Introduction: Generalities About ‘Broken Health Systems’ Despite Covid-19 Successes

A large number of detailed and candid conversations with scientists, clinicians, public health specialists, and industrialists were underway in India well before the pandemic was upon us. Although the world went awry in 2020 with Covid-19, I realised how awry it was in 2018. What everyone kept saying in their own distinct way was that India’s ‘health system was broken’. Each expert described this problem in their own professional language. Doctors often said ‘we can’t get supplies when we need them’, or ‘those big companies are just making profits’; public health researchers said ‘it’s about inequalities and health equities’, ‘patients are marginalised’; scientists said ‘we need to talk more with industry’, or ‘start-up culture is helping’ (some said it was hurting). The overall impression I had, was that despite considerable brilliance and professional credibility arrayed before me, and plenty of good-heartedness and hard work, people were speaking in strange generalities about health as if the problem was so complex or diffuse that it was an utter mystery. This was especially intriguing because India has, over the years, many health successes to its credit and emerged during Covid-19 with substantial technology, industrial, and health system gains.

Despite over two decades on these issues in the context of industrial policies and economic development, I was finding that even sincere senior scientists and clinicians and many bureaucrats who oversee important policies, philanthropies, or grant-making, were still speaking in peculiar hand-wringing generalities about major policy gaps. Cynicism, depression, or apathy? Lack of training in social sciences and policy analysis? I had a strong feeling that the generalities reflected their perception that this implementation of science or health technology, or ensuring the availability of therapeutic products, was/should be someone else’s problem (perhaps even those irrational patients!). Perhaps because some scientists and engineers considered that making policies more effective was somehow trivial, we just had to get someone to put their minds to it? A problem below their pay grade, as we say. After all, they were solving the world’s most complex problems in the lab or becoming Directors, senior ministry officials, CEOs, wasn’t that sufficient?

One exercise I recall initiating with a clinical co-author, first on a napkin, then a clear sheet of paper, then a whiteboard, was a map of gaps from first arrival of a patient to a doctor. This in a social context when many people, especially women, postpone visiting a healthcare practitioner. We wanted to think through the delay in arrival, then the hit-or-miss process of diagnosis, then referral which itself reflects serious delays and misdiagnoses, and then the patient returning (if at all) for treatment(s). I focused not on the complexity of cancers but on ‘simpler’ dengue.

Furthermore, my concerns were that these technological capabilities had to serve either the patient or national economic goals and ideally, both. These meant real attention to the combination of skills, new knowledge, techniques, equipment, service, and delivery quality or location. Groups that more notably engaged in these realistic terms about gaps and hurdles in practical, non-utopian, language were family physicians, surgeon-engineers, or surgeon-entrepreneurs. As with many innovators-by-necessity, they understood the importance of iterative work, the limits of instrument availability or breakdown, and importance of instinct and skills, the materials and location challenges of prototyping, the practical issues of technical design in the real world. Surgeons in particular were also conscious of extensive procedural bottlenecks in working within hospitals, such as dealing with tissue samples or varied techniques for tumour removal which had to fit with approved protocols but needed to be inventive. Similarly, companies in commodity manufacturing or distribution understood the practical challenges well and spoke in specifics because they had created routinised systems of rules to work by. Still, by and large, the claim that the ‘health system was broken’ was as imprecise and unsatisfying in 2018 as it had been when I first entered the field myself over two decades ago, and so I spent my time listening and making notes to extend the earlier work.

Days passed as I thought about the dengue discussion and late referrals, where the Cupboard question started emerging, asking doctors and others in informal conversations how they knew what to use when a patient walked in and what if they didn’t have items such as drugs or diagnostic tests when they needed them. It was evident that the industrial background was getting little conceptual attention. Worse, industry was being referred to in terms of private industry, which misled one to think that corporate R&D was the solution to all problems.

