Tag: healthcare

At the frontier, with my numbers

And so I am working on two business concepts in parallel. One of them is EneFin, my own idea of a FinTech utility in the market of energy, with a special focus on promoting the development of new, local providers in renewable energies. The other is MedUs, a concept I am developing together with a former student of mine, and this one consists in creating an online platform for managing healthcare services, as well as patients’ medical records, in the out-of-pocket market of medical services.

The basic concept of EneFinis to combine trade in futures contracts on retail supply of electricity, with trade in participatory deeds in the providers of said electricity. My sort of idée fixeis to create a FinTech utility that allows, in turn, creating local networks of energy production and distribution as cooperative structures, where the end-users of energy are, in the same time, shareholders in the local power installations. I want to use FinTech tools in order to extract all the advantages of a cooperative structure (low barriers to entry for new projects and investors, low prices of energy) with those of a typically capitalist one (high liquidity and adaptability).

After a cursory review of the available options in terms of legal and financial schemes (see Traps and loopholesas well as Les séquences, ça me pousse à poser cette sorte des questions), I came up with two provisional conclusions. Firstly, a crypto-currency, internal to EneFin looks like the best way of organising smooth trade in both the futures contracts on energy and the participatory shares in the energy providers. Secondly, the whole business has better chances to survive and thrive if the essential concept of EneFin is being offered to users as a set of specific options in an otherwise much broader trading platform.

EneFin as a business in itself can make profits on trading fees strictly spoken, like a percentage on every transaction, still, if the underlying technological platform develops really well, EneFin could grow an engineering branch, supplying that technology in itself to other organizations. This is an option to take into account in any business with ‘tech’ in its description.

MedUs, on the other hand, is based on the idea that the strictly spoken medical services, I mean the out-of-pocket paid ones, tend to be quite chaotic, at least in the context of European markets. In Europe, most healthcare is being financed via public pooled funds, accompanied by private pooled funds (or via network structures that operate de facto as pooled funds). The out-of-pocket paid healthcare is frequently an emergency or a luxury, usually not the bulk of medical care we use. Medical records generated in the out-of-pocket healthcare are technically there (each doctor has to create a file for a patient, even for one visit), and yet they have sort of a nebular structure: it is bloody hell of a nightmare to recreate your personal, medical history out of these.

The basic concept of MedUs consists in using Blockchain technology in order to create a dynamic ledger medical records. Blockchain acts as an archive in itself, very resilient to unlawful modifications. If my otherwise a bit accidental, dispersed medical visits, paid in the out-of-pocket system, are being arranged and paid via a Blockchain-based platform, it is possible to attach a ledger of medical records to the strictly spoken ledger of transactions. I say ‘possible’ because in that nascent business we still don’t have a clear idea of technological feasibility: Blockchain is cool in simple semantic structures, like cryptocurrencies, but becomes really consuming, in terms of energy and disk-space, if we want to handle large, complex sets of data.

MedUs, as we see it now, is supposed to earn money in three essential ways: a) through trading visit-coupons for private healthcare (i.e. coupons that serve to pay for medical care), in the form of coupons strictly spoken or of a cryptocurrency b) through running a closed platform accessible to medical providers after they pay for the initial software package and a monthly, participatory fee, and c) as a provider of the technology of creating local structures in (a) and (b). I can also see a possible carryover from the EneFin concept to MedUs: new, local providers of healthcare could sell their participatory shares to patients together with those visit-coupons, and thus create cooperative structures in local markets.

In this update I am focusing on one specific issue regarding both concepts, namely on the basic, quantitative market research, which I understand as the study of prices and quantities. My point is that you have two fundamental strategies of developing a new business. Your business can grow as your market grows, for one. That’s the classical approach, to find, for example, with Adam Smith. Still, there are businesses which flourish in slowly dying markets. The market of oil is a good example: there is no prospects for big growth, this is certain, and yet there are companies that still make profits in oil.

In a few past updates, I took something like a cursory set of 13 European countries and I calculated their various, quantitative attributes regarding EneFinand the European market of energy. These countries are: Austria, Switzerland, Czech Republic, Germany, Spain, Estonia, Finland, France, United Kingdom, Netherlands, Norway, Poland, Portugal. I am going to keep my focus on this set of countries and run a comparative market research, in terms of basic prices and quantities, for both concepts (i.e. EneFin and MedUS) together.

