In Hampshire, Solent NHS Trust and Commissioners are making use of advanced Health Systems Analytics to visualise their demand, and support their decisions about the how many sexual health clinics should be funded to meet future patient need.
The logistics and operations of sexual health services
Sexual health care in Hampshire is provided through a network of hub and spoke clinics. The hubs are large clinics that are located within the urban centres. The spokes are smaller clinics located in more rural areas of Hampshire. Journey times to urban centres vary substantially across Hampshire. This can make life difficult for patients, particularly as young adults are often the ones with sexual health concerns. They may have limited access to transport or may be put off from making a long journey to receive treatment or advice. The spokes aim to increase the equity of access to services for patients who live in outside of the major cities. The more spokes that are in operation the more likely it is that a patient lives near a clinic. Of course, there are also facility, workforce and travel costs to consider. More clinics increase all of these costs. Large networks of services are also incredibly difficult to manage. A patient who walks to a clinic in the west of Hampshire should receive the same standards of care and access to treatment as patients in the East.
In early 2016 the NHS in Hampshire faced the difficult task of reviewing the provision of sexual health care. A balance was needed between equity of access, quality of care and costs. The NHS teamed up with Health System Analytics experts from the University of Southampton to explore how to meet these aims. The team were part of the National Institute for Health Research’s (NIHR) CLAHRC Wessex. The NIHR CLAHRC programme is a five year £10 million research programme in health funded until 2018. The Wessex region has developed an Analytics team to support the NHS in complex issues regarding operations and logistics that affect patient care. The team has experience in analytics for improving the quality and reducing the cost of patient care.
The untapped potential of Health Systems Analytics
Health systems routinely collect a wealth of data about patient usage of services. Records are often kept on patient demographics, referrals, appointments, diagnoses, prescriptions, procedures performed, time spent in hospitals and follow-up actions. Given financial and time pressures, the NHS rarely exploit such data to their full potential. Analytics offers the NHS a suite of easy to use tools and solutions for data ‘wrangling’ and exploring new ways of delivering patient care.
Analytics can also be delivered quickly. CLARHC Wessex’s Health Systems Analytics team delivered results within two months.
Potential uses of analytics for health data include building a geospatial understanding of demand for health services; identifying areas with greater care needs; identifying inequities in service provision; increasing the quality of services (such as reduced waiting times or better patient outcomes); forecasting future demand; understanding the causes of poor and good performance; predicting pathway usage and health needs, and identifying safe opportunities for disinvestment.
Descriptive Analytics: Visualising the usage services
Each financial year the Hampshire service collected information on over 200,000 appointments from 28 regularly held sexual health clinics.
To investigate demand, a Geographic Information System (GIS) was used to visualise both the clinic locations and patient population centres. Population demographic information could also be added to maps to illustrate factors such as deprivation; car ownership; and patient age or gender. A GIS also provides estimates of car and public transport (bus) travel times between locations. This enhances the investigation of the equity of service provision.
Example visualisations can be seen in Figures 1-2.
Figure 1 illustrates a map of clinic hubs and spokes (red dots); demand (blue dots) and car ownership (shaded green) by postcode sectors e.g. SO16 4. It illustrates that large proportions of the population that live further away from the hubs own at least a single car. While the population that live in cities near the hubs can rely on more frequent public transport.
Figure 2: Example journey time visualisation
Figure 2 illustrates car travel time to clinics using a tool called a box and whisker plot. These are simple graphics to illustrate the spread of data. The middle line of each plot is the median (50% of journey times lie above and below it). The shaded box represents the middle 50% of all journey times that patients undertook. The lines or ‘whiskers’ represent the typical range of journey times seen. Lastly the dots outside the lines represent the odd few patients who undertook unusually long travel times.
Speedy and Greedy: The Power of Predictive Analytics
So how do we identify which clinics could be closed and identify if potential new clinic locations provide a better option for patients?
Enter predictive analytics. These are powerful data centric approaches to ask what if we ran health services differently or what will the population look like in 10 years from now.