Covid-19 was suddenly upon us. I had entered a gruelling two years of work that had only abated as I wrote in mid-2022: advisory boards, technical meetings, an avalanche of data, and suddenly people shouting themselves hoarse over the same issues, but now in confined expert rooms, spitting distance as it were, that ‘the health system is broken!’. Where I was asked to intervene in meetings, there was a serious breakdown in training, of disciplines and professionals establishing hierarchies, of differentiating which professions or methods should speak, but unable to speak to each other. At the same time, a practical set of professionals and new teams were trying very hard to build gadgets, and new science, and do the much-needed field work to assess the urgency of treatment, plus the urgent treatment protocols required for Covid-19 patients. In some of the major problems of routine healthcare, science per se was not at the heart of every problem. I was repeatedly invited to global health meetings, but most discussions ignored the economics of industrial organisation or focused on discussing diagnostic kits or vaccines primarily in mechanistic ‘technology-push’ or ‘supply-driven’ terms as if assuming that in any emergency, an entire industry ‘behind the Cupboard’ (see below) would resolve itself. Many in the science and engineering professions were ignoring how demand and delivery had to consider a dynamic uncertainty about consumers shaping the longevity of their business models or delivery systems.

Here too, several opportunities to build long-term economic development were being sidelined. This was the case even in multilateral forums on the specifics of technology transfer where precise economic development goals could be set for technology acquisition in the short or long term. Strangely, many experts assumed that national production capabilities would resolve demand and delivery, and those countries without technological capabilities should somehow await global health management systems or advanced market commitment mechanisms (such as COVAX or other global coordination). This global health role, as the world was witnessing in real time, was not working particularly well in a context of export bottlenecks and supply chain breakdowns. It was witnessing industrial chaos, but only slowly recognizing how the health systems were built atop it.

I was committed almost 18–20 hours/day in advisory roles, pro bono teams, and round-the-clock data debates on social media, email, phone, and in policy groups. With my clinical colleague who was an infectious disease specialist, and with frequent debates, we finally wrote a working paper to make sense of what we were seeing at the time, to frame the problem of responding to dramatic shifts occurring in the design and manufacture of Covid-19 diagnostic kits. Despite all the handwringing, India has had considerable successes in this area and many others, generating critical services and manufacturing in a highly truncated period.1 Yet, the same uncertainty of Covid-19 had derailed stronger health systems and richer economies than India.

In part this success was because the Indian central and state governments were able to, in a variety of ways, manage a centralised, yet coordinated system of action. They converted from plans, protocols, emergency production shifts, and new procurement rules. New organizations and innovations emerged. In short, at least for a time, a new set of norms and rules was being attempted with substantial national and sometimes municipal, or district-level experimentation, and open partnerships with the private sector. Likewise, the acknowledged strengths of India’s community health workers were evident during Covid-19 at significant personal and social costs, raising questions of how decentralised industrial decision-making would respond to this agile community expertise. Questions that were critical in my mind seemed to be getting little or no explicit debate in meetings. Who is the user? Who is the consumer? Doctors, hospitals, individuals, or other firms? What are the error margins in estimating Covid-19 demand? How will the demand be sustained? Will superfluous product variety and competition be resolved among these firms once the pandemic is over?

Cupboard Full, Cupboard Empty: A Thought Experiment

While there have been attempts in the health policy world to define minimum service baskets, there is arguably little bridge to the dynamic domain of technology development, investment, or practical choices of ensuring availability of product A or B. This chapter is thus focused on shifting from the imprecise complaint that ‘the health system is broken’ towards a more precise discussion of the interface between health policies and industrial capabilities. The ‘Cupboard Full, Cupboard Empty’ (CFCE) thought experiment fills in some of the too-quickly glossed over conceptual and methods questions in health systems. The introduction of the CFCE is offered as one step to future methodologies that can more effectively bridge industrial and health policies in public health design. While the early chapters have identified critical health gaps, this chapter pushes towards clarifying how we then bridge to what is an ‘appropriately full’ cupboard of products, procedures, techniques, and services.

India of course had some luxury of choice developed over decades of building technological capabilities in the health industry. Many countries, however, had no choice except to import under severe domestic constraints and global export curbs. Although each country context is different, the India case is relevant for other industrialising countries and those traditionally offered unsolicited or generic international development advice. India’s private sector is now immense: numbers of firms whatever their size, clinics, hospitals, medical and nursing colleges, private research labs, some private venture capital, and diversified industrial capabilities relevant to healthcare from plastics to robotics. This volume, variety, and number of firms, from micro-level service and product providers to large corporate firms, is both a curse and a boon. It makes policy goal-setting and regulation noisy and complex, but it also makes much public health ideology economically archaic and notably mismatched to the technology dynamism and the diversity of service and customisation options.

Significantly furthermore, within health and in related industries, public sector investments drove private sector growth, making for a rich tapestry in the evolution of institutions, from norms and customs to technical standards, market varieties, and regulation. In this sense, India has much to offer the world as a unique case but also provides useful policy framing and cautions about global health and international development one-size-fits-all generic prescriptions. In some respects, India’s similarities are with the US rather than with UK, much farther from Cuba and distant from China, and closer to some features of Germany, Nigeria or South Africa.