Now, I will try to move forward along that narrow crest that separates educational content from strictly spoken market research for business purposes. I want this blog to be educational, so I am going to give some methodological explanations as I run my quantitative analysis, and yet, in the same time, I want material, analytical progress for both business plans. Thus, here we go.

Both concepts address a similar relation suppliers and their customers. Households are the target customers in both cases. As for EneFin, the category of ‘households’ is a bit more flexible: it can encompass small businesses, small local NGOs, and farms as well. Still, in both of those business concepts populationis the most fundamental metric for measuring quantities. I usually reach to the demographics published by the World Bank: this source is quick to dig info out of it (I mean the interface is handy), and, as far as I know, it is reliable. I am a big fan of using demographics in market research, by the way: they can tell us much more than it superficially appears.

Demographic data from the World Bank covers the window since 1960 through 2016. Quantitative market research is about dynamics in time, as well as about cross-sectional differences. Here below, in Table 1, there is a bit of demographic info about my 13 countries:

Table 1 – Demographic analysis

Country Population headcount in 2016 Demographic growth since 1960 through 2016
Austria 8 747 358 24,1%
Switzerland 8 372 098 57,1%
Czech Republic 10 561 633 10,0%
Germany 82 667 685 13,5%
Spain 46 443 959 52,5%
Estonia 1 316 481 8,7%
Finland 5 495 096 24,1%
France 66 896 109 42,9%
United Kingdom 65 637 239 25,3%
Netherlands 17 018 408 48,2%
Norway 5 232 929 46,1%
Poland 37 948 016 28,0%
Portugal 10 324 611 16,6%
Total 366 661 622 29,3%

Good, now what do those demographics tell? In am interested in growth rates in the first place. Anyone who knows at least a little about the demographics of Europe can intuitively grasp the difference between, let’s say, the headcount of Switzerland as compared to that of Germany. On the other hand, growth rates are less intuitive. I start from the bottom line, i.e. from that compound rate of demographic growth in all the 13 countries taken together. It is 29,3% since 1960 through 2016, which makes a CAGR (Compound Annual Growth Rate) equal to CAGR = 29,9% / (2016 – 1959) = 0,51%. Nothing to write home about, really. The whole sample of 13 countries makes quite a placid demographic environment. Yet, the overall placidity is subject to strong cross-sectional disparities. Some countries, like Switzerland, or Spain, display strong demographic growth, whilst others are like really placid in that respect, e.g. Germany.

How does it matter? Good question. If each consecutive generation has a bigger headcount than the preceding one, in each such consecutive generations new social roles are likely to form. The faster the headcount grows, the more pronounced is that aspect of social change. On the other hand, we are talking about populations that grow (or not really) in constant territories. More people in a constant space means greater a density of population, which, in turn, means more social interactions and more learning in one unit of time. Summing up, the rate of demographic growth is one of those (rare) quantitative indicators that reflect true structural change.

Now, we can go a bit wild in our thinking and do something I call ‘social physics’. An elephant running at 10 km per hour represents greater a kinetic energy than a dog running at the same speed. Size matters, and speed matters. The size of the population, combined with its growth rate, makes something like a social force. Below, I am presenting a graph, which, I hope, expresses this line of thinking. In that graph, you can see a structure, where a core of 5 countries (Austria, Finland, Estonia, Czech Republic, and Portugal) sort of huddles against the origin of the manifold, whilst another set of countries sort of maxes out along some kind of frontier, enveloping the edges of the distribution. These max-outs are France and Spain, in the first place, followed by Switzerland and Netherlands on the side of growth, as well as by Germany and UK on the side of numerical size.

Some social phenomena behave like that, i.e. like a subset of frontier cases, clearly differentiating themselves from the subset of core cases. Usually, the best business is to be made at the frontier. Mind you, the entities of such a frontier analysis do not need to be countries: they can be products, business concepts, regions, segments of customers. Whatever differs by absolute size and its rate of change can be observed like that.

Demogr13_1 

My little demographic analysis shows me that whichever of the two projects I think about – EneFin or MedUs – sheer demographics make some countries (the frontier cases) in my set of 13 clearly better markets than others. After demographics, I turn towards metrics pertinent to energy in general, renewable energies, and to the out-of-pocket market in healthcare. I am going to apply consistently that frontier-of-size-versus-growth-rate approach you could see at work in the case of demographic data. Let’s see where it leads me.

