TECHNICAL FIELD

The present invention relates to the general technical field of the biological analyses of a patient, used in the medical sector.

Practice of medicine essentially consists of acquiring, interpreting, integrating and ultimately transmitting data in order to improve knowledge of the case to be treated. One can appreciate the importance of managing an electronic medical record that collects, throughout an individual's life, all his/her medical data and makes the latter accessible, at any time and in any place, to any designated and legitimate caregiver to enable him/her to take timely cognizance thereof, in order to assist him/her in his/her medical, diagnostic or therapeutic decisions. This is the whole point of a personal electronic medical record (PEMR), centred and focused on the individual.

The importance of biology in medicine is obvious.

The duty of such a record is to collect together, schematically and in various forms, three types of data: i) clinical, ii) biological and iii) imaging.

The clinical data, obtained from the practitioner's observations and thoughts, are essential but extremely subjective and operator-dependent.

Imaging data are perfectly objective and factual, but late in arrival, since they indicate a declared disease.

Biology therefore appears to be the essential component, since it is involved in making 70% of medical decisions*. * source: Marc Silverstein, Centers for Disease Control, Atlanta Ga., April 2003: “An approach to medical errors and patient safety in laboratory services” in “Henry's Clinical diagnosis and management by laboratory methods”22nd Edition Saunders, Elsevier.

Biology is the most frequently performed additional examination (more than 7 billion tests per year in the USA and more than 500 million in France), sometimes even outside any context of disease. One of every 6 medical consultations results in prescription of biological tests.

As the most readable medical data, biology is a powerful indicator of health since it summarises and adds up

i) the effects (achieved or being achieved) of our genetic predispositions,

ii) behavioural aspects specific to the individual, either protective or harmful, which are the consequences of a virtuous personal attitude of prevention or, conversely, of an addiction,

iii) the possible consequences of external influences (attacks suffered actively or passively, accidentally, due to the environment, etc.) and finally,

iv) the inevitable effects of advanced age.

Biology forms the cornerstone of the personal electronic medical record. It forms the latter's framework to which textual records and imaging files are attached in a continuous chronological fashion. This record is called “personal”, since it is simultaneously:

the focal point of the medical data,

the reference centre for the various healthcare professionals who are allowed to manage this unique patient record and

the sole property of the person in question;

In this respect, it is independent from or linked to the records of the healthcare professionals, depending on the individual's choice and in compliance with the currently applicable legal provisions.

Biology is essential for caring for people. It provides other indirect benefits however: it sheds light on new data arising from genome sequencing or from identification of tissue groups that announce predispositions to certain diseases but remain, more often than not, silent regarding the probability of fulfilling said predisposition (“Brave new genome» E. S. Lander, NEJM, 2015, 373, 5-8; “Public Health in the Precision-Medicine era” R. Bayer, S. Galea, NEJM, 2015, 373, 499-501).

Other examples include development of therapeutic strategies based on analysis of massive data, particularly within the context of individualised medicine, precision medicine or totally personalised medicine. At a time when personalised therapies lead to institution of anti-cancer treatments not based on tumour location but on subclassifications based solely on genetic mutations (regardless of the “nature” and location of the tumour), knowledge of the individual's longitudinal biological follow-up will make it its own witness for assessing therapeutic efficacy, without resorting to comparisons that have become impossible according to the conventional methods of clinical studies (“Let's not put all our eggs in one basket” D J Hunter, R B d'Agostino, NEJM, 2015, 373, 691-693; “Vermurafenib in multiple nonmelanoma cancers with BRAF V600mutation” D M Hyman and others, NEJM, 2015, 373, 726-736),

However, massive data are difficult to acquire in medicine for a series of reasons among which the technical and ethical dimensions occupy a forefront position. Biology is the most abundant source, with legitimate respect for and protection of anonymity.

The medical significance of a biological test should be properly understood.

A test provides an initial answer depending on whether or not the measured value falls within the normal range, the latter being synonymous with a pathological situation, either by excess or by default. This raises the fundamental question of determining this so-called normal range.

Currently, in theory, the reference values for medical biology tests are determined on the basis of examinations performed in healthy subjects, considered collectively or distributed by age groups and according to other criteria: gender, race and living conditions, etc.

They are defined by the EPTRV (Expert Panel on Theory of Reference Values) and by the IFCC (International Federation of Clinical Chemistry and Laboratory Medicine) which have proposed a reference interval for each test, bounded by two limits, upper and lower, or by only one (value less than . . . or greater than . . . ), covering 95% of the population studied and considered healthy.

