in an average of 80 information needs per interview. We categorized them into 6 information need goal types: (1) situation understanding, (2) care networking, (3) planning, (4) tracking/monitoring, (5) navigating the health care system, and (6) learning, and 32 subtypes. DISCUSSION AND CONCLUSION: Caring for CYSHCN generates a large amount of information needs that require significant effort from physicians, care coordinators, parents, and various other individuals. CYSHCN are often chronically ill and face developmental challenges that translate into intense demands on time, effort, and resources. Care coordination for CYCHSN involves multiple information systems, specialized resources, and complex decision-making. Solutions currently offered by health information technology fall short in providing support to meet the information needs to perform the complex care coordination tasks. Our findings present significant opportunities to improve coordination of care through multifaceted and fully integrated informatics solutions.

Over the last 5 years, stimulated by the changing healthcare environment and the Health Information Technology for Economic and Clinical Health (HITECH) Meaningful Use (MU) Electronic Health Record (EHR) Incentive program, EHR adoption has increased remarkably, and there is early evidence that such adoption has resulted in healthcare safety and quality benefits.1,2 However, with this broad adoption, many clinicians are voicing concerns that EHR use has had unintended clinical consequences, including reduced time for patient-clinician interaction,3 new and burdensome data entry tasks being transferred to front-line clinicians,4,5 and lengthened clinician workdays.6–8 Additionally, interoperability between different EHR systems has languished despite large efforts towards that goal.9,10 These challenges are contributing to physicians’ decreased satisfaction with their work lives.11–13 In professional journals,14 press reports,15–17 on wards, and in clinics, we have heard of the difficulties that the transition from paper records to EHRs has created.18 As a result, clinicians are seeking help to get through their work days, which often extend into evenings devoted to writing notes. Examples of comments we have received from clinicians and patients include: “Computers always make things faster and cheaper. Not this time,” and “My doctor pays more attention to the computer than to me.”

OBJECTIVE: The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain. MATERIALS AND METHODS: MEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation. RESULTS: Of 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural language processing (17; 50%) and a hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation. DISCUSSION: This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization. CONCLUSION: Recent research has focused on a hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings.

OBJECTIVE: Clinical documentation is central to the medical record and so to a range of healthcare and business processes. As electronic health record adoption expands, computerized provider documentation (CPD) is increasingly the primary means of capturing clinical documentation. Previous CPD studies have focused on particular stakeholder groups and sites, often limiting their scope and conclusions. To address this, we studied multiple stakeholder groups from multiple sites across the USA. METHODS: We conducted 14 focus groups at five Department of Veterans Affairs facilities with 129 participants (54 physicians or practitioners, 34 nurses, and 37 administrators). Investigators qualitatively analyzed resultant transcripts, developed categories linked to the data, and identified emergent themes. RESULTS: Five major themes related to CPD emerged: communication and coordination; control and limitations in expressivity; information availability and reasoning support; workflow alteration and disruption; and trust and confidence concerns. The results highlight that documentation intertwines tightly with clinical and administrative workflow. Perceptions differed between the three stakeholder groups but remained consistent within groups across facilities. CONCLUSIONS: CPD has dramatically changed documentation processes, impacting clinical understanding, decision-making, and communication across multiple groups. The need for easy and rapid, yet structured and constrained, documentation often conflicts with the need for highly reliable and retrievable information to support clinical reasoning and workflows. Current CPD systems, while better than paper overall, often do not meet the needs of users, partly because they are based on an outdated 'paper-chart' paradigm. These findings should inform those implementing CPD systems now and future plans for more effective CPD systems.

OBJECTIVE: Online health knowledge resources contain answers to most of the information needs raised by clinicians in the course of care. However, significant barriers limit the use of these resources for decision-making, especially clinicians' lack of time. In this study we assessed the feasibility of automatically generating knowledge summaries for a particular clinical topic composed of relevant sentences extracted from Medline citations. METHODS: The proposed approach combines information retrieval and semantic information extraction techniques to identify relevant sentences from Medline abstracts. We assessed this approach in two case studies on the treatment alternatives for depression and Alzheimer's disease. RESULTS: A total of 515 of 564 (91.3%) sentences retrieved in the two case studies were relevant to the topic of interest. About one-third of the relevant sentences described factual knowledge or a study conclusion that can be used for supporting information needs at the point of care. CONCLUSIONS: The high rate of relevant sentences is desirable, given that clinicians' lack of time is one of the main barriers to using knowledge resources at the point of care. Sentence rank was not significantly associated with relevancy, possibly due to most sentences being highly relevant. Sentences located closer to the end of the abstract and sentences with treatment and comparative predications were likely to be conclusive sentences. Our proposed technical approach to helping clinicians meet their information needs is promising. The approach can be extended for other knowledge resources and information need types.

OBJECTIVES: Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. MATERIALS AND METHODS: We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements. RESULTS: System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). DISCUSSION: An EF value of <40% can be accurately identified in VA echocardiogram reports. CONCLUSIONS: An automated information extraction system can be used to accurately extract EF for quality measurement.

