Patient admission prediction

What if you knew who would walk into your hospital today, when and why?

How the partnership between Austin Health, CSIRO and Health IQ works to provide Austin Health these answers.

Without the ability to accurately predict patient admissions, hospitals are limited in their ability to effectively plan for changing demand. As a result, they face a range of complex issues that negatively impact patient access and care, the financial position of the hospital, and their ability to comply with national targets.

The Austin Hospital has been seeking a way to accurately predict patient admissions for a long time.

To better understand of how the challenges of planning ahead plays out for hospital decision makers, I spoke to Fiona Webster, Executive Director, Acute Operations at Austin Hospital.

It’s been a long time since Fiona began looking for a way of predicting hospital demand more accurately, but still, like for most Australian hospitals, the Austin Hospital is limited in its ability to accurately forecast what the demand for even just the next day will be.

“We look to what happened the day before, at the average number ED presentations, the number of booked elective surgeries to try and estimate the demand for the next day. We also look at the number of patients waiting in the hospital and the number of estimated discharges for the day to gain visibility of the hospitals’ capacity. If it looks like there’s going to be significant gap between the expected demand and available capacity, the issue is escalated – so that actions can be identified to reduce the imbalance.”

Why can’t hospitals effectively predict demand?

Many disparate systems

Because of the large number of disparate systems in use, it becomes quite difficult to consolidate all the information required to reach a real picture of the demand they can expect. “The real swings in bed requirements happen not because of a single factor, but due to a coalescence of four or five factors. For example, we may reduce beds because a large number of surgeons are away on leave, but if there is suddenly an influx of patients coming in through ED, then we’re left with too few beds. Currently, effectively tracking such disparate factors that may impact demand in the future is extremely challenging.”

Limited understanding of what causes demand fluctuations

The disparate systems and limited visibility also means that the hospital has very little ability to identify the root cause of the various fluctuations – and by the time a cause is identified, the situation may have already changed significantly. There is no opportunity to identify and leverage past trends intelligently. “At the end of the day, we’re simply making an informed guess.”

What does that mean for Austin Health?

The limited ability to accurately predict demand is the cause of some fundamental challenges.

Last-minute decisions

Currently, demand predictions are often only possible the day before they are expected to play out. Due to this, decision makers only have a limited time to plan against these predictions – meaning that they are constrained in the actions they can take to address the expected fluctuations in demand.

Unfortunately though, elective surgery is one of these factors, and it’s often the one that gets cut in order create more capacity. “Our ability to optimally flex beds and other resources is limited, so the default is cancelling elective surgery. Nobody wants to cancel surgeries: they take a lot of effort to set up, both on the patient’s side and the hospital’s. Cancelling them is very inefficient”. In addition, cancelling surgeries can compromise a hospital’s ability to meet their NEST.

Inability to effectively plan ahead

As a key decision maker, there are a number of levers Fiona has to manage as part of her planning – such as budgets, staff, beds, and other resources. However without long term demand forecasts to plan against, the opportunity to proactively optimise these levers is lost. For instance, take the holiday season. With many hospital staff going on leave, and demand generally reducing, a number of beds are usually closed. However each year, it’s difficult to know exactly how many beds to close. Even with past years’ data and allowing for standard population growth, it’s still a guessing game. If too many beds are closed, agency staff may need to be hired at the last minute. “If we did have the visibility of a large surge in demand in May for instance, then I would be able to plan ahead, hire the right number of staff now without relying on agency – but currently that’s not possible.”

Inefficiencies that cannot be resolved

As you know, hospitals are full of highly valuable resources, human and otherwise, and everyone does their job because they care about delivering the best outcome for their patients. In such an environment, not being able to address daily inefficiencies due to lack of demand prediction is very frustrating.

However, more often than not, there’ll either be elective surgeries cancelled due to too much demand or surplus beds that are not utilised… because demand can’t be accurately predicted!