For each test, determination of its reference interval is a task that must meet international recommendations with regard to the following criteria:

determination of all the pre-analytical and physiological factors liable to interfere with the result,

establishment of the metrological characteristics of the measurement technique to be analysed,

collection and analysis of the specimens collected in this panel,

consequent determination of the reference interval, i.e. the area of normality included between the upper and lower bounds.

The question must therefore be asked as to whether there is a degree of normality for the measured values.

At a further stage, the question may even arise of assessing the meaning and relevance of variations within the range of normality. Is it possible to be more or less normal than normal? In other words, should a variation within this range of normality—which remains however within it—be taken into account or not? Should it be attributed any significance or not? It is not wrongful to question the relevance of variations within this buffer zone. This approach therefore makes it possible to go one step further in the analysis of evolution of biological tests by eliminating all references other than “normality” and “abnormality”. In practical terms, what is not normal is abnormal and what is not abnormal is normal.

On the other hand, regarding the latter eventuality (abnormality), it is wise to take account of any relative variation from one examination to another in order to assess deterioration (increase in deviation) or improvement (reduction in deviation) of the situation. Various intuitive representations are possible aimed at simplicity and speed of reading. They form the basis for the present patent.

Account must be taken of the fact that different laboratories provide biological analysis results for the same individual.

Indeed, the same individual may frequently undergo examinations in several separate laboratories, independent from one another, outside his/her local laboratory, in so-called “community-based” outpatient medicine.

Thus, these biological data collected in private practice will be joined by data collected in the event that the patient's condition requires a stay in a hospital, which will call on its own laboratory.

This is also the case if a new physician, consulted by this patient in private practice, works with a different laboratory from that which the patient usually attends.

This is finally the case, throughout life, owing to a change of residence, during systematic health assessments (on joining an occupational, civil or military structure . . . ) or when an examination of this kind proves necessary, owing to a health problem when the individual is temporarily away from his/her usual place of residence.

Furthermore, it is a fact that advancing age leads to more frequent recourse to a greater number of practitioners, particularly on appearance of the signs of the aging process and/or with the onset of a chronic disorder. This situation often initiates recourse to one or several new privately practising specialists and subsequently a stay or stays in a hospital ward.

Finally, the individual may undergo examinations in other laboratories outside any context of disease at the time of marriage, recruitment, application for a loan, or a permit to practise a critical activity . . . , etc.

This is without counting all the “forgotten” or “unrecognised” examinations that have marked our existence since our birth. Examples particularly include contemporary biological tests to determine one's own blood group or systematic screening for signs of kidney weakness before any vaccination.

The question also arises of the consequences of making results available that originate from different laboratories.

As the price to pay for the ubiquity of biological tests performed in large numbers and by many different teams, the technical difficulties faced in consolidating the results are considerable and lie in benchmarks, practices and habits that are difficult to isolate.

The reference values and consequently the normal ranges can thus vary from one laboratory to another and within the same laboratory, when a change of equipment, consumable (reagent) or analysis method is adopted. Quite apart from these changes, regularly recalibrating the various tests—and thus possibly updating their values—is integral to good clinical biology practice and is part of accreditation procedures. The terms “normal range” or “range of normality”, which have exactly the same meaning, will be used hereinafter.

Currently, in practice, one can measure the disparities encountered that appear already by consulting the results presented examination by examination, for the same patient, by two different laboratories. This operation will be performed by successively consulting the results provided on documents that are different and separate from each other.

This is because currently, the presenters of biological results available on the market in the form of spreadsheets allowing comparisons over time for one or more given tests are limited to displaying results from a single laboratory in the form of a comparative table, such as a spreadsheet, or in graphical form. As a general rule, this is the laboratory of the hospital in which the patient is hospitalised. In private practice, it is laboratory with whom the prescribing physician usually works, or the one chosen by the patient him/herself. In all cases, the data transmitted by the laboratory, whatever the latter is, are limited to its own production.

Whatever the case may be, this precludes an overall view allowing immediate comparative analysis on the same medium of the results of any individual who has had recourse to several laboratories (2 to N) and thus does not meet the needs of the various doctors and caregivers in charge of the patient.

PRIOR ART

For example, integration of all a patient's biological data is known through document U.S. Pat. No. 8,688,476. The aim of this invention is to ensure interoperability of biological data that are similar (a same test), but disparate (different in the way they are produced or expressed) and allow their presentation in a form structured over time in order to perform comparisons over time.

