WO2020106693A1 - Optimisation des coûts d'opérations d'instruments de diagnostic - Google Patents

Optimisation des coûts d'opérations d'instruments de diagnostic

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Publication number
WO2020106693A1
WO2020106693A1 PCT/US2019/062164 US2019062164W WO2020106693A1 WO 2020106693 A1 WO2020106693 A1 WO 2020106693A1 US 2019062164 W US2019062164 W US 2019062164W WO 2020106693 A1 WO2020106693 A1 WO 2020106693A1
Authority
WO
WIPO (PCT)
Prior art keywords
reagent
analyzer
diagnostic
tests
diagnostic instrument
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2019/062164
Other languages
English (en)
Inventor
Eric Varlet
Sohrab FARAMARZI OGHANI
Pr. El Ghazali TALBI
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beckman Coulter Inc
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Beckman Coulter Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beckman Coulter Inc filed Critical Beckman Coulter Inc
Publication of WO2020106693A1 publication Critical patent/WO2020106693A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N35/00—Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/00 - G01N33/00; Handling materials therefor
    • G01N35/00584—Control arrangements for automatic analysers
    • G01N35/0092—Scheduling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N35/00—Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/00 - G01N33/00; Handling materials therefor
    • G01N35/00584—Control arrangements for automatic analysers
    • G01N35/0092—Scheduling
    • G01N2035/0094—Scheduling optimisation; experiment design
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

