Understanding Paper-Based Documentation Practices in Medical Resuscitations to Inform the Design of Electronic Documentation Tools.

OBJECTIVE: Despite growing use of electronic health records, many resuscitation settings still use paper-based documentation. The fast-paced and safety-critical nature of trauma and medical resuscitation environments pose challenges for real-time documentation. This study aims to understand paper-based documentation practices and inform the design of efficient electronic documentation solutions for supporting safety-critical medical processes. METHODS: Data were collected through in situ observations of nurse documenters during resuscitation events and postevent interviews with nurses. These data were analyzed using frequency distribution and qualitative, open-coding techniques. Data analysis focused on the following 3 main documentation factors: temporal distribution of documentation, total number of filled out sections on the paper flow sheet across all resuscitations, and completeness of documentation per resuscitation. RESULTS: Findings from this study highlight the time-critical nature of these settings, showing that 74% of the documentation was completed within the first 15 minutes of the resuscitation. Some sections of the paper flow sheet were filled out more than others, and a few sections were left incomplete across all events. Interviews with nurses provided insight about documentation experiences in a fast-paced environment, including variable usage of flow sheet based on nurse experience level and patient scenarios, supplemental documentation mechanisms, and information needs and preferences. CONCLUSIONS: Several design implications are discussed to inform the design of effective electronic documentation systems. Design implications focus on layout structure, prepopulating items, section placement, and completion status of the flow sheet. Future plans for research focus on combining video review with in situ observations and conducting detailed interviews with nurses to better understand their documentation experiences and preferences.

Classification strategies for non-routine events occurring in high-risk patient care settings: A scoping review.

INTRODUCTION: Non-routine events (NREs) are atypical or unusual occurrences in a pre-defined process. Although some NREs in high-risk clinical settings have no adverse effects on patient care, others can potentially cause serious patient harm. A unified strategy for identifying and describing NREs in these domains will facilitate the comparison of results between studies. METHODS: We conducted a literature search in PubMed, CINAHL, and EMBASE to identify studies related to NREs in high-risk domains and evaluated the methods used for event observation and description. We applied The Joint Commission on Accreditation of Healthcare Organization (JCAHO) taxonomy (cause, impact, domain, type, prevention, and mitigation) to the descriptions of NREs from the literature. RESULTS: We selected 25 articles that met inclusion criteria for review. Real-time documentation of NREs was more common than a retrospective video review. Thirteen studies used domain experts as observers and seven studies validated observations with interrater reliability. Using the JCAHO taxonomy, "cause" was the most frequently applied classification method, followed by "impact," "type," "domain," and "prevention and mitigation." CONCLUSIONS: NREs are frequent in high-risk medical settings. Strengths identified in several studies included the use of multiple observers with domain expertise and validation of the event ascertainment approach using interrater reliability. By applying the JCAHO taxonomy to the current literature, we provide an example of a structured approach that can be used for future analyses of NREs.

An Analysis of Speech as a Modality for Activity Recognition during Complex Medical Teamwork.

We analyzed the nature of verbal communication among team members in a dynamic medical setting of trauma resuscitation to inform the design of a speech-based automatic activity recognition system. Using speech transcripts from 20 resuscitations, we identified common keywords and speech patterns for different resuscitation activities. Based on these patterns, we developed narrative schemas (speech "workflow" models) for five most frequently performed activities and applied linguistic models to represent relationships between sentences. We evaluated the narrative schemas with 17 new cases, finding that all five schemas adequately represented speech during activities and could serve as a basis for speech-based activity recognition. We also identified similarities between narrative schemas of different activities. We conclude with design implications and challenges associated with speech-based activity recognition in complex medical processes.