This representation allows any practitioner authorised by the patient to carry out exhaustive consultation of his/her collected biology results. This means that s/he will be able for the first time to read directly the results that his/her colleagues have prescribed to their common patient.

Strictly speaking, the curve that results from the combination of points corresponding to each measured value is artificial in that it combines heterogeneous elements that do not fit into the same ranges of normality.

Relevant reading of the represented data therefore implies an intellectual construction which consists in associating chronological reading of the successive points joined by a continuous line (despite their heterogeneity) with simultaneous reading of the vertical graphic element, located in the background for each measured value, indicating the interval within which this value is considered normal.

Such integration, for the same biological test, has a number of limitations however that can be summarised as follows:ethically, it can be criticised for plotting an “artificial” curve, since it does not link homogeneous points, which are at first sight comparable,intellectually, consultation of these data requires a form of gymnastics involving making a longitudinal transversal reading of this “value curve” at the same time as one needs to identify, point by point, the values which leave their own range of normality,the quantitative variations from one examination to another are difficult to assess, since two consecutive values do not have the same range of normality,several given examinations cannot be compared when they are not expressed in the same logic of units, either molar or weight or other,if this chronological representation of values is still possible, it is difficult to extrapolate a useful trend curve to assess the progression, stabilisation or regression of a chronic condition or acute episode.

This results in constraints related to the difficult reading of measured values, which lose much of their meaning due the fact that they are to be related, for each of the values, to different normal ranges.

DISCLOSURE OF THE INVENTION

The object of the present invention therefore aims to overcome the drawbacks of the prior art by proposing a new method of processing data obtained from biological analyses in order to facilitate utilisation of said data.

Another object of the present invention aims to offer healthcare personnel, by means of a new presentation of the results of biological analyses, another means of ensuring biological monitoring of the patient through consistent follow-up over time.

The objects assigned to the invention are achieved by means of a method for processing initial measured values A(n) corresponding to results of biological analyses of the same patient from one or more laboratories, said analyses each relating to the same biological parameter, the method consisting in:a) using for each initial value A(n), measured and expressed by a given laboratory according to a unit of measurement proposed by said laboratory and a normal range (BI(n), BS(n)), provided by the same laboratory, materialised by a range delimited between a lower bound BI(n) and an upper bound BS(n), or by a range lower than the upper bound BS(n) only, or by a range higher than the lower bound BI(n) only,b) recording data, encrypted or not, sent by a messaging system, advantageously secure, for their consolidation on a server, advantageously secure, according to a format determined based on heterogeneous presentations specific to each of these laboratories, wherein this format comprises, in addition to the measured value A(n), metadata attached to it indicating the corresponding normal range and, opportunely, a certain number of data relating in particular to the corresponding laboratory, specifically to the conditions for carrying out the examination and/or analysis and indications on the identity of the patient and/or that of the prescriber,
characterised in that it consists in:c) using a mathematical model, with or without the aid of computer processing, to transform each of the initial values A(n) obtained from each laboratory into a normalised value Anorm(n), wherein a single laboratory is capable of providing several initial values A(n) over time for the same patient,d) associating with all these normalised values Anorm(n), the homogenised common normal range (BInorm, BSnorm) determined and retained for this homogenisation process, wherein said range is defined by a common simple homogenised lower bound BInorm or by a common simple homogenised upper bound BSnorm or, more frequently, by a common homogenised upper bound BSnorm associated with a common homogenised lower bound, BInorm, with the knowledge that the normal ranges (BI(n),BS(n)) provided by the same laboratory may vary over time,e) subsequently proposing, by means of any method of chronological or synthetic graphic representation appropriate to the use and/or to the user and/or to the data medium, a display comprising the successive different normalised values Anorm(n) for the given biological parameter in combination with at least one element of data, visible, permanent or ephemeral, but always accessible, relating to the common homogenised normalised normal range (BInorm,BSnorm), determined and adopted for the homogenisation process, which forms the basis of the invention.

According to an embodiment in accordance with the invention, the method consists in using under a) for each value A(n), measured by the laboratory having performed the biological analysis, the normal range (BI(n),BS(n)) provided by this same laboratory.

According to an embodiment in accordance with the invention, the method consists in reading and recording for each value A(n), the corresponding normal range (BI(n),BS(n)) provided by the laboratory having performed the analysis and recording additional elements of data specific to the laboratory having performed the analysis, which relate in particular to the method of analysis used by this laboratory, the equipment employed and the reagents associated with the latter. Determination of this normal range, related to each biological test analysed, under defined conditions, is generally based on a sample representative of a population considered normal, according to a methodology specific to the laboratory or complying with a protocol defined by an accreditation body.

