Centralised procurement for the health care sector -- bang for your pound or siphoning off scarce resources?

The National Health Service (NHS) has been running a centralised model for health care procurement in England for a few years now. The current system resulted from a redesign of the NHS supply chain that has been operational since 2019 [for details, see A Sanchez-Graells, ‘Centralisation of procurement and supply chain management in the English NHS: some governance and compliance challenges’ (2019) 70(1) NILQ 53-75.]

Given that the main driver for the implementation and redesign of the system was to obtain efficiencies (aka savings) through the exercise of the NHS’ buying power, both the UK’s National Audit Office (NAO) and the House of Commons’ Public Accounts Committee (PAC) are scrutinising the operation of the system in its first few years.

The NAO published a scathing report on 12 January 2024. Among many other concerning issues, the report highlighted how, despite the fundamental importance of measuring savings, ‘NHS Supply Chain has used different methods to report savings to different audiences, which could cause confusion.’ This triggered a clear risk of recounting (ie exaggeration) of claims of savings, as detailed below.

In my submission of written evidence to the PAC Inquiry ‘NHS Supply Chain and efficiencies in procurement’, I look in detail at the potential implications of the use of different savings reporting methods for the (mis)management of scarce NHS resources, should the recounting of savings have allowed private subcontractors to also overclaim savings in order to boost the financial return under their contracts. The full text of my submission is reproduced below, in case of interest.

nao’s findings on recounting of savings

There are three crucial findings in the NAO’s report concerning the use of different (and potentially problematic) savings reporting methods. They are as follows:

DHSC [the Department of Health and Social Care] set Supply Chain a cumulative target of making £2.4 billion savings by 2023-24. Supply Chain told us that it had exceeded this target by the end of 2022-23 although we have not validated this saving. The method for calculating this re-counted savings from each year since 2015-16. Supply Chain calculated its reported savings against the £2.4 billion target by using 2015-16 prices as its baseline. Even if prices had not reduced in any year compared with the year before, a saving was reported as long as prices were lower than that of the baseline year. This method then accumulated savings each year, by adding the difference in price as at the baseline year, for each year. This accumulation continued to re-count savings made in earlier years and did not take inflation into account. For example, if a product cost £10 in 2015-16 and reduced to £9 in 2016-17, Supply Chain would report a saving of £1. If it remained at £9 in 2017-18, Supply Chain would report a total saving of £2 (re-counting the £1 saved in 2016-17). If it then reduced to £8 in 2018-19, Supply Chain would report a total saving of £4 (re-counting the £1 saved in each of 2016-17 and 2017-18 and saving a further £2 in 2018-19) […]. DHSC could not provide us with any original sign-off or agreement that this was how Supply Chain should calculate its savings figure (para 2.4, emphasis added).

Supply Chain has used other methods for calculating savings which could cause confusion. It has used different methods for different audiences, for example, to government, trusts and suppliers (see Figure 5). When reporting progress against its £2.4 billion target it used a baseline from 2015-16 and accumulated the amount each year. To help show the savings that trusts have made individually, it also calculates in-year savings each trust has made using prices paid the previous year as the baseline. In this example, if a trust paid £10 for an item in 2015-16, and then procured it for £9 from Supply Chain in 2016-17 and 2017-18, Supply Chain would report a saving of £1 in the first year and no saving in the second year. These different methods have evolved since Supply Chain was established and there is a rationale for each. Having several methods to calculate savings has the potential to cause confusion (para 2.6, emphasis added).

When I read the report, I thought that the difference between the methods was not only problematic in itself, but also showed that the ‘main method’ for NHS Supply Chain and government to claim savings, in allowing recounting of savings, was likely to have allowed for excessive claims. This is not only a technical or political problem, but also a clear risk of siphoning off NHS scarce budgetary resources, for the reasons detailed below.

Submission to the pac inquiry

00. This brief written submission responds to the call for evidence issued by the Public Accounts Committee in relation to its Inquiry “NHS Supply Chain and efficiencies in procurement”. It focuses on the specific point of ‘Progress in delivering savings for the NHS’. This submission provides further details on the structure and functioning of NHS Supply Chain than those included in the National Audit Office’s report “NHS Supply Chain and efficiencies in procurement” (2023-24, HC 390). The purpose of this further detail is to highlight the broader implications that the potential overclaim of savings generated by NHS Supply Chain may have had in relation to payments made to private providers to whom some of the supply chain functions have been outsourced. It raises some questions that the Committee may want to explore in the context of its Inquiry.