Definitions

  • reagents are the most important consumable materials which include the main part of the test cost.
  • Reagents are chemical materials used by diagnostic instruments (e.g., analyzers) to perform clinical tests. In better words, diagnostic instruments require reagents to be able to perform tests on patients’ samples.
  • diagnostic instruments e.g., analyzers
  • These reagents are provided in bottles with different sizes. Normally, for each test type, a specific type of reagent is needed. Assigning reagent bottles to the diagnostic instruments in a clinical laboratory in order to satisfy the daily test demand is a challenging issue as in one side, it imposes configuration costs to the organization and on the other side, it directly affects the operational decisions such as tube-instrument assignment and consequently, operational activities such as tube movements within the laboratory.
  • Diagnostic instrument configuration costs include the costs of different reagent bottles used in the instruments as well as costs of calibrating each available test type on each instrument.
  • ACP analyzer configuration problem
  • each diagnostic instrument may be configured to perform one or more tests on a biological sample
  • each diagnostic instrument may be configured to hold at least one reagent pack
  • the reagent pack may comprise reagents for performing the one or more tests on that diagnostic instrument.
  • FIG. 1 schematically illustrates the analyzer configuration problem.
  • some embodiments may provide a method that could comprise steps such as determining a type of diagnostic instrument from a plurality of diagnostic instruments in a laboratory environment, and for each diagnostic instrument from the plurality of diagnostic instruments, determining a correct reagent pack and loading a required number of the correct reagent pack into that diagnostic instrument, wherein the correct reagent pack depends on the type of that diagnostic instrument.
  • each diagnostic instrument may be configured to perform one or more tests on a biological sample
  • each diagnostic instrument may be configured to hold at least one reagent pack
  • the reagent pack may comprise reagents for performing the one or more tests on that diagnostic instrument.
  • some embodiments may provide methods such as described in the context of the first aspect that may comprise calibrating each of the plurality of diagnostic instruments in the laboratory environment based on one or more types corresponding to the one or more tests performed on that diagnostic instrument.
  • some embodiments may provide methods such as described in the context of either of the first or second aspects which comprise determining a configuration for each of the diagnostic instruments based on an amount of reagent determined for each of the at least one reagent packs based on a set of pre-defined parameters.
  • some embodiments may provide methods such as described in the context of the third aspect in which the set of pre-defined parameters may include at least one of: a daily reagent consumption statistic for the diagnostic instrument, a number of reagent packs accommodated in the diagnostic instrument, a number of tests performed on the diagnostic instrument per hour and a number of tests performed on the diagnostic instrument per day, reagent bottle size, reagent efficiency, and reagent price.
  • some embodiments may provide methods such as described in the context of any of the first through fourth aspects wherein determining an amount of reagent usage per day avoids the diagnostic instrument from multiple calibration.
  • some embodiments may provide methods such as described in the context of fourth aspect in which the daily reagent consumption statistic for the diagnostic instrument may be average daily reagent consumption over a period of time.
  • some embodiments may provide methods such as described in the context of the fourth aspect in which the daily reagent consumption statistic for the diagnostic instrument may be maximum daily reagent consumption over a period of time.
  • some embodiments may provide methods such as described in the context of the fourth aspect in which the daily reagent consumption statistic for the diagnostic instrument may be a percentile daily reagent consumption over a period of time.
  • some embodiments may provide methods such as described in the context of any of the sixth through eighth aspects wherein the period of time may be a month.
  • some embodiments may provide methods such as described in the context of any of the first through ninth aspects wherein the diagnostic instrument configuration may be further determined based on parameters for reagents provided by multiple suppliers.
  • some embodiments may provide methods such as described in the context of any of the first through tenth aspects wherein the method may comprise determining one more demand profiles wherein each demand profile defines a set of tests commonly performed together based on test statistics.
  • determining configurations for diagnostic instruments may comprise balancing costs of reagents with which the diagnostic instruments are configured and costs of transporting tubes between diagnostic instruments to complete sets of tests as defined in the one or more demand profiles.
  • some embodiments may provide a system comprising one or more computers configured by computer executable instructions stored on a non-transitory computer readable medium to perform the method as claimed in any of the first through eleventh aspects.
  • some embodiments may provide a system comprising at least one diagnostic instrument configured based on performance of methods as described in the context of any of the first through eleventh aspects.
  • some embodiments may provide a system comprising at least one diagnostic instrument configured to perform a method as described in the context of any of the first through eleventh aspects.
  • each analyzer may belong to one or more test disciplines which implies the potential capability of an analyzer to perform clinical tests.
  • analyzers are able to do the tests of a discipline to which they belong.
  • a Chemistry analyser may potentially be able to carry out only Chemistry tests.
  • a matching reagent may be required in the analyzer.
  • the Triglyceride reagent must be available on the analyzer.
  • Reagents are materials used by the analyzers to conduct the tests on patient’s samples.
  • a specific type of reagent may be needed for each test type.
  • reagents may be available in bottles with different sizes. It is worth noting that, in some embodiments, each analyzer may have only a certain number of positions to store reagent bottles. In some embodiments, reagent bottles with different sizes may occupy different positions in analyzers.
  • the efficiency of analyzers in terms of reagent consumption to perform a test may be different.
  • the cost of a regent bottle may rely on the test type, bottle size, and analyzer in which the bottle is loaded.
