WO2019166953A1

WO2019166953A1 - Efficient management of patient samples in a laboratory

Info

Publication number: WO2019166953A1
Application number: PCT/IB2019/051553
Authority: WO
Inventors: Vishwesh B VISHNUMURTHY
Original assignee: Beckman Coulter Inc
Current assignee: Beckman Coulter Inc
Priority date: 2018-02-27
Filing date: 2019-02-27
Publication date: 2019-09-06
Anticipated expiration: 2020-08-27

Abstract

Embodiments of the present disclosure relate to a method and system for tracking samples in real time in a laboratory by creating a smart tube, wherein the smart tube comprises a biological sample embedded with a sample identification label, wherein the sample identification label is associated with a patient; and embedding the smart tube with a sensor and associating the sensor with the sample identification label for tracking the smart tube in the laboratory.

Description

FIELD OF TECHNOLOGY

This disclosure relates to management of patient samples in a laboratory. BACKGROUND

Generally, laboratory instruments are used for performing tests on patient samples. Typically tests performed by laboratory instruments fall under various categories such as biochemistry, microbiology, pathology and so on. Usually, in performing such tests using laboratory instruments, biological samples may be collected from patients, which may include living organisms covering humans and animals, and collected samples form the patients may be tested using these laboratory instruments for various know parameters. After testing of the samples, a report may be generated on the various parameters that the patient sample was tested for, and may be provided to the patient or a physician.

Embodiments of the present disclosure relate to a system and method for tracking samples, preferably biological samples, real time in a laboratory. One embodiment may include creating or generating a smart tube, wherein the smart tube may include a simple tube (such as a test tube) comprising a biological sample that may be further embedded with a sample identification label, wherein the sample identification label may be associated with a particular patient. A further embodiment may include embedding the smart tube with a sensor and associating the sensor with the sample identification label for tracking the presence of the smart tube within the laboratory. Other embodiments are also disclosed.

For a better understanding of the nature and desired objects of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawing figures wherein like reference character denote corresponding parts throughout the several views. Objects, features, and advantages of embodiments disclosed herein may be better understood by referring to the following description in conjunction with the accompanying drawings. The drawings are not meant to limit the scope of the claims included herewith. For clarity, not every element may be labeled in every Figure. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments, principles, and concepts dims, features and advantages of the present disclosure will become more apparent from the following detailed descripti on of exemplary embodiments thereof taken in conjunction with the accompanying drawings in which:

FIGURE 1 illustrates an exemplary smart tube 100 in accordance with the embodiments of the present disclosure;

FIGURE 2 illustrates an exemplary sample collection model 200 in accordance with the embodiments of the presen t disclosure; and

FIGURE 3 illustrates tin exemplary laboratory workflow model in accordance with the embodiments of the present disclosure.

DETAILED DESCRIPTION

Hereinafter, various embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be noted that all of these drawings and description are only presented as exemplary embodiments. It is to note that based on the subsequent description, alternative embodiments may be conceived that may have a structure and method as disclosed herein, and such alternative embodiments may be used without departing from the principle of the disclosure as claimed herein.

It may be appreciated that these exemplar}' embodiments are provided herein only for enabling those skilled in the art to better understand and then further implement the present disclosure, and is not intended to limit the scope of the present disclosure in any manner. Besides, in the drawings, for a purpose of illustration, optional steps, modules, and units are illustrated in dotted-line blocks.

The terms “comprise(s),”“include(s)”, their derivatives and like expressions used herein should be understood to be open, i.e.,“comprising/ including, but not limited to.” The term“based on” means“at least in part based on.” The term“one embodiment” means“at least one embodiment”; and the term “another embodiment” indicates “at least one further embodiment.” Relevant definitions of other terms will be provided in the description below'.

