Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients.

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

Who should see the patient? on deviations from preferred patient-provider assignments in hospitals.

In various organizations including hospitals, individuals are not forced to follow specific assignments, and thus, deviations from preferred task assignments are common. This is due to the conventional wisdom that professionals should be given the flexibility to deviate from preferred assignments as needed. It is unclear, however, whether and when this conventional wisdom is true. We use evidence on the assignments of generalist and specialists to patients in our partner hospital (a children's hospital), and generate insights into whether and when hospital administrators should disallow such flexibility. We do so by identifying 73 top medical diagnoses and using detailed patient-level electronic medical record (EMR) data of more than 4,700 hospitalizations. In parallel, we conduct a survey of medical experts and utilized it to identify the preferred provider type that should have been assigned to each patient. Using these two sources of data, we examine the consequence of deviations from preferred provider assignments on three sets of performance measures: operational efficiency (measured by length of stay), quality of care (measured by 30-day readmissions and adverse events), and cost (measured by total charges). We find that deviating from preferred assignments is beneficial for task types (patients' diagnosis in our setting) that are either (a) well-defined (improving operational efficiency and costs), or (b) require high contact (improving costs and adverse events, though at the expense of lower operational efficiency). For other task types (e.g., highly complex or resource-intensive tasks), we observe that deviations are either detrimental or yield no tangible benefits, and thus, hospitals should try to eliminate them (e.g., by developing and enforcing assignment guidelines). To understand the causal mechanism behind our results, we make use of mediation analysis and find that utilizing advanced imaging (e.g., MRIs, CT scans, or nuclear radiology) plays an important role in how deviations impact performance outcomes. Our findings also provide evidence for a "no free lunch" theorem: while for some task types, deviations are beneficial for certain performance outcomes, they can simultaneously degrade performance in terms of other dimensions. To provide clear recommendations for hospital administrators, we also consider counterfactual scenarios corresponding to imposing the preferred assignments fully or partially, and perform cost-effectiveness analyses. Our results indicate that enforcing the preferred assignments either for all tasks or only for resource-intensive tasks is cost-effective, with the latter being the superior policy. Finally, by comparing deviations during weekdays and weekends, early shifts and late shifts, and high congestion and low congestion periods, our results shed light on some environmental conditions under which deviations occur more in practice.

Digital Twins in Healthcare: Is It the Beginning of a New Era of Evidence-Based Medicine? A Critical Review.

Digital Twins (DTs) are used in many different industries (e.g., manufacturing, construction, automotive, and aerospace), and there is an initial trend of applications in healthcare, mainly focusing on precision medicine. If their potential is fully unfolded, DTs will facilitate the as-yet-unrealized potential of connected care and alter the way lifestyle, health, wellness, and chronic disease will be managed in the future. To date, however, due to technical, regulatory and ethical roadblocks, there is no consensus as to what extent DTs in healthcare can introduce revolutionary applications in the next decade. In this review, we present the current applications of DTs covering multiple areas of healthcare (precision medicine, clinical trial design, and hospital operations) to identify the opportunities and the barriers that foster or hinder their larger and faster diffusion. Finally, we discuss the current findings, opportunities and barriers, and provide recommendations to facilitate the continuous development of DTs application in healthcare.

Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients.

Clinicians urgently need reliable and stable tools to predict the severity of COVID-19 infection for hospitalized patients to enhance the utilization of hospital resources and supplies. Published COVID-19 related guidelines are frequently being updated, which impacts its utilization as a stable go-to resource for informing clinical and operational decision-making processes. In addition, many COVID-19 patient-level severity prediction tools that were developed during the early stages of the pandemic failed to perform well in the hospital setting due to many challenges including data availability, model generalization, and clinical validation. This study describes the experience of a large tertiary hospital system network in the Middle East in developing a real-time severity prediction tool that can assist clinicians in matching patients with appropriate levels of needed care for better management of limited health care resources during COVID-19 surges. It also provides a new perspective for predicting patients' COVID-19 severity levels at the time of hospital admission using comprehensive data collected during the first year of the pandemic in the hospital. Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April 2021. The study uses comprehensive COVID-19 patient-level clinical data from the hospital electronic medical records (EMR), vital sign monitoring devices, and Polymerase Chain Reaction (PCR) machines. The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time. Finally, this study provides results from a prospective validation test conducted by clinical experts in the hospital. The proposed prediction framework shows excellent performance in concurrent validation (n=462 patients, March 2020-April 2021) with highest discrimination obtained with the random forest classification model, achieving a macro- and micro-average area under receiver operating characteristics curve (AUC) of 0.83 and 0.87, respectively. The prospective validation conducted by clinical experts (n=185 patients, April-May 2021) showed a promising overall prediction performance with a recall of 78.4-90.0% and a precision of 75.0-97.8% for different severity classes.

