Transforming Healthcare Supply Chains: Lessons Learned and the Path to a Digitalized Future

The pandemic shone a light on just how important it is for organizations across every industry to get their supply chains right, but nowhere was this seen and felt more than in the healthcare sector. With the increased demand for PPE and other medical materials essential to the frontline response to Covid-19, hospitals and healthcare trusts saw their logistics and requisition operations stretched to breaking point. What lessons can we learn about the supply chain models used during the pandemic, how did they evolve, and what does the digitalized supply chain of the future now look like?

Traditionally, the supply chain has been seen through the lens of two models: the push model and the pull model. At their most basic, the push model uses defined forecasts to predict demand, whilst the pull model bases its approach on actual stock usage. Both were used to some extent in the healthcare sector during the pandemic, to varying degrees of success.

The push model

The supply chain push model uses a defined forecast of what you will need in the future and automatically brings in materials when it determines they will be needed. This model requires a greater use of digitalization and data analytics due to its reliance on creating forecasts and generating data-based assumptions about future usage and demand.

During the pandemic, the push model was used sparingly in the healthcare sector in the UK, mainly due to the lack of required digital infrastructure in some parts of the NHS. However, some decisions were taken away from NHS trusts and centralized to NHS Supply Chain, which used a forecasting model based on demand for protective equipment in different parts of the country. While this approach had its benefits the lack of comprehensive, accurate and timely data across the whole healthcare landscape meant that it was not as effective as it might have been.

The pull model

In contrast, the pull model is based on real-time usage data rather than forecasts. This data, in a healthcare setting, would typically be more granular; for example individual hospital wards would have a pre-set inventory level, which would generate an automatic replenishment order if stock fell below that number.

This model was more common in the healthcare sector during the pandemic. It would typically see wards keep, for example, 100 masks at any one time, with a replenishment trigger whenever they fell below 20 masks. Once stock levels fell below this threshold, new materials would be ordered to bolster stock back up to the original 100 masks.

The pull model has recently received something of a digital upgrade itself, with a number of hospitals and trusts moving away from manual stock counts to use automated digital scanners. Using these scanners, details of each medical procedure can be recorded, from consultant information through to materials used (from PPE through to higher value consumables such as implantable devices). This information is then transmitted wirelessly to a central digital database, which allows for an automatically-generated report to ensure accurate patient records are free from human error, as well as an updated stock level to allow for stock replenishment routines and purchase orders to be run automatically.

Whilst this is simpler to implement, the model is based purely on usage and so is reactive, rather than proactively seeking to avoid shortages. During the pandemic, this inevitably led to problems in the supply chain where orders could not be fulfilled fast enough to prevent shortages of critical care equipment such as PPE.

A hybrid approach

There are undoubtedly pros and cons to both approaches, with the key to both being the proper collection of good quality inventory data. By acknowledging the power of data, supply chain professionals working across different sectors, but particularly in healthcare, should now be looking toward a new, hybrid approach which combines the best elements of the push and pull models to mitigate the risk of stock shortages.

By combining actual ward level data with the power of predictive analytics, the healthcare industry can continue the journey towards automation of its supply chain processes whilst avoiding shortages of critical materials during unusual spikes in demand.

This hybrid system could see a flexible pull model, which adapts its target threshold for stock replenishment based on forecasted demand. This combination of real-time data and predictive analytics could be revolutionary for the healthcare industry: the ability to gain real-time insight into stock levels, assess the likelihood of an increase in the need for medical supplies and then pre-emptively order the required supplies. All without diverting clinical staff away from their frontline roles, which is crucial in balancing healthcare’s need to maintain supplies of critical medical equipment while still meeting demand for better patient care.

Integrating such a hybrid system would not be difficult. Healthcare settings already amass huge amounts of data, and the difficulties faced during Covid-19 galvanized the sector to develop better data strategies. Healthcare providers analyzing case data to better predict demand became a central part of the NHS’ Covid response and procurement teams improved their ability to collect, maintain and properly analyze reliable, reproducible and secure data. For example, as the pandemic progressed, healthcare teams became better at identifying areas more susceptible to Covid-19 cases based on a combination of real-time data and existing information on the makeup of populations. This then influenced decisions on how many resources to stockpile in a specific location. Such progress would not be difficult to continue into the post-pandemic era.

The pandemic has seen the rapid growth of digitalization across a number of different sectors, with the supply chain, and in particular the healthcare supply chain, being no different. If supply chain professionals are to best utilize this growth in digitalization and data management, we need to move to a new model of supply chain management which plays to its strengths.

Kevin Sample is a Senior Consulting and Business Development Manager, GHX Europe. Building on decades of collaboration between providers, manufacturers, distributors and other industry stakeholders, Global Healthcare Exchange, LLC (GHX) is leading the charge in helping organizations run the new business of healthcare. By automating key business processes and translating evidence-based analytics and data into meaningful action, GHX is helping the healthcare ecosystem to move faster, operate more intelligently and achieve greater outcomes. With the support of GHX, healthcare organisations have removed billions of dollars of wasteful healthcare spend.
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