Engaging Clinicians and Commissioners in Bed Management

Isle of Wight NHS Trust is the only integrated acute, community, mental health and ambulance health care provider in England. The Trust provides a full range of health services to an isolated, elderly offshore population of 140,000, creating a unique, challenging healthcare system to manage.

Based at the heart of the Island, with 246 beds St Mary’s Hospital is their main base for acute service delivery handling 22,685 admissions a year. The Isle of Wight aims to maximize patient flow across the entire Health and Care System to improve the delivery of urgent and elective care and helping people stay as independent as possible.

In this year’s demand and capacity planning round, the Isle of Wight was determined to engage effectively with clinicians and to provide assurance to the CCG that the Trust could cope with demand. They turned to Bed.P.A.C. for a new approach to bed management because of the increased accuracy the cutting edge simulation algorithm could bring to their planning, coupled with the ability to rapidly test a wide range of scenarios from reducing Length of Stay to managing changing demand profiles.

They were confident Bed.P.A.C. was the tool they needed to allow them to involve and engage their CCG, consultants and clinicians throughout the process to create a plan that was robust, backed with solid evidence and consequently everyone trusted and was invested in executing.

“Bed.P.A.C. has stood up to scrutiny from clinicians and managers within the Trust, consequently the results output have led to constructive discussions about solutions to issues rather than ongoing debates about the integrity of the modeling. Consultants have been particularly impressed by our ability to recognize the maximum bed requirements and how often a certain number of beds will be utilized rather than referring to average bed requirements.

Bed.P.A.C. has given our commissioners assurance on capacity that exists to deliver agreed demand plan with the exception of specific identified areas.”

Deputy Director - Information
Finance & Performance Information Service
Isle of Wight NHS Trust

The Objectives

The Isle of Wight Decision Support Team’s main driver in using Bed.P.A.C. was to move away from planning on averages with simple deterministic spreadsheet calculations and ensure their analysis was taking into account the variation seen in the real world.

The IOW team had 7 key questions to answer using Bed.P.A.C.:

  1. What is the impact of variation on the planned number of beds needed for different specialties for 2016/17?
  2. What is the effect of winter pressures on inpatient beds?
  3. What is the effect on inpatient bed utilization of reducing delayed discharge patients?
  4. How can we reduce mixed sex accommodation breaches?
  5. Is there sufficient capacity to meet demand for a New Step Down Unit within the current footprint and what will be the frequency of medical outliers to this unit?
  6. What is the downstream capacity constraint of community nursing beds?
  7. How can we increase performance objectives, such as ED wait times, by using short term predictions to show where inpatient bed capacity problems may arise over the next 7 days.

The Process

The Isle of Wight began their analysis by using their Trust Demand tool to forecast arrivals of elective and emergency patients across each specialty. These forecasts along with 12 months of historical data of arrivals, length of stay and discharge times were uploaded to Bed.P.A.C. which automatically created daily patient arrival patterns and profiles of typical length of stay depending on time and day or arrival and specialty.

Bed.P.A.C.’s simulation engine generated both individual specialty and aggregate results. The results enabled them to look at the impact of demand on:

  • Hourly capacity for emergency and elective beds separately “unconstrained”, providing results which show how many beds would be needed
  • Hourly capacity if beds were constrained to the actual beds available, for emergency and electives together and separately
  • Where patients would need to be placed in another speciality having waiting 12 hours for a bed

The Isle of Wight grouped their results into four main areas, representing the main departments in the hospital: Trauma & Orthopedics, Surgery, Medicine and ITU

Bed.P.A.C.’s results were directly shared with the clinical staff in each of these teams. Through combinations of group workshops and individual meetings Bed.P.A.C.'s results were used to facilitate discussions around bed allocation and investigate many “what if?” scenarios. These were highly successful sessions with clinical staff enthusiastic and engaged throughout. The sophistication of Bed.P.A.C.'s report gained immediate respect and confidence and enabled productive discussions about the current state and how this could be improved.

In particular the confidence intervals provided for each result were pivotal at securing the clinical team’s buy in. Bed.P.A.C. can produce these because for each scenario it does 50 individual runs introducing different variation each time to give an average view on required capacity with confidence intervals. These confidence intervals are critical in showing the level of risk you are taking with your bed planning choices and how vulnerable you are to fluctuations in demand.


The Results

Bed.P.A.C. clearly demonstrated that more beds were required to meet the Isle of Wight’s demand and also showed these beds didn’t necessarily have to be hospital beds but could be in the community or in fact the patient’s own bed a home with appropriate support. Getting the complex balance between acute and community care right has been one of the key success points of their work with Bed.P.A.C.

Bed.P.A.C. has led to...

  • Constructive system wide discussions via the systems resilience group to resolve bed capacity issues, recognizing this is a system issue not just an issue with bed capacity in the hospital.
  • Reconfiguration of beds in hospital to allow increased our bed capacity for medical patients by approximately 17%.
  • Extension of the provision of winter resilience step down bed capacity in the community through Q1 of 16/17 funded by system resilience.
  • Increase community bed capacity based on identified need through the redesign of existing surplus capacity.
  • Increased home care packages delivering services to people in their own homes and in localities, to avoid attendance and admission to hospital for episodes that can be safely managed elsewhere.

Why Bed.P.A.C.

When asked why Isle of Wight would choose Bed.P.A.C. over alternative bed planning and management tools they said that it was:


Bed.P.A.C. is quicker than any other tool Isle of Wight had previously used. In particular the ability to upload new demand profiles, select pre-built scenarios with a single click and then press run to regenerate bed performance metrics, has been critical in allowing the Trust to rapidly produce their annual demand and capacity plan.

Results for any hospital, specialty, elective and/or emergency can be run in a morning and produced in a format that can be used for the meeting that afternoon.


Many of the scenarios are already built into Bed.P.A.C. this means experimentation is rapid giving the flexibility to respond to “what if” questions from key stakeholders. Bed.P.A.C.’s results are also visually compelling and easily understood by clinical staff.

Being able to interact with clinical staff in this way is incredibly powerful as a mechanism to not only gain their trust but to fully engage them in the planning process ensuring a far better end analysis as a result.


Bed.P.A.C.'s powerful simulation engine provides a level of detail and accuracy not previously available for bed planning. The Isle of Wight no longer has to plan on or report only averages which their sophisticated clinical staff rightly never bought into.

The Isle of Wight now have a full suite of detailed system performance metrics with confidence intervals provided for every result giving confidence in their analysis and ensuring buy in from clinical staff. In addition it means they have solid evidence to generate and have the difficult conversations that must happen to produce the right plan for optimal hospital performance.

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