阅读说明：本技术 实验室仪器的选择和配置 (Laboratory instrument selection and configuration ) 是由 苏赫拉布·法拉马齐奥哈尼 埃里克·瓦尔莱 马丁·比埃 皮尔·埃尔·加扎利·塔勒比 于 2018-12-17 设计创作，主要内容包括：实验室仪器的选择和试剂至实验室仪器的分配可以通过使用相对于各种类型的约束使成本函数最小化的计算机程序来优化。这些约束可以包括针对测试、实验室仪器操作能力、实验室仪器试剂能力的需求等。在一些实施方式中,该优化可以将试剂分配与机器选择相结合以帮助选择合适的实验室仪器。(The selection of laboratory instruments and the distribution of reagents to laboratory instruments may be optimized by using a computer program that minimizes a cost function with respect to various types of constraints. These constraints may include requirements for testing, laboratory instrument operational capabilities, laboratory instrument reagent capabilities, and the like. In some embodiments, the optimization can combine reagent dispensing with machine selection to help select the appropriate laboratory instrument.)

1. A method implemented on an apparatus comprising at least a processor, the method comprising:

a) defining a formal representation of requirements for a test to be performed in a laboratory; and

b) based on the formal representation including a predefined set of parameters, recommendations are provided for one or more laboratory instruments for performing the test and allocation of reagents among the one or more laboratory instruments to optimize costs associated with performing the test.

2. The method of claim 1, wherein the recommendation comprises a recommended reagent allocation among the one or more laboratory instruments.

3. The method of any one of claims 1 or 2, wherein the recommendation comprises a recommendation to purchase one or more laboratory instruments not already present in the laboratory.

4. The method of any of claims 1 to 3, wherein the recommendation comprises a recommendation to use at least one laboratory instrument already present in the laboratory.

5. The method of any of claims 1-4, wherein the one or more laboratory instruments comprise at least one laboratory instrument capable of performing tests in a plurality of disciplines.

6. The method of claim 5, wherein the plurality of disciplines comprises at least two disciplines selected from the group consisting of:

a) immunology;

b) chemical treatment;

c) hematology; and

d) the department of coagulation.

7. The method of any of claims 1-6, wherein the recommendation is based on minimizing a cost function representing one or more costs for performing one or more pre-analysis operations.

8. The method of claim 7, wherein the one or more pre-analysis operations comprise sorting and centrifugation.

9. A computer program product comprising a non-transitory computer readable medium storing instructions operable to configure a computer to perform the method of any of claims 1-8.

10. A machine comprising a computer configured with a set of instructions operable, when executed, to perform a method according to any one of claims 1 to 8.

Technical Field

The techniques disclosed herein may be applicable to the selection and/or configuration of various types of laboratory instruments.

Background

In general, a laboratory, such as one that may perform tests on various types of substances, may require both laboratory instruments that perform the tests and reagents that may be used to perform the tests. Various ways of balancing competing considerations related to laboratory instrument selection and reagent dispensing have been attempted. However, it is believed that no method of selecting laboratory instruments and deploying reagents or other consumables thereto as described herein has been previously used in the art.

Disclosure of Invention

The disclosed technology may be used to implement various methods, systems, machines, and computer program products. For example, in some aspects, based on the present disclosure, one of ordinary skill in the art can implement a method comprising: defining a formal representation of requirements for a test to be performed in a laboratory; and providing recommendations for one or more laboratory instruments to be used in performing the test and reagent allocations among the one or more laboratory instruments based on the formal representation including the predefined set of parameters to optimize costs associated with performing the test. In some aspects, a computer program product comprising instructions operable to configure a computer to perform such a method and a machine comprising a computer configured with instructions operable, when executed, to cause a computer to perform such a method may also be implemented.

Additional information on how the disclosed techniques can potentially be implemented is set forth herein, and based on the materials set forth in this document, variations in samples will be immediately apparent to one of ordinary skill in the art and can be practiced without undue experimentation. Accordingly, the exemplary methods and machines described in this summary should be understood as being illustrative only and should not be taken as limiting the scope of protection afforded by this document or any related document.

