Development of a maturity model for demand and capacity management in healthcare.

BACKGROUND: The aim of this paper is to develop a maturity model (MM) for demand and capacity management (DCM) processes in healthcare settings, which yields opportunities for organisations to diagnose their planning and production processes, identify gaps in their operations and evaluate improvements. METHODS: Informed by existing DCM maturity frameworks, qualitative research methods were used to develop the MM, including major adaptations and additions in the healthcare context. The development phases for maturity assessment models proposed by de Bruin et al. were used as a structure for the research procedure: (1) determination of scope, (2) design of a conceptual MM, (3) adjustments and population of the MM to the specific context and (4) test of construct and validity. An embedded single-case study was conducted for the latter two - four units divided into two hospitals with specialised outpatient care introducing a structured DCM work process. Data was collected through interviews, observations, field notes and document studies. Thematic analyses were carried out using a systematic combination of deductive and inductive analyses - an abductive approach - with the MM progressing with incremental modifications. RESULTS: We propose a five-stage MM with six categories for assessing healthcare DCM determined in relation to patient flows (vertical alignment) and organisational levels (horizontal alignment). Our application of this model to our specific case indicates its usefulness in evaluating DCM maturity. Specifically, it reveals that transitioning from service activities to a holistic focus on patient flows during the planning process is necessary to progress to more advanced stages. CONCLUSION: In this paper, a model for assessing healthcare DCM and for creating roadmaps for improvements towards more mature levels has been developed and tested. To refine and finalise the model, we propose further evaluations of its usefulness and validity by including more contextual differences in patient demand and supply prerequisites.

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.

Supporting capacity management decisions in healthcare using data-driven process simulation.

Healthcare managers are confronted with various Capacity Management decisions to determine appropriate levels of resources such as equipment and staff. Given the significant impact of these decisions, they should be taken with great care. The increasing amount of process execution data - i.e. event logs - stored in Hospital Information Systems (HIS) can be leveraged using Data-Driven Process Simulation (DDPS), an emerging field of Process Mining, to provide decision-support information to healthcare managers. While existing research on DDPS mainly focuses on the fully automated discovery of simulation models from event logs, the interaction between process execution data and domain expertise has received little attention. Nevertheless, data quality issues in real-life process execution data stored in HIS prevent the discovery of accurate and reliable models from this data. Therefore, complementary information from domain experts is necessary. In this paper, we describe the application of DDPS in healthcare by means of an extensive real-life case study at the radiology department of a Belgium hospital. In addition to formulating our recommendations towards the radiology management, we will elaborate on the experienced challenges and formulate recommendations to move research on DDPS within a healthcare context forward. In this respect, explicit attention is attributed to data quality assessment, as well as the interaction between the use of process execution data and domain expertise.

The other side of the crisis: organizational flexibility in balancing Covid-19 and non-Covid-19 health-care services.

BACKGROUND: Many healthcare systems have been unable to deal with Covid-19 without influencing non-Covid-19 patients with pre-existing conditions, risking a paralysis in the medium term. This study explores the effects of organizational flexibility on hospital efficiency in terms of the capacity to deliver healthcare services for both Covid-19 and non-Covid-19 patients. METHOD: Focusing on Italian health system, a two-step strategy is adopted. First, Data Envelope Analysis is used to assess the capacity of hospitals to address the needs of Covid-19 and non-Covid-19 patients relying on internal resource flexibility. Second, two panel regressions are performed to assess external organizational flexibility, with the involvement in demand management of external operators in the health-care service, examining the impact on efficiency in hospital capacity management. RESULTS: The overall response of the hospitals in the study was not fully effective in balancing the needs of the two categories of patients (the efficiency score is 0.87 and 0.58, respectively, for Covid-19 and non-Covid-19 patients), though responses improved over time. Furthermore, among the measures providing complementary services in the community, home hospitalization and territorial medicine were found to be positively associated with hospital efficiency (0.1290, p < 0.05 and 0.2985, p < 0.01, respectively, for non-Covid-19 and Covid-19 patients; 0.0026, p < 0.05 and 0.0069, p < 0.01, respectively, for non-Covid-19 and Covid-19). In contrast, hospital networks are negatively related to efficiency in Covid-19 patients (-0.1037, p < 0.05), while the relationship is not significant in non-Covid-19 patients. CONCLUSIONS: Managing the needs of Covid-19 patients while also caring for other patients requires a response from the entire healthcare system. Our findings could have two important implications for effectively managing health-care demand during and after the Covid-19 pandemic. First, as a result of a naturally progressive learning process, the resource balance between Covid-19 and non-Covid-19 patients improves over time. Second, it appears that demand management to control the flow of patients necessitates targeted interventions that combine agile structures with decentralization. Finally, untested integration models risk slowing down the response, giving rise to significant costs without producing effective results.

Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation.

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke's hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

Simulation-based evaluation of operating room management policies.

BACKGROUND: Since operating rooms are a major bottleneck resource and an important revenue driver in hospitals, it is important to use these resources efficiently. Studies estimate that between 60 and 70% of hospital admissions are due to surgeries. Furthermore, staffing cannot be changed daily to respond to changing demands. The resulting high complexity in operating room management necessitates perpetual process evaluation and the use of decision support tools. In this study, we evaluate several management policies and their consequences for the operating theater of the University Hospital Augsburg. METHODS: Based on a data set with 12,946 surgeries, we evaluate management policies such as parallel induction of anesthesia with varying levels of staff support, the use of a dedicated emergency room, extending operating room hours reserved as buffer capacity, and different elective patient sequencing policies. We develop a detailed simulation model that serves to capture the process flow in the entire operating theater: scheduling surgeries from a dynamically managed waiting list, handling various types of schedule disruptions, rescheduling and prioritizing postponed and deferred surgeries, and reallocating operating room capacity. The system performance is measured by indicators such as patient waiting time, idle time, staff overtime, and the number of deferred surgeries. RESULTS: We identify significant trade-offs between expected waiting times for different patient urgency categories when operating rooms are opened longer to serve as end-of-day buffers. The introduction of parallel induction of anesthesia allows for additional patients to be scheduled and operated on during regular hours. However, this comes with a higher number of expected deferrals, which can be partially mitigated by employing additional anesthesia teams. Changes to the sequencing of elective patients according to their expected surgery duration cause expectable outcomes for a multitude of performance indicators. CONCLUSIONS: Our simulation-based approach allows operating theater managers to test a multitude of potential changes in operating room management without disrupting the ongoing workflow. The close collaboration between management and researchers in the design of the simulation framework and the data analysis has yielded immediate benefits for the scheduling policies and data collection efforts at our practice partner.

Addressing overestimation and insensitivity in the 85% target for average bed occupancy.

BACKGROUND: An established finding suggests that, in balancing variability in patient demand and length of stay, an average bed occupancy of 85% should be targeted for acute hospital wards. The notion is that higher figures result in excessive capacity breaches, while anything lower fails to make economic use of available resources. Although concerns have previously been raised regarding the generic use of the 85% target, there has been little research interest into alternative derivations that may better represent the diverse range of conditions that exist in practice. OBJECTIVE: To quantify a continuum of average occupancy targets for use within the acute hospital setting. METHODS: Computer simulation is used to model the process of acute patient admission and discharge. Patient arrivals are assumed to be independent of one another (i.e. random) with length of stay distributions obtained through fitting to patient-level data from all of England. RESULTS: Target average occupancy increases with ward size, ranging from 45% to 79% for a relatively small 15-bed ward to 64-84% for a relatively large 50-bed ward. Regarding ward speciality, for a typical 25-bed ward, values range from 57-58% for Gynaecology to 67-74% for Adult Mental Health. These increase to 62-63% and 75-82%, respectively, if the tolerance on breaching capacity is relaxed from 2% to 5% of days per year. CONCLUSION: An unconditional 85% target serves as an overestimate across the vast majority of settings that typically exist in practice. Hospital planners should consider ward size, speciality and capacity-breach tolerance in determining a more sensitive assessment of bed occupancy requirements. This study provides hospital planners with a means to reliably assess the operational performance and readily calculate optimal capacity requirements.

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care.

Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these 'capacity-dependent' deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional 'capacity-independent' deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service.

Failure demand: a concept evaluation in UK primary care.

