The pan-impact on diagnostics

This is an open thread to co-create an understanding on the wake-up call the current pandemic has brought on our understanding of the diagnostics sector potential. All inputs, corrections and challenges are welcome.

Context >

Let’s gain perspective. The global healthcare industry (10Trillion) is an 8% of the total global economy. It makes sense, after all we care about our health. Of that 10T a 12% (1.2T) goes to pharma and only a 0.9% (93B) to diagnostics whose market size is 7% the value of pharma’s. In other words, we are focused on treatment not prevention. As we learned from a pandemic while treatment capacity is limited, diagnostics doesn’t have to be. 

Just imagine that the total probable cost of the pandemic could be around 3T or x3 the size of the diagnostics sector. If the diagnostics sector was bigger how much smaller would pharma be and how much less would the pandemic have cost us?

It’s only logical, that up to now, the whole healthcare sector has been organized around epidemiologic incidence. Given the probability of occurrence we develop our healthcare system to cure diseases. So, in accordance, money flows creating incentives that determine proportionally the capability (amount and level of professionals) and capacity (amount and quality of hospitals and medicine) that is necessary to “cure” the expected cases. Thus, we get the status quo system design, which may be effective in its own fragmented terms, not necessarily efficient as a whole.

The current pandemic has revealed the system’s natural bottleneck in treatment capacity in a specific disease area and then in the system as a whole. It would not make sense to expect a bottleneck free system, it would be unbearably inefficient. That is why we need to rethink the system from a diagnostics perspective in order to improve its input quality and once diagnostics screening rebalances treatment phase distribution, we could rethink medical attention capacity distribution.  

At the same time the current design has set investment return standards that respond to the cure research and development risk and patent model. That standard sets expectation’s that help push cost above global inflation rates. Without re-thinking the system as a whole we will never find the incentives from within for a full sector transformation and we will keep on living on a cure-first based model.

The pandemic >

Our current way of life, which is more intricate, interconnected and complex than ever before, is only more conducive for the spread of this or any other future disease. Technology and in particular platform business models (Uber, Amazon or DoorDash) have created network organizational models (moving away from traditional linear value chain logic) that operate like constellations which reinforce inter-transmissions, because they have more connections as the network gets stronger. We were already seeing an increase of sexual transmitted diseases within any given constellation due to the growth in casual relations organized through dating apps. We only needed 50M flights a year (or 135k a day) connecting cities, communities and those constellations to increase the odds of contagious diseases spreading. 

The natural behavior of a fast spreading disease did not take into account all the human organizational friction that politics, business and misinformation could create in its favor. Helping the spread to accelerate, or at least slowing risk consciousness and response time. This could take us to a whole different chapter of organizational misalignments.

The current pandemic has showed us the relevance of diagnostics even though its importance is underrecognized but paramount to health in general. Virus life span, incubation time and asymptomatic carriers combined created a perfect cocktail that has only evidenced the cracks in our current system.

Asymptomatic virus carriers are one of the main reasons the spread is so effective in multiplying infected people faster. Countries that have introduced social distancing, the use of cloth mask (90% of virus incubate in nose and throat), and massive testing have been more effective controlling spread and in particular controlling asymptomatic silent spreaders better. Of all measures taken by different countries it seems that the subtleties in testing could have one of the biggest impacts in reducing the spread.  

In order to apply massive testing, you need installed diagnostics equipment, reagents and trained professionals. There are few companies that have the installed base of right factory equipment and are capable of deploying the reagents in time. Most kits going around are not validated, require manual handling limiting the number of tests that can be done and take way more time than necessary to provide a result which is not always accurate or valid. Bottom line, when we talk about testing, we are talking about diagnostics automated testing. 

Its logical that health officials want to avoid the system’s collapse while trying to control the herding resistance. In other words, they want to flatten the adoption curve as a majority of the population gets infected and develops resistance to the virus. Collateral deaths of this strategy could be avoided with massive testing too. In most countries, in particular underdeveloped, the diagnostics sectors are still too small or immature, to have the right capacity to deal with the required level of testing. A probable hypothesis would state that a more effective pandemic control strategy would be possible if your country had the proper diagnostics sector development.

Diagnostics >

Sectors usually organize around profit pools, where value exists. Up to now most value, in most sectors, has been captured when dealing with realized conditions. Most parts of insurance, legal or health sectors get in motion when something undesirable and unexpected happens. Then those same parts work to become efficient in “solving” those issues. Most of these sectors could be a lot more efficient if they focused on prevention. Like insurance scoring is used promote preventive behaviors. Diagnostics in a way could work like scoring. 

There are a lot of valuable startups innovating in the diagnostics environment specially in sequencing and genetic diagnostics. At the same time, we are still recovering from Therano’s failed promise (aka fraud) which taught us that retail sample collection innovation is possible but diagnostics testing miracles are unlikely. There is an array of startups promising mobile diagnostics apps which sell statistical probability as valid diagnostics which are only as good as a google search result. We need to reinforce the relevance of test accuracy and validity. Because of that, when we talk about diagnostics, we are talking about scientific based, regulator approved, medically understandable, diagnostic tests.