COVID-19 Intelligence-Driven Operational Response Platform: Experience of a Large Tertiary Multihospital System in the Middle East.

The COVID-19 pandemic has resulted in global disruptions within healthcare systems, leading to quick dynamic fluctuations in hospital operations and supply chain management. During the early months of the pandemic, tertiary multihospital systems were highly viewed as the go-to hospitals for handling these rapid healthcare challenges caused by the rapidly increasing number of COVID-19 cases. Yet, this pandemic has created an urgent need for coordinated mechanisms to alleviate increasing pressures on these large multihospital systems and ensure services remain high-quality, accessible, and sustainable. Digital health solutions have been identified as promising approaches to address these challenges. This case report describes results for developing multidisciplinary visualizations to support digital health operations in one of the largest tertiary multihospital systems in the Middle East. The report concludes with some lessons and insights learned from the rapid development and delivery of this user-centric COVID-19 multihospital operations intelligent platform.

Hospital transfusion service operations during the SARS-CoV-2 pandemic: Lessons learned from the AABB hospital survey in preparation for the next infectious disease outbreak.

BACKGROUND: The SARS-CoV-2 pandemic disrupted hospital operations, affected the blood supply, and challenged the health care system to develop new therapeutic options, including convalescent plasma (CCP). The aim of this study is to describe and analyze blood supply fluctuations and the use of convalescent plasma in 2020. METHODS: AABB distributed a weekly and biweekly questionnaire through email to hospital-based members (HBM). RESULTS: The survey was sent to 887 HBM with 479 unique respondents, most of the hospitals served pediatric and adult patients, and all states of the country participated, except Idaho and Vermont. Fifty four percent of HBM reported increased wastage in the early phase of the pandemic (May), which decreased to 4% by the end of June and throughout the rest of the year. The majority of HBM reported receiving alerts from their blood suppliers reporting blood shortages throughout the year. During March and April, only 12% of HBM were performing elective surgical procedures. The top reasons to delay procedures were: bed availability (28%); COVID-19 caseload (23%; and blood availability (19%). By mid-April, 42% HBM had transfused CCP and reported >24 h delay in getting the units; the vast majority obtained CCP using the Expanded Access Protocol, and later, the Emergency Use Authorization. HBM consistently prioritized the most severe patients to receive CCP, but the proportion of severely ill recipients fell from 52% to 37% between May and October, with an increase from 5% to 21% of HBM providing CCP transfusion early in the course of the disease. DISCUSSION: Blood utilization and availability fluctuated during the pandemic. The fluctuations appeared to be related to the number of COVID-19 in the community. The use and regulatory landscape of CCP rapidly evolved over the first 8 months of the pandemic.

Short-Stay Hospitalizations for Patients with COVID-19: A Retrospective Cohort Study.

OBJECTIVE: Patients requiring hospital care for COVID-19 may be stable for discharge soon after admission. This study sought to describe patient characteristics associated with short-stay hospitalization for COVID-19. METHODS: We performed a retrospective cohort study of patients with COVID-19 admitted to five United States hospitals from March to December 2020. We used multivariable logistic regression to identify patient characteristics associated with short hospital length-of-stay. RESULTS: Of 3103 patients, 648 (20.9%) were hospitalized for less than 48 h. These patients were significantly less likely to have an age greater than 60, diabetes, chronic kidney disease; emergency department vital sign abnormalities, or abnormal initial diagnostic testing. For patients with no significant risk factors, the adjusted probability of short-stay hospitalization was 62.4% (95% CI 58.9-69.6). CONCLUSION: Identification of candidates for early hospital discharge may allow hospitals to streamline throughput using protocols that optimize the efficiency of hospital care and coordinate post-discharge monitoring.