PURPOSE: The purpose of this paper is to assess failure demand as a lean concept that assists in waste analysis during quality improvement activity. The authors assess whether the concept's limited use is a missed opportunity to help us understand improvement priorities, given that a UK Government requirement for public service managers to report failure demand has been removed. DESIGN/METHODOLOGY/APPROACH: The authors look at the literature across the public sector and then apply the failure demand concept to the UK's primary healthcare system. The UK National Health Service (NHS) demand data are analysed and the impact on patient care is elicited from patient interviews. FINDINGS: The study highlighted the concept's value, showing how primary care systems often generate failure demand partly owing to existing demand and capacity management practices. This demand is deflected to other systems, such as the accident and emergency department, with a considerable detrimental impact on patient experience. RESEARCH LIMITATIONS/IMPLICATIONS: More research is needed to fully understand how best to exploit the failure demand concept within wider healthcare as there are many potential barriers to its appropriate and successful application. PRACTICAL IMPLICATIONS: The authors highlight three practical barriers to using failure demand: first, demand within the healthcare system is poorly understood; second, systems improvement understanding is limited; and third, need to apply the concept for improvement and not just for reporting purposes. ORIGINALITY/VALUE: The authors provide an objective and independent insight into failure demand that has not previously been seen in the academic literature, specifically in relation to primary healthcare.

A decision support simulation model for bed management in healthcare.

PURPOSE: In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources. such as beds. Bed management is a key to the effective delivery of high quality and low-cost healthcare. The purpose of this paper is to develop a discrete event simulation to assist in planning and staff scheduling decisions. DESIGN/METHODOLOGY/APPROACH: A discrete event simulation model was developed for a hospital system to analyze admissions, patient transfer, length of stay (LOS), waiting time and queue time. The hospital system contained 50 beds and four departments. The data used to construct the model were from five years of patient records and contained information on 23,019 patients. Each department's performance measures were taken into consideration separately to understand and quantify the behavior of departments individually, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time and LOS of patients. FINDINGS: Using the simulation model, it was determined that reducing the bed turnover time by 1 h resulted in a statistically significant reduction in patient wait time in queue. Further, reducing the average LOS by 10 h results in statistically significant reductions in the average patient wait time and average patient queue. A comparative analysis of department also showed considerable improvements in average wait time, average number of patients in queue and average LOS with the addition of two beds. ORIGINALITY/VALUE: This research highlights the applicability of simulation in healthcare. Through data that are often readily available in bed management tracking systems, the operational behavior of a hospital can be modeled, which enables hospital management to test the impact of changes without cost and risk.

Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions.

Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories' uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the "complexity generators" in the "complexity metrics". Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve.

Operations research in intensive care unit management: a literature review.

The intensive care unit (ICU) is a crucial and expensive resource largely affected by uncertainty and variability. Insufficient ICU capacity causes many negative effects not only in the ICU itself, but also in other connected departments along the patient care path. Operations research/management science (OR/MS) plays an important role in identifying ways to manage ICU capacities efficiently and in ensuring desired levels of service quality. As a consequence, numerous papers on the topic exist. The goal of this paper is to provide the first structured literature review on how OR/MS may support ICU management. We start our review by illustrating the important role the ICU plays in the hospital patient flow. Then we focus on the ICU management problem (single department management problem) and classify the literature from multiple angles, including decision horizons, problem settings, and modeling and solution techniques. Based on the classification logic, research gaps and opportunities are highlighted, e.g., combining bed capacity planning and personnel scheduling, modeling uncertainty with non-homogenous distribution functions, and exploring more efficient solution approaches.

Enhancing outpatient appointment scheduling system performance when patient no-show percent and lateness rates are high.