OBJECTIVE: Fatal errors can occur in intensive care units (ICUs). Researchers claim that information integration at the bedside may improve nurses' situation awareness (SA) of patients and decrease errors. However, it is unclear which information should be integrated and in what form. Our research uses the theory of SA to analyze the type of tasks, and their associated information gaps. We aimed to provide recommendations for integrated, consolidated information displays to improve nurses' SA. MATERIALS AND METHODS: Systematic observations methods were used to follow 19 ICU nurses for 38 hours in 3 clinical practice settings. Storyboard methods and concept mapping helped to categorize the observed tasks, the associated information needs, and the information gaps of the most frequent tasks by SA level. Consensus and discussion of the research team was used to propose recommendations to improve information displays at the bedside based on information deficits. RESULTS: Nurses performed 46 different tasks at a rate of 23.4 tasks per hour. The information needed to perform the most common tasks was often inaccessible, difficult to see at a distance or located on multiple monitoring devices. Current devices at the ICU bedside do not adequately support a nurse's information-gathering activities. Medication management was the most frequent category of tasks. DISCUSSION: Information gaps were present at all levels of SA and across most of the tasks. Using a theoretical model to understand information gaps can aid in designing functional requirements. CONCLUSION: Integrated information that enhances nurses' Situation Awareness may decrease errors and improve patient safety in the future.

OBJECTIVE: The evidence base for information technology (IT) has been criticized, especially with the current emphasis on translational science. The purpose of this paper is to present an analysis of the role of IT in the implementation of a geriatric education and quality improvement (QI) intervention. DESIGN: A mixed-method three-group comparative design was used. The PRECEDE/PROCEED implementation model was used to qualitatively identify key factors in the implementation process. These results were further explored in a quantitative analysis. METHOD: Thirty-three primary care clinics at three institutions (Intermountain Healthcare, VA Salt Lake City Health Care System, and University of Utah) participated. The program consisted of an onsite, didactic session, QI planning and 6 months of intense implementation support. RESULTS: Completion rate was 82% with an average improvement rate of 21%. Important predisposing factors for success included an established electronic record and a culture of quality. The reinforcing and enabling factors included free continuing medical education credits, feedback, IT access, and flexible support. The relationship between IT and QI emerged as a central factor. Quantitative analysis found significant differences between institutions for pre-post changes even after the number and category of implementation strategies had been controlled for. CONCLUSIONS: The analysis illustrates the complex dependence between IT interventions, institutional characteristics, and implementation practices. Access to IT tools and data by individual clinicians may be a key factor for the success of QI projects. Institutions vary widely in the degree of access to IT tools and support. This article suggests that more attention be paid to the QI and IT department relationship.

OBJECTIVE: Computerized Provider Order Entry (CPOE) with electronic documentation, and computerized decision support dramatically changes the information environment of the practicing clinician. Prior work patterns based on paper, verbal exchange, and manual methods are replaced with automated, computerized, and potentially less flexible systems. The objective of this study is to explore the information management strategies that clinicians use in the process of adapting to a CPOE system using cognitive task analysis techniques. DESIGN: Observation and semi-structured interviews were conducted with 88 primary-care clinicians at 10 Veterans Administration Medical Centers. MEASUREMENTS: Interviews were taped, transcribed, and extensively analyzed to identify key information management goals, strategies, and tasks. Tasks were aggregated into groups, common components across tasks were clarified, and underlying goals and strategies identified. RESULTS: Nearly half of the identified tasks were not fully supported by the available technology. Six core components of tasks were identified. Four meta-cognitive information management goals emerged: 1) Relevance Screening; 2) Ensuring Accuracy; 3) Minimizing memory load; and 4) Negotiating Responsibility. Strategies used to support these goals are presented. CONCLUSION: Users develop a wide array of information management strategies that allow them to successfully adapt to new technology. Supporting the ability of users to develop adaptive strategies to support meta-cognitive goals is a key component of a successful system.

Computerized decision support and order entry shows great promise for reducing adverse drug events (ADEs). The evaluation of these solutions depends on a framework of definitions and classifications that is clear and practical. Unfortunately the literature does not always provide a clear path to defining and classifying adverse drug events. While not a systematic review, this paper uses examples from the literature to illustrate problems that investigators will confront as they develop a conceptual framework for their research. It also proposes a targeted taxonomy that can facilitate a clear and consistent approach to the research of ADEs and aid in the comparison to results of past and future studies. This paper outlines the ambiguity in definitions of ADEs that has arisen from the conflation of regulatory and quality terminology. It proposes a typology for ADEs by drug and disease effect and outlines problems inherent in the study of ADEs related to disease effects, errors, and omitted therapies. The paper also highlights difficulty in assessing seriousness and causality and the problems with commonly used scales for these assessments. Finally, although national or international agreement on taxonomy for ADEs is a distant or unachievable goal, individual investigations and the literature as a whole will be improved by prospective, explicit classification of ADEs and inclusion of the study's approach to classification in publications.