If we wanted to minimise both the number of clinics needed and maximise the number of patient journey times within 30 minutes there would be 1.9 x 1040 combinations to explore in Hampshire. To put it mildly, this is a trifle tedious to do by hand. Predictive analytics offers the ability to produce good solutions in less than one second using a greedy algorithm. To explain this approach, you can think of the algorithm as an individual who has the ability do arithmetic very quickly and who never regrets any of their decisions. Think of the map we saw in Figure 1 as a completed jigsaw where each jigsaw piece represents a discrete geographic region (for example, a postcode sector or a super output area). Each piece of the jigsaw has a number of patients who live there and a known journey time to every other piece of the jigsaw. Our person quickly completes a few calculations and removes the jigsaw piece that has the most patients living within 30 minutes journey time of it. That area is then selected as a location for a clinic. The pieces that are within 30 minutes journey time are also removed. As our person has no regrets, once a jigsaw piece has been removed it is never returned. This process is repeated with the remaining pieces until no further pieces of the jigsaw can be removed.
The great advantage of a greedy approach is that it does not rely on complex mathematics. It also works extremely well for problems on a regional scale when compared to more complex approaches. The approach is actually part of the wider analytics field of optimisation and heuristics. These are powerful approaches used to solve complex combinatorial problems.
Figure 3 gives an example of the greedy algorithm in action. It illustrates the trade-off in 15 minute journey times versus the number of clinics that are funded. In this case it is possible to reduce the number of funded clinics from 28 to 14 and still have an equitable journey times for 95% of the population. If 90% were acceptable than only 9 clinics would be needed; although it is advisable to consider the equivalent results that use public transport times.
Your algorithm is clever, but I want to do something slightly different…
The great thing about the process of Analytics is that it doesn’t end with a dull report stating “here is the answer”. To support the complexity of decisions in the real world Analytics has to provide adaptable solutions. In a sense you can think of Analytics as a set of tools to facilitate decision making. In this case, the NHS are making use of a predictive analytics tool to adapt the ‘solutions’ proposed by the greedy algorithm. Why is this necessary? It is because of the ‘we couldn’t possibly put a facility there!’ phenomenon. There are many reasons why a mathematically equitable solution doesn’t work in the real world ranging from ‘there is no car parking in area x’ to ‘this facility requires expensive renovation’. Predictive analytics algorithms can be written to take account of such factors, but it is often difficult to (quickly) gather the right data to make it work or indeed include everything relevant. Worse still, complex models often make it difficult for decision makers to understand and trust results.
The best part is that predictive analytics tool can be implemented in Microsoft Excel which is on the desktop of every NHS employee. The tool, in this case, allows the NHS to use the solution suggested by the greedy algorithm as a starting point. Clinics can then be‘turned on’ or ‘turned off’ to see the impact on travel times. This flexibility allows for qualitative information about parking, facility conditions or good old fashioned politics to be quickly taken into account. The tool also provides guidance on capacity requirements as well by quantifying likely shifts in demand on each clinic.
Analytics is making a real impact on the NHS
The financial pressures on the NHS are increasing by the day. Public scrutiny of decisions affecting the equity and quality of patient care is also at an all-time high. The good news is that Health Systems Analytics is showing signs of making a real tangible impact in the way decisions are made regarding the future of healthcare services in the UK.
“The advanced Health System Analytics work undertaken by the NIHR CLAHRC Wessex Methodological Hub was a key element of our transformation project to review and redesign local sexual health services in response to reducing financial resources and increasing demand.
The analysis gave us a detailed understanding of how far our residents were travelling to access different services within existing clinics and identified inequities in service access in some parts of the county which we have now been able to address. The analysis also enabled us to forecast future demand for services and to model the impact on footfall and operational capacity of introducing alternative models of service delivery, including home-sampling kits for STI testing.
As a result of the analysis we have a much better understanding of current and future demand for sexual health services and the optimal location of clinics to provide equitable access. This knowledge has informed our recent procurement requirements and provided us with the information and confidence to reduce the number of clinics we require to meet the evolving needs of our population, providing savings in relation to estates, workforce efficiency and travel costs across the local system.”
Rob Carroll – Public Health Manager, Hampshire County Council
Marion, Rudabeh and Thomas are researchers who work for NIHR CLAHRC Wessex, Faculty of Health Sciences, University of Southampton. They can be contacted via Thomas.firstname.lastname@example.org