Purpose High lateness and no-show percentages pose great challenges on the patient scheduling process. Usually this is addressed by optimizing the time between patients in the scheduling process and the percent of extra patients scheduled to account for absent patients. However, since the patient no-show and lateness is highly stochastic we might end up with many patients showing up on time which leads to crowded clinics and high waiting times. The clinic might end up as well with low utilization of the doctor time. The purpose of this paper is to study the effect of scheduled overload percentages and the patient interval on the waiting time, overtime, and the utilization. Design/methodology/approach Actual data collection and statistical modeling are used to model the distribution for common dentist procedures. Simulation and validation are used to model the treatment process. Then algorithm development is used to model and generate the patient arrival process. The simulation is run for various values of basic interval scheduled time between arrivals for the patients. Further, 3D graphical illustration for the objectives is prepared for the analysis. Findings This work initially reports on the statistical distribution for the common procedures in dentist clinics. This can be used for developing a scheduling system and for validating the scheduling algorithms developed. This work also suggest a model for generating patient arrivals in simulation. It was found that the overtime increases excessively when coupling both high basic interval and high overloading percentage. It was also found that: to obtain low overtime we must reduce the basic interval. Waiting time increases when reducing the basic scheduled appointment interval and increase the scheduled overload percentage. Also doctors' utilization is increased when the basic interval is reduced. Research limitations/implications This work was done at a local clinic and this might limit the value of the modeled procedure times. Practical implications This work presents a statistical model for the various procedures and a detailed technique to model the operations of the clinics and the patient arrival time which might assist researches and developers in developing their own model. This work presents a procedure for troubleshooting scheduling problems in outpatient clinics. For example, a clinic suffering from high patient waiting time is directly instructed to slightly increase their basic scheduled interval between patients or slightly reduce the overloading percentage. Social implications This work is targeting an extremely important constituent of the health-care system which is the outpatient clinics. It is also targeting multiple objectives namely waiting times, utilization overtime, which in turn is related to the economics and doctor utilization. Originality/value This work presents a detailed modeling procedure for the outpatient clinics under high lateness and no-show and addresses the modeling procedure for the patient arrivals. This 3D graphical charting for the objectives includes a study of the multiple objectives that are of high concern to outpatient clinic scheduling interested parties in one paper.

Comparing Choice and Partnership Approach assumptions to Child and Adolescent Mental Health Services in NHS Greater Glasgow and Clyde.

PURPOSE: The purpose of this paper is to compare NHS Greater Glasgow and Clyde (NHSGGC) Child and Adolescent Mental Health Service (CAMHS) activity data over a one-year period to the Choice and Partnership Approach (CAPA) demand and capacity model assumptions, providing an evaluation of CAPA model implementation and its effects on actual demand and capacity of the service. DESIGN/METHODOLOGY/APPROACH: Three assumptions within the CAPA model are tested against activity data extracted from the patient management system. Analysis by patient record assesses the number of appointments the patients received and the patients' journey from assessment to treatment. A combination of community CAMHS data are combined to compare actual activity against assumed capacity required to meet demand according to the CAPA model. FINDINGS: Tested against an audit of 2,896 patient records, CAMHS average 7.76 core appointments per patient compared to the CAPA assumption of 7.5 appointments at a 0 per cent DNA rate. The second CAPA assumption states that 66 per cent of assessments will result in treatment, compared to 73.55 per cent in NHSGGC CAMHS. Finally, the workforce model in CAMHS has clinical capacity to meet demand according to the CAPA assumption of weekly accepted referral rates not exceeding the number of clinical whole time equivalent. ORIGINALITY/VALUE: The data allow for identification of inefficiencies within CAMHS and highlights how capacity can be increased, without increasing budgets, to meet a rising clinical demand. The results allow managers and clinicians to improve job planning to ensure more children and young people have quick access to services.

Tactical capacity planning under uncertainty - a capacity limitation analysis.

PURPOSE: Tactical capacity planning is crucial when hospitals must cope with substantial changes in patient requirements, as recently experienced during the Covid-19 pandemic. However, there is only little understanding of the nature of capacity limitations in a hospital, which is essential for effective tactical capacity planning. DESIGN/METHODOLOGY/APPROACH: We report a detailed analysis of capacity limitations at a Norwegian tertiary public hospital and conducted 22 in-depth interviews. The informants participated in capacity planning and decision-making during the Covid-19 pandemic. Data are clustered into categories of capacity limitations and a correspondence analysis provides additional insights. FINDINGS: Personnel and information were the most mentioned types of capacity limitations, and middle management and organizational functions providing specialized treatment felt most exposed to capacity limitations. Further analysis reveals that capacity limitations are dynamic and vary across hierarchical levels and organizational functions. RESEARCH LIMITATIONS/IMPLICATIONS: Future research on tactical capacity planning should take interdisciplinary patient pathways better into account as capacity limitations are dynamic and systematically different for organizational functions and hierarchical levels. PRACTICAL IMPLICATIONS: We argue that our study possesses common characteristics of tertiary public hospitals, including professional silos and fragmentation of responsibilities along patient pathways. Therefore, we recommend operations managers in hospitals to focus more on intra-organizational information flows to increase the agility of their organization. ORIGINALITY/VALUE: Our detailed capacity limitation analysis at a tertiary public hospital in Norway during the Covid-19 pandemic provides novel insights into the nature of capacity limitations, which may enhance tactical capacity planning.