  • each available test type on an analyzer must be calibrated which imposes a cost to the system, called calibration cost.
  • Analyzer configuration is the problem of specifying the type and quantity of reagent bottles in each analyzer to satisfy the daily average demand optimizing one or more objectives.
  • objectives can be defined as minimizing the total cost of reagent bottles used in the analyzers as well as analyzers calibration costs, and minimizing the total number (cost) of tube movements within the laboratory.
  • the first type focuses on cost-related objectives.
  • the type is multi-objective models, such as a bi-objective model which looks to the operational issues inside the laboratory and tries to minimize tube movements as well as minimizing analyzer configuration costs.
  • input data may be test-based data
  • input data may be tube-based data.
  • test-based data the demand is expressed for each test type and it is assumed that average daily demand is given for each test type. In this case, no information is available about the arriving tubes to the system; however, in tube- based data, the daily demand pattern is described through a tube-test matrix in which the requested tests of each arriving tube to the laboratory are known in advance.
  • reagents are chemical material used by the analyzers to perform the test.
  • the daily average operational capacity of analyzer j equals the minimum of daily average nominal capacity of the analyzer and the total number of tests that can be analyzed by the analyzer regarding the number of reagent bottles assigned to that analyzer x hsj) ⁇
  • the nominal capacity of an analyzer may be computed based on the multiplication of the analyzer capacity (g j ) provided by the manufacturer in terms of the average number of tests per hour by the total daily available working hours ( j ) .
  • the total daily available working time for each analyzer implies the time that the analyzer is available for operating and analyzing.
  • Each analyzer has a certain number of reagent positions to room reagent bottles.
  • Each reagent bottle occupies a certain number of positions in the analyzer depending on the reagent type and bottle size.
  • a Reagent bottle cost relies on the reagent type, bottle size and the analyzer in which the reagent bottle is used.
  • the number of tests that can be analyzed using a bottle of reagent depends on the reagent type, bottle size and the analyzer in which the reagent bottle is used.
  • the configuration of the analyzers is performed on the daily basis and is independent from previous days implying that the remaining reagents in the analyzers are discarded at the end of the day.
  • T j the daily available working hours of analyzer j 9j the average number of tests that can be analyzed by analyzer j per hour
  • Table 2 sets forth equations that may be used in some embodiments and that represent a cost to minimize as well as constraints on that minimization.
  • Equation (1) is the objective function which minimizes the total costs of reagent bottles used in the analyzers and the total calibration costs of each test type on each analyzer.
  • Constraint (2) and constraint (3) demonstrate the daily operational capacity of each analyzer which can neither be more than the analyzer’ s nominal capacity (constraint (2)), nor more than the capacity created by the number of reagent bottles assigned to the analyzer denoting the total number of tests that can be processed by the analyzer (constraint (3)).
  • Constraint (4) assures that there is a sufficient capacity (capability to analyze a certain number of tests) in the laboratory to handle all the daily requested tests from different disciplines.
  • Constraint (5) guarantees that there are sufficient reagents in the existing analyzers to calibrate the analyzers and to analyze each requested test within a day.
  • Constraint (6) assures that the number of reagent bottles positioned into each analyzer must not exceed the available number of reagent positions on each analyzer.
  • Constraint (7) demonstrates whether test h is available on analyzer j or not to provide useful information to compute calibration cost of each test type on the analyzers.
  • Constraint (8) presents the potential eligibility of each analyzer to perform a test. Analyzers of each discipline are only able to analyze the tests which belong to the associated discipline.
  • Constraints (9) to (11) specify the type of decision variables used in the model.
  • first model may be applied in a case where multi-part analyzers exist in the laboratory through considering each part of an analyzer as an independent analyzer with certain test and reagent capacity.
  • W h denotes the amount of remaining reagent for test type h at the end of the day in all the existing analyzers.
  • some embodiments may add the following objective function to the previous model:
  • each reagent type is provided by a specific supplier; however, in some embodiments, there might be more than one supplier to supply reagent bottles. Reagent bottles for a specific test from various suppliers might differ in price, efficiency and size.
  • index r is used to indicate the supplier.
  • Table 3 presents all the necessary modifications in notations of the previous model to construct the new one.
  • some embodiments may perform calibration for each test type provided by each supplier on each analyzer. In other words, if for a specific test type on an analyzer reagents from two different suppliers exist, some embodiments may calibrate this test type for both reagents provided by two different suppliers.
  • model model model Explanation x hsj x hrsj the number of reagent bottle type h from supplier r with size s assigned to analyzer j d hsj S hrsj the average number of test h that can be analyzed using one bottle of reagent provided by supplier r with size s in analyzer j l hs X hrs the number of reagent positions occupied by reagent bottle h provided by supplier r with size s
  • a model for addressing the analyser configuration problem that some embodiments may use in a context where there are multiple suppliers may be formulated using the objective function and constraints set forth below in table 4.
  • Table 4 Objective function and constraints for multi-supplier analyser configuration model.
  • a cost-based configuration only minimizes configuration costs which might lead to excessive operational costs in the system. For instance, a cost-based configuration might assign reagent bottles to the analyzers in a way that arriving tubes have to be moved many times from one analyzer to another until all their ordered tests can be analyzed.
  • tests a, b and c have been assigned to three different analyzers in order to minimize the total configuration costs. In this case, all the tubes which require tests a, b and c have to be transported among these three analyzers to be completely analyzed.
  • tube movements inside a laboratory increase operational costs and reduce tube traceability in the system.
