Bed modelling tool

Study the impact of elective admission deferrals to alleviate bed need in favor of Covid hospitalizations. This model is conceptual in nature and designed to be easy-to-use for hospital leadership to quickly understand and test such Covid implications. As new developments are being released hour by hour, we will continue updating this model with the newest information. If so, we are here to help through our NBBJ foundation. Our consulting studio has been busy writing blog posts related to Covid and tips for working from home.

Enjoy these brief articles below. EHRN is a 21st-century medical communication platform, designed to bring the wealth of medical knowledge available in EHR data directly to researchers, healthcare professionals, and learners. Electronic health record data collected over decades, spanning millions of patients, could provide clues to help solve medical problems.

Our firm has been working with several clients on a process to get them back to business as quickly and as safely as possible. This document includes several white papers and blog posts that outline potential solutions that we are seeing not just in healthcare, but also incorporate and commercial projects.

One of the fastest ways to adjust with a small amount of effort with a big impact is with the entrances, lobbies, and waiting areas. As always, our teams will work with you to develop the best possible design interventions to achieve your intended outcome, keep your facility safe, and adapt to future changes.

Latest FGI press release on emergency guidelines. We have curated a set of our favorite dashboards and data resources below. Use tab to navigate through the menu items. Get in Touch! Disclaimer: This tool is designed to help you evaluate bed need during this critical time and is not to be used for all your inpatient bed planning needs as the parameters are general in nature and may not reflect actual site specific cases. Kelly Griffin Jan 14, Focus, Collaborate, Learn, Socialize and Rest.

Nate Holland Dec 19, Working Together, Apart? Doug Grove Dec 15, Full pooling of the current bed stock reduces p delay for both acute and rehabilitation patients to 0. If an additional four beds were available and pooled the likelihood of delays drops to 1 in 64 patients. Table 3 reports this result along with results from scenarios where the units are co-located, but only a subset of the 26 beds are pooled range 0 to 9 beds. This demonstrates that pooling can be beneficial, but that there is also a trade-off between acute delays and rehabilitation delays.

As more beds are pooled this trade-off diminishes. The final scenario analyses the impact of the complex-neurological patients on delays in the stroke pathway. These transferred patients have an effect on the delays experienced accessing rehabilitation in a 12 bed unit: increasing the number experiencing delay from 1 in every 17 patients to 1 in every 9. An effect is also seen in the acute stroke with 10 beds with the number experiencing delay increasing from 1 in every 11 patients to 1 in every 7.

To achieve a 0. A full table of results is provided in the Additional file 1. This simplification is at the heart of the models usefulness: it allows users to understand the actual capacity requirements in different parts of the pathway. At our study hospital the model demonstrates that an increase from 10 to 14 acute stroke unit beds reduces the number of patients experiencing delays from 1 in every 7 patients to 1 in every This is a substantial improvement in smoothing the flow of patients through the stroke unit and significantly increases the time clinicians can focus on patient care as opposed to bed management.

The modelling also predicts a capacity shortfall in the inpatient rehabilitation wards. An increase from 12 to at least 14 beds is again required to smooth the flow and reduce the likelihood of transfer delays.

The study hospital was also planning to co-locate the acute stroke unit and rehabilitation wards. Even if bed pooling between the two units is not officially sanctioned, in practice it is likely that some temporary bed pooling will happen in order to cope with the spontaneous variation in rates of patient admissions and discharges. The model therefore provides a prospective way to plan the implementation of bed pooling and to fully understand the trade-offs when pooling only a subset of beds.

The model was also used to analyse the impact of complex-neurological patients on flow through the pathway. The utility of such information is in the dialog between clinicians and healthcare commissioners to understand the implications of service provision to different patient subgroups on overall performance.

There are several further ways in which our model can be used, depending on the issues seen to be important in different contexts. The unfettered demand approach used in our model is generalizable and hence is applicable to other relevant wards. For example, a second use for our model would be to adapt it for other hospital wards, such as those for the cardiac surgery, where timely admission and discharge are important.

The strengths of our approach to capacity planning are threefold. First, the model provides a sophisticated analysis of capacity requirements accounting for the spontaneous and unpredictable variability in patient arrivals and lengths of stay.

This level of detail is often missing from capacity calculations. Planning models that rely on average occupancy only will greatly underestimate bed requirements as they take insufficient account of variability. In this study average occupancy of the bedded acute stroke unit was nine patients, corresponding to delays for one in every five patients.

Our study provides a scientific methodology for analysing how many beds above average occupancy are necessary in order to limit the probability of delay. Second, although sophisticated, the model is driven by routinely collected data that is readily available from patient administration systems. Last, as the planning model has no capacity constraints, it is not necessary to model what happens to patients when stroke wards are full. Its independence of these details, which can vary considerably across hospitals, greatly increases the applicability of the model to other settings.

If sensitivity analyses show that these discrepancies are likely to cause misleading results, a small prospective sample of times where patients are fit for transfer to rehabilitation versus when they are transferred, or a historic sample of lengths of stay during periods of time when beds are not blocked can be used.