As for energy, I start with a classic, namely the final consumption of energy per capita, as published by the World Bank. This metric is given in kg of oil equivalent per person per year. You want to convert it into kilowatt hours, like in electricity? Just multiply it by 11,63. Anyway, I take a pinch of that metric, just enough for those 13 countries, and I multiply it by another one, i.e. by the percentage share of renewable energies in that final consumption, also from the website of the World Bank. I stir both of these with the already measured population, and I have like: final consumption of energy per capita * share of renewable energies * population headcount = total final consumption of renewable energies [tons of oil equivalent per year].

Table 2, below, summarizes the results of that little arithmetical rummaging. Is there another frontier? Hell, yes. Germany and United Kingdom are the clear frontier cases. Looks like whatever anyone would like to do with renewable energies, in that set of 13 countries, Germany and UK are THE markets to go.

Table 2 – National markets of renewable energies

Country Final consumption of renewable energies in 2015, tons of oil equivalent Final consumption of renewable energies, compound growth rate 1990 – 2015
Austria 11 296 981,38 80,7%
Switzerland 6 200 709,18 48,6%
Czech Republic 6 036 384,16 241,0%
Germany 44 301 158,29 501,2%
Spain 19 412 734,75 104,4%
Estonia 1 508 374,57 359,5%
Finland 14 036 145,55 101,8%
France 33 167 337,48 42,3%
United Kingdom 15 682 329,72 1069,6%
Netherlands 4 223 183,03 434,9%
Norway 17 433 243,73 39,8%
Poland 11 267 553,99 336,8%
Portugal 5 996 364,89 32,6%

 Good, time to turn my focus to the other project: MedUs. I take a metric available with the World Health Organization, namely ‘Out-of-Pocket Expenditure (OOPS) per Capita in PPP Int$ constant 2010’.  Before I introduce the data, a bit of my beloved lecturing about what it means. So, ‘PPP’ stands for purchasing power parity. You take a standard basket of goods that most people buy, in the amounts they buy it per year, and you measure the value of that basket, in local currencies of each country, at local prices. You take the coefficient of national income per capita in the given country, and you divide it by the monetary value of that basket. It tells you how many such baskets can your average caput(Latin singular from the plural ‘capita’) purchase for an average chunk of national income. That ratio, or purchasing power, makes two ‘Ps’ out of the three. Now, you take the PP of United States as PP = 1,00 and you measure the PP of each other country against the US one. This is how you get the parity of PPs, or PPP.

PPP is handy for converting monetary aggregates from different countries into a common denominator made of US dollars. When we compare national markets, PPP dollars are better than those calculated with the exchange rates, as the former very largely get rid of local inflation, as well as local idiosyncrasies in pricing. With those international dollars being constant for 2010, inflation is basically kicked out of the model. The final point is that measuring national markets in PPP dollars is almost like measuring quantities, sort of standard units of medical services in this case.

So, I take the OOPS and I multiply it by the headcount of the national population, and I get the aggregate OOPS, for all the national capita taken together, in millions of PPP dollars, constant 2010. You can see the results in Table 3, below, once again approached in terms of the latest size on record (2015 in this case) vs. the compound growth rate (2000 – 2015 for this specific metric, as it is available with WHO). Once again, is there a frontier? Yes, it is made of: United Kingdom, Germany and Spain, followed respectfully by Netherlands, Switzerland and Poland. The others are the core.

Question: how can I identify a frontier without making a graph? Answer: you can once again refer to that concept of social physics. You take the size of the market in each country, or its aggregate OOPS. You compute the share of this national OOPS in the total OOPS of all the 13 countries taken together. This is the relative weight of that country in the sample. Next, you multiply the compound growth rate of the national OOPS by its relative weight and you get the metric in the third numerical column, namely ‘Size-weighted growth rate’. The greater value you obtain in that one, the further from the centre of the manifold, the two variables combined, you would find the given country.

Table 3 – Aggregate Out-Of-Pocket Expenditure on Healthcare

Country Aggregate OOPS in millions of PPP dollars in 2015 Compound growth rate in the aggregate OOPS, 2000 -2015 Size-weighted growth rate
Austria 7 951 105,8% 3,8%
Switzerland 17 802 124,9% 9,9%
Czech Republic 3 862 300,2% 5,2%
Germany 54 822 104,9% 25,7%
Spain 35 816 146,9% 23,5%
Estonia 565 308,6% 0,8%
Finland 4 356 98,5% 1,9%
France 20 569 84,7% 7,8%
United Kingdom 39 935 275,5% 49,1%
Netherlands 11 027 227,7% 11,2%
Norway 4 607 100,3% 2,1%
Poland 15 049 124,1% 8,3%
Portugal 7 622 86,8% 3,0%