  • excessive tube movements affect operational issues such as job and operator scheduling and increase test turnaround time in the system.
  • some embodiments may take these issues into consideration while configuring the analyzers.
  • some embodiments may use a bi-objective model such as described in this section.
  • bi-objective models such as described in this section may use tube-based data implying that the average number of tubes with their ordered tests construct the demand input data.
  • tests of tubes are assigned to the analyzers, then, to support this assignment, required reagents are assigned to the analyzers.
  • reagents are chemical material used by the analyzers to perform the test.
  • the total daily available working time for each analyzer implies the time that the analyzer is available for operating and analyzing.
  • Each analyzer has a certain number of reagent positions to store reagent bottles.
  • the daily demand is characterized by the number of tubes and their requested tests.
  • the tube-test matrix is a matrix with binary elements where requested tests of each tube is determined.
  • Each reagent bottle occupies certain positions in the analyzer depending on the reagent type and bottle size.
  • the reagent bottle cost relies on the reagent type, bottle size and the analyzer in which the reagent bottle is used.
  • the number of tests that can be analyzed using a bottle of reagent depends on the reagent type, bottle size and the analyzer in which the reagent bottle is used.
  • T j the daily available working time of analyzer j g j the average number of tests that can be analyzed by analyzer j per hour
  • Table 5 Notations that can be used in multi-objective models.
  • Table 6, below sets forth equations that may be used in some embodiments which incorporate multi-objective models representing costs to minimize as well as constraints on that minimization.
  • Table 6 Costs to minimize and minimization constraints for multi-objective models.
  • Constraint (28) presents which tests are analyzed by which analyzer(s) to provide us computing the calibration cost of the tests on the analyzers.
  • a test is analyzed by an analyzer only if there is at least one reagent bottle of that test in the analyzer.
  • Constraint (29) demonstrates whether test h is potentially analyzed by analyzer j or not. This is the eligibility constraint to avoid assigning a test to an analyzer which is not able to analyze that test.
  • Constraint (30) presents that the total number of tests of type h done by the analyzer j must not exceed the total available reagents assigned to the analyzer for test h. Note that for each test type on each analyzer, a portion ( a hj ) is used for calibration.
  • Constraint (31) assures that each analyzer of a discipline in the laboratory receives a minimum number of tests proportional to the analyzer capacity so that a minimum amount of reagent must be assigned to the analyzer. In this constraint, coefficient
  • Constraint (32) reflects that the total number of tests assigned to an analyzer must not exceed the analyzer capacity which is defined in terms of the total average number of tests that can be done by the analyzer per day. Constraints (33) to (36) imply the type of decision variables used in the model.
  • a multi-objective model may be shown as follows:
  • Some embodiments may solve a multi-objective problem using a weighted sum method in which all objectives are aggregated in a way to make the model as single-objective as follows:
  • n k is the weight of kt objective function implying the importance of objective k.
  • objectives may be normalised.
  • each objective function should be optimized separately for both minimization and maximization directions to find out the extreme points.
  • PIS or fTM positive ideal solution
  • NIS or / k maji negative ideal solution
  • the normalised value of a minimization objective function may be computed using the following formula:
  • the aim is to find out the most appropriate assignment of different reagent bottles to the analyzers where the type and quantity of reagent bottles assigned to each analyzer is determined considering the two objectives which are (i) minimizing the total configuration costs, and (ii) minimizing the total tube movements among the analyzers within the laboratory.
  • DxI600 and DxI800 are the selected Immunology analyzers and AU480 and AU5822 are the selected Chemistry analyzers. Potential test capability, test capacity and reagent capacity of each analyzer is extracted from the analyzers manufacturer website. The daily available working time of each analyzer is fixed to eight hours.
  • each reagent type is supplied by a single supplier and for each type, two bottle sizes are available. Cost of each reagent bottle type for different sizes have been extracted from brochure provided by reagent suppliers. In addition, efficiency of each analyzer in terms of reagent consumption for a test has been adapted from the analyzers’ manufacturer’s website.
  • Table 8 Value of objective functions under different importance factors.
  • Table 9 Portion of exemplary analyzer configuration solution.
  • analyzer configuration(s) could be based on maximum demand (e.g., maximum daily demand for a particular type of test observed over a period such as a month or a week), thereby reducing the risk that variations from average would result in an analyzer running out of reagents and needing to be refilled during any given day.
  • maximum demand e.g., maximum daily demand for a particular type of test observed over a period such as a month or a week
  • analyzer configuration could be based on a percentile demand measurement. For example, measurements of the number of tests required each day could be taken for a set period, and the user could pick a percentile (e.g., fiftieth percentile, sixtieth percentile, seventieth percentile, seventy fifth percentile, eightieth percentile, ninetieth percentile, ninety fifth percentile, ninety ninth percentile) measurement that would be used to configure the analyzers, thereby allowing the user to balance between avoiding having reagents left over at the end of the day and avoiding having to refill an analyzer before a day was complete. Other statistical measures could also be used.
  • a percentile e.g., fiftieth percentile, sixtieth percentile, seventieth percentile, seventy fifth percentile, eightieth percentile, ninetieth percentile, ninety fifth percentile, ninety ninth percentile
  • a user could specify that analyzers should be configured based on demand information equal to the average daily demand plus one standard deviation of demand measurements taken over a set period of time (e.g., one month).
  • this may be done by redefining that parameter as the number defined by the alternative statistic used in that embodiment (e.g., as the maximum observed daily demand for test h, as the specified percentile value of daily demand for test h, etc.).

