Fairhaven, The River

Defining Metadata

Summary:The Dublin Core work leaves out the importance of establishing an intended use as context for metadata.  Having this context then makes their level of interoperability and some of the issues around metadata storage much clearer.

Dublin Core leaves out the importance of intended use when discussing metadata.  It may be too obvious to those close to the problem. Their definition
      "Metadata is data about data>"
while correct, is insufficient.  All data is metadata from some context.  A clearer definition is:
      "Metadata is data about data, that is useful in a specific context of intended use."

Johm Moehrke's post gives good examples of the kinds of intended use that are important for medical records.

It makes sense to say that PatientID is metadata about a document in different contexts:

• It could mean that "This document is about PatientID"
• It could mean that "This document references PatientID", e.g., a document about a child references the mother.

You need the context of a use to understand metadata.

The context of use also explains the levels of interoperability that are otherwise left dangling by the Dublin Core.  The degree of interoperability is in the context of the intended use.  An example of the lowest level of interoperability might be a piece of metadata called "license".

At the lowest level, that word "license" is all you know about the metadata.  You can only guess about possible meanings.  You don't know the format of "license".  Maybe it is a text blob that contains legal language.  Maybe it's a URL to a document in an unknown format.  Maybe it's a UUID.  This is the lowest level of interoperability and it makes automated processing nearly impossible. But, it's an important improvement over having nothing.  There are many situations where this vague hint is sufficient information for a person to figure out what to do.

At the highest level, you find something like "diagnosticCode", with a specification that it is to be encoded as an HL7 CWE, with a value selected from the 2011 XYZ profile value set.  Now I have the semantic meaning, the format, the vocabulary, complete version information, and can perform extensive automatic processing.

It's important to separate the discussion of metadata, intended use, and degree of interoperabilty needed in early discussions defining metadata.  They are different concepts.

Another issue that is not mentioned in Dublin Core is the decision of how metadata is stored and conveyed.  This is an interface and exchange problem only.  Within any processing system you don't need agreement with others about how any data is stored or conveyed.  But metadata discussions do need to understand that when exchanging metadata there are three possible situations:

• The metadata may be embedded in the document, and not otherwise exposed.  This means that it is only accessible to systems and people that understand the document format.  An example of this could be "patient's mother" or "KVP setting".  These are metadata for some rather specialized uses in genomics and procedure analysis.  An indexing registry for medical records is unlikely to maintain these as a separately stored metadata index.
• The metadata might only be available as a separate item.  The hash value for a document is almost never stored as part of the document.  It's use is as a separate piece of metadata used by the privacy, security, and integrity systems.
• The metadata might be stored both as part of the document and as a separate item.  PatientID is often stored both ways.  When using patientID as part of finding and selecting documents, it is appropriate to have separate indices for many reasons.  But when processing those documents, it is necessary to have that patientID information in context within the document.  This does lead to some considerations about consistency rules when defining how the metadata is to be used, and that is normal.

 

Definitions matter (median income statistics)

Summary: definitions matter much more than I expected.

There have been lots of public opinions about the change in median income in the US, and what it means for policies.  It turns out that the definition of median income matters much more than I expected.

This table shows the increase in percentage from 1979 to 2007, for those who want the answer up front:

Income Included	Tax Unit	Household	Size Adjusted Tax Unit	Adjusted Household
pre tax, pre-transfer	3.2	12.5	14.5	20.6
pre tax, post-transfer	6.0	15.2	17.0	23.6
post tax, post-transfer	9.5	20.2	25.0	29.3
post both, plus health insurance	18.2	27.3	22.0	36.7

The widely reported figure is the 3.2.  This is used to argue that there has been no improvement.  All the gains have gone to the top 1%.  The middle class is being hollowed out.

The different definitions make for a more nuanced answer, and reflect difficulties in getting data.

