Comparison of a prototype for indications-based prescribing with 2 commercial prescribing systems

Importance: The indication (reason for use) for a medication is rarely included on prescriptions despite repeated recommendations to do so. One barrier has been the way existing electronic prescribing systems have been designed. Objective: To evaluate, in comparison with the prescribing modules of 2 leading electronic health record prescribing systems, the efficiency, error rate, and satisfaction with a new computerized provider order entry prototype for the outpatient setting that allows clinicians to initiate prescribing using the indication. Design, Setting, and Participants: This quality improvement study used usability tests requiring internal medicine physicians, residents, and physician assistants to enter prescriptions electronically, including indication, for 8 clinical scenarios. The tool order assignments were randomized and prescribers were asked to use the prototype for 4 of the scenarios and their usual system for the other 4. Time on task, number of clicks, and order details were captured. User satisfaction was measured using posttask ratings and a validated system usability scale. The study participants practiced in 2 health systems\u27 outpatient practices. Usability tests were conducted between April and October of 2017. Main Outcomes and Measures: Usability (efficiency, error rate, and satisfaction) of indications-based computerized provider order entry prototype vs the electronic prescribing interface of 2 electronic health record vendors. Results: Thirty-two participants (17 attending physicians, 13 residents, and 2 physician assistants) used the prototype to complete 256 usability test scenarios. The mean (SD) time on task was 1.78 (1.17) minutes. For the 20 participants who used vendor 1\u27s system, it took a mean (SD) of 3.37 (1.90) minutes to complete a prescription, and for the 12 participants using vendor 2\u27s system, it took a mean (SD) of 2.93 (1.52) minutes. Across all scenarios, when comparing number of clicks, for those participants using the prototype and vendor 1, there was a statistically significant difference from the mean (SD) number of clicks needed (18.39 [12.62] vs 46.50 [27.29]; difference, 28.11; 95% CI, 21.47-34.75; P \u3c .001). For those using the prototype and vendor 2, there was also a statistically significant difference in number of clicks (20.10 [11.52] vs 38.25 [19.77]; difference, 18.14; 95% CI, 11.59-24.70; P \u3c .001). A blinded review of the order details revealed medication errors (eg, drug-allergy interactions) in 38 of 128 prescribing sessions using a vendor system vs 7 of 128 with the prototype. Conclusions and Relevance: Reengineering prescribing to start with the drug indication allowed indications to be captured in an easy and useful way, which may be associated with saved time and effort, reduced medication errors, and increased clinician satisfaction

Comparison of a prototype for indications-based prescribing with 2 commercial prescribing systems

Importance: The indication (reason for use) for a medication is rarely included on prescriptions despite repeated recommendations to do so. One barrier has been the way existing electronic prescribing systems have been designed. Objective: To evaluate, in comparison with the prescribing modules of 2 leading electronic health record prescribing systems, the efficiency, error rate, and satisfaction with a new computerized provider order entry prototype for the outpatient setting that allows clinicians to initiate prescribing using the indication. Design, Setting, and Participants: This quality improvement study used usability tests requiring internal medicine physicians, residents, and physician assistants to enter prescriptions electronically, including indication, for 8 clinical scenarios. The tool order assignments were randomized and prescribers were asked to use the prototype for 4 of the scenarios and their usual system for the other 4. Time on task, number of clicks, and order details were captured. User satisfaction was measured using posttask ratings and a validated system usability scale. The study participants practiced in 2 health systems\u27 outpatient practices. Usability tests were conducted between April and October of 2017. Main Outcomes and Measures: Usability (efficiency, error rate, and satisfaction) of indications-based computerized provider order entry prototype vs the electronic prescribing interface of 2 electronic health record vendors. Results: Thirty-two participants (17 attending physicians, 13 residents, and 2 physician assistants) used the prototype to complete 256 usability test scenarios. The mean (SD) time on task was 1.78 (1.17) minutes. For the 20 participants who used vendor 1\u27s system, it took a mean (SD) of 3.37 (1.90) minutes to complete a prescription, and for the 12 participants using vendor 2\u27s system, it took a mean (SD) of 2.93 (1.52) minutes. Across all scenarios, when comparing number of clicks, for those participants using the prototype and vendor 1, there was a statistically significant difference from the mean (SD) number of clicks needed (18.39 [12.62] vs 46.50 [27.29]; difference, 28.11; 95% CI, 21.47-34.75; P \u3c .001). For those using the prototype and vendor 2, there was also a statistically significant difference in number of clicks (20.10 [11.52] vs 38.25 [19.77]; difference, 18.14; 95% CI, 11.59-24.70; P \u3c .001). A blinded review of the order details revealed medication errors (eg, drug-allergy interactions) in 38 of 128 prescribing sessions using a vendor system vs 7 of 128 with the prototype. Conclusions and Relevance: Reengineering prescribing to start with the drug indication allowed indications to be captured in an easy and useful way, which may be associated with saved time and effort, reduced medication errors, and increased clinician satisfaction

