"Are we there yet?" Ten persistent hazards and inefficiencies with the use of medication administration technology from the perspective of practicing nurses.

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.

Modeling patient-related workload in the emergency department using electronic health record data.

INTRODUCTION: Understanding and managing clinician workload is important for clinician (nurses, physicians and advanced practice providers) occupational health as well as patient safety. Efforts have been made to develop strategies for managing clinician workload by improving patient assignment. The goal of the current study is to use electronic health record (EHR) data to predict the amount of work that individual patients contribute to clinician workload (patient-related workload). METHODS: One month of EHR data was retrieved from an emergency department (ED). A list of workload indicators and five potential workload proxies were extracted from the data. Linear regression and four machine learning classification algorithms were utilized to model the relationship between the indicators and the proxies. RESULTS: Linear regression proved that the indicators explained a substantial amount of variance of the proxies (four out of five proxies were modeled with R2 > 0.80). Classification algorithms also showed success in classifying a patient as having high or low task demand based on data from early in the ED visit (e.g. 80 % accurate binary classification with data from the first hour). CONCLUSION: The main contribution of this study is demonstrating the potential of using EHR data to predict patient-related workload automatically in the ED. The predicted workload can potentially help in managing clinician workload by supporting decisions around the assignment of new patients to providers. Future work should focus on identifying the relationship between workload proxies and actual workload, as well as improving prediction performance of regression and multi-class classification.

Ten Best Practices for Improving Emergency Medicine Provider-Nurse Communication.

BACKGROUND: The current state of scientific knowledge regarding communication between emergency medicine (EM) providers indicates that communication is critical to safe and effective patient care. OBJECTIVES: In this study, we identified communication needs of EM nurses and physicians; in particular, what information should be conveyed, when, how, and to whom. METHODS: Five semi-structured focus groups and one interview were conducted with nine nurses, eight attending physicians, and four residents. Questions addressed how EM personnel use and share information about patients and clinical work, what information tends to be exchanged, and what additional information would be helpful to share. Sessions were audio recorded. Transcripts were generated and analyzed using a concept mapping approach (a visual qualitative analysis technique to represent and convey synthesized knowledge). RESULTS: Eleven concept maps were produced summarizing: information physicians needed from nurses and vice versa; methods of communication that could be utilized; barriers or obstacles to effective communication; strategies to enhance or ensure effective communication; and environmental or situational factors that impact communication. CONCLUSIONS: Our main finding of this research is that communication ensures shared awareness of patient health status, the care plan, status of plan steps and orders, and, especially, any critical changes or "surprises" regarding the health of a patient. Additionally, the research identified shared information needs; communication methods, strategies and barriers; and factors affecting successful communication, and has implications for both system and training design. Key implications for emergency nursing practice from this research are distilled in 10 'best practice' strategies for improving EM nurse-physician communication.

Informatics and interaction: Applying human factors principles to optimize the design of clinical decision support for sepsis.

In caring for patients with sepsis, the current structure of electronic health record systems allows clinical providers access to raw patient data without imputation of its significance. There are a wide range of sepsis alerts in clinical care that act as clinical decision support tools to assist in early recognition of sepsis; however, there are serious shortcomings in existing health information technology for alerting providers in a meaningful way. Little work has been done to evaluate and assess existing alerts using implementation and process outcomes associated with health information technology displays, specifically evaluating clinician preference and performance. We developed graphical model displays of two popular sepsis scoring systems, quick Sepsis Related Organ Failure Assessment and Predisposition, Infection, Response, Organ Failure, using human factors principles grounded in user-centered and interaction design. Models will be evaluated in a larger research effort to optimize alert design to improve the collective awareness of high-risk populations and develop a relevant point-of-care clinical decision support system for sepsis.

Design and Evaluation of an Integrated, Patient-Focused Electronic Health Record Display for Emergency Medicine.

BACKGROUND: Hospital emergency departments (EDs) are dynamic environments, involving coordination and shared decision making by staff who care for multiple patients simultaneously. While computerized information systems have been widely adopted in such clinical environments, serious issues have been raised related to their usability and effectiveness. In particular, there is a need to support clinicians to communicate and maintain awareness of a patient's health status, and progress through the ED plan of care. OBJECTIVE: This study used work-centered usability methods to evaluate an integrated patient-focused status display designed to support ED clinicians' communication and situation awareness regarding a patient's health status and progress through their ED plan of care. The display design was informed by previous studies we conducted examining the information and cognitive support requirements of ED providers and nurses. METHODS: ED nurse and provider participants were presented various scenarios requiring patient-prioritization and care-planning tasks to be performed using the prototype display. Participants rated the display in terms of its cognitive support, usability, and usefulness. Participants' performance on the various tasks, and their feedback on the display design and utility, was analyzed. RESULTS: Participants provided ratings for usability and usefulness for the display sections using a work-centered usability questionnaire-mean scores for nurses and providers were 7.56 and 6.6 (1 being lowest and 9 being highest), respectively. General usability scores, based on the System Usability Scale tool, were rated as acceptable or marginally acceptable. Similarly, participants also rated the display highly in terms of support for specific cognitive objectives. CONCLUSION: A novel patient-focused status display for emergency medicine was evaluated via a simulation-based study in terms of work-centered usability and usefulness. Participants' subjective ratings of usability, usefulness, and support for cognitive objectives were encouraging. These findings, including participants' qualitative feedback, provided insights for improving the design of the display.

