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.
This article describes how trust among team members and in the technology supporting them was eroded during implementation of an electronic health record (EHR) in an adult outpatient oncology practice at a comprehensive cancer center. Delays in care of a 38-year-old woman with high-risk breast cancer occurred because of ineffective team communication and are illustrated in a case study. The case explores how the patient's trust and mutual trust between team members were disrupted because of inaccurate assumptions about the functionality of the EHR's communication tool, resultant miscommunications between team members and the patient, and the eventual recognition that care was not being effectively coordinated, as it had been previously. Despite a well-established, team-based culture and significant preparation for the EHR implementation, the challenges that occurred point to underlying human and system failures from which other organizations going through a similar process may learn. Through an analysis and evaluation of events that transpired before and during the EHR rollout, suggested interventions for preventing this experience are offered, which include: a thorough crosswalk between old and new communication mechanisms before implementation; understanding and mitigation of gaps in the communication tool's functionality; more robust training for staff, clinicians, and patients; greater consideration given to the pace of change expected of individuals; and development of models of collaboration between EHR users and vendors in developing products that support high-quality, team-based care in the oncology setting. These interventions are transferable to any organizational or system change that threatens mutual trust and effective communication.
Background: Although timeliness of care was one of the aims of quality improvement in the Institute of Medicine’s 2001 “Crossing the Quality Chasm” report, a significant proportion of patients with cancer still experience delays in diagnosis and treatment. For example, in a study of 3,831 older adults diagnosed with myeloma in the United States, the median time between the first myeloma-related symptom and diagnosis was 99 days (Friese CR, Leukemia & Lymphoma 2009). Such delays are associated with substantial anxiety, poor patient-reported outcomes, and increased cost. Methods: A novel adult cancer diagnostic service (CDS) was established by the Dana-Farber/Brigham and Women’s Cancer Center in October 2017. The clinic is embedded in the Department of Medicine at the Brigham and Women’s Hospital, with the aim of expediting the cancer diagnostic work-up and treatment for clinically complex patients with symptoms concerning for cancer, but for whom the next diagnostic steps are unclear. This clinic is comprised of an internist, a physician assistant, and a practice assistant. The clinic staff conduct a weekly phone conference with a multidisciplinary team—including a solid tumor oncologist, a hematologic oncologist, and a radiologist—to discuss the work-up for each patient. For every patient evaluated who receives a cancer diagnosis, we measure the diagnostic interval (days from CDS referral to diagnosis date). We define diagnosis date as the date the pathologic report is signed. We also measure the interval between CDS referral and first oncology appointment. Results: From the inception of the CDS to October 1, 2018, 221 patients were seen in the clinic and 91 (41.2%) were diagnosed with cancer. The top 3 cancer diagnoses were lymphoma (31%), gastrointestinal cancers (20%), and lung cancer (19%; Figure 1). The median number of days from CDS referral to diagnosis was 14 days (interquartile range [IQR], 10, 21; Table 1). Finally, the median time between referral to CDS and first oncology appointment was 20 days (IQR, 14, 27). Conclusion: This novel cancer diagnostic service substantially shorted the diagnostic trajectory (∼2 weeks) compared to existing literature with median diagnostic intervals often lasting more than 3 months (Friese CR, Leukemia & Lymphoma 2009). Our findings suggest that a cancer diagnostic care model, grounded in internal medicine, with engagement of oncologists and radiologists, has significant potential to improve delays in cancer diagnostic care.
140 Background: The implementation of electronic medical records (EMR) has been noted to disrupt clinical workflows as providers acclimate to a new EMR. On May 30, 2015, Dana-Farber Cancer Institute (DFCI) implemented a new EMR. Using our Real Time Location System (RTLS), we sought to identify the time required to stabilize the experience for providers. We identified factors that may speed the stabilization rate to guide EMR implementations elsewhere. Methods: DFCI uses an RTLS to timestamp patient and provider locations throughout the day. To adjust for variation in appointment types, we measured the ratio of the actual exam duration (recorded by the RTLS) to the scheduled exam duration. We compared to a 3-month baseline average to quantify the immediate impact of implementation. We tracked the ratio over time to identify when stabilization occurred and compare to baseline performance. To infer influential factors, we performed a regression analysis based on RTLS data from the first 6 months post implementation. Results: The stabilization curve fits the “classical” power function model. Rapid improvement over the first ten days of clinical practice was followed by a gradual period of ongoing stabilization. The EMR impact on exam duration required approximately 30 clinical days for each provider to reach the baseline value with continued improvement over the next 30 clinical days. Factors with a potential to improve the rate of stabilization were provider type (MD, NP or PA), provider gender and provider age. Conclusions: The first ten clinical days experience a fast rate of improvement. Thus, while the initial impact is disruptive, operations improve rapidly. Initial improvement may be attributed to fixing “bugs” in the EMR and rapid learning by providers. Our presentation will explore factors that impact the rate of improvement. Understanding the stabilization rate and factors can aid organizations in training, implementation, and ongoing improvement to minimize the impact of EMR disruption. [Table: see text]
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.