Variation is undermining your bed capacity planning

Simulation in Healthcare



Managing hospital bed capacity is crucial to the smooth running of a hospital, since all departments are in some way dependent on bed availability.

Hospital beds are required for patients needing emergency care as well as procedures and the requirements are different depending on the time of arrival, by hour, day, week and month and the needs and characteristics of the particular patient.Given this complexity and the wealth of evidence on variation, it is surprising that so many hospital planners still use deterministic spreadsheets to plan their bed capacity, based on average demand. Any report of arrivals to an Emergency Department will show the variation in arrivals – one such published example below – and this is replicated in demand for surgical beds as well.

RMB_Arrivals

The net impact of demand for beds from both elective and emergency patients, together with patient discharge delays creates a pressure on hospital beds which builds over the course of the day, usually culminating in the mid-afternoon. On average, it might look as if there are enough beds, but the reality is that patients are often placed in a non-ideal unit if the right bed in the right unit is not available at peak demand, or they may have to wait in ER until the bed becomes available. The example below demonstrates this effect, as demand for beds sometimes surpasses actual capacity.

BedOccupancySShot

This non-ideal placement has serious implications for patients and for costs.  A patient placed in a bed in the wrong clinical specialty can have an additional length of stay of 1 or 2 days (Alameda 2009; Blay 2002) and inpatient hospital mortality doubles if patients are in the wrong bed (Santamaria 2014). Patient waits for scheduled surgery, due to a lack of beds, can cause cancellations impacting patient outcomes and creating significant cost and revenue implications as a result.It is staggering how costs will accumulate and escalate for patients placed in the wrong bed. It is estimated in an average sized hospital with 400 beds, patients placed in the wrong bed costs $1.6 million per month. Try our bed management ROI Calculator yourself to understand the impact of bed management for your organization.

This is compelling evidence to drive better planning of beds. Every hospital has this data but very few are using it to drive and improve bed management decisions.

 

Staff have lost hope

We have spoken to hospital management and clinicians in American, Canadian and UK hospitals about bed management and got some very surprising insights. Most felt that there was room for improvement. The staff responsible for day to day operations used different methodologies from those leading transformational change and those in charge of strategic planning, and there was often no overall sense of how the daily “flow” in the hospital could be understood by each of these groups to drive improvement. More worrying was that some felt that there was not much you could really do to make the situation any better.

Professor Matthew Cooke, Deputy Medical Director (Strategy and Transformation) at the Heart of England Foundation Trust and Professor of Clinical Systems Design at the University of Warwick, summarizes some of the behaviors, both positive and negative, that arise as staff try and manage the difficult situation they find themselves in.

The system is perhaps partially rescued by its adaptive nature. If admissions are not possible then people may be sent home earlier; more may be managed as out-patients; less may be added to the waiting list, but, as soon as beds become available, a pool of unmet need floods through the doors. But there is also maladaptive behavior – a doctor may keep a patient in hospital to block the bed until he next needs one; patients may be sent home before they are ready to make an extra bed. A simple formula is in reality a complex, interacting system of negative and positive feedback.

Clinical staff are constantly managing bed capacity. They take corrective action to deal with unexpected arrivals, this causes cancellations, and increases demand later. As Cooke explains they then get used to operating in a system like this and start to develop behaviors which can exaggerate the problem of variation in an already overloaded system.

Planning bed capacity is incredibly complex – managing bed availability on the wards is crippling staff and there is no simple solution. As simulation specialists, we believe there is a better way of managing the variation in demand that is experienced by hospitals, and that it is possible to produce results which are much more accurate than a spreadsheet.

We have combined our experience with the insights of our customers to produce a tool that can quickly forecast bed capacity requirements more accurately than a spreadsheet, both for long term and near-real time planning. If you believe that your organization has similar opportunities for improvement, watch the webinar and test out your own ROI to see if there is value in using this approach for your hospital.

Alameda C et al, Clinical outcomes in medical outliers admitted to hospital with heart failure, Eur J Intern Med. 2009 Dec;20(8):764-7

Blay N et al, A retrospective comparative study of patients with chest pain and intra ward transfers, Aust Health Rev. 2002;25(2):145-54

Santamaria J et L, Do Outlier Inpatients Experience More Emergency Calls In Hospital? An Observational Cohort Study, Med J Aust 2014; 200 (1): 45-48 .

 

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