According to an embodiment consistent with the invention, the method consists in defining the “normalised” normal range for the initial value A(n), by choice or calculation, represented either by a common normalised lower bound of normality BInorm, or by a common normalised upper bound of normality BSnorm, or more commonly, by combination of a common normalised lower bound of normality BInorm and a common normalised upper bound of normality BSnorm.

According to an embodiment consistent with the invention, the transformation under (c) consists in calculating or determining, by means of a calculation based on linear correlations, the normalised value Anorm(n) for each corresponding initial value A(n).

According to an embodiment consistent with the invention, the method consists in, as part of determining the normalised values Anorm(n), calculating a linear correlation coefficient Cbs(n) based on the upper bound of normality BS(n) and the initial value A(n) and the difference between the upper bound BS(n) and the lower bound BI(n), with the knowledge that it is also possible to calculate the linear correlation coefficient Cbi(n) based on the lower bound of normality BI(n) and on the initial value A(n) and the difference BS(n)−BI(n) and that either coefficient Cbs(n) or Cbi(n) may be used interchangeably for the subsequent operations.

According to an embodiment consistent with the invention, the method consists in performing calculation of the linear correlation coefficient for each initial value A(n) using the following equation:
Cbs(n)=(A(n)−BS(n))/(BS(n)−BI(n))
or
Cbi(n)=(A(n)−BI(n))/(BS(n)−BI(n))
where BI(n) and BS(n) are respectively the lower and upper bounds of normality of the normal range in relation to the initial value A(n).

According to an embodiment consistent with the invention, the method consists in calculating the normalised values Anorm(n) using the following equation:
Anorm(n)=BSnorm+Cbs(n)×(BSnorm−BInorm)
or
Anorm(n)=BInorm+Cbi(n)×(BSnorm−BInorm)
where BInorm and/or BSnorm are respectively the common lower and/or upper bounds of normality of the common normalised normal range.

According to another embodiment, the method according to the invention consists in calculating, when the bound of normality BS(n) is infinite, a linear correlation coefficient Cb0(n) based on the lower bound of normality BI(n) and the initial value A(n) and on the difference between the lower bound BI(n) and the origin 0, by calculating the linear correlation coefficient for each initial value A(n), using the equation:
Cb0(n)=(A(n)−0)/(BI(n)−0)
where BI(n) is the lower bound of normality in relation to the initial value A(n), with BS(n) being an infinite value.

The method according to the invention subsequently consists in calculating the normalised values Anorm(n) using the following equation:
Anorm(n)=BInorm+(Cb0(n)×(BInorm−0))
where BInorm is the common normalised lower bound of normality of the common normalised normal range.

According to an embodiment consistent with the invention, the method consists in determining the common normalised upper bound of normality BSnorm and/or the common normalised lower bound of normality BInorm by arbitrary selection of their values. Preferentially and non-restrictively, the bounds BSnorm and BInorm are for example set respectively at 75 and 25 on a scale of between 0 and 100.

According to an embodiment consistent with the invention, the method consists in selecting from among the upper bounds of normality BS(n), the one having the highest value among all the BS(n) communicated by the different laboratories for the same measured biological test and/or from among the lower bounds of normality BI(n), the one having the lowest value among all the BI(n) in order to determine respectively the normalised upper bound of normality BSnorm and the normalised lower bound of normality BInorm.

According to an embodiment consistent with the invention, the method consists in determining the common normalised normal range (BInorm,BSnorm) by calculating the arithmetic mean over the values of all the normal ranges (BI(n),BS(n)) communicated by the different laboratories for the same measured biological test, without taking into account the respective positioning of the different BI(n) and BS(n) but only the intervals between the latter.

According to an embodiment consistent with the invention, the method consists in determining the common normalised upper bound of normality BSnorm by calculating the arithmetic mean of all the values of the upper bounds of normality BS(n) followed by that of the common normalised lower bound of normality BInorm by taking as the basis all the values of the lower bounds of normality BI(n), wherein the operation may also be performed by taking as the basis a limited number of these selectively chosen bounds BI(n) and/or BS(n).