As our model focuses on capacity requirements, a limitation is that it cannot predict the length of a delay that a patient experiences. Although creation of such models is possible the complexity increases by several orders of magnitude and will inevitably require data that is not routinely collected — for example regarding the management and repatriation of outlying stroke patients.

The model is easily adaptable to other acute stroke units which transfer patients to multiple inpatient rehabilitation wards in the community and could be used to explore the impact of introducing new cost effective services such as ESD [ 33 ]. The simulation-based method used here was chosen in preference to attempting to derive heuristics based on queueing theory for calculating the uplifts to associate with different occupancy levels as a more direct way to incorporate the characteristics of the particular problem.

However, the simulation model development was guided by a knowledge of relevant queueing theories, in the spirit of complementary use of simulation and queueing theory [ 34 ].

In recent years some aspects of stroke services have been modelled using discrete-event simulation approaches, [ 8 , 19 — 25 ] including access to time-sensitive treatments such as thrombolysis. Our method, with its focus on capacity, is complementary to these models and will be particularly useful for cases of stroke service reconfigurations where acute stroke units will face substantially increased admissions, including patients for whom the final diagnosis is not stroke.

To enable cost-effective and efficient provision planning decisions in such complex systems requires all of the relevant information to be considered in a way that is not possible for simple average-based estimates. Our method accounts for the variation in admission patterns, length of stay by patient type and eligibility for ESD, greatly increasing the precision with which services can be planned and the ability to predict and respond to short and long-term variation in demand for emergency stroke services.

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Implement Sci. Booked inpatient admissions and hospital capacity: mathematical modelling study. Analytical methods for calculating the capacity required to operate an effective booked admissions policy for elective inpatient services. Health Care Managment Science. Understanding target-driven action in emergency department performance using simulation. Emerg Med J. Article PubMed Google Scholar. A modeling framework that combines markov models and discrete-event simulation for stroke patient care.

Facilitating stroke care planning through simulation modelling. Health Informatics J. Hunter RM, et al. Impact on clinical and cost outcomes of a centralized approach to acute stroke care in London: a comparative effectiveness before and after model. Morris S. Impact of centralising acute stroke services in English metropolitan areas on mortality and length of hospital stay: difference-in-differences analysis.

NHS England. Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. When should physical rehabilitation commence after stroke: a systematic review. Int J Stroke. Fearon P, Langhorne P. Services for reducing duration of hospital care for acute stroke patients. Cochrane Database Syst Rev. Fisher RJ, et al. A consensus on stroke: early supported discharge. Corporation S. Accessed 27 Sept Churilov L, Donnan GA.

Operations research for stroke care systems: an opportunity for the science of better to do much better. Oper Res Health Care. Decision support in pre-hospital stroke care operations: a case of using simulation to improve eligibility of acute stroke patients for thrombolysis treatment.

Comput Oper Res. Simulating the impact of change: implementing best practice in stroke care. London J Primacy Care. A simulation-based approach for improving utilization of thrombolysis in acute brain infarction. Med Care. Thrombolysis in acute ischemic stroke: a simulation study to improve pre- and in-hospital delays in community hospitals. Maximizing the population benefit from thrombolysis in acute ischemic stroke: a modeling study of in-hospital delays.

Will delays in treatment jeopardize the population benefit from extending the time window for stroke thrombolysis? Is it beneficial to increase the provision of thrombolysis? Utley M, Worthington D.

Capacity Planning. In: Hall R, editor. Handbook of Healthcare System Scheduling. New York: Springer; Gross D, Harris CM. Fundamentals of Queueing Theory. Hoboken: Wiley; Robinson S. Simulation: The practice of model development and use. London: John Wiley and Sons; Law AM. Simulation Modelling and Analysis. Boston: McGraw-Hill International; Geer Mountain Software. Pidd M. Computer Simulation in Management Science. Cost-effectiveness of stroke unit care followed by early supported discharge.

Worthington D. Reflections on queue modelling from the last 50 years. J Oper Res Soc. Download references. The model is highly generalizable to other stroke pathways. Specific results can be recreated as follows. Model logic and arrival rates for patient classes are detailed in the main text. For length of stay distributions and patient routing see the online Additional file 1.

The model has a run length of 5 years. A warm-up period of 3 years was used with replications. TM designed the study, performed the analysis and wrote the paper. DW designed the study, oversaw the analysis and contributed to writing the paper.

MJ provided clinical guidance and oversight and contributed to writing the paper. All authors have read and approved the final manuscript. This publication presents the results of a service evaluation project conducted in collaboration with an NHS Trust in the UK using routinely collected administrative data only, and thus did not require ethical approval or individual participant consent.

No patients were involved or identified, no new data were generated or collected, and no care pathways were altered. You can also search for this author in PubMed Google Scholar. Correspondence to Thomas Monks.

Reprints and Permissions. Monks, T.