Towards a more accurate future

Fiona, who had long sought a better solution for patient admission prediction, was aware of the work CSIRO were doing on Patient Admission Prediction in Queensland. “I thought, if you can forecast the weather, surely you can forecast the number of patients who we can expect to come through the door. When I heard that patient admissions could be predicted, and with accuracy, I was immediately interested.”

The former Minister for Technology, Gordon Rich-Phillips, with Paul Phelan of Health IQ and the teams from the Austin Health and CSIRO at the launch of the PAPT trial.

What resulted was a partnership between Austin Health, CSIRO and Health IQ, who are working together to implement the Patient Admission Prediction Tool (PAPT) at Austin Health. Launched in October 2014, the tool is expected to provide Fiona and her colleagues the ability to better predict bed demand, optimise resource allocation, and maximise patient access in their hospitals. Fiona is looking to achieve accurate demand forecasts as much as one year in advance.

How it works

PAPT is a software tool that utilises complex algorithms by applying them to historical data in order to predict the number of patients admitted and discharged in the future. The tool runs unsupervised and updates data regularly, allowing new information to improve efficiency without draining staff resources. With 90% accuracy, PAPT can predict the number of expected presentations with specific injuries or illnesses, facilitating efficient planning of staff, beds and other resources.

Expected outcomes

Fiona looks forward to realising the trial’s goals: “The ability to anticipate emergency department attendances and inpatient beds is an important aid not only to the daily challenges of bed management but it will forecast a year ahead which will assist with winter planning, hospital staffing and longer term capacity planning” (Austin Health, 2014).

While we’re waiting to see exactly what outcomes Austin will experience at the conclusion of the trial in mid-2015, we can already see the benefits that have been experienced by Queensland hospitals who already leverage a version of this tool.

Specifically, it has helped these facilities improve their bed management, staff resourcing, and scheduling of elective surgery (CSIRO, 2014) – exactly the outcomes Fiona is looking for. From a patient standpoint, PAPT has enabled the delivery of improved healthcare outcomes, such as the timely delivery of emergency care, improved quality of care, and less time spent in the hospital (CSIRO, 2014).

The Patient Admission Prediction Tool helped Gold Coast doctors plan for the influx of patients during Schoolies.

For example, the tool played a central role at Gold Coast Health in managing the influx of patients during Schoolies. Dr James Lind, Director of Access and Patient Flow at Gold Coast Health says that of with PAPT’s prediction technology, they are able to expect “around 2,700 presentations to our emergency department in total and around 20% of these will be school leavers in just the first week of the celebrations” (as quoted in CSIRO, 2013). This knowledge enables Dr Lind and his team to better plan the staff, medical supplies, and beds for this increased demand while also catering to the needs of other non-schoolies patients (CSIRO, 2013).

If you would like to find out more about how your hospital can take advantage of such improved demand prediction ability, please call me on 03 9425 8012 or send me an email to dave.piggott@healthiq.com.au.

Dave Piggott is the Executive Director of Health IQ, and is focused on helping Australian health services achieve better visibility and communication within and across their hospitals. Dave has over 20 years’ experience in Health IT. A graduate of the Australian Institute of Company Directors (AICD) and with a Masters in Open Systems (IT), Dave has worked extensively in the Patient Flow area, and helped over 30 Australian hospitals to improve their flow of patients.

Cited sources:

Austin Health. (2014, October 31). Newsroom: New technology will boost Austin Health service delivery. Retrieved from Austin Health: http://www.austin.org.au/newsroom

CSIRO. (2013, November 15). CSIRO. Retrieved from The one Toolie that’s welcome at Schoolies: http://www.csiro.au/Portals/Media/Technology-predicts-surge-of-sick-schoolies.aspx

CSIRO. (2014, February 26). CSIRO. Retrieved from Cutting hospital waiting time: http://www.csiro.au/Organisation-Structure/Flagships/Digital-Productivity-and-Services-Flagship/Health-services/PAPT–case-study.aspx

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