"Working Against Gravity": The Uphill Task of Overcapacity Management.

While most health systems have implemented interventions to manage situations in which patient demand exceeds capacity, little is known about the long-term sustainability or effectiveness of such interventions. A large multi-jurisdictional study on patient flow in Western Canada provided the opportunity to explore experiences with overcapacity management strategies across 10 diverse health regions. Four categories of interventions were employed by all or most regions: overcapacity protocols, alternative locations for emergency patients, locations for discharge-ready inpatients, and meetings to guide redistribution of patients. Two mechanisms undergirded successful interventions: providing a capacity buffer and promoting action by inpatient units by increasing staff accountability and/or solidarity. Participants reported that interventions demanded significant time and resources and the ongoing active involvement of middle and senior management. Furthermore, although most participants characterized overcapacity management practices as effective, this effectiveness was almost universally experienced as temporary. Many regions described a context of chronic overcapacity, which persisted despite continued intervention. Processes designed to manage short-term surges in demand cannot rectify a long-term mismatch between capacity and demand; solutions at the level of system redesign are needed.

Improving the Efficiency of an Emergency Department Based on Activity-Relationship Diagram and Radio Frequency Identification Technology.

Emergency department crowding has been one of the main issues in the health system in Taiwan. Previous studies have usually targeted the process improvement of patient treatment flow due to the difficulty of collecting Emergency Department (ED) staff data. In this study, we have proposed a hybrid model with Discrete Event Simulation, radio frequency identification applications, and activity-relationship diagrams to simulate the nurse movement flows and identify the relationship between different treatment sections. We used the results to formulate four facility layouts. Through comparing four scenarios, the simulation results indicated that 2.2 km of traveling distance or 140 min of traveling time reduction per nurse could be achieved from the best scenario.

A case-based methodology for lean implementation in hospital operations.

PURPOSE: Although there are general methodologies for lean implementation in manufacturing companies, a specific methodology for the implementation of lean healthcare in hospitals has not been addressed by the literature. Addressing this gap, the purpose of this paper is to develop a practice-driven methodology for implementing lean in hospital operations. DESIGN/METHODOLOGY/APPROACH: Three case studies were conducted to collect evidence on the lean implementation process in Brazilian hospitals. From empirical evidence and literature, the implementation methodology was proposed and submitted to critical assessment by experts from the field. FINDINGS: The process of lean implementation was very similar in all cases, triggered by strategic planning and operationalized by continuous improvement projects. On the other hand, in all cases, the lean implementation teams had to deal with employees' resistance. These findings were valuable inputs to the development of the implementation methodology. After refinement, it was proposed a feasible, useful and user-friendly methodology. RESEARCH LIMITATIONS/IMPLICATIONS: The proposed methodology was raised from the practice through case study research. However, the proposed methodology was not fully applied, and the associated performance measures were not elaborated in this paper. Therefore, more case studies and applications will be necessary to generalize the findings. PRACTICAL IMPLICATIONS: The methodology provides practical guidelines that support lean implementation in hospital operations. Although it demands adaptations for each specific hospital setting, this initial step may encourage hospital managers to start the lean journey. ORIGINALITY/VALUE: This study addressed the gap in the literature regarding the lack of methodologies for implementing lean healthcare in hospital operations. The methodology synthesizes the knowledge, principles and tools of lean thinking that can be applied in hospital operations.

What is full capacity protocol, and how is it implemented successfully?

