Team training improved communication, task coordination and perceptions of efficiency, quality, safety and interactions among team members as well as patient perception of care coordination.

No abstract

Background Patients referred to comprehensive cancer centers arrive with clinical data requiring review. Radiology consultation for second opinions often generates additional imaging requests, however the impact of this service on breast cancer management remains unclear. We sought to identify the incidence of additional imaging requests and the effect additional imaging has on patients’ ultimate surgical management. Methods Between November 2013 and March 2014, 153 consecutive patients with breast cancer received second opinion imaging reviews and definitive surgery at our cancer center. We identified the number of additional imaging requests, the number of fulfilled requests, the modality of additional imaging completed, the number of biopsies performed, and the number of patients whose management was altered due to additional imaging results. Results Of 153 patients the mean age was 55; 98.9 % were female; 23.5% (36) had in situ carcinoma (35 DCIS/ 1 LCIS) and 76.5% (117) had invasive carcinoma. Additional imaging was suggested for 47.7% (73/153) of patients. After multi-disciplinary consultation, 65.8% (48/73) of patients underwent additional imaging. Imaging review resulted in biopsy in 43.7% (21/48) of patients and ultimately altered preliminary treatment plans in 37.5% (18/48) of patients. (Figure 1) Changes in management included: conversion to mastectomy or to breast conservation, neoadjuvant therapy, additional wire placement, and need for contralateral breast surgery. Conclusions Our analysis of second opinion imaging consultation demonstrates the significant value this service has on breast cancer management. Overall, 11.7% (18/153) of patients who underwent breast surgery had management changes as a consequence of radiologic imaging review.

Service systems are often stochastic and preplanned by appointments, yet implementations of their appointment systems are prevalently deterministic. At the planning stage of healthcare services, for example, customer punctuality and service durations are often assumed equal their means—and this gap, between planned and reality, motivated our research. Specifically, we consider appointment scheduling and sequencing under a time-varying number of servers, in a data-rich environment where service durations and punctuality are uncertain. Our data-driven approach, based on infinite-server queues, yields tractable and scalable solutions that accommodate hundreds of jobs and servers. We successfully test our approach against near-optimal algorithms (which exist for merely single-servers). This entails the development of a data-driven robust optimization approach with novel uncertainty sets. To test for practical performance, we leverage a unique data set from a cancer center that combines real-time locations, electronic health records, and appointments log. Focusing on one of the center’s infusion units (roughly 90 daily appointments, 25+ infusion chairs), we reduce cost (waiting plus overtime) on the order of 15%–40% consistently, under a wide range of experimental setups. This paper was accepted by Assaf Zeevi, stochastic models and simulation.