Drawings

FIG. 1 is a block diagram of an environment in which aspects of the technology described herein may be deployed in some implementations.

Fig. 2 illustrates a method that may be used in some embodiments to determine how an analyzer instrument in a laboratory may be stocked with a reagent.

Detailed Description

Turning now to the drawings, FIG. 1 is a block diagram of an environment in which aspects of the technology described herein may be deployed in some embodiments, in which a user computer 101 (e.g., in a laboratory) may connect to a server 102 over a network 103. the server 102 may provide an interface to the user computer in response to a request from the user computer 101. for example, the server 102 may be a web server connected to the user computer 101 over the Internet, the server 102 being configured to send, in response to a GET command, an HTM L, JavaScript, and/or other code to be interpreted by a browser on the user computer 101. in such implementations, code to be sent to the user computer 101 may be stored on the server 102 or on one or more databases 104, or may be dynamically generated by the server 102 in response to a request from the user computer 101. once code is received at the user computer 101, the user may use the interface provided by the computer 101 to view information provided by the server 102, and/or participate in further implementations, the information may be provided back to the server 102 in processing of the user computer 101, e.g., by providing instructions to the server 102 (e.g., providing the results to the server 102) in execution of one or to the server 101.

Similarly, it is possible that rather than using the architecture shown in FIG. 1 with remote servers, in some implementations, functionality such as that described with respect to the architecture of FIG. 1 may be provided locally on the user computer 101 itself.

Turning now to fig. 2, this figure illustrates a method that may be used in some embodiments to determine how an analyzer instrument in a laboratory may be stocked with a reagent. As shown in fig. 2, in some embodiments, such methods may include the step of defining 201 a reagent that may be dispensed between laboratory instruments. This may include, for example, specifying attributes of the reagent, such as the size of the container into which the reagent enters (e.g., small, medium, large bottles), the number of locations that various containers may occupy in the analyzer instrument, the number of tests that may be performed using a particular type of laboratory instrument with a reagent in a container of a particular size, and the cost of various reagent containers. Similarly, a method such as that shown in fig. 2 may also include the step of defining 202 a laboratory instrument to which a reagent may be dispensed. This may include, for example, specifying which laboratory instruments are to be configured (e.g., which analyzers are present in the laboratory that are to be assigned to reagents), and various attributes of those laboratory instruments, such as what tests they may potentially perform, the cost for calibrating them for a particular test, and their ability to hold reagents. The method of fig. 2 also includes defining 203 a demand profile for the laboratory, which may include defining the demand for various types of tests at the laboratory as equal to an average number of each of those types of tests that the laboratory performs (or is expected to perform) in a given day (or other time period between reagent refills).

In the process of fig. 2, a formal representation of requirements, laboratory instruments and reagent information will be created 204 with the requirements, laboratory instruments and reagents 201, 202, 203 defined. This may be done, for example, by taking the information obtained during the aforementioned defining steps 201, 202, 203 and using it as a parameter of a set of equations that has been developed to enable modeling of laboratory activities. For example, in some embodiments, equations 1-6 (below) may be used to create 204 a formal representation, and parameters for those equations may be specified during the reagent, laboratory instrument, and demand profile defining steps 201, 202, 203.

Continuing with the discussion of FIG. 2, in the process described in this figure, once formal representations of reagents, laboratory instruments, and requirements are created 204 for a laboratory, these representations can be used with a linear solver (e.g., a Cplex solver from IBM) to minimize functions representing laboratory costs for various configurations. For example, in some embodiments, constraints such as set forth above in equations 1-6 may be combined with a cost function such as shown in equation 7 to create a set of equations that may generate 205 an optimized distribution of reagents between laboratory instruments when the cost function is minimized.

Further, as shown in FIG. 2, in some embodiments, after the optimal allocation has been generated 205, the steps of defining 203 the demand profile, creating 204 the formal representation, and generating 205 the optimal allocation may be repeated. This may be useful, for example, where a laboratory organizes its work to perform certain types of tests on certain days of the week, which may lead to better results if different demand profiles are created for different days of the week.