Listening and question-asking behaviors in resident and nurse handoff conversations: A prospective observational study

OBJECTIVE: To characterize interactivity during resident and nurse handoffs by investigating listening and question-asking behaviors during conversations. MATERIALS AND METHODS: Resident (n = 149) and nurse (n = 126) handoffs in an inpatient medicine unit were audio-recorded. Handoffs were coded based on listening behaviors (active and passive), question types (patient status, coordination of care, clinical reasoning, and framing and alignment), and question responses. Comparisons between residents and nurses for listening and question-asking behaviors were performed using the Wilcoxon rank-sum tests. A Poisson regression model was used to investigate differences in the question-asking behaviors between residents and nurses, and the association between listening and question-asking behaviors. RESULTS: There were no significant differences between residents and nurses in their active (18% resident vs 39% nurse handoffs) or passive (88% resident vs 81% nurse handoffs) listening behaviors. Question-asking was common in resident and nurse handoffs (87% vs 98%) and focused primarily on patient status, co-ordination, and framing and alignment. Nurses asked significantly more questions than residents (Mresident = 2.06 and Mnurse = 5.52) by a factor of 1.76 (P \u3c 0.001). Unit increase in listening behaviors was associated with an increase in the number of questions during resident and nurse handoffs by 7% and 12%, respectively. DISCUSSION AND CONCLUSION: As suggested by the Joint Commission, question-asking behaviors were common across resident and nurse handoffs, playing a critical role in supporting resilience in communication and collaborative cross-checks during conversations. The role of listening in initiating question-asking behaviors is discussed

Differentiating signals to make biological sense – a guide through databases for MS-based non-targeted metabolomics

Metabolite identification is one of the most challenging steps in metabolomics studies and reflects one of the greatest bottlenecks in the entire workflow. The success of this step determines the success of the entire research, therefore the quality at which annotations are given requires special attention. A variety of tools and resources are available to aid metabolite identification or annotation, offering different and often complementary functionalities. In preparation for this article, almost 50 databases were reviewed, from which 17 were selected for discussion, chosen for their on-line ESI-MS functionality. The general characteristics and functions of each database is discussed in turn, considering the advantages and limitations of each along with recommendations for optimal use of each tool, as derived from experiences encountered at the Centre for Metabolomics and Bioanalysis (CEMBIO) in Madrid. These databases were evaluated considering their utility in non-targeted metabolomics, including aspects such as ID assignment, structural assignment and interpretation of results

Interdisciplinary handover between obstetric nursing and neonatal physician teams: An observational study

Objective: We investigated the content and quality of communication of interservice interprofessional handover between obstetric nurses and neonatal physicians for high-risk deliveries. Design: Observational study. Setting: Labour and delivery unit at a tertiary care hospital. Method: We audio-recorded handovers between obstetric and neonatal teams (n=50) and conducted clinician interviews (n=29). A handover content framework was developed and used to qualitatively code missing core and ancillary content and their potential for adverse events. Results: 26 (52%) handovers missed one or more clinical content elements; a third of the handovers missed at least one core clinical content element. Increase in the number of missed clinical content elements increased the odds of potential adverse events by 2.39 (95% CI1.18 to 5.37). Both residents and nurses perceived handovers to be of low quality and inconsistent and attributed it to the lack of a structured handover process. Conclusion: Streamlining handover processes by instituting standardisation approaches for both information organisation and communication can improve the quality of neonatal handovers

Potential uses of AI for perioperative nursing handoffs: A qualitative study

OBJECTIVE: Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are keys to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used interviews to better understand how AI could work in this context. MATERIALS AND METHODS: Eleven nurses participated in semistructured interviews. Mixed inductive-deductive thematic analysis was used to extract major themes and subthemes around roles for AI supporting postoperative nursing. RESULTS: Five themes were generated from the interviews: (1) nurse understanding of patient condition guides care decisions, (2) handoffs are important to nurse situational awareness, but multiple barriers reduce their effectiveness, (3) AI may address barriers to handoff effectiveness, (4) AI may augment nurse care decision making and team communication outside of handoff, and (5) user experience in the electronic health record and information overload are likely barriers to using AI. Important subthemes included that AI-identified problems would be discussed at handoff and team communications, that AI-estimated elevated risks would trigger patient re-evaluation, and that AI-identified important data may be a valuable addition to nursing assessment. DISCUSSION AND CONCLUSION: Most research on postoperative handoff communication relies on structured checklists. Our results suggest that properly designed AI tools might facilitate postoperative handoff communication for nurses by identifying specific elevated risks faced by a patient, triggering discussion on those topics. Limitations include a single center, many participants lacking of applied experience with AI, and limited participation rate