Embodiment of tire present disclosure relate to a system and a method for tracking samples, preferably biological samples, real time in a laboratory. In one embodiment, tracking samples real time in a laboratory and/or hospital (referred to in this application generally as laboratories) is a current challenge that is faced by many laboratories. One embodiment may include creating a smart tube, wherein the smart tube essentially comprises a simple test tube containing a biological sample of the patient. A further embodiment may include embedding the simple test tube containing the patient sample with a sample identification label, wherein the sample identification label is associated with a particular patient. In one embodiment, each patient may have a unique sample identification label. A further embodiment may include embedding the smart tube with a sensor. A further embodiment may include associating the sensor with the sample identification label for tracking the smart tube in the laboratory.

In a further embodiment, the sensor may include at least one of a MEMS (micro electromechanical system), a NEMS (nano-electromechanical system), or an RFID (radio frequency identifier). In a further embodiment, the MEMS may be within a range of 0.01 millimeter to 1 millimeter. In a further embodiment the NEMS may be within a range of 1 nanometer to 100 nanometers. In a further embodiment, the RFID may be a bar code. In a further embodiment, the MEMS and/or the NEMS may include at least one of a carbon allotrope or smart dust. In a further embodiment, the MEMS and/or NEMS may comprise material that have semiconducting properties.

In a further embodiment, the MEMS and/or NEMS may be configured to store at a large number of parameters that may be associated with the patient sample or tests from the laboratory instruments. In one further embodiment, the MEMS/NEMS may be configured to store a up to 32 parameters hi a further embodiment, the parameters may include at least one of a patient Identification, a volume of the patient sample, a residual volume of the patient sample after a test is perfonned, a cell layer, a plasma volume of the sample, a temperature of tire sample, a serum clarity of the sample, a clot in the sample, a STAT sample, an ER (emergency room or casualty ward) sample for work flow' assignment, a time spent on track, a time spent in an analyzer, a time spent in a storage, a time from collection of a sample to a first time the sample is analyzed, a temperature changes associated with the samples, a volume changes associated with the sample, a location associated with the sample at any given point in time, an identity associated with the sample. In a further embodiment, STAT may refer to all emergency laboratory test, wherein there may be an urgent need to obtain results for those marked tests as STAT, so that immediate treatment may be provided to the patients.

In a further embodiment, data gathered from the smart tubes may be used to reduce a mix up of patients. In a further embodiment, data collected from the smart tubes may also be used to reduce Pre-Analytical, Analytical and Post-Analytical errors in the laboratory. In a further embodiment, data may also provide useable and accurate data for any artificial intelligence or machine learning tools.

In an example embodiment, considering data usage for AI and for Machine Learning, since the Smart tubes are associated to patients, where one patient could have more than one test ordered for him/her, the smart tube may be able to help .41 tools to group tests based on clinical condition of patient to suggest similar group of tests for other patients with comparable clinical parameters, and thereby reduce time taken by the clinician to arrive at a diagnosis. In a further embodiment, data gathered from these smart tubes may be able to identify a pattern in tire blood results to suggest physician appropriate tests that could probably turn positive for a current patient based on the‘machine learnt’ pattern from past data.

In a further embodiment, the smart tube may be coupled to a hub, in a hub and spoke arrangement, wherein each hub has a number of spokes and each of the smart tube being represented by a spoke. In an example embodiment, each hub may advantageously include six smart tubes. In a further embodiment, each hub may be configured to retain information of the smart tubes. In a further embodiment, each hub may be configured to be coupled to a laboratory computer. In one embodiment, the laboratory computer may include a server or a middleware. In a further embodiment, die laboratory computer may include a at least a memory and a processing device configured to process instruction and perform specified tasks. In a further embodiment, the hub may be configured to track a smart tube and report a location of the smart tubes within the hub to the laboratory computer.

A further embodiment may include a system comprising a plurality of smart tubes, wherein the smart tubes form a hub, the system further comprising a plurality of hubs that may be communicatively coupled to a laboratory computer, which may be a server or a middleware, and configured to perform the method described above.