1. NHS Supply Chain operating structure

01. The NAO report analyses the functioning and performance of NHS Supply Chain and SCCL in a holistic manner and without considering details of the complex structure of outsourced functions that underpins the model. This can obscure some of the practical impacts of some of NAO’s findings, in particular in relation with the potential overclaim of savings generated by NHS Supply Chain (paras 2.4, 2.6 and Figure 5 in the report). Approaching the analysis at a deeper level of detail on NHS Supply Chain’s operating structure can shed light on problems with the methods for calculating NHS Supply Chain savings other than the confusion caused by the use of multiple methods, and the potential overclaim of savings in relation to the original target set by DHSC.

02. NHS Supply Chain does not operate as a single entity and SCCL is not the only relevant actor in the operating structure.[1] Crucially, the operating model consists of a complex network of outsourcing contracts around what are called ‘category towers’ of products and services. SCCL coordinates a series of ‘Category Tower Service Providers’ (CTSPs), as listed in the graph below. CTSPs have an active role in developing category management strategies (that is, the ‘go to market approach’ at product level) and heavily influence the procurement strategy for the relevant category, subject to SCCL approval.

03. CTSPs are incentivised to reduce total cost in the system, not just reduce unit prices of the goods and services covered by the relevant category. They hold Guaranteed Maximum Price Target Cost (GMPTC) contracts, under which CTSPs will be paid the operational costs incurred in performing the services against an annual target set out in the contract, but will only make a profit when savings are delivered, on a gainshare basis that is capped.

Source: NHS Supply Chain - New operating model (2018).[2]

04. There are very limited public details on how the relevant targets for financial services have been set and managed throughout the operation of the system. However, it is clear that CTSPs have financial incentives tied to the generation of savings for SCCL. Given that SCCL does not carry out procurement activities without CTSP involvement, it seems plausible that SCCL’s own targets and claimed savings would (primarily) have been the result of the simple aggregation of those of CTSPs. If that is correct, the issues identified in the NAO report may have resulted in financial advantages to CTSPs if they have been allowed to overclaim savings generated.

05. NHS Supply Chain has publicly stated that[3]:

• ‘Savings are contractual to the CTSPs. As part of the procurement, bidders were asked to provide contractual savings targets for each year. These were assessed and challenged through the process and are core to the commercial model. CTSPs cannot attain their target margins (i.e. profit) unless they are able to achieve contractual savings.’

• ‘The CTSPs financial reward mechanism [is] based upon a gain share from the delivery of savings. The model includes savings generated across the total system, not just the price of the product. The level of gain share is directly proportional to the level of savings delivered.’

06. In view of this, if CTSPs had been allowed to use a method of savings calculation that re-counted savings in the way NAO details at para 2.4 of its report, it is likely that their financial compensation will have been higher than it should have been under alternative models of savings calculation that did not allow for such re-count. Given the volumes of savings claimed through the period covered by the report, any potential overcompensation could have been significant. As any such overcompensation would have been covered by NHS funding, the Committee may want to include its consideration within its Inquiry and in its evidence-gathering efforts.

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[1] For a detailed account, see A Sanchez-Graells, “Centralisation of procurement and supply chain management in the English NHS: some governance and compliance challenges” (2019) 70(1) Northern Ireland Legal Quarterly 53-75.

[2] Available at https://wwwmedia.supplychain.nhs.uk/media/Customer_FAQ_November_2018.pdf (last accessed 12 January 2024).

[3] Ibid, FAQs 24 and 25.

Some further thoughts on setting procurement up to fail in 'AI regulation by contract'

The next bit of my reseach project concerns the leveraging of procurement to achieve ‘AI regulation by contract’ (ie to ensure in the use of AI by the public sector: trustworthiness, safety, explainability, human rights compliance, legality especially in data protection terms, ethical use, etc), so I have been thinking about it for the last few weeks to build on my previous views (see here).

In this post, I summarise my further thoughts — which have been prompted by the rich submissions to the House of Commons Science and Technology Committee [ongoing] inquiry on the ‘Governance of Artificial Intelligence’.

Let’s do it via procurement

As a starting point, it is worth stressing that the (perhaps unsurprising) increasingly generalised position is that procurement has a key role to play in regulating the adoption of digital technologies (and AI in particular) by the public sector—which consolidates procurement’s gatekeeping role in this regulatory space (see here).

More precisely, the generalised view is not that procurement ought to play such a role, but that it can do so (effectively and meaningfully). ‘AI regulation by contract’ via procurement is seen as an (easily?) actionable policy and governance mechanism despite the more generalised reluctance and difficulties in regulating AI through general legislative and policy measures, and in creating adequate governance architectures (more below).