The following sections provide an abstract sketch of the practical aspects of uncertainty and possible conceptual building blocks behind investment decisions, required knowledge and technologies, skills and learning, and local administrative context—whether for community workers, nurses, and doctors, or district procurement agencies. The Choosing Wisely India initiative, discussed later, promisingly identifies policy and product mixes for Indian cancer patients. The preliminary ‘Cupboard Full, Cupboard Empty’ (CFCE) framework introduced in this chapter can extend Choosing Wisely further into the industrial policy domain.

The Building Blocks and Methods Behind a Health-Industry Interface

Policy Frames

Health policy scholars, including clinicians and social scientists, see that health delivery has many moving parts. However, new insights have emerged about institutions and evolution in several domains of economics, especially those concerned with how knowledge is generated, and technological change occurs. Industrial history has focused disproportionately on production capabilities and especially on manufacturing. In contrast, public health arguably has focused on service delivery. Scholarship of industrial development and of health both recognize that technological capabilities matter, but the first is more acutely focused on when and how they matter.

A newer institutional and evolutionary economics argues that to recognise systemic behaviour is to recognise health as a natural domain of study with unique dynamics (Hodgson, 2007). Specifically, we have known for some time that an institutional triad (Srinivas, 2012, p. 8) of three different co-evolving institutional domains of production, demand, and delivery all reveal industrial features that can be studied in snapshots of time and can usefully contrast countries. Each of the institutional domains co-evolves with the other; involves private and public actors, some mix of new organisation types; or contributes to distinctive types of technological capabilities. Analysing the uneven fits and starts of building these technological capabilities is an essential element of scientific argument in national comparison, where institutional variety (IV)—including the norms and regulations of firms, universities, corporate R&D, or new technical standards—must be studied more explicitly (Srinivas, 2020). For cancer, these dynamic features and the uncertain identification, categorization, and progression of the disease make the metaphor of a ‘War on Cancer’ outdated and misleading (Srinivas, 2021b). Updating economic frameworks for cancer is also tightly linked to prevention concerns and the rise in carcinogens, making cross-industry regulation concerns more urgent to combine ecology, health, and industrial development (Srinivas, 2021a).

Some medical specialists do recognise that economics and structural issues shape cancer’s systemic problems, but these are framed in response to global health priorities:

Following the UN High Level Summit, the global call to embed all non-communicable diseases, including cancer, in the post-2015 development agenda has been followed rapidly by a plethora of indicators and targets … Unfortunately, there is little insight into the complex economic and structural issues that emerging economies such as India have to deal with to deliver an affordable cancer care and control system. The provision of affordable cancer care in India needs a deep understanding of the substantial differences between spending on health across individual states and union territories, and the gaps in basic health indicators and outcomes (eg, infant mortality rates, health resources, numbers of clinical staff, and physical infrastructure). (Pramesh et al., 2019)

The quote above well recognises the autonomy and specificity of context and allows a useful bridge to building specific long-term technological capabilities. National context matters in shaping indicators and targets. Probing the institutional context of the triad may identify the scope for greater autonomy for industrialising countries to develop their technological capabilities and health policy choices, and to recognise their national differences. However, over-engaging with the global health straitjacket of national health and spending comparisons is one policy challenge; another is under-engaging with the analytical differences between the subset of countries that possess or are aggressively investing in greater technological and industrial depth. This special sub-set of countries not only supports the global supplies of essential medicines, vaccines, diagnostics, and devices but ironically may still have some distance to solving their own problems. Those countries with democratic pressures struggle in their own peculiar ways with both the framing of health as well as industrial and other policies. India is one of these countries, which for many reasons therefore may have inspirational as well as cautionary lessons for other countries.

In the policy analysis world, this type of detailed unmasking of different perspectives on institutions and organisations involves a deliberate ‘frame reflection’ which accommodates different experiential or professional perspectives on the same problem (Rein & Schön, 1994). This unmasking can make the framing process an essential part of design, whether of policy design or engineering design. Some policy challenges persist because their policy frames are different and involve both epistemic and ontological challenges of language in plans, with conflicting normative assumptions of how plans and their methods are developed. Here heuristics such as the ‘institutional triad’ can open a conversation about methodologies (and generative metaphors [Schön, 1979]). Policy disagreements may continue to occur within seemingly mutual policy frames, and controversies exist because different policy frames are applied but not communicated. Notably, ‘more data’, a favourite among scientists of all stripes and policy-makers, will not dispel such controversy (Rein & Schön, 1994).