Finally, after the appropriate dispense has been generated 205, reagents may be dispensed 206 to the instrument according to the generated dispense. For example, if the solver identifies a solution to x that will minimize the cost function of equation 7_hsjOf (a) a set of values (each of which would represent the number of reagent bottles of size s that should be allocated to analyser j for test h), then for a period of time (e.g. one month) for which the demand profile for deriving the set of values is deemed valid, an analyser instrument in the laboratory may be stocked with x generated by the generation of_hsjThe set of values provides the number and size of reagent bottles. Then, in the event that the demand profile is no longer valid (e.g., at the end of the month), a new profile may be defined 203, a new allocation may be generated 205, and the process may be repeated.

It should be understood that while the above discussion provides a set of equations that some embodiments may use to determine an optimal distribution of reagents to a laboratory instrument, the use of the above equations is not mandatory, and other embodiments may use other equations. For example, in some embodiments, a set of equations, such as equations 1-7 above, may be decomposed into separate sets of equations for each of the disciplines of the tests performed by the laboratory, and those separate sets of equations may be optimized separately to find an assignment for each of the instruments in the laboratory. For purposes of illustration, consider table 1 below, which presents a set of equations that may be used to determine an optimal allocation for a particular discipline.

TABLE 1

In the equation set of Table 1, X and H^dLaboratory instruments and tests specific to the particular discipline under consideration will be indicated, respectively.

In some embodiments, other variations are also possible. For example, some embodiments may apply techniques similar to those described above for determining optimal dispensing of reagents to the problem of determining optimal selection of instruments for a laboratory. This may be done, for example, where a new laboratory is being established and a consultant or sales representative is making recommendations for equipment that the laboratory may want to purchase. In this type of case, equations such as equations 8-14 below may be used to represent the constraints to be considered in the instrument selection problem, while equation 15 may represent a cost function that will be minimized to determine the best choice of instrument.

Some embodiments may also combine optimized instrument selection with optimized reagent dispensing. Some embodiments may include a step of pre-calculating the number of each type of instrument to be included in the set of potential analyzers to ensure that enough potential instruments of each type are included and also to provide a margin that may help reduce the risk that the optimization problem will become incalculable. In embodiments where this type of pre-calculation occurs, equations 16-19 below may be used to define the number of potential instruments of each type.

Defining a set of potential instruments (M) using equations, such as equations 16-19, and using u_jIn an embodiment where the decision variable is 1 if instrument j is selected from set M and 0 otherwise, a set of equations such as shown in table 2 (below) can be used to define the constraints that the laboratory must satisfy and the cost function that is minimized by appropriate selection of instruments and dispensing of reagents.

TABLE 2

In the set of equations of Table 2, if instrument j is selected, u_jIs 1, otherwise u_j0, HM if test h is potentially done by analyzer j_hjIs 1, otherwise HM_hjIs 0, L S_jRepresenting the life of machine j, ND represents the number of lab working days per year (e.g., ND 365), the remaining parameters having the same meaning as set forth in equations 3-7 and 12-14 and in the context of table 1.

Of course, it should be understood that additional variations for organizing equations for optimizing instrument selection and/or configuration are possible in some embodiments. For example, in some embodiments, the problem of selecting a non-analytical instrument (e.g., centrifuge) can be isolated and solved by minimizing equation 20 below, constrained by equations 12 and 13.

The problem of selecting analytical instruments (and the configuration of what reagents should be assigned to which of those instruments) can then be solved using equations such as set forth in table 1 to optimize the necessary instruments separately for each discipline having tests to be processed by the laboratory.