The different terms are:

• Tax Unit is the tax filing unit.  This is what the IRS tax statistics report.
•  Household is what you would expect.  It's all the people in the house.  So everyone in the household is combined.  This captures the effects of grandparents, parents, and children all being potential earners and sharing income and expenses.  It also captures unmarried couples, shared custody, etc.  The IRS statistics don't capture this, but the monthly Census survey does.
• Size adjustment modifies the income using the same adjustment as is used for cost of living.   A family of four needs more income than a single person, but not four times more.
• The kinds of income reflect regular wages/dividends, transfer payments like social security or food stamps, and finally health insurance benefits.  These variations also reflect data gathering.  The IRS can measure some transfer income, like the EITC, but not other transfer income, like food stamps.  EITC and food stamps are two very large social welfare programs in the US.

A recent paper is interesting in that it works from the census bureau data rather than the tax data.  This lets it measure households, transfer payments, and health insurance.  The tax information can only measure tax units.  They compared their results with the tax data and confirmed that they matched when measuring the categories that the IRS can measure.

My Conclusions:

• There is no "right" number.  The proper issue is what is the question that you are trying to answer.  The shifts in households, with grandparents and adult children moving back together with parents may be a compensation for economic hard times.  These numbers show that it works and has more than compensated for income loss.  Health insurance costs have gone up dramatically, as these numbers show.  Transfer payments and a progressive tax rate do appear to have a significant effect.
• The "middle class is vanishing" is at best misleading. 

Paper is at http://www.nber.org/papers/w17164

There is some more data on trends in household sizes, etc.  There is also a breakdown of quintiles.  For the all included houehold category, the bottom quintile saw 26.4% growth and the top quintile saw 52.6% growth.  The top 5% saw 63% growth.  There is no data for the top 1% because privacy related data blinding was applied by the census bureau, and only larger aggregates are reported.

So you can argue that all parts of the population saw significant improvement, or that the rich saw a larger improvement, or that the middle class is suffering.  The data shows that the progressive tax rate (EITC included) does have an effect, transfer payments and the social programs do make a difference, and that healthcare benefits do make a difference.

 

News has a problem with economic reporting

News, whether web or paper, needs a story.  It's extremely hard to transform very slow processes into an interesting story, and even harder to explain complex slow processes.  Unemployment is an example.

All of the economic reporting that I see on unemployment tries to bring some excitement to the story.  Big news.  Sudden change. Get people scared or excited.  They present as complicated a diagram as possible, or find some chart that looks really bad or suddenly good.  But there is rarely any effort to take apart the complex system and show the separate parts in a way that can be understood.

This diagram illustrates the problem with using a clearer presentation.

Click on graph for larger image.
It separates out one important component in employment from the others. This shows how many people are working, and in a way that lets you compare it with other recessions.

There's no exciting story here.  It shows that this is a really severe recession, much worse than anything since World War Two.  It also shows that all the fuss and excitement over this plan or that change has made no difference.  You don't see changes due to elections either.  The one and only government driven change is the employment bubble from the 2010 census.

You also see an interesting evolution in the nature of recessions.  The last three have been very smooth and without the sudden jumps of previous ones.  They are also lasting much longer.

There are probably some very important lessons to be learned from this that would help in making decisions.  But there is no story.  There is no cause for sudden joy or sorrow.  There is no reason for panic and fear.  It's a long slow process that needs understanding.

As a result, it does not make the general news.  They need big excitement like "Unemployment reaches X".  A difficult to explain slowly changing increase in the number of actual jobs is not "news".

Graph from Calculated Risk.

Modern Human, Neanderthal, and Denisovan cross-breeding

I went to talk by David Reich, Harvard Med School, last Thursday on genetic evidence measuring interbreeding among modern humans, Neanderthals, and Denisovans.  His talk also included significant background information.