In my own research I have argued that where technology issues are central, policy frames can only be more rigorous if we first debate which technological capabilities are more relevant for health or could be put to better use in public terms. Such articulation of our existing knowledge and technological capabilities should make for a more rigorous process of comparison of long-term health outcomes and greater clarity on which types of translation are required. Such systematic comparisons can still satisfy the routine investment and accountability imperatives that make up the everyday decisions of start-ups, spin-offs, large firms, firm clusters, or decisions on competition, IP, price regulation, and other industrial policy concerns. Countries can approach policy framing as a multi-technology, evolutionary process, where new science may be required only for some problems, and could decide to coordinate multiple knowledge systems that co-exist to solve the same problem. From a health policy or clinical standpoint, co-existing intervention choices or multi-technology interventions provide policy framing because they do not easily reduce to simple cause and effect (e.g. Greenhalgh & Papoutsi, 2018) nor as being substitutes or complements of each other.

In the absence of appreciating this institutional variety, ‘innovation’ cannot be specified in a systematic way, nor any credible commitment made to healthcare quality. In terms of microeconomics, firms and even hybrid consortia must know both how and how much to produce. The ‘institutional triad’ ties between production, demand, and delivery, can be combined in many different ways across time. This type of heuristic establishes a policy framing and helps view the industrial organisation as it changes, identifying countries by their technological capabilities and forcing differentiation among industrialising LMICs. Import and export shutdowns started the pandemic domestic clock, and isolated countries in terms of needed inputs. Only some emerged capable of amassing existing resources to work towards finished products. Notably, not all health problems required either new science or new manufacturing.

Therefore, uncertainty and demand remain untended in the institutional gaps between health policy and industrial policy. What is the cupboard that is proverbially empty and why is it? Doctors ask firms, why didn’t you give me what I need when I needed it? Firms turn around and say, you didn’t clarify what you needed. Clinicians are not able to articulate what they need to industry: the exception may be clinician engineers such as surgeons who experiment with devices, or some types of family physicians who see patterns across patients and failure modes in diagnosis and treatment.

The hand-wringing claim that ‘the health system is broken’ was imprecise because it could not explain the more optimistic policy reality, that many parts of the system were surprisingly agile under high uncertainty, unevenness and fragmentation. From an economics standpoint, this was far more than a pricing and market clearing policy frame. Rather, it exhibits evolving, institutional features of how demand becomes solidified through new norms of collaboration, standards and rules.

Uncertainty and Demand in the Pandemic

Covid-19 pointed to a rapid escalation of conflicting needs but uncertain demand. The public policy and administrative response was to consolidate supply (where many countries failed badly) and somehow anticipate needs and institutional shifts (norms, customs, guidelines, rules) to quickly convert this under high biological uncertainty into more certain demand. Market design was fluid but required any firm or public research centre with substantial capabilities to step up. India’s response could be seen reflecting a decades-long investment (however uneven or unsubstantial) in science, engineering and technology development, combined with public research and private sector first-mover practice. Rather than ideological lenses on the pandemic, the Indian response was pragmatic: all hands on deck. While too many lost their lives and many parts of the healthcare system were revealed as pulled together with duct tape, the country emerged surprisingly robust relative to wealthier, more technologically and industrially superior countries.

It should be emphasised therefore that the ‘frame’ was not global inequality of access in a vacuum of possibilities, although this argument has been made by many; it was inequality of access precisely because only some countries had industrial production capabilities built over decades before Covid-19 arrived. Neither were the countries with such capabilities and Cupboard Full cases only wealthier ones.

This unevenness across countries has not been well explained through equilibrium economics or even the claim that all health breakthroughs came from the biological sciences. During Covid-19 the clinical-industrial interface demonstrated a wide variety of ways that countries responded to Covid-19 diagnostics production, but also several different ways in which policy intervention could be sanctioned based on what was available when trade was impossible, and what was domestically possible to produce. In that instance, starting conditions under trade stoppages defined and separated those countries with distinct domestic capabilities from others and forced a reconfiguration of institutional variety (IV).