 Time to wrap up the writing and serious thinking for today. You had an example of quantitative market analysis, in the form of ‘frontier vs. core’ method. When we talk about the relative attractiveness of different markets, that method, i.e. looking  for frontier markets, is quite logical and straightforward.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French versionas well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon pageand become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

Crossbreeds, once they survive the crossbreeding process

 

As a bit of a surprise, I presently have two business plans on the board, instead of just one. A former student of mine asked me to mentor a business project he is starting up with his friend. The basic concept is that of an online platform for managing medical visits, and the innovation consists in using the Blockchain technology to create, for each patient using that functionality, a digital, trusted ledger of all their medical documentation, i.e. their medical visits, diagnoses, treatments received etc. all in one set of data, properly secured and available from any place on Earth.

Additionally, an educational project – a book on the FinTech industry accompanied by an educational toolkit – which I am running with a friend of mine, has gained in maturity and we will be giving it a definitive form. All in all, ideas and projects abound, and I decided to use my blog for conveying as accurate an account of my intellectual journey into all of these three realms. From now on, I am doing my best to weave an interesting story of scientific research out of three distinct stories, namely: a) my EneFinproject b) that medical ledger project, which I provisionally name MedUs, and c) the FinTech educationalpackage.

As for the EneFinproject, in my last update in French, namely in Les séquences, ça me pousse à poser cette sorte des questions, I came to the conclusion that the best way of starting with the EneFin concept is to create or to join an existing generalist trading platform, possibly using a cryptocurrency, such as Katipult, and include in its general features some options, which, in turn, are likely to spur the emergence of new suppliers in renewable energies.

A little pill of update for those who didn’t follow that update in French: I used a technique that data scientists frequently use, and which consists in expressing something we want as a sequence of events, actions and decisions. When I did this with the general concept, to be found in Traps and loopholes, I discovered that at least some potential users of the EneFinfunctionality are likely to have and want a bit more choice and freedom of movement in their financial decisions. I came to the (provisional) conclusion that the strictly spoken EneFinscheme, i.e. promoting the development of new suppliers in renewable energies, will sell better when expressed as a set of financial incentives, placed in the environment of an otherwise general, well-running platform of exchange, rather than as a closed system.

Right now, I am working through the issue of contracts and the legal rules that accompany them. I am deconstructing the typical contracts signed for the supply of energy, in order to have a very precise idea of what should the smart, crypto-coined contracts at EneFinlook like. Contracts are about securing a precise pattern of behaviour from the part of the other contracting party. I want to understand thoroughly the patterns of behaviour, those wanted as well as those unwanted ones, in the relation between a supplier of energy and his customers.

The business plan I am preparing form the MedUsconcept, I am at the phase of evaluating the size and the value of the market, together with defining, progressively, the core business process. Let me present a bit of the initial idea, and a few openings that it creates. The idea has its roots in the observation of the Polish healthcare market, which is a maze of mutually interweaving public funding and private schemes. An average Polish patient seldom can rely exclusively on the public provision of medical care. Frequent blood diagnostics, dental care, post-surgery rehabilitation – sooner or later, you just need to stop waiting for the public funding of these, and pay privately, either in the out-of-pocket formula, or in some kind of pooled funding scheme.

Those entangled, disparate funding patterns results in the dissipation of the patients’ medical records. The initial ides of MedUsis to take the already known functionality of online arrangement of medical appointments, and combine it with the aggregation and proper handling of digitalized medical records. You make an appointment with one doctor via MedUs, you are being diagnosed and treated, then you make an appointment with another doctor, another diagnosis and treatment ensue, and the record of all that is being stored with MedUs.

This is where the Blockchain technology becomes interesting. Blockchain is basically a ledger, and in handling medical records we need, precisely, a ledger. Medical records contain legally sensitive data, and improper handling can lead to a lot of legal trouble. Every single action taken regarding that data has to be properly documented, and secured against fraud. The basic digital architecture of medical records is that of a database, with the identity of the patient as the leading variable.

In those databases, well, s**t happens, let’s face it. I had a good example of that in my own recent experience. As some of you could have read in ‘The dashing drip of Ketonal, or my fundamental questions for the New Year’, due to a complicated chain of events, involving me, some herrings, and the New Year’s party, I spent the New Year’s night in an emergency ward of the district hospital, with the symptoms of acute food poisoning. As I was being released, on the New Year’s day, I had my official discharging documents. In those documents, space and time warped a little. It started with my data, and then I could read that I had been taken in charge three days earlier, in Berlin, with acute cardiac symptoms, and subsequently transferred to the very same hospital, and then, all of a sudden, my own (real) description followed.