This is very clear in several submissions to the ongoing Parliamentary inquiry (above). Without seeking to be exhaustive (I have read most, but not all submissions yet), the following points have been made in written submissions (liberally grouped by topics):

Procurement as (soft) AI regulation by contract & ‘Market leadership’

• ‘Procurement processes can act as a form of soft regulation … Government should use its purchasing power in the market to set procurement requirements that ensure private companies developing AI for the public sector address public standards. ’ (Committee on Standards in Public Life, at [25]-[26], emphasis added).

• ‘For public sector AI projects, two specific strategies could be adopted [to regulate AI use]. The first … is the use of strategic procurement. This approach utilises government funding to drive change in how AI is built and implemented, which can lead to positive spill-over effects in the industry’ (Oxford Internet Institute, at 5, emphasis added).

• ‘Responsible AI Licences (“RAILs”) utilise the well-established mechanisms of software and technology licensing to promote self-governance within the AI sector. RAILs allow developers, researchers, and companies to publish AI innovations while specifying restrictions on the use of source code, data, and models. These restrictions can refer to high-level restrictions (e.g., prohibiting uses that would discriminate against any individual) as well as application-specific restrictions (e.g., prohibiting the use of a facial recognition system without consent) … The adoption of such licenses for AI systems funded by public procurement and publicly-funded AI research will help support a pro-innovation culture that acknowledges the unique governance challenges posed by emerging AI technologies’ (Trustworthy Autonomous Systems Hub, at 4, emphasis added).

Procurement and AI explainability

• ‘public bodies will need to consider explainability in the early stages of AI design and development, and during the procurement process, where requirements for transparency could be stipulated in tenders and contracts’ (Committee on Standards in Public Life, at [17], emphasis added).

• ‘In the absence of strong regulations, the public sector may use strategic procurement to promote equitable and transparent AI … mandating various criteria in procurement announcements and specifying design criteria, including explainability and interpretability requirements. In addition, clear documentation on the function of a proposed AI system, the data used and an explanation of how it works can help. Beyond this, an approved vendor list for AI procurement in the public sector is useful, to which vendors that agree to meet the defined transparency and explainability requirements may be added’ (Oxford Internet Institute, at 2, referring to K McBride et al (2021) ‘Towards a Systematic Understanding on the Challenges of Procuring Artificial Intelligence in the Public Sector’, emphasis added).

Procurement and AI ethics

• ‘For example, procurement processes should be designed so products and services that facilitate high standards are preferred and companies that prioritise ethical practices are rewarded. As part of the commissioning process, the government should set out the ethical principles expected of companies providing AI services to the public sector. Adherence to ethical standards should be given an appropriate weighting as part of the evaluation process, and companies that show a commitment to them should be scored more highly than those that do not (Committee on Standards in Public Life, at [26], emphasis added).

Procurement and algorithmic transparency

• ‘… unlike public bodies, the private sector is not bound by the same safeguards – such as the Public Sector Equality Duty within the Equality Act 2010 (EA) – and is able to shield itself from criticisms regarding transparency behind the veil of ‘commercial sensitivity’. In addition to considering the private company’s purpose, AI governance itself must cover the private as well as public sphere, and be regulated to the same, if not a higher standard. This could include strict procurement rules – for example that private companies need to release certain information to the end user/public, and independent auditing of AI systems’ (Liberty, at [20]).

• ‘… it is important that public sector agencies are duly empowered to inspect the technologies they’re procuring and are not prevented from doing so by the intellectual property rights. Public sector buyers should use their purchasing power to demand access to suppliers’ systems to test and prove their claims about, for example, accuracy and bias’ (BILETA, at 6).

Procurement and technical standards

• ‘Standards hold an important role in any potential regulatory regime for AI. Standards have the potential to improve transparency and explainability of AI systems to detail data provenance and improve procurement requirements’ (Ada Lovelace Institute, at 10)

• ‘The speed at which the technology can develop poses a challenge as it is often faster than the development of both regulation and standards. Few mature standards for autonomous systems exist and adoption of emerging standards need to be encouraged through mechanisms such as regulation and procurement, for example by including the requirement to meet certain standards in procurement specification’ (Royal Academy of Engineering, at 8).

Can procurement do it, though?

Implicit in most views about the possibility of using procurement to regulate public sector AI adoption (and to generate broader spillover effects through market-based propagation mechanisms) is an assumption that the public buyer does (or can get to) know and can (fully, or sufficiently) specify the required standards of explainability, transparency, ethical governance, and a myriad other technical requirements (on auditability, documentation, etc) for the use of AI to be in the public interest and fully legally compliant. Or, relatedly, that such standards can (and will) be developed and readily available for the public buyer to effectively refer to and incorporate them into its public contracts.