First, he covered the techniques and difficulty of getting DNA samples from old bones.  Typically, the body has been completely contaminated with bacteria, fungi, plants, and insects.  Even after all their efforts at selecting protected interior portions of protected bones, less than 3% of the recovered DNA is mammalian.  This also explains the early emphasis on mitochondrial DNA.  There are thousands of mitochondria per copy of cell DNA.  This makes it much easier to get an acceptable sample of mitochondrial DNA.

Then he explained significant differences between Neanderthals and Denisovans.  For those who missed the recent news, the Denisovans are another form of human.  Remains were found in the Denisovan caves in southern Siberia in 2010.  Based on DNA clustering, the Denisovans, Neanderthals, and Modern Humans are each distinct. Within these three the various DNA samples cluster tightly and overlap in variations.  There is a substantial and statistically significant separation between the three. 

There are multiple samples of DNA for all these human variations.  These are sufficient to obtain reasonably complete genomes, despite the limited samples.

Neanderthal range is Europe and to an unknown extent eastward into Central and Southern Asia.  So far, the only Denisovan source is the caves in southern Siberia.

He then explained his terminology.  When comparing different animals, like man vs chimpanzees, they look at gene overlap.  When measuring interbreeding, they look at base-pair matching.  The basic measure is to find single base-pair changes in a gene, then determine how often that change is found in Modern vs Neandertal vs Denisovan.  This is used to derive percent sources.

Results:

• Africans have the largest genetic diversity of modern humans and no measurable contribution of DNA from Neanderthal or Denisovan.
• Non-Africans have about 2.5% Neanderthal DNA.  This level is relatively uniform across all non-Africans.
• There is a cluster of Denisovan contribution in Papua, New Guinea, Australia and neighboring islands.  This level is not uniform.  Outside this region there is no Denisovan contribution.  Within these clusters there is one group at about 2.5% Denisovan, plus two other groups with different substitutions but both at about 5% Denisovan.

Based on change rates for base-pair substitutions, these interbreedings took place about 50K ya for both Neanderthals and Denisovans.

 Speculations

The simplest explanation for the Neanderthal mix is the "out of Africa" theory, with the interbreeding taking place in the Levant.  There is paleontological evidence for both Neanderthal and Modern humans living in the same hills at the same time, about 35-50K ya.  This is consistent with migration from Africa.  The lack of any Neanderthal contribution to Africans makes other mixing unlikely.

More speculatively, it also supports the hypothesis that the southern route (arabia, india, southeast asia, to australia) was first for modern human expansion.  This would explain multiple interbreeding events with Denisovans that affect only southeast asia and australia.  There was a second wave of modern humans from china later.  This shows up in the genetics, and shows that this was a separate event from the earlier wave.

Unrelated Comment

There is increasing evidence that interbreeding events are the norm, not unusual.  Genetics show events where Europeans substantially contributed to India.  There is presently a major interbreeding event between Africans and Amerindians in south america.  This is a change from previous theories that interbreeding events were rare.

Audience  Questions:

What percent of interbreeding couples does this indicate?  At most 2%, probably less than 1%, of children would be from interbreeding.  The genetic match is close enough that this must have been a social effect, not a viability effect.  There should have been no biological problems with interbreeding.  If the percent was above 2% the overlap the genetic overlap would be higher.

What about the "hobbits" of Indonesia?  No usable DNA could be recovered.  Hot climates degrade DNA too fast.  There are no usable DNA samples from any prehistoric bones of humans in hot climates.

Internet trust lecture

Summary: A lecture by Miriam Meckel reminded me of the importance of reciprocity in healthcare relationships.

The lecture by Miriam Meckel presented results of a study on building trust on internet.  They picked ten realistic things that are part of establishing trust.  Then they examined surveys over 1 year for a variety of B2B and consumer organizations.  These were studied for primacy component analysis to see what drives trust the most.

Biggest factor is "reciprocity".  This is the agreement by both sides that their expected actions make sense and are appropriate for the nature of the relationship.  A very simple example is that customers expect to pay for goods delivered.  This reciprocity is a factor independent of contract or other terms.  It applies to later discoveries, undisclosed activity, etc. 

