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BACKGROUND: In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians. OBJECTIVE: Addressing the limited exposure of healthcare providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality. METHODS: The workshop focuses on meaningful topics for clinicians, such as model performance evaluation and introduces machine learning through hands-on exercises using free, interactive python notebooks. Clinical case studies on sepsis recognition and opioid overdose death provide relatable contexts for applying data science concepts. RESULTS: Positive feedback from over 300 participants across various settings highlights the workshop's effectiveness in making complex topics accessible to clinicians. CONCLUSIONS: Our approach prioritizes engaging content delivery and practical application over extensive programming instruction, aligning with adult learning principles. This initiative underscores the importance of equipping clinicians with data science knowledge to navigate today's data-driven healthcare landscape, offering a template for integrating data science education into healthcare informatics programs or continuing professional development.

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OBJECTIVE: Efforts to reduce documentation burden (DocBurden) for all health professionals (HP) are aligned with national initiatives to improve clinician wellness and patient safety. Yet DocBurden has not been precisely defined, limiting national conversations and rigorous, reproducible, and meaningful measures. Increasing attention to DocBurden motivated this work to establish a standard definition of DocBurden, with the emergence of excessive DocBurden as a term. METHODS: We conducted a scoping review of DocBurden definitions and descriptions, searching six databases for scholarly, peer-reviewed, and gray literature sources, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extensions for Scoping Review (PRISMA-ScR) guidance. For the concept clarification phase of work, we used the American Nursing Informatics Association (ANIA)'s 6-Domains of Burden Framework. RESULTS: A total of 153 articles were included based on a priori criteria. Most articles described a focus on DocBurden, but only 18% (n=28) provided a definition. We define excessive DocBurden as the stress and unnecessarily heavy work a HP or healthcare team experiences when usability of documentation systems and documentation activities (i.e., generation, review, analysis and synthesis of patient data) are not aligned in support of care delivery. A negative connotation was attached to burden without a neutral state in included sources, which does not align with dictionary definitions of burden. CONCLUSIONS: Existing literature does not distinguish between a baseline or required task load to conduct patient care resulting from usability issues(DocBurden), and the unnecessarily heavy tasks and requirements that contribute to excessive DocBurden. Our definition of excessive DocBurden explicitly acknowledges this distinction, to support development of meaningful measures for understanding and intervening on excessive DocBurden locally, nationally and internationally.

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BACKGROUND: Technology in health care is pervasive and growing exponentially. Simulation in academic settings also continues to expand with the use of advancing technologies. PROBLEM: Technology appropriate usage and implementation can become problematic without experts with the right skills in nursing simulation labs. Gaps in technology utilization, simulation best practices, and data analytics now exist. APPROACH: A school of nursing employed a nurse informaticist in its simulation laboratory to address the complexities of technology usage and aid in data analytics. OUTCOMES: The nurse informaticist was effective at promoting simulation use, increasing faculty and learner usage of the simulation space. The initiation of data capturing was also achieved. CONCLUSIONS: Highly trained nurse informaticists are ready to address the unique complexities of advanced simulation environments.

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A panel sponsored by the American College of Medical Informatics (ACMI) at the 2021 AMIA Symposium addressed the provocative question: "Are Electronic Health Records dumbing down clinicians?" After reviewing electronic health record (EHR) development and evolution, the panel discussed how EHR use can impair care delivery. Both suboptimal functionality during EHR use and longer-term effects outside of EHR use can reduce clinicians' efficiencies, reasoning abilities, and knowledge. Panel members explored potential solutions to problems discussed. Progress will require significant engagement from clinician-users, educators, health systems, commercial vendors, regulators, and policy makers. Future EHR systems must become more user-focused and scalable and enable providers to work smarter to deliver improved care.

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BACKGROUND: Documentation burden is a common problem with modern electronic health record (EHR) systems. To reduce this burden, various recording methods (eg, voice recorders or motion sensors) have been proposed. However, these solutions are in an early prototype phase and are unlikely to transition into practice in the near future. A more pragmatic alternative is to directly modify the implementation of the existing functionalities of an EHR system. OBJECTIVE: This study aims to assess the nature of free-text comments entered into EHR flowsheets that supplement quantitative vital sign values and examine opportunities to simplify functionality and reduce documentation burden. METHODS: We evaluated 209,055 vital sign comments in flowsheets that were generated in the Epic EHR system at the Vanderbilt University Medical Center in 2018. We applied topic modeling, as well as the natural language processing Clinical Language Annotation, Modeling, and Processing software system, to extract generally discussed topics and detailed medical terms (expressed as probability distribution) to investigate the stories communicated in these comments. RESULTS: Our analysis showed that 63.33% (6053/9557) of the users who entered vital signs made at least one free-text comment in vital sign flowsheet entries. The user roles that were most likely to compose comments were registered nurse, technician, and licensed nurse. The most frequently identified topics were the notification of a result to health care providers (0.347), the context of a measurement (0.307), and an inability to obtain a vital sign (0.224). There were 4187 unique medical terms that were extracted from 46,029 (0.220) comments, including many symptom-related terms such as "pain," "upset," "dizziness," "coughing," "anxiety," "distress," and "fever" and drug-related terms such as "tylenol," "anesthesia," "cannula," "oxygen," "motrin," "rituxan," and "labetalol." CONCLUSIONS: Considering that flowsheet comments are generally not displayed or automatically pulled into any clinical notes, our findings suggest that the flowsheet comment functionality can be simplified (eg, via structured response fields instead of a text input dialog) to reduce health care provider effort. Moreover, rich and clinically important medical terms such as medications and symptoms should be explicitly recorded in clinical notes for better visibility.