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Abstract

Des coûts de fonctionnement pour des instruments de laboratoire peuvent être optimisés à l'aide d'un procédé comprenant des étapes telles que la détermination d'un type d'analyseur à partir d'une pluralité d'analyseurs dans un environnement de laboratoire, l'étalonnage de chacun de la pluralité d'analyseurs dans l'environnement de laboratoire sur la base du type de performance de test sur l'analyseur, la détermination d'une configuration d'analyseur sur la base d'une quantité de réactif déterminée pour chacun des paquets de réactifs sur la base d'un ensemble de paramètres prédéfinis, et sur la base de la détermination ci-dessus, le chargement d'un nombre requis de paquets de réactifs dans l'analyseur. Dans certains de ces procédés, chaque analyseur peut être configuré pour effectuer un test spécifique sur un échantillon de patient et chaque analyseur peut être configuré pour contenir au moins un bloc de réactifs, lesdits paquets de réactifs pouvant être utilisés pour effectuer un test sur l'échantillon de patient.
PCT/US2019/062164 2018-11-21 2019-11-19 Optimisation des coûts d'opérations d'instruments de diagnostic Ceased WO2020106693A1 (fr)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021015881A1 (fr) * 2019-07-24 2021-01-28 Siemens Healthcare Diagnostics Inc. Procédés et systèmes d'optimisation de plan de chargement de jeux de réactifs
CN113517978A (zh) * 2021-07-16 2021-10-19 安徽伊普诺康生物技术股份有限公司 一种体外诊断设备试剂卡的安全防护与重用方法
US11698380B2 (en) 2019-07-24 2023-07-11 Siemens Healthcare Diagnostics Inc. Optimization-based load planning systems and methods for laboratory analyzers
WO2023150440A1 (fr) * 2022-02-07 2023-08-10 Siemens Healthcare Diagnostics Inc. Système et procédé de planification de charge centrée sur le patient
US12580054B2 (en) 2020-11-13 2026-03-17 Siemens Healthcare Diagnostics Inc. Computationally-efficient load planning systems and methods of diagnostic laboratories

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EP0833278A2 (fr) * 1996-09-19 1998-04-01 Abbott Laboratories Support d'information
EP1062551A1 (fr) * 1998-03-12 2000-12-27 Abbott Laboratories Technique d'allocation de ressources
WO2014110282A1 (fr) * 2013-01-09 2014-07-17 Siemens Healthcare Diagnostics Inc. Distribution de réactif pour l'optimisation de débit
WO2014127269A1 (fr) * 2013-02-18 2014-08-21 Theranos, Inc. Méthodes, systèmes, et dispositifs pour l'exécution en temps réel et l'optimisation de protocoles d'essai concurrents sur un seul dispositif
WO2019126033A1 (fr) * 2017-12-19 2019-06-27 Beckman Coulter, Inc. Configuration et sélection d'instrument de laboratoire

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Publication number Priority date Publication date Assignee Title
EP0833278A2 (fr) * 1996-09-19 1998-04-01 Abbott Laboratories Support d'information
EP1062551A1 (fr) * 1998-03-12 2000-12-27 Abbott Laboratories Technique d'allocation de ressources
WO2014110282A1 (fr) * 2013-01-09 2014-07-17 Siemens Healthcare Diagnostics Inc. Distribution de réactif pour l'optimisation de débit
WO2014127269A1 (fr) * 2013-02-18 2014-08-21 Theranos, Inc. Méthodes, systèmes, et dispositifs pour l'exécution en temps réel et l'optimisation de protocoles d'essai concurrents sur un seul dispositif
WO2019126033A1 (fr) * 2017-12-19 2019-06-27 Beckman Coulter, Inc. Configuration et sélection d'instrument de laboratoire

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021015881A1 (fr) * 2019-07-24 2021-01-28 Siemens Healthcare Diagnostics Inc. Procédés et systèmes d'optimisation de plan de chargement de jeux de réactifs
US11698380B2 (en) 2019-07-24 2023-07-11 Siemens Healthcare Diagnostics Inc. Optimization-based load planning systems and methods for laboratory analyzers
US12580054B2 (en) 2020-11-13 2026-03-17 Siemens Healthcare Diagnostics Inc. Computationally-efficient load planning systems and methods of diagnostic laboratories
CN113517978A (zh) * 2021-07-16 2021-10-19 安徽伊普诺康生物技术股份有限公司 一种体外诊断设备试剂卡的安全防护与重用方法
WO2023150440A1 (fr) * 2022-02-07 2023-08-10 Siemens Healthcare Diagnostics Inc. Système et procédé de planification de charge centrée sur le patient

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