According to an embodiment consistent with the invention, the method consists in, when at least one value A(n) is associated with a normal range having only a lower bound of normality BI(n), BS(n) being an infinite value, e1) on the one hand, when the value of A(n) is less than or equal to the value of BI(n), calculating a linear correlation coefficient Cb0(n) based on the lower bound of normality BI(n) and the initial value A(n) and on the difference between the lower bound of normality BI(n) and the origin 0, by calculating the linear correlation coefficient for each initial value A(n), using the equation:
Cb0(n)=(A(n)−0)/(BI(n)−0)

and calculating the normalised values Anorm(n), using the following equation:
Anorm(n)=BInorm+(Cb0(n)×(BInorm−0))
e2) and on the other hand, when the value A(n) is greater than or equal to the value of BI(n), calculating the normalised value Anorm(n) using a mathematical equation verifying the condition that if the value of A(n) is between BI(n) and infinity, then Anorm(n) will be less than or equal to the value adopted for the normalised upper bound of normality BSnorm, thereby associating all the values Anorm(n) with a common normalised normal range (BInorm, BSnorm).

According to an embodiment consistent with the invention, the normalised value Anorm(n) is calculated using the following equation:
Anorm(n)=BSnorm−((BSnorm−BInorm)2/(A(n)+BSnorm−BInorm−BI(n)))

The common normalised bounds of normality, lower BInorm and upper BSnorm, are by way of example determined arbitrarily.

According to another embodiment, the method of the invention consists in, when at least one value A(n) is greater than or equal to its associated bound of normality BS(n):calculating the linear correlation coefficient for each initial value A(n), using the following equation:
Cbs(n)=(A(n)−BS(n))/(BS(n)−BI(n))
or
Cbi(n)=(A(n)−BI(n))/(BS(n)−BI(n))and subsequently calculating for said value A(n) greater than or equal to its associated bound of normality BS(n), the normalised value Anorm(n), using the following equation:
Anorm(n)=BSnorm+Ln(Cbs(n)×(BSnorm−BInorm))
or
Anorm(n)=BInorm+Ln(Cbi(n)×(BSnorm−BInorm))
where BInorm and/or BSnorm are respectively the common lower and/or upper bounds of normality of the common normalised normal range.

According to another embodiment, the method of the invention consists in, when at least one value A(n) is less than or equal to its associated bound of normality BI(n):calculating the linear correlation coefficient for each initial value A(n), using the following equation:
Cbs(n)=(A(n)−BS(n))/(BS(n)−BI(n))
or
Cbi(n)=(A(n)−BI(n))/(BS(n)−BI(n))and subsequently calculating for said value A(n) less than or equal to its associated bound of normality BI(n), the normalised value Anorm(n), using the following equation:
Anorm(n)=BSnorm−Ln(−Cbs(n)×(BSnorm−BInorm))
or
Anorm(n)=BInorm−Ln(−Cbi(n)×(BSnorm−BInorm))
where BInorm and/or BSnorm are respectively the common lower and/or upper bounds of normality of the common normalised normal range.

The “Ln” function shown above corresponds to the Napierian logarithm function. This function can be replaced, without going beyond the ambit of the present invention, by the “Log” function corresponding to the decimal logarithm function. The above equations are therefore not repeated for this purpose.

Furthermore, in addition to the two preceding paragraphs, the method according to the invention consists in, when a value A(n) is on the one hand less than or equal to its associated bound of normality BS(n) and on the other hand greater than or equal to its associated bound of normality BI(n):calculating the linear correlation coefficient for each initial value A(n), using the following equation:
Cbs(n)=(A(n)−BS(n))/(BS(n)−BI(n))
or
Cbi(n)=(A(n)−BI(n))/(BS(n)−BI(n))and subsequently calculating for said value A(n) less than or equal to its associated bound of normality BS(n) and greater than or equal to its associated bound of normality BI(n), the normalised value Anorm(n), using the following equation:
Anorm(n)=BSnorm+Cbs(n)×(BSnorm−BInorm)
or
Anorm(n)=BInorm+Cbi(n)×(BSnorm−BInorm)
where BInorm and/or BSnorm are respectively the common lower and/or upper bounds of normality of the common normalised normal range.

According to an embodiment consistent with the invention, the method allows use of all the raw initial values A(n), without prior processing or transformation and moreover independently of the fact that these values A(n) are each defined by a finite range of normality (between BS(n) and BI(n)) or simply in relation to a single lower bound BI(n) or upper bound BS(n), without any detriment to the homogeneity of the values Anorm(n) thus obtained.