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BACKGROUND: Electronic health record (EHR) system implementation is complex. Strong support for clinicians and other end-users during the initial phase of implementation requires dedicated resources and commitment to ensure a smooth transition, reduce frustration, and assist in adoption. OBJECTIVES: Evaluate end-user support processes and personnel employed during the initial phase of EHR implementation at an academic medical center and identify facilitators of success, challenges, and lessons learned. METHODS: Using the model for improvement framework, this case report describes the activities that follow the concepts of planning, doing, studying, and acting (PDSA) when providing on-site support to system end users during an EHR implementation in a complex health care system. RESULTS: Strengths included engaged and supportive leadership, use of internal support personnel, use of zone leaders, daily huddles for effective dissemination of information, and an evaluation survey tool to provide data supporting rapid changes in support personnel allocation. Challenges primarily surrounded the management of over 1,000 external support personnel which included limited EHR system knowledge, scheduling, and transportation logistics. CONCLUSION: Implementation of EHR systems continues. Supporting end users is one aspect of these complex projects that require dedicated resources to manage effectively. Organizations taking a more proactive approach, based on learning from experiences, as well as from other health care organizations, can improve their ability to take on this challenge armed with best practices and lessons learned.

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INTRODUCTION: The emerging operational role of the "Chief Clinical Informatics Officer" (CCIO) remains heterogeneous with individuals deriving from a variety of clinical settings and backgrounds. The CCIO is defined in title, responsibility, and scope of practice by local organizations. The term encompasses the more commonly used Chief Medical Informatics Officer (CMIO) and Chief Nursing Informatics Officer (CNIO) as well as the rarely used Chief Pharmacy Informatics Officer (CPIO) and Chief Dental Informatics Officer (CDIO). BACKGROUND: The American Medical Informatics Association (AMIA) identified a need to better delineate the knowledge, education, skillsets, and operational scope of the CCIO in an attempt to address the challenges surrounding the professional development and the hiring processes of CCIOs. DISCUSSION: An AMIA task force developed knowledge, education, and operational skillset recommendations for CCIOs focusing on the common core aspect and describing individual differences based on Clinical Informatics focus. The task force concluded that while the role of the CCIO currently is diverse, a growing body of Clinical Informatics and increasing certification efforts are resulting in increased homogeneity. The task force advised that 1.) To achieve a predictable and desirable skillset, the CCIO must complete clearly defined and specified Clinical Informatics education and training. 2.) Future education and training must reflect the changing body of knowledge and must be guided by changing day-to-day informatics challenges. CONCLUSION: A better defined and specified education and skillset for all CCIO positions will motivate the CCIO workforce and empower them to perform the job of a 21st century CCIO. Formally educated and trained CCIOs will provide a competitive advantage to their respective enterprise by fully utilizing the power of Informatics science.

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The primary aim of this performance improvement project was to determine whether the electronic health record implementation of stroke-specific nursing documentation flowsheet templates and clinical decision support alerts improved the nursing documentation of eligible stroke patients in seven stroke-certified emergency departments. Two system enhancements were introduced into the electronic record in an effort to improve nursing documentation: disease-specific documentation flowsheets and clinical decision support alerts. Using a pre-post design, project measures included six stroke management goals as defined by the National Institute of Neurological Disorders and Stroke and three clinical decision support measures based on entry of orders used to trigger documentation reminders for nursing: (1) the National Institutes of Health's Stroke Scale, (2) neurological checks, and (3) dysphagia screening. Data were reviewed 6 months prior (n = 2293) and 6 months following the intervention (n = 2588). Fisher exact test was used for statistical analysis. Statistical significance was found for documentation of five of the six stroke management goals, although effect sizes were small. Customizing flowsheets to meet the needs of nursing workflow showed improvement in the completion of documentation. The effects of the decision support alerts on the completeness of nursing documentation were not statistically significant (likely due to lack of order entry). For example, an order for the National Institutes of Health Stroke Scale was entered only 10.7% of the time, which meant no alert would fire for nursing in the postintervention group. Future work should focus on decision support alerts that trigger reminders for clinicians to place relevant orders for this population.

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OBJECTIVE: To understand how nurses respond to alerts that detect attempts to enter into electronic health records patient weights that vary significantly from previously recorded weights. METHODS: Examination of subsequent patient weights to determine if the alerts were true positive (TP) or false positive (FP), and whether nurses overrode alerts, changed their entry or quit without storing a value. RESULTS: Alerts occurred 2.74%, with 41.9% TP and 58.1% FP. Nurses overrode 30.3% of TP and 97.3% of FP alerts. CONCLUSIONS: The alert has an acceptable FP rate and does not appear to cause nurses to change entries to satisfy the alert. The alert improves recording of patient weights.

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The medication error dilemma has come to the forefront of most hospitals' improvement agendas. The most often cited solution to the problem has been computerized provider order entry (CPOE) systems. These systems have significant potential to improve errors associated with illegibility as well as inappropriate drug use and dosing. On the other hand, CPOE system implementation is fraught with barriers that impede acceptance and use of these systems. Knowing what strategies have proven successful and what upfront analysis is required can help increase the chances of success and ultimately improve the quality of patient care.

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