Identifying influential individuals on intensive care units: using cluster analysis to explore culture.

AIM: The objective of this paper is to identify attribute patterns of influential individuals in intensive care units using unsupervised cluster analysis. BACKGROUND: Despite the acknowledgement that culture of an organisation is critical to improving patient safety, specific methods to shift culture have not been explicitly identified. METHODS: A social network analysis survey was conducted and an unsupervised cluster analysis was used. RESULTS: A total of 100 surveys were gathered. Unsupervised cluster analysis was used to group individuals with similar dimensions highlighting three general genres of influencers: well-rounded, knowledge and relational. CONCLUSIONS: Culture is created locally by individual influencers. Cluster analysis is an effective way to identify common characteristics among members of an intensive care unit team that are noted as highly influential by their peers. To change culture, identifying and then integrating the influencers in intervention development and dissemination may create more sustainable and effective culture change. Additional studies are ongoing to test the effectiveness of utilising these influencers to disseminate patient safety interventions. IMPLICATIONS FOR NURSING MANAGEMENT: This study offers an approach that can be helpful in both identifying and understanding influential team members and may be an important aspect of developing methods to change organisational culture.

Communication in the Electronic Age: an Analysis of Face-to-Face Physician-Nurse Communication in the Emergency Department.

We describe the patterns and content of nurse to physician verbal conversations in three emergency departments (EDs) with electronic health records. Emergency medicine physicians and nurses were observed for 2 h periods. Researchers used paper notes to document the characteristics (e.g., partners involved, location of communication, who initiated communication) and content of nurse to physician conversations. Eighteen emergency nurses and physicians (nine each) were observed for a total of 36 h. Two hundred and fifty-five unique communication events were recorded across three emergency departments spread evenly across day, evening, and night shifts. A qualitative analysis of communication event content revealed 5 types of communication and 13 content themes. Content themes covered a broad range of topics including exchange of patient health information, management of the ED, and coordination of orders. Physician participants experienced significantly more communication events than nurse participants, while nurses initiated significantly more communication events than physicians. Most of the communication events occurred at the physician workstation followed by patient treatment areas. This study describes nature of verbal nurse to physician communication in the ED. Direct communication is still used to communicate important information, such as information about patients' status, in EDs with established electronic health records. Our results provide an overview of information exchanged in the ED which can serve as a basis for designing improved information support systems.

Evidence-based practices to increase hand hygiene compliance in health care facilities: An integrated review.

BACKGROUND: Hand hygiene (HH) in health care facilities is a key component to reduce pathogen transmission and nosocomial infections. However, most HH interventions (HHI) have not been sustainable. AIMS: This review aims to provide a comprehensive summary of recently published evidence-based HHI designed to improve HH compliance (HHC) that will enable health care providers to make informed choices when allocating limited resources to improve HHC and patient safety. METHODS: The Medline electronic database (using PubMed) was used to identify relevant studies. English language articles that included hand hygiene interventions and related terms combined with health care environments or related terms were included. RESULTS: Seventy-three studies that met the inclusion criteria were summarized. Interventions were categorized as improving awareness with education, facility design, and planning, unit-level protocols and procedures, hospital-wide programs, and multimodal interventions. Past successful HHIs may not be as effective when applied to other health care environments. HH education should be interactive and engaging. Electronic monitoring and reminders should be implemented in phases to ensure cost-effectiveness. To create hospitalwide programs that engage end users, policy makers should draw expertise from interdisciplinary fields. Before implementing the various components of multimodal interventions, health care practitioners should identify and examine HH difficulties unique to their organizations. CONCLUSIONS: Future research should seek to achieve the following: replicate successful HHI in other health care environments, develop reliable HHC monitoring tools, understand caregiver-patient-family interactions, examine ways (eg, hospital leadership, financial support, and strategies from public health and infection prevention initiatives) to sustain HHC, and use simulated lab environments to refine study designs.

Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile.

BACKGROUND: Although many risk factors are well known, Clostridium difficile infection (CDI) continues to be a significant problem throughout the world. The purpose of this study was to develop and validate a data-driven, hospital-specific risk stratification procedure for estimating the probability that an inpatient will test positive for C difficile. METHODS: We consider electronic medical record (EMR) data from patients admitted for ≥24 hours to a large urban hospital in the U.S. between April 2011 and April 2013. Predictive models were constructed using L2-regularized logistic regression and data from the first year. The number of observational variables considered varied from a small set of well known risk factors readily available to a physician to over 10 000 variables automatically extracted from the EMR. Each model was evaluated on holdout admission data from the following year. A total of 34 846 admissions with 372 cases of CDI was used to train the model. RESULTS: Applied to the separate validation set of 34 722 admissions with 355 cases of CDI, the model that made use of the additional EMR data yielded an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% confidence interval [CI], .79-.83), and it significantly outperformed the model that considered only the small set of known clinical risk factors, AUROC of 0.71 (95% CI, .69-.75). CONCLUSIONS: Automated risk stratification of patients based on the contents of their EMRs can be used to accurately identify a high-risk population of patients. The proposed method holds promise for enabling the selective allocation of interventions aimed at reducing the rate of CDI.