Oxysterol-Binding Protein-1 (OSBP1) Modulates Processing and Trafficking of the Amyloid Precursor Protein

BACKGROUND Evidence from biochemical, epidemiological and genetic findings indicates that cholesterol levels are linked to amyloid-β (Aβ) production and Alzheimer's disease (AD). Oxysterols, which are cholesterol-derived ligands of the liver X receptors (LXRs) and oxysterol binding proteins, strongly regulate the processing of amyloid precursor protein (APP). Although LXRs have been studied extensively, little is known about the biology of oxysterol binding proteins. Oxysterol-binding protein 1 (OSBP1) is a member of a family of sterol-binding proteins with roles in lipid metabolism, regulation of secretory vesicle generation and signal transduction, and it is thought that these proteins may act as sterol sensors to control a variety of sterol-dependent cellular processes. RESULTS We investigated whether OSBP1 was involved in regulating APP processing and found that overexpression of OSBP1 downregulated the amyloidogenic processing of APP, while OSBP1 knockdown had the opposite effect. In addition, we found that OSBP1 altered the trafficking of APP-Notch2 dimers by causing their accumulation in the Golgi, an effect that could be reversed by treating cells with OSBP1 ligand, 25-hydroxycholesterol. CONCLUSION These results suggest that OSBP1 could play a role in linking cholesterol metabolism with intracellular APP trafficking and Aβ production, and more importantly indicate that OSBP1 could provide an alternative target for Aβ-directed therapeutic.National Institute on Aging (AG/NS17485

Comparative assessment of content overlap between written documentation and verbal communication: An observational study of resident sign-outs

OBJECTIVE: Effective sign-outs involve verbal communication supported by written or electronic documentation. We investigated the clinical content overlap between sign-out documentation and face-to-face verbal sign-out communication. METHODS: We audio-recorded resident verbal sign-out communication and collected electronically completed ( written ) sign-out documentation on 44 sign-outs in a General Medicine service. A content analysis framework with nine sign-out elements was used to qualitatively code both written and verbal sign-out content. A content overlap framework based on the comparative analysis between written and verbal sign-out content characterized how much written content was verbally communicated. Using this framework, we computed the full, partial, and no overlap between written and verbal content. RESULTS: We found high a high degree of full overlap on patient identifying information [name (present in 100% of sign-outs), age (96%), and gender (87%)], past medical history [hematology (100%), renal (100%), cardiology (79%), and GI (67%)], and tasks to-do (97%); lesser degree of overlap for active problems (46%), anticipatory guidance (46%), medications/treatments (15%), pending labs/studies/procedures (7%); and no overlap for code status (\u3c1%), allergies (0%) and medical record number (0%). DISCUSSION AND CONCLUSION: Three core functions of sign-outs are transfer of information, responsibility, and accountability. The overlap-highlighting what written content was communicated-characterizes how these functions manifest during sign-outs. Transfer of information varied with patient identifying information being explicitly communicated and remaining content being inconsistently communicated. Transfer of responsibility was explicit, with all pending and future tasks being communicated. Transfer of accountability was limited, with limited discussion of written contingency plans

Self-explaining Hierarchical Model for Intraoperative Time Series

Major postoperative complications are devastating to surgical patients. Some of these complications are potentially preventable via early predictions based on intraoperative data. However, intraoperative data comprise long and fine-grained multivariate time series, prohibiting the effective learning of accurate models. The large gaps associated with clinical events and protocols are usually ignored. Moreover, deep models generally lack transparency. Nevertheless, the interpretability is crucial to assist clinicians in planning for and delivering postoperative care and timely interventions. Towards this end, we propose a hierarchical model combining the strength of both attention and recurrent models for intraoperative time series. We further develop an explanation module for the hierarchical model to interpret the predictions by providing contributions of intraoperative data in a fine-grained manner. Experiments on a large dataset of 111,888 surgeries with multiple outcomes and an external high-resolution ICU dataset show that our model can achieve strong predictive performance (i.e., high accuracy) and offer robust interpretations (i.e., high transparency) for predicted outcomes based on intraoperative time series