Other types of variations are also possible. For example, in some embodiments that populate a set of potential instruments for optimization as described above, existing instruments that should be included in the set and always selected as constraints may be specified. This may be beneficial, for example, if a laboratory considers the expansion of its capabilities and wants to know what additional instruments should be purchased to do so, rather than what instruments would be optimal if operated on a completely blank board. Similarly, if a laboratory attempts to make long-term strategic decisions regarding purchasing various types of instruments, it may utilize techniques such as those disclosed herein as well as predictions of the need for various types of tests in the coming year. As another example of the types of variations possible in some embodiments, some embodiments may be used to optimize the selection and/or configuration of an instrument that includes analyzers associated with multiple disciplines. This may be accomplished, for example, by modeling the individual analyzers as having multiple components, where each of the components is associated with a particular discipline and has the capability to hold reagents that will be used to perform the tests in that discipline. A set of equations that may be used in some embodiments to identify the optimal instrument selection and configuration for these types of multidisciplinary instruments is presented in table 3 below.

TABLE 3

In table 3, P serves as an index to an analyzer component in the set P of analyzer components, and other parameters including references to component P should be understood to be similar to similar parameters previously discussed in the context of a single component analyzer. For example, APOC_jpShould be regarded as similar to APOC_jAnd is used in table 3 to represent the operational capabilities of component p of analyzer j. Similarly, x_hsjpThe number h of reagent bottles with size s that should be understood as assigned to the part p of the analyzer j; MPD_jpdShould be understood as a value that represents whether the component p of analyzer j is in discipline d (1 if it is, 0 otherwise); HMP_hjpShould be understood as a value that represents whether the test h can potentially be done by the component p of analyzer j (and if so would be 1, otherwise 0); g_jpShould be understood as the hourly capacity of the part p of the analyzer j in terms of testing; RK_jpThe number of reagent bottle positions that should be understood as part p of analyzer j; and y is_hjpShould be understood to mean that the test h is done by the part p of the analyzer j (1 if yes, 0 otherwise)_jHave the same meanings as when these parameters are used in table 2.

As yet another example of a potential variation, in some embodiments, the constraints and cost function may be such as to omit calibrationModeled in a cost-wise manner (previously incorporating parameters)Discussed). This may be beneficial, for example, where calibration costs are associated with particular tests run on instruments (e.g., those in hematology disciplines). An exemplary equation that can be used to identify optimized instrument selection and reagent dispensing while ignoring calibration costs that may be used in certain embodiments is listed in table 4 below. In the table, u_jWill be an integer having a value of 0 or 1 depending on whether a particular instrument is selected, and all other parameters will have the same meaning as previously discussed in the context of equations 2-5 and 12-14 and table 2.

TABLE 4

Other variations, features, and potential implementations and applications of the inventors' technology will be apparent to those of ordinary skill in the art in light of this disclosure, and may be practiced without undue experimentation. Thus, this document, and any documents that claim the benefit of the disclosure of this document, should not be construed as limited to the specific implementations of the inventors' technology described herein.

As used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. The invention has now been described in detail for purposes of clarity and understanding. It should be understood, however, that certain changes and modifications may be practiced within the scope of the appended claims.

As used herein, "laboratory instrument" or "instrument" refers to a device for analyzing a sample or facilitating or effecting such analysis. Examples of these types of devices include analyzer instruments, centrifuges, and sorting and routing machines (sorting and routing machines).

As used herein, the term "machine" refers to an apparatus or combination of apparatuses.

As used herein, the term "set" refers to a number, group, or combination of zero or more things of similar nature, design, or function.

As used herein, the statement that "does things" without any human intervention means that the things are done automatically.

As used herein, the term "based on" means that something is determined, at least in part, by what is indicated as "based on". To indicate that something must be determined entirely based on something else, it can be described as being "exclusively" based on what is determined entirely by it.

As used herein, modifiers such as "first," "second," etc. are simply labels to improve readability, and are not intended to imply any temporal or substantial difference between the modified items. For example, the terms "first program" and "second program" in the claims should not be construed to indicate that the "first program" is created first, or that the two programs will necessarily cause different things to happen when executed by a computer. Similarly, the terms "computer" and "server" when used in the claims should be understood as synonyms, where different terms are used to enhance the readability of the claims, and do not imply any physical or functional differences between the items referred to using those different terms.