Coping with COVID-19: the role of hospital care structures and capacity expansion in five countries.

This contribution examines the responses of five health systems in the first wave of the COVID-19 pandemic: Denmark, Germany, Israel, Spain and Sweden. The aim is to understand to what extent this crisis response of these countries was resilient. The study focuses on hospital care structures, considering both existing capacity before the pandemic and the management and expansion of capacity during the crisis. Evaluation criteria include flexibility in the use of existing resources and response planning, as well as the ability to create surge capacity. Data were collected from country experts using a structured questionnaire. Main findings are that not only the total number but also the availability of hospital beds is critical to resilience, as is the ability to mobilise (highly) qualified personnel. Indispensable for rapid capacity adjustment is the availability of data. Countries with more centralised hospital care structures, more sophisticated concepts for providing specialised services and stronger integration of the inpatient and outpatient sectors have clear structural advantages. A solid digital infrastructure is also conducive. Finally, a centralised governance structure is crucial for flexibility and adaptability. In decentralised systems, robust mechanisms to coordinate across levels are important to strengthen health care system resilience in pandemic situations and beyond.

Creating Coherence-Based Nurse Planning in the Perinatology Care System.

The combination of increasing demand and a shortage of nurses puts pressure on hospital care systems to use their current volume of resources more efficiently and effectively. This study focused on gaining insight into how nurses can be assigned to units in a perinatology care system to balance patient demand with the available nurses. Discrete event simulation was used to evaluate the what-if analysis of nurse flexibility strategies and care system configurations from a case study of the Perinatology Care System at Radboud University Medical Center in Nijmegen, the Netherlands. Decisions to exercise nurse flexibility strategies to solve supply-demand mismatches were made by considering the entire patient care trajectory perspective, as they necessitate a coherence perspective (i.e., taking the interdependency between departments into account). The study results showed that in the current care system configuration, where care is delivered in six independent units, implementing a nurse flexibility strategy based on skill requirements was the best solution, averaging two fewer under-/overstaffed nurses per shift in the care system. However, exercising flexibility below or above a certain limit did not substantially improve the performance of the system. To meet the actual demand in the studied setting (70 beds), the ideal range of flexibility was between 7% and 20% of scheduled nurses per shift. When the care system was configured differently (i.e., into two large departments or pooling units into one large department), supply-demand mismatches were also minimized without having to implement any of the three nurse flexibility strategies mentioned in this study. These results provide insights into the possible solutions that can be implemented to deal with nurse shortages, given that these shortages could potentially worsen in the coming years.

Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic.

OBJECTIVE: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. METHODS: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event, simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths, under changing pandemic situations, from getting ready to turning all of its resources to pandemic care. RESULTS: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described. CONCLUSIONS: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed.

Predicting next-day discharge via electronic health record access logs.

OBJECTIVE: Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users' granular interactions with patients' records by communicating various semantics and has been neglected in outcome predictions. MATERIALS AND METHODS: This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction. RESULTS: The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919-0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860-0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features. CONCLUSION: EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.

One year with the COVID-19 pandemic - Lessons learnt? Intersectoral collaboration measures established during the crisis could benefit capacity and patient flow management in daily clinical practice.

PURPOSE: The COVID-19 pandemic has changed the way hospitals work. Strategies that were detached from the boundaries of departments and responsibilities in the COVID-19 pandemic have proven themselves under extreme conditions and show a beneficial influence on patient flow and resource management as well as on the communication culture. The continuation of closer interdisciplinary and cross-sectoral co-operation in a "new clinical routine" could have a positive impact on personnel concepts, communication strategies, and the management of acute care capacities and patient pathways. DESIGN/METHODOLOGY/APPROACH: The aim of the paper is to critically discuss the knowledge gained in the context of the COVID-19 pandemic from the various approaches in patient flow and capacity management as well as interdisciplinary co-operation. More recent research has evaluated patient pathway management, personnel planning and communication measures with regard to their effect and practicability for continuation in everyday clinical practice. FINDINGS: Patient flows and acute care capacities can be more efficiently managed by continuing a culture change towards closer interdisciplinary and intersectoral co-operation and technologies that support this with telemedicine functionalities and regional healthcare data interoperability. Together with a bi-directional, more frequent and open communication and feedback culture, it could form a "new clinical routine". ORIGINALITY/VALUE: This paper discusses a holistic approach on the way away from silo thinking towards cross-departmental collaboration.