This is a BIG implicit assumption, at least in relation with non trivial/open-ended proceduralised requirements and in relation to most of the complex issues raised by (advanced) forms of AI deployment. A sobering and persuasive analysis has shown that, at least for some forms of AI (based on neural networks), ‘it appears unlikely that anyone will be able to develop standards to guide development and testing that give us sufficient confidence in the applications’ respect for health and fundamental rights. We can throw risk management systems, monitoring guidelines, and documentation requirements around all we like, but it will not change that simple fact. It may even risk giving us a false sense of confidence’ [H Pouget, ‘The EU’s AI Act Is Barreling Toward AI Standards That Do Not Exist’ (Lawfare.com, 12 Jan 2023)].

Even for less complex AI deployments, the development of standards will be contested and protracted. This not only creates a transient regulatory gap that forces public buyers to ‘figure it out’ by themselves in the meantime, but can well result in a permanent regulatory gap that leaves procurement as the only safeguard (on paper) in the process of AI adoption in the public sector. If more general and specialised processes of standard setting are unlikely to plug that gap quickly or ever, how can public buyers be expected to do otherwise?

seriously, can procurement do it?

Further, as I wrote in my own submission to the Parliamentary inquiry, ‘to effectively regulate by contract, it is at least necessary to have (i) clarity on the content of the obligations to be imposed, (ii) effective enforcement mechanisms, and (iii) public sector capacity to establish, monitor, and enforce those obligations. Given that the aim of regulation by contract would be to ensure that the public sector only adopts trustworthy AI solutions and deploys them in a way that promotes the public interest in compliance with existing standards of protection of fundamental and individual rights, exercising the expected gatekeeping role in this context requires a level of legal, ethical, and digital capability well beyond the requirements of earlier instances of regulation by contract to eg enforce labour standards’ (at [4]).

Even optimistically ignoring the issues above and adopting the presumption that standards will emerge or the public buyer will be able to (eventually) figure it out (so we park requirement (i) for now), and also assuming that the public sector will be able to develop the required level of eg digital capability (so we also park (iii), but see here)), does however not overcome other obstacles to leveraging procurement for ‘AI regulation by contract’. In particular, it does not address the issue of whether there can be effective enforcement mechanisms within the contractual relationship resulting from a procurement process to impose compliance with the required standards (of explainability, transparency, ethical use, non-discrimination, etc).

I approach this issue as the challenge of enforcing not entirely measurable contractual obligations (ie obligations to comply with a contractual standard rather than a contractual rule), and the closest parallel that comes to my mind is the issue of enforcing quality requirements in public contracts, especially in the provision of outsourced or contracted-out public services. This is an issue on which there is a rich literature (on ‘regulation by contract’ or ‘government by contract’).

Quality-related enforcement problems relate to the difficulty of using contract law remedies to address quality shortcomings (other than perhaps price reductions or contractual penalties where those are permissible) that can do little to address the quality issues in themselves. Major quality shortcomings could lead to eg contractual termination, but replacing contractors can be costly and difficult (especially in a technological setting affected by several sources of potential vendor and technology lock in). Other mechanisms, such as leveraging past performance evaluations to eg bar access to future procurements can also do too little too late to control quality within a specific contract.

An illuminating analysis of the ‘problem of quality’ concluded that the ‘structural problem here is that reliable assurance of quality in performance depends ultimately not on contract terms but on trust and non-legal relations. Relations of trust and powerful non-legal sanctions depend upon the establishment of long-term … relations … The need for a governance structure and detailed monitoring in order to achieve co-operation and quality seems to lead towards the creation of conflictual relations between government and external contractors’ [see H Collins, Regulating Contracts (OUP 1999) 314-15].

To me, this raises important questions about the extent to which procurement and public contracts more generally can effectively deliver the expected safeguards and operate as an adequate sytem of ‘AI regulation by contract’. It seems to me that price clawbacks or financial penalties, even debarment decisions, are unilkely to provide an acceptable safety net in some (or most) cases — eg high-risk uses of complex AI. Not least because procurement disputes can take a long time to settle and because the incentives will not always be there to ensure strict enforcement anyway.

More thoughts to come

It seems increasingly clear to me that the expectations around the leveraging of procurement to ‘regulate AI by contract’ need reassessing in view of its likely effectiveness. Such effectiveness is constrained by the rules on the design of tenders for the award of public contracts, as well as those public contracts, and mechanisms to resolve disputes emerging from either tenders or contracts. The effectiveness of this approach is, of course, also constrained by public sector (digital) capability and by the broader difficulties in ascertaining the appropriate approach to (standards-based) AI regulation, which cannot so easily be set aside. I will keep thinking about all this in the process of writing my monograph. If this is of interested, keep an eye on this blog fior further thougths and analysis.