According to an embodiment consistent with the invention, the method allows use of all the raw initial values A(n), without prior processing or transformation and moreover independently of the unit of measurement in which each of the initial values A(n) is expressed, which has the effect, for example non-restrictively, of rigorously amalgamating results expressed in a volume logic (molar) or weight logic (grams and multiples or sub-multiples).

According to an embodiment consistent with the invention, the method consists in displaying a graph on which the corresponding dates and results of the analyses (n) for the same test and the same patient are plotted along a horizontal axis, in chronological order, and on the other hand, along a vertical axis, for all these analyses, the single common normalised normality range (BInorm, Bsnorm) associated with normalised values Anorm(n), the positions of the different neighbouring normalised values Anorm(n) being connected by line segments ranging from one normalised value Anorm(n) to the next Anorm (n+1), while the single common normalised normality range (BInorm, Bsnorm) is represented in the form of a vertical rectangle or any other line or geometrical figure or materialisation representative of this “normalised” range of normality.

According to an embodiment consistent with the invention, the method consists in defining on the location area of each normalised value Anorm(n), an active area, which when pointed to or selected by means of a mouse or any other instrumental or digital action, makes it possible to display ephemerally a window containing at least some of the data forming the element of metadata corresponding to the measured value in question.

According to an embodiment consistent with the invention, the method consists in ensuring or reinforcing safeguarding of the data by using any intrinsic data encryption process or, extrinsically, during transfer or storage of data on location and during the processing phases and operations under the terms of the invention or during the subsequent stages of making available raw or processed data concerning the patient in question in order to maintain and ensure the integrity, confidentiality and security of such personal health data according to the best means available according to the state of the art.

According to an embodiment consistent with the invention, the method consists in importing additional medical data from a patient's personal electronic medical record (PEMR), such as start/end of medical treatment, health problem, stay in regions with a health risk or others, in order to complete the graphic representation at the request of health personnel, by combining—insofar as necessary—different types of medical data from either clinical diagnostic or imaging registers or therapeutic registers, in particular medicinal product registers, in order to create tables of “preferences” corresponding to the uses and needs of caregivers for optimal patient management.

According to an embodiment consistent with the invention, the method allows, for a given biological test or for a selected set of tests and/or for concatenation with data other than biological data as described in claim20, consolidation of corresponding comparable data between a number of patients in order to create a silo of massive data previously rendered completely and definitively anonymous without possible backtracking to individual identities, for the purpose of scientific, industrial or public health studies independently of the initial technical degree of heterogeneity of the data thus taken into account.

According to one embodiment, the method of the invention consists in importing additional medical data from a patient's personal electronic medical record (PEMR), such as start/end of medical treatment, health problem, stay in regions with a health risk or others, in order to complete the graphic representation at the request of health personnel. These associations, the list of which is non-exhaustive, are intended to facilitate monitoring and management of a treatment, by adapting doses for example, or to generate alerts or reminders.

The method according to the invention therefore offers the remarkable advantage of employing a relevant graphic construction allowing immediate comparability of the measured values for which their associated range of normality may be different.

Several major advantages resulting from the method according to the invention should be mentioned, namely:it becomes easy to compare a given examination or examinations carried out in different laboratories,it becomes easy for each practitioner caring for the patient to consult all the biological data of his/her patient, including those requested by his/her colleagues, whether known or unknown,graphic representation of the values within identical ranges of normality allows easy and immediate observation of a highly significant evolutionary curve in order to assess the stabilisation, progression or regression of a chronic disorder or an acute episode,this representation makes it possible to monitor the onset and appearance of a disorder by observing the evolution of specific markers performed in several laboratories,there is no need to multiply the examination codes according to the LOINC® or other classification (SNOMED, X12 etc.,); the semantic reconciliation process can be performed based on different computer codes, at a later date on a dedicated server,the measured value rates can be expressed on a single graph, even if they are expressed based on analytical samples with different volumes,It even becomes possible to precisely and accurately compare test results, the values of which are expressed in different units, for example, in moles or grams,homogenisation of all these values allows their relevant integration in a massive data thesaurus (Big Data), by eliminating the heterogeneity of the data acquired in totally independent series, within the framework of public health data,other advantages will become apparent, particularly for selection and management of the participants in clinical trials, completely transforming the logic of the inclusion or withdrawal processes,hence, reading an individual's longitudinal biological record, a thesaurus of all the examinations carried out during his/her life, will give full meaning to the theoretical predispositions displayed by genomic data or by tissue groups.