Figure 1 illustrates an exemplary smart tube 100 in accordance with the embodiments of the present disclosure. Figure 1 illustrates a simple tube, for example a test tube. Tire tube contains biological sample 110 collected and stored in the tube by the laboratory. The tube has a barcode 130, for example in the fomi of a sticker printed and pasted on the tube, to identify the tube. Additionally, the tube contains one or more sensors 120 embedded with the tube thereby making the present tube a smart tube. Sensor 120 may preferably be at least one of a MEMS, NEMS, RFID or smart dust.

In one embodiment, smart tube has a sensor 120 embedded into sample identifying label or marker 130, wherein sample identifying label 130 has a pre-existing bar code, which is printed on a label and attached to the tube. Sensor 120 may be printed on any material, which for example may be a patient label that identifies a patient hr one embodiment, the smart tube may be configured to communicate via RFID technology with a node (not shown in the figure), which in turn is configured to communicated to a processing device, for example a laboratory'· computer or a tablet or the likes. In one embodiment, this is a waterproof, battery less and wireless mode of communication.

In one embodiment, the smart tube system may be configured to manage patient sample quality, to identify volume of a sample, sub-volume of gel separator, sub-volume of cells and sub-volume of plasma temperature of sample, micro-clots and serum index. In a further embodiment, the smart tube system may be enabled such that an exact location of the tube may be obtained at any point in time within the laboratory, in real time. In an example embodiment, in a Power Express track, the patient sample may be in Analyzer‘A’, or‘B’ etc. In a further embodiment, the smart tube may further quantify the residual volume of sample in a tube. In a further embodiment, the smart tube may bond a soft relation between aliquots so that there is no missing or mixing of samples and patients. In a further embodiment, the smart tube may be configured to identify clots in any of the patient sample . In a further embodiment, the smart tube may be configured to separate between regular and STAT/A & E samples. In a further embodiment, the smart tube may be configured to reduce any pre-anaiytieal errors.

In one embodiment, tire pre-analytical space may include a number of parameters or activities such as sample collection, Sample handling may include the time from collection of the sample to loading the sample into the analyzers or automation track, sample workflow that may include aliquoting, any re-runs, reflex and reflective testing and sample storage.

In a further embodiment, the analytical space may include a number of parameters or activities such as samples for the entire duration they are in the lab, samples while they are in the automation track, and samples while they are being stored until they are discarded from the system. In a further embodiment, the present disclosure may include a disruptive technology that is not used presently in order to achieve significant benefits in sample management and workflow area to address most of the latent needs of our customers from large to small volume laboratories.

In one embodiment, sensors, for example MEMS and/or NEMS, which are tiny energy efficient systems that work wirelessly through RFID and do not need power to work themselves may be used in the present disclosure for creating a smart tube. In a further embodiment, the sensors may be waterproof and may be embedded on to materials like bandages, paper stickers (labels or markers) or skin patches and the likes. In a further embodiment, this may be considered as a disruptive technology product that is yet to be used for tracking samples efficiently in a laboratory, which may also include a hospital . In one embodiment, artificial intelligence, machine learning or rule based systems may be used to perform the operation mentioned above . In one embodiment Artificial intelligence (AI) may be representative of a simulation of human intelligence processes by machines, especially computer systems and/or hub used in the present disclosure. In one embodiment, tills processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self- correction In a further embodiment, the AI techniques used may be weak AI or strong AT In one embodiment, machine learning may be used, wherein ML is a method of data analysis that automates analytical model building. In a further embodiment, the systems can learn from data, identify patterns and make decisions with minimal human intervention. In a further embodiment, rule based systems may be used, such as a Bayesian belief network or the likes using the data that may be captured and stored in a repository.