As for me, I wouldn’t care, but my wife said: ‘Look, if you have any complications, or if you need any follow up in treatment, that official discharge will matter. Go to that hospital and make them get your records straight’. So I did, and you would really like to see the faces of people, in the hospital’s administration, when I showed them what I am coming with and for. It was that specific ‘Oh, f**k, not again!’ look. They got it straight, and so I stopped being that cardiac patient hospitalized in Berlin, but as far as I know, it all required a little bit of IT acrobatics.

As I described the situation to a friend of mine, an IT engineer, he explained me that this sort of things happen all the time. Our sensitive data is being stored in a lot of databases, and errors happen recurrently. Technically, once they happen, they should be bound to stay happened. Still, what do we have those IT engineers for? What you do, in such a case, is either to run ‘a minor reloading of the database, just to remove some holes in the security systems’, or you deliberately put the system to failure, and reboot it. Both manoeuvres allow miraculous disappearance of embarrassing data. A lot of institutions do it, like hospitals, banks, even ministries, apparently on a recurrent basis. This is, for example, the way that banks hush up the traces of hacking attacks on their customers’ accounts.

Databases with medical records are basically proprietary, i.e. each database has to have a moral entity clearly owning it and being responsible for it. That’s the law. If I use the services of many different medical providers, each of them runs their own database of medical records, and each such database is proprietary, which, in turn, means that my personal medical data is being owned by many entities in the same time. Each of these entities holds one piece of the puzzle, and the law prohibits any sharing between them, basically, unless a chain of official requests for information is being put in motion. As strange as it seems, such a request cannot be issued by the patient, whose medical records are in question. Only doctors can put my dispersed medical records into one whole, and I have no leverage upon that process.

Strange? Absurd? Well, yes, still no more than the promises, which some politicians make during elections. Anyway, that student of mine came up with the idea of using Blockchain to revolutionize the system. There is that digital platform, MedUs, which starts innocently, as a simple device to make appointments for private medical care. Now, revolution begins: each action taken by the patient, and about the patient, via MedUs, is considered as a transaction, to be stored in a ledger powered by the Blockchain technology. The system allows the patient to be effectively in charge of his own medical record, pertaining to all the medical visits, tests, diagnoses and treatments arranged via MedUs.

A sequence comes to my mind. A patient joins the MedUsplatform, and buys a certain number of tokens in its internal cryptocurrency. Let’s call them ‘Celz’. Each Celzcan buy medical services from providers who have joined MedUs. As it is a token of cryptocurrency, each Celzis being followed closely in all its visits and acquaintances: the medical history of the patient is being written in the hash codes of the Celzeshe or she is using in the MedUsplatform.

Crossbreeds, once they survive the crossbreeding process strictly spoken, are the strongest, the meanest, and the toughest players in the game of existence, and so I am crossbreeding my business concepts. The genes (memes?) of EneFingently make their way inside MedUs, and the latter sends small parcels of its intellectual substance into EneFin. Yes, I know, the process of crossbreeding could be a shade more fun, but I am running a respectable scientific blog here. Anyway, strange, cross-bred ideas are burgeoning in my mind. Each subscriber of the EneFinplatform could have all the history of their transactions written into the hash codes of the cryptocurrency used there, and thus the EneFinutility could become something like a CRM system (Customer Relationship Management), where each token held is informative about the past transactions it changed hands in. How would the reading of such data, out of the hash code, work in the (legal) light of General Data Protection Regulation (GDPR)?

On the other hand, why couldn’t patients, who join the MedUsplatform, use their Celzesto buy participation in the balance sheet of those medical providers who wish such a complex deal? Celzes used to buy equity in medical providers could generate extra purchasing power – more Celzes – to pay for medical services.

In both projects, which I am currently preparing business plans for, namely in EneFin, and in MedUs, the Blockchain technology comes as a simplifying solution, for transforming complex sets of transactions, functionally interconnected, into a smooth flow of financial deeds. When I find a common denominator, I tend to look for common patterns. I am asking myself, what do these two ideas have in common. What jumps to my eye is that both pertain to that special zone of social interactions, when an otherwise infrastructural sector of the social system gently turns into something more incidental and mercantile. It is about giving some spin to those portions of the essential energy and healthcare systems, which can tolerate, or even welcome, some movement and liquidity, without compromising social stability.