Reciprocity was 1/3 of the determination of trust.

Also noted was the penalty for violating reciprocity expectations.  A slimey crook who presents as a crook is not trusted to begin with.  A trusted relationship that then has a reciprocity failure is treated as a major betrayal.  The betrayer becomes much worse than an untrusted crook.  They are an enemy to society.

Three factors are responsible for the next 1/3 of establishing trust:

• Technical reliability.  This means all aspects of the relationship work smoothly and without problems.  It's much more than just web site stabiltiy.
• Customer control.  The more that the customer determines the relationship and activities, the greater the trust.
• 3rd party recommendations.

So, with four reasonably well defined areas you get 2/3 of the trust establishment, and reciprocity is the dominating factor. 

This has some relevance to healthcare and its security.  The trust relationship is important to most aspects of healthcare. 

One result is that the risk assessment priorities for security analysis need reconsideration.  It's true that inappropriate disclosure is a risk. I would consider that a technical reliability problem. But, reciprocity, patient control, and 3rd party recommendations are also assets to be protected. 

This also points to a flaw in an argument that I hear often regarding data losses.  The many disclosures due to stolen laptops are discounted because the data is rarely actually disclosed.  In practice the laptop is wiped, because the thief stole it for resale.  Wiped laptops are easier to sell.  That's an argument dealing with the 10% factor of technical reliability failure.  This argument ignores the reciprocity failure, and leaves the vendor open to enemy of society treatment.  That's a big loss of an important asset.

The solution to the reciprocity failure is some mix of

• have the customer accept that the loss is reasonable.  This is the rather unpopular "there is no privacy any more" argument.
• make sure the customer knows that you cannot be trusted with their data.  This downgrades you from enemy of society to merely not trustworthy.
• don't lose data on laptops

We need to add the assets of reciprocity, reliability, customer control, and 3rd party assessment to the risk analysis mix.  It's more than loss of data and data disclosure.

I've seen two other related problems in healthcare.

1.  "Consent"s, which are important but generally bungled.  Reciprocity does not mean that you told me something would happen.  Reciprocity means that when I later learn about it I agree that it was appropriate.  Consent is only part of reciprocity to the extent that it ensures that the customer understands the other side and knows who not to trust. 
2. The "patient control" implementations that I've seen have generally asked the patient to do the impossible.  The patient is expected to make an agreement while under extreme stress, inadequately informed, and with no time to get proper advice or more information.  Then the agreement is used to rationalize all kinds of reciprocity failures.  They would do better to deal with the reciprocity failures in most cases, and concentrate patient control on situations where the patient is not under stress, has adequate information, and the time to make an informed decision (including getting 3rd party advice).

Medicine and Aviation

     Summary: Both healthcare and aviation are system engineering problems.  Healthcare does have things to learn from aviation, some simple and some very hard.  One of the simple things is crew resource management techniques.  The extremely hard is transitioning from a vengeance based quality system to a modern quality system.

 There are three very different kinds of engineering problems:

• Complex part engineering problems, like mechanical design engineering:  In this kind of problem there are a small number of different interacting parts, and each part is extraordinarily complex.  A single camera housing part may reflect hundreds of man years of engineering, with stress analysis, temperature flow profiles, manufacturing plastic fluid flow simulations, etc.
• Huge number engineering problems, like financial software:  In this kind of problem there is an extraordinarly huge number of interacting parts, but each part is fairly simple.  Financial software and web commerce applications routinely deal with millions of transactions every hour.  The rules for any one individual transaction are fairly simple.
• System engineering problems, like aerospace and medicine:  In this kind of problem there are a large number of interacting parts and each part is complex.

It's unfortunate that many people see the recent successes solving huge number engineering problems and think that these huge number approaches will solve a system engineering problem. 