A mixed economics and clinical collaboration, begun early in the pandemic, argued for the urgency of generating a first conceptual bridge between health realities and different windows on industrial uncertainty (Srinivas et al., 2020). We specified seven types of observable uncertainties visible in field conditions for Covid-19, with experts acting on patchy data, and decisions in boardrooms, labs, and university offices where different disciplines were speaking past each other. We took as given the following clinical procedures: (a) screening during asymptomatic/pre-symptomatic phase; (b) diagnosis of symptomatic disease; (c) determination of viral shedding in the convalescence phase for decision-making on de-isolation; and (d) epidemiologic surveillance. Uncertainty arises not only because a suitable test may not be available, but given the availability of a suitable test, a clear clinical decision may not follow. We argued that clinical indecision draws from and compounds these uncertainties, leading to marked challenges of industrial supply and demand and the logistical bottlenecks of viable delivery. Table 6.1 describes clinical foreground and industrial background uncertainties we identified to make sense (as a policy frame) of how diagnostic tools, firms, and clinics were operating on the ground. Importantly, many industrialised wealthier countries had little clarity of their own.

Table 6.1 Seven interface uncertainties in the clinical foreground and industrial background of Covid-19
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The policy frame should help us translate between clinical challenges, biological uncertainties, and economic and policy fundamentals of what the industry was facing. The industrial policy frame better emerges only once the uncertainties are explicitly discussed, and the inherent institutional variety is catalogued and its dynamics studied. These seven uncertainties thus required clear enunciation of a type of policy frame with several types of decisions from plan-making to strategy with a changing biological context: dynamic changes of organisations testing under uncertainty; agnosticism about public and private sector technological capability; push to coordinate and consolidate scale-up in response to centralised orders on building demand. These seven uncertainties observed under fast-moving conditions are more useful framing responses to the WHO’s generic policy prescription of ‘test, test, test’. They demonstrate the importance of heuristics and theoretical working frameworks to probe the simplistic, despairing idea that ‘the health system is broken’, because many uncertainties were in fact surprisingly well-managed during Covid-19 through new institutional variety.

The reason this requires intermediate frameworks and discussion is that uncertainty’s twin is risk. The uncertainties once articulated and appreciated, as opposed to fixed theory blind to local changes, suggest taxonomies and heuristics to sift through the institutional variety (IV). This IV reflects accommodations in the economy and its culture through which a society changes. Any study can then generate real-world hypotheses of how the ‘institutional triad’ domains of production, consumption (demand), and delivery evolved during the pandemic since only some countries had technological capabilities and experience managing risk through their public management capabilities. Technological capabilities in the private sector, while independently surging ahead, relied on enormous public administration spread across many services, organisations, and regions not only for industrial coordination but for every signal of wider institutional change—from how to reward health workers, to individual health responsibility, to ‘acceptable’ profits and pricing, to media and ‘expert’ information and misinformation. Such institutional variety, with some common features, is nationally defined.

Cupboard Full, Cupboard Empty

Framing why and how the national context matters is crucial to understanding the social appetite and public and private capabilities to manage uncertainties and risk. These situate the specific investments for building long-term technological capabilities while establishing national autonomy in decision-making. The Cupboard Full, Cupboard Empty (CFCE) approach in this section is a closer scrutiny of the clinical-industrial interface to frame the seven uncertainties in relationships between production, demand, and delivery (the ‘institutional triad’). Not all industrial and economic contexts are the same in making medicines, diagnostic kits, therapies, medical equipment, and vaccines, nor are supplier countries and democracies the same as other ‘developing’ countries when it comes to health technologies and the health industry.

I began speaking about a ‘cupboard’ to describe what I thought people were discussing without being explicit about ‘products they needed’. When I tested this idea in coffee-led discussions, people often picked it up and responded with phrases such as ‘yes, we need to know what’s in the cupboard’, ‘how can we be effective if the cupboard is empty?’, or ‘why should we care what’s behind the cupboard?’. So the metaphor seemed to resonate.

The proverbial cupboard (outside pandemic times) is supposedly filled with products such as antibiotics, analgesics, and possibly a diagnostic kit for routine analysis, from inexplicable fevers to skin cultures. Let us for the moment say that the Cupboard is located in a publicly funded Primary Health Centre (PHC) in rural India or even underserved parts of municipal areas. This does not rule out private sector coordination or partnership which occur in many PHCs.2 Essential medicines, universal immunisation and other guidelines inform what is stocked at the PHC. The Cupboard is a conceptual building block. In reality, some of the products are stored in a refrigerator, or may even be outsourced, such as some pathology diagnostics, to private labs down the road. But at its bare conceptual minimum, the Cupboard is refrigerated or temperature controlled and everything is stocked in one place. The problem frame (CFCE) is what determines the conditions under which the Cupboard is full or empty? Countries which import their CFCE supplies are particularly vulnerable to logistics bottlenecks, but they nevertheless have to decide what to stock.