Reference is now made to Figure 2, which illustrates an exemplary sample collection model 200 in accordance with the embodiments of the present disclosure. In one embodiment, multiple patient samples in the smart tubes, i.e., the tubes with patient samples with sensors embedded to the tubes, can be easily tracked by a single hub device. In one embodiment, there could be many such hubs m a laboratory. Each hub in the laboratory includes central hub computer 210 that has at least six (6) samples 220-1, ... , 220-6 coupled to central hub computer 210. The smart tubes 220-1, . . , 220-6, which are multiple sample tubes are coupled through central hub computer 210 to main laboratory computer 230. In one embodiment, laboratory computer 230 may include a laboratory information services system, a middleware, a server, a laptop computer or a portable electronic device that configured to process information. IN one embodiment, the present arrangement allows a seamless flow' of information (data) across the smart tube containing the patient sample and the laboratory computer (server or LIS or middleware).

In one embodiment, the arrangement as disclosed in the embodiments herein works as a hub and spoke model, wherein multiple such smart tubes containing the sensors may be connected to a node, and multiple nodes may be in turn connected to a computer. In a further embodiment, bidirectional flow of information may be enabled in this architecture presented herein. In one embodiment, sensors may be embedded for creating the smart tubes and typically these sensors may be typically having a dimension of about 1 / 10^th the size of a pinhead. In a further embodiment, these sensors may be attached to patient labels that may be stuck on the tubes that are used to collect blood or other patient samples. In a further embodiment, these smart tubes may be configured to gather information about light, temperature, vibration, magnetism, or chemical properties etc., based on any requirement. In a further embodiment, these smart tubes may be configured to gather information and transmit the information/data to the laboratory computer or a processing device coupled to the smart tubes for further processing the information.

In one embodiment, vendors may choose to replace existing tubes that may be used to collect patient samples. In a further embodiment, vendors may paste waterproof stickers or labels containing at least one sensor on each of the tube to create a smart tube. In a further embodiment, the cost for one such sensor may be about 5 cents or lesser, but advantageously attaching such sensors to the tubes brings in tremendous value in terms of tracking samples and/or efficiently managing samples, and customer are not impacted by this change in the system . In a further embodiment, quality of smart tube including parameters like its temperature, moisture content, and air quality can be gathered even before collecting a patient sample in the tube .

In one embodiment, once a sample is collected from a patient it may be linked to a patient using a unique patient ID and from there on be linked to that specific patient. In a further embodiment, if there is a need to be aliquoted, it can be programmed to create a‘child’ number that may be linked to the original or‘parent’ number hi a further embodiment, once these tubes are filled with a patient sample, for instance blood, they may be configured to capture the quality of blood in terms of its volume, temperature and many other parameters. In a further embodiment, if the blood is centrifuged, it may further go onto measuring the volume of ceils and plasma and may he able to sense any micro clots within the sample. In a further embodiment, it may be configured to provide information on serum indices. In a further embodiment, accidental spillage in the de-capped stage or while manually handling may be recorded. In a further embodiment, once all the tests are completed for a given patient sample, the sample may be usually stored for a defined period, for any further tests to be conducted under specific conditions to preserve the samples. In a further embodiment, if the laboratory technician or the physician needs to know about the quality of the patient sample, the sensors on the tube may help provide the information required until the samples are finally discarded hi a further embodiment, if some equipment is reused the sensor may be reprogrammed to bear a new' patient ID or number thereby make it re-usable.

In one embodiment, smart tubes may significantly impact on the way samples are handled within a lab. In a further embodiment, the present disclosure addresses most of the latent needs of customers. In a further embodiment, with a sensible use of a safe and energy efficient technology the present disclosure brings about a huge change to customer experience and thereby improve the overall process of handling and managing samples. In a further embodiment, being reusable makes this system more desirable and cost efficient to manufacturers and vendors.

Reference is now made to Figure 3, which is an exemplar)'· laboratory' workflow model in accordance with the embodiments of the present disclosure. Once a patient sample is collected in a smart tube, the smart tube containing the patient sample either goes into an analyzer through laboratory automation or may be manually loaded into the analyzer. The smart tube can be tracked throughout the laboratory' as it is constantly in communication with the central hub computer and the laboratory computer, wherein the laboratory_'· computer and the central hub computer is configured to constantly exchange information to and from the smart tubes.