Aerospace engineering and aviation problems are 99% hidden from the traveling public.  Being a major traveler does not mean you understand aviation engineering.  I was on the system engineering teams for several satellites and radars.  This gives a different perspective.  There are things that medicine can learn from avaiation.

One simple but difficult step that will have very large benefit is introducing the medical equivalent of crew resource management.  There are some providers making progress here, but right now it's tiny little islands of effective management in a huge sea of mismanagement.

In the 1950's and 60's aviation was similar to medicine.  In aviation the pilot was god, just as in medicine the doctor is god.  Investigations of accidents showed that this was leading to major accidents and significant loss of life.  Pilots were ignoring important information and making mistakes of judgement.  There was serious human factors and psychological research into changing this.  Simulations and practices  from other industries were studied.  Airlines forced a change in pilot and crew behaviors through training, explanation, practice, and supervision. 

The first really big public success for crew resource management was the Sioux City DC-10 crash.  After a mechanical failure that should have been unsurvivable and caused 296 deaths, an amazing ad-hoc crew managed a crash landing with only 111 deaths.

I've once seen a doctor listen respectfully to the advice from a transport orderly, take the advice, change patient treatment, and thank the orderly.  Once, at one hospital.  Most of medicine is still struggling with maintaining civil relationships between nurses and doctors.  Only a few hospitals have reached the point where doctors will tolerate and participate on a working committee that is chaired by a grounds keeper.

Medicine and aviation are at a similar maturity level for equipment.  My experience with making products for the aviation industry prepared me for medical equipment.  The two are similar.  Medicine can learn from aviation in this area, but it is a learning between equals.  Both aviation and medicine have mature modern quality systems for their equipment.  Each can learn some things from the other.

The really hard problem is switching the industry from a vengeance based quality system to a modern quality system.  The vengeance based systems go back into pre-history.  They also work.  Rome grew to a major civilization that lasted for centuries with the simple vengeance quality system:

If the slave does low quality work, torture them.

It's sad that this works.

Modern quality systems date to Demming and Juran.  They are presently entering medical practice under names like the Toyota Production System.  The details evolve, but one principle is abandoning the vengeance based quality system.

One example of the difference between aviation and medicine is that there is an NTSB that examines all aviation accidents involving fatalities or major losses.  The accidents are impartially investigated, causes analyzed, and mitigations evaluated.  These reports provide input to other organizations.  These provided a sound basis for things like the investment in crew resource management.  There are islands of vengeance, but the sea is a modern quality system.

In contrast, public evaluation of medical errors is deeply feared and avoided because of fears of malpractice, retaliation, loss of status, and other harms.  This fear is well justified.  Any doctor who has been through a malpractice suit will agree that it is a form of torture.  Similarly, when the government decided that something needed to be done about "never" events, there was no investigation of causes or evaluation of mitigations.  There were threats by medicare and payment retaliation for failures.  A medicare audit is another form of torture.  There is a sea of vengeance based quality systems.  You only find occasional small islands of modern quality systems.

The change from vengeance based to modern quality system is needed before large improvements can be made in patient safety.  Other industries have made this transition.  It has happened in aviation, electronics, and manufacturing. 

Ownership and copyright

This weekend's work finds yet another oddball use for "own".  In the world of BPEL and BPMN the "owner" of a task is the person who is currently working on it.  Another way to spread confusion.

John asked about an "XML schema" for privacy.   There are two answers to this.

1. I think a consent structure similar to the Creative Commons copyright licenses is feasible.
2. A general XML encoding for privacy, consent, or copyright remains a matter for research and exploration.  The translation of law and decisions into a structured form has been a matter of legal research since the mid 1970's or earlier.  It's still research.  There is also ongoing research into forms of license and contract.  None of these are ready for routine use.