An industrial economics standpoint has evolving, dynamic features: industries and their investments re-organise and change. They also have an institutional perspective since decisions within uncertainty still must prioritise health problems through norms, customs, guidelines, standards, regulations, and laws. The CFCE can thus be stated as a microeconomics problem at the outset about unspecified consumers and producers but also unspecified minimum institutional and organizational units of analysis to judge what product is essential and how that system changes to become more responsive to new technologies. Unlike more generic systems analysis or operations research problems, the CFCE cannot be technology-neutral. Because only some countries have the technological capabilities or the industrial policy priority for building them to scale, the CFCE is framed within the context of existing and near horizon technological capabilities. Again, this ties in with minimal but reliable production and delivery for a given (albeit uncertain) demand. The CFCE can thus be considered to act as a policy frame towards questioning both industrial and health methodology, a way of observing the daily interface, for localising ambiguity about decisions and public responsibility.

Let’s begin with the intermediate situation of a cupboard of one product, say an antibiotic, and a cupboard that is partially full, neither full nor empty. The person who discovers that the cupboard needs restocking is an attendant, nurse, or doctor. This could be analysed as a routine stock-flow problem taught in basic logistics, engineering, economics, or management. The system is the PHC plus a pharmacy and the joint personnel of the two. However, even in this simple one-product small system, further microeconomics and industrial organization problems emerge. For example, antibiotics availability can be further analysed in a wider system of firms' closures or firms switching out of antibiotics to more profitable pastures, or the messier problem of antibiotics and antimicrobial resistance. Even with the simpler problem of an existing supply chain of PHC to pharmacy and beyond, health administrators and clinicians recognise and bemoan such gaps in health access to minimum required medicines. But the problem is usually unsolved because of upstream challenges, despite recognition here too that the health and industrial systems are interlinked. As noted in Table 6.1, several types of uncertainty including those of systematically building demand, already begin to be visible here.

Alternately, the Cupboard Empty case is the extreme case of a single product missing where no stocks exist at the PHC. In reality, there are multiple CEi, CEii, CEiii,….Cn of products i to n, where one or more is unavailable at any time to a patient at the PHC. These arise from various levels of uncertainty in Table 6.1 and generate corresponding uncertainties for patients, usually in stocking uncertainties and repeat, costly, visits. Simply put, loss in time, money, increased stigma, or even future likelihood of seeking care at all. Either the n includes the basic Essentials list deemed by national or state health policy to be available at every PHC, else the patient is told to buy it at the closest pharmacy, at market rate or subsidised price with prescription, such as with the new Jan Aushadi Kendras where a limited set of products is stocked but at vastly subsidised prices. Nevertheless, each uncertainty diminishes the better healthcare outcome.

Note, CFCE problems of the kind above exist even for the simplest of illnesses and treatments that can be fully and appropriately diagnosed and treated. The more difficult the diagnosis and treatment (where personnel skills become important, such as in Covid-19), the less obvious it is that n medicines are needed, or that i to n should not also include basic medical equipment or diagnostics. The several uncertainties 1–7 even in the simplest of cases can thus be compounded when a single product is missing, making it far less likely that reliable demand can be built for the PHC or wider health system.

Thus, the PHC faces policy translation confusion. Medical training to malpractice enforcement determines the range of how a doctor makes do or improvises when something is unavailable. Staff often have limited autonomy and many rules to follow. They must, with existing training and reliability, determine what is an essential minimum product quantity to decide whether a Cupboard is Empty or Full. They report upward through a state-level health bureaucracy, and have no direct links to firms, to a market of products, or services. Arguably, this building block is the first instance where an explicit industrial and health systemic linkage begins to go awry. First, it is unclear about the bare minimum product, service, or combination needed to satisfy a positive health outcome. Second, even with good intentions, the health and industrial suppliers are deliberately kept apart so there are no conflicts of interest from procurement routines to prescription biases. This separation is understandable in that only selective discretion is provided to medical personnel for addressing health challenges. The first feature, however, of ignoring how we know whether the Cupboard is appropriately Full or Empty is dependent not merely on a stocking list but on a diffuse policy goal and measurable outcomes which are handed down to the PHC to resolve with the tools at hand. As health is a predominantly state subject in India, state governments do have some autonomy to decide what and how to stock.