BACKGROUND: Full capacity protocol (FCP) is an internationally recognized intervention designed to address emergency department (ED) crowding. Despite FCP international recognition and positive effects on hospital performance measures, many hospitals, even the most crowded ones, have not implemented FCP. We conducted this study to identify the core components of FCP, explore the key barriers and facilitators associated with the FCP implementation, and provide practical recommendations on how to overcome those barriers. METHODS: To identify the core components of FCP, we used a non-experimental approach. We conducted semi-structured interviews with key informants (e.g., division chiefs, medical directors) involved in the implementation of FCP. We used the Consolidated Framework for Implementation Research (CFIR) to guide data collection and analysis. We used a template analysis approach to determine the relevance of the CFIR constructs to implementing the FCP. We analyzed the responses to the interview questions about FCP definition and FCP key principles, compared different hospitals' FCP official documents, and consulted with the original FCP developer. We then used an adaptation framework to categorize the core components of FCP into three main groups. Finally, we summarized practical recommendations for each barrier based on information provided by the interviewees. RESULTS: A total of 32 interviews were conducted. We observed that FCP has evolved from the idea of transferring boarded patients from ED hallways to inpatient hallways to a practical hospital-wide intervention with several components and multiple levels. The key determinant of successful FCP implementation was collaboration with inpatient nursing staff, as they were often reluctant to have patients boarded in inpatient hallways. Other determinants of successful FCP implementation were reaching consensus about the criteria for activation of each FCP level and actions in each FCP level, modifying the electronic health records system, restructuring the inpatient units to have adequate staffing and resources, complying with external regulations and policies such as fire marshal guidelines, and gaining hospital leaders' support. CONCLUSIONS: The key determinant in implementing FCP is creating a supportive and cooperative hospital culture and encouraging key stakeholders, including inpatient nursing staff, to acknowledge that crowding is a hospital-wide problem that requires a hospital-wide response.

Modeling the critical care pathway for cardiothoracic surgery.

The west of Scotland heart and lung center based at the Golden Jubilee National Hospital houses all adult cardiothoracic surgery for the region. Increased demand for scheduled patients and fluctuations in emergency referrals resulted in increasing waiting times and patient cancellations. The main issue was limited resources, which was aggravated by the stochastic nature of the length of stay (LOS) and arrival of patients. Discrete event simulation (DES) was used to assess if an enhanced schedule was sufficient, or more radical changes, such as capacity or other resource reallocations should be considered in order to solve the problem. Patients were divided into six types depending on their condition and LOS at the different stages of the process. The simulation model portrayed each patient type's pathway with sufficient detail. Patient LOS figures were analyzed and distributions were formed from historical data, which were then used in the simulation. The model proved successful as it showed figures that were close to actual observations. Acquiring results and knowing exactly when and what caused a cancellation was another strong point of the model. The results demonstrated that the bottleneck in the system was related to the use of High Dependency Unit (HDU) beds, which were the recovery beds used by most patients. Enhancing the schedule by leveling out the daily arrival of patients to HDUs reduced patient cancellations by 20%. However, coupling this technique with minor capacity reallocations resulted in more than 60% drop in cancellations.

The Boarding Patient: Effects of ICU and Hospital Occupancy Surges on Patient Flow.

Patients admitted to a hospital's intensive care unit (ICU) often endure prolonged boarding within the ICU following receipt of care, unnecessarily occupying a critical care bed, and thereby delaying admission for other incoming patients due to bed shortage. Using patient-level data over two years at two major academic medical centers, we estimate the impact of ICU and ward occupancy levels on ICU length of stay (LOS), and test whether simultaneous "surge occupancy" in both areas impacts overall ICU length of stay. In contrast to prior studies that only measure total LOS, we split LOS into two individual periods based on physician requests for bed transfers. We find that "service time" (when critically ill patients are stabilized and treated) is unaffected by occupancy levels. However, the less essential "boarding time" (when patients wait to exit the ICU) is accelerated during periods of high ICU occupancy and, conversely, prolonged when hospital ward occupancy levels are high. When the ICU and wards simultaneously encounter bed occupancies in the top quartile of historical levels-which occurs 5% of the time-ICU boarding increases by 22% compared to when both areas experience their lowest utilization, suggesting that ward bed availability dominates efforts to accelerate ICU discharges to free up ICU beds. We find no adverse effects of high occupancy levels on ICU bouncebacks, in-hospital deaths, or 30-day hospital readmissions, which supports our finding that the largely discretionary boarding period fluctuates with changing bed occupancy levels.

The accountable care organization summit: a white paper on findings, outcomes, and challenges.

The authors sought to explore the implications of the Patient Protection and Affordable Care Act's establishment of Accountable Care Organizations (ACO). Summit participants, who discussed best practices and issues to be addressed when designing and implementing ACOs. Healthcare leaders from across the country in charge of running, developing, and/or implementing ACOs for health systems. Participants were asked to consider the challenges, benefits, and strategies to ACO implementation.