Bedside Nurses' Perceptions of Effective Nurse-Physician Communication in General Medical Units: A Qualitative Study.

Background There is a dearth of research on successful interventions to improve nurse-physician communication (NPC). An important step is identifying what matters to bedside nurses and their perceptions of effective NPC communications and actions. Methods We conducted three focus groups with a total of 19 medical unit nurses across two hospitals in one academic medical center in the United States. Using a convenience sampling strategy, five to eight nurses voluntarily participated in each focus group. The recording was transcribed verbatim and two independent coders performed coding and resolved any discrepancies in codes. Qualitative content analysis was pursued to identify themes and associated quotes. Results The presence of direct communication between physicians and nurses was identified as the first theme and perceived by nurses as very important. Additional themes related to physician communication and attributes emerged including collegiality and respect (e.g., engaging nurses as partners in patient care), attentiveness and responsiveness (e.g., listening carefully and addressing concerns), and directness and support (e.g., backing nurses up in difficult situations). Effective NPC is further facilitated by organizational structure, relationship development separate from patient care, and consistent/timely use of technology. Conclusions Hospital bedside nurses provided valuable insight into improved physician communication and what attributes contribute to more effective NPC. Most importantly, they emphasized the significance of physicians in supporting them with difficult patients.

Patient Perceptions and Real-Time Observations of Bedside Rounding Team Communication: The Interprofessional Teamwork Innovation Model (ITIM).

BACKGROUND: Interdisciplinary rounds are designed to address barriers to teamwork, communication, and quality patient care. This study used multiple methods (observations, patient surveys) in two hospital sites to examine communication and teamwork in the Interprofessional Teamwork Innovation Model (ITIM). METHODS: Observations of 68 ITIM teams that completed 685 patient visits were conducted in a 302-bed community-based acute care hospital (CH) and a 569-bed academic medical center (AMC) in one academic health care system. Patients were asked to complete surveys about their experience with their ITIM team. RESULTS: Length of stay (LOS) in the CH was significantly and negatively associated with team structures and communication processes. LOS in the AMC was associated with communication processes. Geographic cohorting was a system factor associated with teamwork and communication processes that affect patient care and quality. A variety of communication processes were operating in ITIM teams, including soliciting questions from patients and staff, politeness, rapport, speaking percentages, and team-oriented communication. Patients were satisfied with their ITIM experience, indicating that their encounters were collaborative and supportive and contributed to their care experience. CONCLUSION: This multimethod study illustrates the value of system-level approaches to structured patient-centered team care delivery and understanding the complexity of communication in team-based patient care. Findings suggest that when patients feel they are given opportunities to ask questions, speak without being interrupted, and have their questions answered, they tend to be satisfied with their experience of care. Health care leaders may consider ITIM to advance their mission of improving patient experiences and quality of bedside care.

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

BACKGROUND: Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. OBJECTIVE: To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. METHODS: We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. RESULTS: Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. CONCLUSIONS: We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.

Interprofessional Teamwork Innovation Model (ITIM) to promote communication and patient-centred, coordinated care.

BACKGROUND: Despite recommendations and the need to accelerate redesign of delivery models to be team-based and patient-centred, professional silos and cultural and structural barriers that inhibit working together and communicating effectively still predominate in the hospital setting. Aiming to improve team-based rounding, we developed, implemented and evaluated the Interprofessional Teamwork Innovation Model (ITIM). METHODS: This quality improvement (QI) study was conducted at an academic medical centre. We followed the system's QI framework, FOCUS-PDSA, with Lean as guiding principles. Primary outcomes included 30-day all-cause same-hospital readmissions and 30-day emergency department (ED) visits. The intervention group consisted of patients receiving care on two hospitalist ITIM teams, and patients receiving care from other hospitalist teams were matched with a control group. Outcomes were assessed using difference-in-difference analysis. RESULTS: Team members reported enhanced communication and overall time savings. In multivariate modelling, patients discharged from hospitalist teams using the ITIM approach were associated with reduced 30-day same-hospital readmissions with an estimated point OR of 0.56 (95% CI 0.34 to 0.92), but there was no impact on 30-day same-hospital ED visits. Difference-in-difference analysis showed that ITIM was not associated with changes in average total direct costs nor average cost per patient day, after adjusting for all other covariates in the models, despite the addition of staff resources in the ITIM model. CONCLUSION: The ITIM approach facilitates a collaborative environment in which patients and their family caregivers, physicians, nurses, pharmacists, case managers and others work and share in the process of care.

Recurrent migration of peripherally inserted central catheter into the azygos vein.

Peripherally inserted central catheter (PICC) migration into azygos vein (AV) is a rare complication. It is recognised only when catheter malfunction occurs or when patients develop associated complications. PICC migration into AV has been reported to be associated with various complications such as catheter malfunction, perforation, haemorrhage, thrombosis, infection and stenosis of AV. Pleural effusion and trachea-azygos fistulas have also been reported. We present a patient with recurrent migration of PICC into AV after an initial corrective repositioning during the same hospital stay. In this case, PICC migration was possibly related to left-sided approach, use of smaller diameter PICC, severe congestive heart failure and her bedbound status. PICC migration should be considered when PICC found be malfunctioning, especially if associated with the above risk factors.

Letter to the Editor, What Can Be Done to Maintain Positive Patient Experience and Improve Residents' Satisfaction? In Reference to: "Standardized Attending Rounds to Improve the Patient Experience: A Pragmatic Cluster Randomized Controlled Trial".

We read the article by Monash et al. published in the March 2017 issue with great interest. This randomized study showed a discrepancy between patients' and residents' satisfaction with standardized rounds; for example, residents reported less autonomy, efficiency, teaching, and longer time of rounds.

Rare case of losartan-induced cough complicated by rectus sheath haematoma: in a patient on rivaroxaban therapy.

Spontaneous rectus sheath haematomas and cough secondary to losartan are individually rare conditions. Abdominal wall haematomas present with abdominal pain and abdominal mass. Most patients are managed conservatively; Surgery or embolisation is indicated for shock, infection, rupture into the peritoneum or intractable pain. This is a man aged 65 years presented with dry cough and right-sided abdominal pain. He started losartan a few weeks prior to the onset of cough and had been on rivaroxaban for prior deep venous thrombosis. The right side of his abdomen was distended, bruised and tender. His haemoglobin dropped from 13.3to 9.5 g/dL. CT abdomen/pelvis showed a large 14.5×9.1×4.5 cm haematoma within the right lateral rectus muscle. His only risk factor for developing rectus sheath haematoma was cough in the setting of anticoagulation. Dry cough due to angiotensin receptor blockers is rare, but can have very serious consequences.