Thus, any diagnosis and treatment would include a straightforward process of better stocking of known items in the Full Cupboard, but it would not solve the bigger challenge of how to assure patients that should they come to the PHC at all. The CF situation when a product or service is reliably available is unusual and a true victory would be reliable test results and follow-up diagnosis the same day with easy access to medicines if needed. The commitment to ensure availability of personnel and CF diagnostics and rapid turnaround test results would be one problem framed and solved. With some diseases, this might make associated debates about vaccines either optional or unnecessary. For instance, one study indicates that if such a Cupboard is stocked with simple low-cost and low-technology approaches for cervical cancer, a 31% mortality reduction is achieved (Poli et al., 2015).3 The absence of such stocks (CE, or partially empty) even in the case of a non-infectious disease, makes it much less likely that the patient will return at all, a sizable demand collapse and knock-on clinical and industrial uncertainties to later stages of healthcare delivery.

It could be argued that for many developing countries, the health policy paradigm (the ‘frame’) has often been one of inequality or lack of resources and this chapter has argued that from an industrial and technological standpoint, this can be debated. Some ‘developing’ countries have industrial supplier capabilities and choices to make and they do so under specific institutional and organisational combinations. As discussed earlier, once institutional variety (IV) is explicitly recognised as a specific confounding feature of economic explanation, it becomes easier to see how misleading inferences may be made. While many paths are in principle possible, only some are actually demonstrated in real life, and fewer still are explicit policy priorities. Health knowledge of some types (e.g. how rural Indian women use scanning services) should deliberately be converted into an industrial system of production or into skill sets that are relevant to small firms or employment opportunities. This translation can generate both agile market and non-market strategies for affordable, reliable, high-quality, health services.

Ideally, procurement reform such as underway in India across many sectors will change the ‘low-cost vendor’ approach to ‘high-quality vendor’, where quality refers not to product quality but to a product and service combination of reliability and speed, since the uncertainties are highly costly to the patient and yet new business models may be slow to emerge. When we consider the technological capability spectrum in a location, rather than a global health paradigm of a one-size-fits-all approach to cancer which is often technology-neutral, there are minimum CF issues to be resolved of a minimum ‘technology basket’. Framed as such, the institutional variety (IV) visible locally and the institutional triad’s combinations that reflect its technological priorities, provide policy menus and catalogues. These practical administrative tools convert the CFCE methodology into routine policy training and public health practices.

There are consequently deeper philosophical problems that can excite economic and policy debate. After all, uncertainty and lost time exact a heavy toll on health prevention as well as for diagnosis and treatment. Should PHCs or equipment vendors be assessed by combined business or tendering criteria that requires both product and service packages to be made available, and should the patient be the end consumer and judge of care quality? Why are R&D grants or subsidies being offered to new technologies, or new investment markets growing, but not easily translated to PHC ‘cupboards’? In the past, such criteria have often been split too neatly into contentious debates about the relevance of PHCs themselves or of privatization threats. However, heavy private sector presence already exists in many public health systems, even in perceived ‘public’ or ‘community’ systems in the Indian states of Tamil Nadu or Kerala. At the same time, the sharp growth of more government-funded regional cancer centres, telemedicine, and private sector partnerships have grown in Uttar Pradesh, and more remote areas such as Assam, where traditionally patients have been dissuaded by distance, rugged terrain, Empty Cupboards or unreliable care.

Choosing Wisely for Stocking the Cancer Cupboard

The CFCE is therefore a localised ‘institutional triad’ of production, demand, and delivery whose abstraction can aid health policy design. Precisely because no long-term policy commitment on production is made, delivery problems persist (or else demand would rise sharply). Likewise, as no existing commitment to reliable delivery is made, no minimum CF ‘basket’ is possible. Thus the national context can be seen to define specific minimum and maximum frames (min CFCE and max CFCE) in which particular clinical interventions are decided. For economists and industrial organization specialists, the consequences are that minimum and maximum CFCE frames require translation into specific industrial policy strategies from the essential medicine and equipment products, the subsidies or other incentives offered for their production, price controls if relevant, procurement guidelines and product quality, competition from suppliers if any, and consistency of large volume suppliers. At a later point, where more stable demand is predicted, other industrial policy interventions may be needed such as competition policy, anti-trust, or IP protections.