A bit of history

While I think that common consents are feasible, it will take a lot of time.  The history of work that led up to the the Creative Commons licenses shows how much time it can take:

• 1974 - The CONTN commission recommends that copyright law be extended to software.
• 1980 - Copyright is extended to include software
• 1988 - The Emacs General Public License is written.  This is the first effort to formalize a publicly usable license.  It is a reaction to the serious problems resulting from inadequacy of the Gosling license for early emacs work.
• 1991 - GPLv2 issued.  This became a major influence on copyright and public domain thinking.
• 2002 - The Creative Commons is established.  The needs for open culture, open publications, etc. are not met by the GPL and similar variants.
• 2009 - The Creative Commons licenses reach version 3.0 (the current version).

Creative Commons is now to the point where there are about a dozen standard copyright licenses.  For a very large number of people it meets their needs.  You answer a few questions and get a recommended license.  You also get an HTML code snippet that identifies the license, and provides links for further information, attribution, etc.

This has reached the same level of ease of use as the typical publisher's copyright assignment forms, with substantially better commonality.  Every publisher, etc. has their own standard assignment forms,  with their own terms, etc.  They all have lawyers who customize things to maximum advantage of the publisher.  Common Criteria took the perspective of the authors, and after a few years experience with actual author preferences, has a set of common licenses for the open culture authors.

A Creative Commons for Consents

There have been occasional discussions of whether privacy rules could be managed like copyright.  Zittrain has written on this, and there was a recent seminar on the topic at the Berkman Center.  These make clear that the larger issue of privacy remains extremely complex.  But in a much narrower domain like patient privacy consents, there is a better chance for success.

I can see a situation where a group defines a set of patient oriented consents.  These consents would differ from much of what I've seen in current work by being patient perspective rather than provider perspective.  Rules like the HIPAA rule are impenetrable to the typical patient.  They deal with the many issues that are visible to the provider, rather than the issues that are visible to the typical patient.

I expect that getting this right will probably take a decade, given that it took two decades for the copyright licenses to evolve a public oriented set.  We can learn much from that effort, but one of the things that you learn is that real experience was crucial to the evolution of these licenses.  Real experience takes time.

"Own" is the wrong verb

The verb "own" takes us down the wrong path in understanding the issues around medical data. This comment is triggered by Keith's post, but this misuse of "ownership"has a long and sad history in medical records and informatics.  It was less of a problem in the 19th century, when copiers and computers did not exist.  Then, ownership of the physical paper holding the record controlled the possible uses of the medical data.  But "own" was never really the right verb.

All of the semantics and law implied by the word "own" are derived from management of atoms.  You "own" things.  These things are made of atoms.  "Ownership" deals with breaking things, stealing things, selling things, lending things, etc.  All of these make sense for collections of atoms.  If you sell or lend something, the atoms are gone.  You no longer have the atoms.  None of these verbs make sense for medical information like patient records.

Copyright is much closer.  It uses the proper verbs.  Copyright deals primarily with two questions:

1. Who is allowed to make copies?  This may be a restricted right, as in allowing a magazine to print copies of an article in one and only one issue of that magazine.  Electronic copies, CDs, DVDs, audio, etc. are not allowed.  Or it may be an unrestricted right. There is a huge body of details, expectations, defaults, and considerations around making copies.
2. What uses are allowed for those copies? A host of potential use restrictions are normal in copyright.  A copyright license normally specified exactly what uses are allowed, and what uses are prohibited.

These are the issues that arise with patient records.  The entire issue of disclosure and disclosure consents is defining who can make copies and what uses can be made of those copies.  Privacy rules are all around allowed copying and use.

Copyright also deals with more minor concepts properly.  In medicine the actual creator of the record is a machine or doctor.  From the legal and social perspective the ownership world would assign ownership to the creator, subject to sales contracts, mechanic's liens, etc.  In the copyright world this is a "work for hire".  The terms found in "work for hire" accomodate all the uncertainties of the creative process in the arts and medicine.  The contract law around manufacturing of things make little sense in medicine.