In most countries’ regulators decide which test are approved and payers decide, to the extent the regulators allow them, which test their doctors can use. The array of available test is much broader than the ones in use. Its logic that approved test have to make sense in terms of epidemiologic incidence and cost reduction. But analyzing each test individually in relation to the current cost it has on a saturated healthcare system, without considering the impact over the sector as a whole, is restrictive. If regulators thought of diagnostics in evolutionary terms driving change towards a clear end game proper of a more balanced system, they would probably make different decisions.

Doctors don’t always know all the diagnostics potential available and don’t always necessarily choose the best test for the current conditions and most of the time are not experts in diagnostics interpretation. Because of this we would expect a more accurate synchronization between diagnostics and doctors, but incentives are not always aligned.

On some specific diseases its necessary to onboard other specialists forming a consultation board capable of reaching consensus on a preliminary diagnose. Some of these specialists are not doctors and are not always recognized by payers. As it happens with genetic counseling. Sometimes patients review their results before seeing a doctor or validate afterwards searching online for interpretation guidelines creating an unnecessary and stressful situation. Patient experience and in particular diagnostics interpretation is far from expected. 

Increasing diagnostics investment by payers usually falls in a chicken and egg dilemma. Even if you invested today you would not see the incidence in clinical facilities for some time while input patient amount grows as population or age does. This dynamic reality would have to change meanwhile the current system would still demand the same or more cash to work. It’s impossible to think the incidence of diagnostics in the total life cycle value of patients without a proper sector transformation plan that allows rebalancing capacities and cost during the transition phase. 

Patient non-centric >

Clinical institutions and labs, as it happens with players in many consolidating sectors, search for scale to avoid rising cost pressure. Medical care institutions integrate vertically having their own labs (like Kaiser Permanente or Mayo Clinic) while Labs companies (like Quest Diagnostics or LabCorp) try to build networks to balance core/reference lab efficiency with decentralized sample collection. Regardless of their origin they are searching for profit in similar ways.

When both network hubs meet, in any given situation, they compete and as a result they create endless friction in patient experience. Because they both work hard for patient retention by making it difficult for patients to move to a different constellation or network. 

On the other end both type of players wants to pay for test throughput squeezing diagnostics companies to a low-cost model that rules out all the potential value they could gain from providers as partners. In some cases, short term cost cutting is impeding accuracy improvement, lab integration, lab efficiency or a better patient experience.

While in the last years Google Deepmind and IBM Watson have been buying unthinkable amounts of medical records. Flying beneath the radar they are amassing huge amounts of data (aka gold) to compete in big data diagnostics predictions that could influence pharma research of clinical capability design. So now we have diagnostics companies, labs and big tech companies competing for the spotlight, the core data behind all testing. Even though competition is good I am not sure if a platform model with a winner takes all reality is the best think for diagnostics.

These forces create narrow crooked pathways patients have to walk through with a lot of effort. The value chain is mostly fragmented, not integrated, lacks proper coordination, and lives with a lot of friction between players. Little price transparency, little or non report flow or minimum operational integration. Doctors suffer all of these but mainly patients when their records are not available when needed by all necessary parties. 

The billing and reimbursement process has been thought from the payer’s perspective, where they are trying to avoid fraud and misuse. Patients always have to prove their right while living a probably stressful process when the doctors are the ones asking for tests.

At the end most players coming from different ends meet in the diagnostics playground only to fight at the cost of patients, who, as we expect should always be first. Regulators should enforce patient defense regulation and advocacy with more determination. By doing so they will most probably allow a healthier competition between diagnostics providers that could leverage their relationship with labs and hospital partners in the industry, forcing a healthier development of the sector. In only regulators could stop running the challenge from behind.

Annexes >

Annex 1: We have seen in the past weeks several non-expert analyses of available epidemiologic data reaching conclusions that support the idea that the current quarantine remedy is worse than the disease itself. Organizing the whole of society around the health of the weakest, even if it works in demerit of the economic wellbeing of society, is and should always be just. This is a moral problem, not a rational or economic one. If the economic post crisis evolves into more indirect deaths, we should ask ourselves what changes are necessary in our social and economic models. Something worth exploring in a different pan-impact thread.

Annex 2:  The negative impact of fake news or massive social behavior manipulation is increasing from crisis to crisis. From market fraud, to elections and now pandemics. We can regulate media, develop checkpoint or create the right incentives, but nothing will beat education. How could a better health care system avoid in the future the impact of misinformation.

Annex 3: The diagnostics endgame requires guarantees in security, privacy, data management and governmental control. All the possible improvements in patient experience could eventually represent a threat in other orders of life. As we have seen in this pandemic some countries have been efficient in solving health risk issues at the cost of privacy.

Annex 4: One of the probable consequences of the pandemic is the deployment of insurance policies by central countries. They could try to influence healthcare policies of no centric countries to control potential outbreaks. They could also impose a diagnostics passport to validate these controls. Countries could start demanding specific test (on top of vaccines) to allow people within their borders. Probably increasing healthcare and travel insurance cost. 

Annex 5: Enforcement of refugee camps as “massive containment dams” have been cracking in the last twenty years. How will public policy from central countries evolve as they enforce global public healthcare policies onto satellite countries where refugees come from. After so many years of imposing occidental commerce and cultural guidelines will the next phase of globalization be health and in particular diagnostics as medium of bio-control. In which ways can sector companies and patients protect themselves from governmental misuse.