Choosing Wisely India (Pramesh et al., 2019) is one practical way in which overall cancer care can map onto a CFCE scenario. The Choosing Wisely India initiative offers guidelines from cancer specialists for deciding on a priority phasing for cancer diagnosis and treatments, in which domestic cancer types and their clinical approach are prioritised. Table 6.2 shows differences between two Canadian/US recommendations accepted for India as adapted, and four new Indian recommendations. The last column begins the process of translation to industrial policy design.

Table 6.2 Selected recommendations from Choosing Wisely India, towards a minimum CFCE
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Given the Choosing Wisely Indian suggestions that a break with UK, US, or Canadian cancer strategies is needed to suit the country, one can ask for a first-step refinement of the CFCE strategy for cancer. For instance, radiotherapy is a good case for future study as an industrial and health priority (Samiei, 2013). A traditional health economics metric such as 25 million people per radiotherapy machine in India vs. 250,000 in higher income countries is more effective only when combined with the fact that radiotherapy should, by Choosing Wisely India guidelines, be placed much higher on the list of equipment priorities. However, it will require industrial policy priority in concert, with attention to prototyping, industrial design, development, and procurement priority into a minimum CFCE list, along with higher standards, innovation criteria, and business models for better patient experience. Pricing, competition, or other considerations follow from, not lead, this perspective.

Conclusion: a Cancer System’s Industrial Building Blocks

‘Cancer policy’ will need more clarity. It is currently poorly defined because of its arguably outdated economics and policy frames (Srinivas, 2021b). Not all countries have the same industrial organization nor existing technological capabilities. The variety of environmental and biological processes behind the clinical symptoms also represent diverse cancer diseases under progression and many potential technology or service interventions (including education) that can identify disease early or prevent most cancers. Therefore, CFCE minimums can be debated per disease (‘cancer’, which is multiple diseases) or by industrial capabilities. Problem-framing, theory, and methodological clarity on ‘cancer policy’ is certainly needed.

As discussed, the CFCE approach offers a localised abstraction of institutional variety, the evolving, institutional details of production, demand, and delivery of the ‘institutional triad’ that is often left unspecified in health policy, and an essential preliminary step towards more integrated policy methods. Moreover, the pandemic has helped clarify at least seven practical uncertainties arising at the clinical-industrial interface and underscores the institutional variety through which societies manage this risk. The CFCE forces the uncomfortable conversations of minimum product-service mixes in policy design towards better decisions of public organisations, rather than vague normative maximums.5 Ideals of equality or all-public systems may not necessarily lead to long-term gains for patients.

Encouragingly, pandemic-induced improvements can now be converted into viable industrial guidelines. But we can only determine how to fill a cupboard if we openly debate questions such as: should we make it at home; can we import it instead? Should this be short or long term and why? What is a good mix for the foreseeable future of local manufacture and imports of specific priority products? Is cancer growing from polluting national industries themselves, with degenerated nature, products and services and what would that CFCE method entail? Can industrial policy goals circumscribe, prevent disease, or enhance health policy and vice versa?

At the very least governments must support and regulate their own health goals and industries. The CFCE thought experiment can underscore this national autonomy, clarity on minimum priorities, and commitments to integrated industrial capabilities.

Notes

  1. 1.

    Global praise for India’s manufacture and administration of over 1 billion vaccine doses (over 2 billion first, second, and precautionary doses by August 2022) https://health.economictimes.indiatimes.com/news/pharma/us-lawmakers-say-indias-success-will-help-world-defeat-covid-19-as-it-achieves-100-crore-jabs-milestone/87216337, last accessed June 4 2022.

  2. 2.

    A rich literature exists on PHCs and public sector health delivery systems around the world. The abstraction here will not do it justice but offers a first step to analyse the interface and the institutional design of the proverbial ‘Cupboard’ and decision-making.

  3. 3.

    Similarly Indian gains for breast cancer examination, https://www.bmj.com/content/372/bmj.n256.

  4. 4.

    National Cancer Grid and tumour boards are now being built into Indian regional cancer centres with standardised multi-specialty recommendations to lower uncertainty for patients and improve health outcomes.

  5. 5.

    A similar point in Srinivas (2016) that Indian regulation design is idealistic and often satisfies neither the practical minimum nor maximum requirements of better care goals.