As I see that similarity, my mind wanders towards that third project I am working on, the book about FinTech. One of the essential questions I have been turning and returning in my head spells: ‘What is FinTech, at the bottom line? What part of FinTech is just digital technology, versus financial innovation in general?’. Those fundamental questions popped in my head some time ago, after some apparently unconnected readings: the Fernand Braudel’s masterpiece book: ‘Civilisation and Capitalism’, ‘The Expression of The Emotions in Man and Animals’ by Charles Darwin, and finally ‘Traité de la circulation et du crédit’ by Isaac da Pinto. It all pushed me towards perceiving financial deeds, and especially money, as some kind of hormones, i.e. systemic conveyors of information about what is currently the best opportunity to jump on.

A hormone is information in solid form, basically, just obtrusive enough to provoke into action, and light enough to be conveyed a long way from the gland it originates from. OK, here I come: gently and quietly, I have drifted towards thinking about the nature and origins of money. Apparently, you cannot be a serious social thinker if you don’t think about it. Mind you, if you just think about the local (i.e. your own) lack of money, you are but a loser. It is only when you ascend beyond your own, personal balance sheet that you become a respectable economist. Karmic economics, sort of.

Being a respectable social thinker does not preclude practical thinking, I hope, and so I am drifting back to business planning, and to the MedUsconcept. My idea is that whatever will be the final span of customers with that online platform, it is going to start in the market of private healthcare, or, as I think about it, peri-healthcare as well (beauty clinics, spa centres, detox facilities etc.). Whatever the exact transactional concept will be finally developed, any payment made by the customers of MedUswill be one of these: a) a margin, paid by the patient over the strictly spoken price of the healthcare purchased b) a margin, paid by the provider of healthcare out of the price they receive from the patient, or, finally, c) a capital expense of the healthcare provider, to be reflected in some assets in their balance sheet. Hence, I need to evaluate the aggregate value of payments made by patients, the distribution of the corresponding expenditure per capita, and the capital investments in the sector. Studying a few cases of healthcare businesses, just to get the hang of their strategies, would do no harm either.

As I browsed through the website of the World Health Organization, I selected 17 indicators which seem relevant to studying the market for MedUs. I list them in Table 1, below. They are given either as straight aggregates (indicators #11 – 17), as per capita coefficients, or as shares in the GDP. When something is per capita, I need to find out about the number of capita, for example with the World Bankand from then on, it is easy: I multiply that thing per capita by the amount of capita in the given country, and I fall on the aggregate. When, on the other hand, I have data in percentages of the GDP, I need the GDP in absolute numbers, and the World Economic Outlook database, by the International Monetary Fund, comes handy in such instances. Once again, simple multiplication follows: % of GDP times GDP equals aggregate.

Table 1 – Selected indicators about national healthcare systems, as provided by the World Health Organization

Indicator #1 Current Health Expenditure (CHE) as % Gross Domestic Product (GDP)
Indicator #2 Health Capital Expenditure (HK) % Gross Domestic Product (GDP)
Indicator #3 Current Health Expenditure (CHE) per Capita in US$
Indicator #4 Domestic Private Health Expenditure (PVT-D) as % Current Health Expenditure (CHE)
Indicator #5 Domestic Private Health Expenditure (PVT-D) per Capita in US$
Indicator #6 Voluntary Financing Arrangements (VFA) as % of Current Health Expenditure (CHE)
Indicator #7 Voluntary Health Insurance (VHI) as % of Current Health Expenditure (CHE)
Indicator #8 Out-of-pocket (OOPS) as % of Current Health Expenditure (CHE)
Indicator #9 Voluntary Financing Arrangements (VFA) per Capita in US$
Indicator #10 Out-of-Pocket Expenditure (OOPS) per Capita in US$
Indicator #11 Voluntary prepayment, in million current US$
Indicator #12 Other domestic revenues n.e.c., in million current US$
Indicator #13 Voluntary health insurance schemes, in million current US$
Indicator #14 NPISH financing schemes (including development agencies), in million current US$
Indicator #15 Enterprise financing schemes, in million current US$
Indicator #16 Household out-of-pocket payment, in million current US$
Indicator #17 Capital health expenditure, in million current US$

 I am wrapping up writing, for today. I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French versionas well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon pageand become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?