The latest figures from NHS Monitor indicate that the NHS is on course for a deficit of almost £3bn. It may seem like a paradox for trusts to have to make long-term decisions with such financial uncertainty, but without effective financial planning the cost pressures will grow and services will become out-dated, argues Mark Smith, Head of Financial Services Product Development at CACI.

BBC Panorama recently highlighted the issues commissioners across the country are facing in their documentary ‘NHS - The Perfect Storm’. The film demonstrated how the Liverpool Clinical Commissioning Group is operating in a climate that does not allow a single mistake, and also how the ‘Healthy Liverpool’ project is working to transform the NHS.

The key in Liverpool - and across the country - is to move from a ‘patching up’ service to an NHS that coordinates efforts based on forecasting the needs of patients. To succeed with this transformation, however, massive quantities of data are needed. And a major challenge for all trusts will be to understand the data and accurately relate it to patient activities and changes.

Multi-disciplinary planning

Forecasting the needs of a hospital is no simple task. Currently, multi-disciplinary teams are required to engage in a daily struggle with vast volumes of data, conflicting needs from different departments and uncertainties in funding. While highly-skilled teams are already in place, inadequate tools for analysis and restricted data sets often hinder their efforts. Cumulatively, these elements make processing fully accurate assessments particularly challenging.

Another sticking point for trusts is how to truly forecast the future needs of the population. In the coming years financial planning will likely require the involvement of many more highly skilled multi-disciplinary teams than before, in order to simultaneously look at population data alongside making sense of data sets such as financial statements, clinical performance KPIs, bed and theatre use, patient level costs and much more.

With that in mind, the cost inefficiencies of highly-skilled teams working with poor data and limited tools for analysis make the prospects of the planning process even more concerning for exec teams.

However, a great example of how trusts have overcome these challenges is the Better Care Together initiative in North England, where costing outputs have successfully been used as the starting point for financial planning. Better Care Together is working to reorganise how 11 trusts deliver health services to over 350,000 people living across sparsely populated territories in North Lancashire and South Cumbria.

Staying ahead of the changing health landscape, like Better Care Together is doing, is essential for succeeding with the data-driven financial planning of the future.

Detailed modelling

Re-organisation plans like Better Care Together have mostly been driven by relocating services between trusts and into the community, but also by achieving specific cost savings. More specifically, experience and research from CACI shows how such cost savings can be achieved, for instance through modelling different nursing configurations and changes in bed numbers or occupancy rates in greater detail.

A hypothetical example is the cost savings proposed in a plan to reduce length of stay for patients by half a day. The savings could simply be calculated by multiplying the daily ward costs by the number of saved ward days. However this approach doesn’t necessarily go far enough and the true savings are only going to be identified by the savings of closing wards, or reducing ward sizes and therefore optimising staffing and reducing consumable costs.

This type of modelling can be applied to theatres, outpatient clinics, radiology, pathology, pharmacy and other clinical support services. Once a plan has been formed around the direct clinical services the scale of change can be assessed and the requirements for non-clinical support services can be better understood.

Demographic tools

Essential for long-term financial planning is being able to forecast the health requirements of the broader population. This includes existing health conditions and risk factors such as high blood pressure, as well as the prevalence of smoking and unhealthy diets, and also living conditions, the likelihood of isolation and loneliness, and exercise levels. All of this data can be used to understand the types of conditions that will be expected.

Using demographic analysis tools to model the population is in this respect a great advantage for trusts. It reduces the uncertainty of patient needs and allows for a more systematic approach to forecasting. It also allows planning teams to have more time to focus on outputs, rather than applying complex Excel formulas to project activity volumes. This time can instead be spent formulating plans to change services and reconfigure wards and clinical teams to meet the growing demand.

In trying financial times such as these, with deficits heading for the billions, configuring services to the needs of patients while optimising spend, instead of forcing the patients’ needs into a predefined service size and specification, is what the NHS needs.

Patient data speaks for itself; listening to and making sense of it will define the future of the NHS.