Don't plan on an average. Include variation in your bed forecasting for accurate planning.
Use Bed.P.A.C. to create your long term bed capacity plans and predict when a bed crisis is looming. With Bed.P.A.C. you know about the bed crisis before it happens, in time to take preventative action.
Inpatient stays are one of the most expensive areas of healthcare and managing bed capacity is a daily challenge for hospital administrators. It is estimated that better bed management can save approximately $370,000 per month per hospital AND improve patient outcomes.
Gain insight into how your policy changes will impact your bed occupancy. Test, plan and experiment in a risk free environment. Know when you’re going to run out of beds need.
Improve the patient experience by testing the impact of improvement decisions on cancellations, waits and costs. Be confident that your decision is the right one for costs and patient care.
Get departments working together on patient placement decisions. Shared forecasts give shared visibility.
Bed.P.A.C. automatically builds demand profiles for patient arrivals and length of stay from your historic data.
Built on a simulation engine we add the variance you'd see in the real world to robustly test your plan.
With one click try different scenarios. Increase patient demand, change staff shift patterns, close wards.
See trends in admissions, LOS, discharge and wait times with dynamically updating control charts.
You can hook up to your live data sources but there's no requirement, you can input directly from csv files.
Bed.P.A.C. is completely online. No install, perfect for cross organizational sharing at every level.
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 modelling. Consultants have been particularly impressed by our ability to recognise the maximum bed requirements and how often a certain number of beds will be utilised rather than referring to average bed requirements.
Iain Hendey, Deputy Director - Information Finance & Performance Information Service Isle of Wight, NHS TrustRead the Case Study
A patient in the wrong bed extends their stay by one day, costing $1,600 per day per patient. If just 10% of patients are in the wrong bed that’s $10,000 per day.
4% of scheduled surgery is cancelled for non surgical reasons. Surgery generates income from around $1,500 per case. That adds up to $75,000 per quarter in lost revenue.
Bed management can seem an unsolvable problem. So many times customers told us, there's nothing to be done, I have a fixed number of beds and too many patients. With a challenge this big, even small improvements bring big gains. Just 10% improvement can save 26 lives a month.
Claire Cordeaux, Executive Director of Healthcare