IMPORTANCE Concurrent chemoradiotherapy is the standard-of-care curative treatment for many cancers but is associated with substantial morbidity. Concurrent chemoradiotherapy administered with proton therapy might reduce toxicity and achieve comparable cancer control outcomes compared with conventional photon radiotherapy by reducing the radiation dose to normal tissues.OBJECTIVE To assess whether proton therapy in the setting of concurrent chemoradiotherapy is associated with fewer 90-day unplanned hospitalizations (Common Terminology Criteria for Adverse Events, version 4 [CTCAEv4], grade Ն3) or other adverse events and similar disease-free and overall survival compared with concurrent photon therapy and chemoradiotherapy.

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BackgroundLittle is known about how team processes impact providers’ abilities to prepare patients for a safe hospital discharge. Teamwork Shared Mental Models (teamwork-SMMs) are the teams’ organised understanding of individual member’s roles, interactions and behaviours needed to perform a task like hospital discharge. Teamwork-SMMs are linked to team effectiveness in other fields, but have not been readily investigated in healthcare. This study examines teamwork-SMMs to understand how interprofessional teams coordinate care when discharging patients.MethodsThis mixed methods study examined teamwork-SMMs of inpatient interprofessional discharge teams at a single hospital. For each discharge event, we collected data from the patient and their discharge team (nurse, physician and coordinator) using interviews and questionnaires. We quantitatively determined the discharge teams’ teamwork-SMM components of quality and convergence using the Shared Mental Model Scale, and then explored their relationships to patient-reported preparation for posthospital care. We used qualitative thematic analysis of narrative cases to examine the contextual differences of discharge teams with higher versus lower teamwork-SMMs.ResultsThe sample included a total of 106 structured patient interviews, 192 provider day-of-discharge questionnaires and 430 observation hours to examine 64 discharge events. We found that inpatient teams with better teamwork-SMMs (ie, higher perceptions of teamwork quality or greater convergence) were more effective at preparing patients for post-hospital care. Additionally, teams with high and low teamwork-SMMs had different experiences with team cohesion, communication openness and alignment on the patient situation.ConclusionsExamining the quality and agreement of teamwork-SMMs among teams provides a better understanding of how teams coordinate care and may facilitate the development of specific team-based interventions to improve patient care at hospital discharge.

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Objective Electronic health record (EHR)-derived data are extensively used in health research. However, the pattern of patient interaction with the healthcare system can result in informative presence bias if those who have poorer health have more data recorded than healthier patients. We aimed to determine how informative presence affects bias across multiple scenarios informed by real-world healthcare utilization patterns. Materials and methods We conducted an analysis of EHR data from a pediatric healthcare system as well as simulation studies to characterize conditions under which informative presence bias is likely to occur. This analysis extends prior work by examining a variety of scenarios for the relationship between a biomarker and a health event of interest and the healthcare visit process. Results Using biomarker values gathered at both informative and noninformative visits when estimating the effect of the biomarker on the event of interest resulted in minimal bias when the biomarker was relatively stable over time but produced substantial bias when the biomarker was more volatile. Adjusting analyses for the number of prior visits within a fixed look-back window was able to reduce but not eliminate this bias. Discussion These results suggest that bias may arise frequently in commonly encountered scenarios and may not be eliminated by adjusting for prior visit intensity. Conclusion Depending on the context, the estimated effect from analyses using data from all visits available may diverge from the true effect. Sensitivity analyses using only visits likely to be informative or noninformative based on visit type may aid in the assessment of the magnitude of potential bias.