Copyright uses the right verbs and structure but the body of rules and expectations is all wrong for medicine.  It distinguishes between owning a book and having the copyright to the contents of a book.  This is the same as the distinction between owning the paper medical records and having the rights to use and make further copies of those records.  It's normal for a doctor to own the paper records, have the rights to use for treatment, and have no right to disclose to the public or use for research.  This falls nicely onto the copyright treatment of owning a copy of a play, having the right to read it to your children, and having no right to put on a public performance or make copies for the actors.  Copyright lacks the proper words for the rights and uses in medicine.

We should be adding words to deal with the kinds of uses that make sense in medicine.  Instead of public performance, derivative work, plaigarism, and "fair use" we need kinds of medical use, purpose for disclosure, etc.  The "work for hire" concept is there, but all the details and contractual rules make sense for employees creating documents, photographers on assignment, and artists painting to a commission.  What are the right words for referrals, treatments, reviews, consultation, billing disputes, etc.?

So forget "own" and the world of atoms.  For medical records in the electronic age we want:

1. Rights to make copies, (who, what, where, why, when, ...)
2. Rights to use copies, (who, how, where, why, when, ...)

For those who want to dig deeper into the world of copyrights, I suggest the Groklaw references or the MIT Open Courseware short course.  Copyright practices are a large subject to cover, and you will find that all of the work and examples has been around the creative arts and more recently software.  The basic concepts and verbs are right for medicine, but the rest needs to be defined. 

 

Are money market funds evil?

One of the things that I enjoy about the LSE lectures is their ability to find extreme and provocative speakers who present sometimes very extreme ideas, to an audience that takes them apart with courtesy and precision.  It's nice to listen to real argument rather than shouting and volume.

Bhide surprised me by being a Harvard economist with some really interesting extreme views.  For instance, the argument that money market funds are a bad idea and should be prohibited.  The lecture was interesting, and it will get me to either borrow or by the book.  His Harvard Business Review precis indicates it will be well written.

Time to close some doors

In a recent standards tcon I was struck by the statement:

Don't close the door to innovative solutions.

I think it is long past time to start closing that door.  Standards are not research.  The successful standards are created when a group of people realize that:

• They agree that there is a common problem (aside from minor details)
• There are multiple well understood solutions to that problem
• There needs to be agreement on one common solution for progress to be made.

Standards should be created after the innovation has occurred and after there is experience with that innovation.  Experience shows that there is a long period of experimentation and experience gathering between the innovation and the successful standard.  If there is still substantial innovation needed, then standardization is premature.

Fortran

This was a highly successful standard.  There were several key dates:

1958 - The successful introduction of Fortran II into operational use.  It was one of multiple, incompatible but similar languages called Fortran.

1966 - The publication of the Fortran 66 standard, more commonly known as "Fortran IV".

That's eight years between the earliest operational use of the one of the versions and the finished standard.

Ethernet

Another successful standard.  It's key dates are:

1968 - Publication of the Alohanet paper, describing the technique and successful experiments.

1973 - Initial operational use of the major contributor to the eventual ethernet standard.

1982 - Completion of the 802.3 standard for 10 Mbit/sec ethernet.

In this case it was nine years from initial operational use, and fourteen years from the initial idea.

TCP/IP

Another successful standard.  It's key dates are:

1973 - Publication of the Catenet paper, describing the internetworking technique and early experiments.

1975 - Initial operational use of the earliest internetworking protocol.

1982 - Publication and transition to TCP/IP, as a finished standard.

This went faster.  It was only nine years from initial idea, and seven years from initial operational use to the point where a standard was ready.

All of these standards continued to evolve and grow.  But the initial innovation was many years before the standardization.  The interim was spent with research, experiments, evaluations, and improvements.  The standards efforts were debates between advocates with experience and experimental results to justify their claims.  The implications of compromises and decisions could be understood.

None of these standards was perfect or complete.  Fortran continued to evolve, Ethernet has grown into a variety of faster versions, and TCP/IP continues to change.  The innovations have been gradual improvements.