Consistency is key: documentation distribution and efficiency in primary care.

OBJECTIVES: We analyzed the degree to which daily documentation patterns in primary care varied and whether specific patterns, consistency over time, and deviations from clinicians' usual patterns were associated with note-writing efficiency. MATERIALS AND METHODS: We used electronic health record (EHR) active use data from the Oracle Cerner Advance platform capturing hourly active documentation time for 498 physicians and advance practice clinicians (eg, nurse practitioners) for 65 152 clinic days. We used k-means clustering to identify distinct daily patterns of active documentation time and analyzed the relationship between these patterns and active documentation time per note. We determined each primary care clinician's (PCC) modal documentation pattern and analyzed how consistency and deviations were related to documentation efficiency. RESULTS: We identified 8 distinct daily documentation patterns; the 3 most common patterns accounted for 80.6% of PCC-days and differed primarily in average volume of documentation time (78.1 minutes per day; 35.4 minutes per day; 144.6 minutes per day); associations with note efficiency were mixed. PCCs with >80% of days attributable to a single pattern demonstrated significantly more efficient documentation than PCCs with lower consistency; for high-consistency PCCs, days that deviated from their usual patterns were associated with less efficient documentation. DISCUSSION: We found substantial variation in efficiency across daily documentation patterns, suggesting that PCC-level factors like EHR facility and consistency may be more important than when documentation occurs. There were substantial efficiency returns to consistency, and deviations from consistent patterns were costly. CONCLUSION: Organizational leaders aiming to reduce documentation burden should pay specific attention to the ability for PCCs to execute consistent documentation patterns day-to-day.

The Effects of a Health Care Chatbot's Complexity and Persona on User Trust, Perceived Usability, and Effectiveness: Mixed Methods Study.

BACKGROUND: The rising adoption of telehealth provides new opportunities for more effective and equitable health care information mediums. The ability of chatbots to provide a conversational, personal, and comprehendible avenue for learning about health care information make them a promising tool for addressing health care inequity as health care trends continue toward web-based and remote processes. Although chatbots have been studied in the health care domain for their efficacy for smoking cessation, diet recommendation, and other assistive applications, few studies have examined how specific design characteristics influence the effectiveness of chatbots in providing health information. OBJECTIVE: Our objective was to investigate the influence of different design considerations on the effectiveness of an educational health care chatbot. METHODS: A 2×3 between-subjects study was performed with 2 independent variables: a chatbot's complexity of responses (eg, technical or nontechnical language) and the presented qualifications of the chatbot's persona (eg, doctor, nurse, or nursing student). Regression models were used to evaluate the impact of these variables on 3 outcome measures: effectiveness, usability, and trust. A qualitative transcript review was also done to review how participants engaged with the chatbot. RESULTS: Analysis of 71 participants found that participants who received technical language responses were significantly more likely to be in the high effectiveness group, which had higher improvements in test scores (odds ratio [OR] 2.73, 95% CI 1.05-7.41; P=.04). Participants with higher health literacy (OR 2.04, 95% CI 1.11-4.00, P=.03) were significantly more likely to trust the chatbot. The participants engaged with the chatbot in a variety of ways, with some taking a conversational approach and others treating the chatbot more like a search engine. CONCLUSIONS: Given their increasing popularity, it is vital that we consider how chatbots are designed and implemented. This study showed that factors such as chatbots' persona and language complexity are two design considerations that influence the ability of chatbots to successfully provide health care information.

Medication errors, critical incidents, adverse drug events, and more: a review examining patient safety-related terminology in anaesthesia.

Literature focused on quantifying or reducing patient harm in anaesthesia uses a variety of labels and definitions to represent patient safety-related events, such as 'medication errors', 'adverse events', and 'critical incidents'. This review extracts and compares definitions of patient safety-related terminology in anaesthesia to examine the scope of this variability and inconsistencies. A structured review was performed in which 36 of the 769 articles reviewed met the inclusion criteria. Similar terms were grouped into six categories by similarities in keyword choice (Adverse Event, Critical Incident, Medication Error, Error, Near Miss, and Harm) and their definitions were broken down into three base components to allow for comparison. Our analysis found that the Medication Error category, which encompasses the greatest number of terms, had widely variant definitions which represent fundamentally different concepts. Definitions of terms within the other categories consistently represented relatively similar concepts, though key variations in wording remain. This inconsistency in terminology can lead to problems with synthesising, interpreting, and overall sensemaking in relation to anaesthesia medication safety. Guidance towards how 'medication errors' should be defined is provided, yet a definition will have little impact on the future of patient safety without organisations and journals taking the lead to promote, publish, and standardise definitions.