In one embodiment a number of parameters may be measured that may include for example volume of the sample, wherein the sample may be blood or urine, residual volume of the sample after a test is performed. cell layer, plasma volume which may be useful for pipetting depth computations, mapping the right patient to the right sample, temperature of the sample, serum clarity, clots in the sample, identify STAT and ER samples for separate workflow, time spent on tract, time spent on analyzer, time spent in storage, time since collection of the sample to first analyzer, temperature changes, volume changes, identifying a sample at any point in time in the laboratory', locating a sample in the laboratory' etc.

In one embodiment a computing system (not shown in the figures) may be integrated wi thin the lab instrument for (not represented in Figures), as being an in tegral part of the lab instrument. In a further embodiment, the computing system may reside outside the lab instrument and may be configured to operate the lab instrument. It should be understood that such a computing system is only intended to depict the representative major components of the computing system and that individual components may have greater complexity. Moreover, m addition other components such as the number, type, and configuration of such components may vary for such computer systems. Several particular examples of such additional complexity or additional variations are disclosed herein; it being understood that these are by way of example only and are not necessarily the only such variations. In one embodiment, the computing system may be interfaced with a laboratory' information system (LIS) or a hospital information system (HIS) and/or a repository which may be part of the LIS/HIS or may be separate.

In one embodiment, artificial intelligence, machine learning or rule based systems may be used to perform the operation mentioned above.

This computing system embodiment comprises a plurality of central processing units (herein generically referred to as a processor or a CPU) connected to a main memory unit, a mass storage interface, a terminal/display interface, a network interface, and an input/output ("I/O") interface by a system bus. In a further embodiment, the mass storage interfaces, in turn, connect the system bus to one or more mass storage devices, such as a direct access storage device or a readable/writable optical disk drive. In a further embodiment, the network interfaces allow the computing system to communicate with other computing systems over the communications medium. In a further embodiment, the main memory_' unit in this embodiment also compri ses an operating system, a plurality of application program s (such as the application component manager that may control the lab instrument), and some program data.

In one embodiment, tire computing system is a general-purpose computing device. Accordingly, the CPU’s may be any device capable of executing program instructions stored in the main memory and may themselves be constructed from one or more microprocessors and/or integrated circuits. In a further embodiment, the computing system may contain multiple processors and/or processing cores, as is typical of larger, more capable computer systems; however, the computing systems may include a single processor system and/or a single processor designed to emulate a multiprocessor system.

In a further embodiment, when the computing system starts up, the associated processor(s) initially execute the program instructions that make up the operating system, which manages the physical and logical resources of the computing system. In a further embodiment, these resources include the main memory, the mass storage interface, the terminal/display interface, the network interface, and the system bus. In a further embodiment, as with the processor(s), some computing system may utilize multiple system interfaces and buses, which in turn, may each include their own separate, fully programmed microprocessors.

In one embodiment, the system bus may be any device that facilitates communication between and among the processors; the ma memory; and the interfaces. In a further embodiment, although the system bus is relatively simple, single bus structure that provides a direct communication path among the system bus, other bus structures are within the scope of the present disclosure, including without limitation, point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, etc.

In a further embodiment, the main memory_' and the mass storage devices w'ork cooperatively in this to store the operating system, the application programs, and the program data. In a further embodiment, the mam memory is a random-access semiconductor device capable of storing data and programs. In a further embodiment, this device as a single monolithic entity, the main memory may be a more complex arrangement, such as a hierarchy of caches and other memory devices. In an example embodiment, the main memor may exist in multiple levels of caches, and these caches may be further divided by function, so that one cache holds instructions while another holds non-instruction data, which is used by the processor or processors. In a further embodiment, the memory may be further distributed and associated with different CPUs or sets of CPUs, as is known in any of various so-called non- uniform memory' access (NUMA) computer architectures. Moreover, some embodiments may utilize virtual addressing mechanisms that allow the computing systems to behave as if it has access to a large, single storage entity instead of access to multiple, smaller storage entities such as the mam memory and the mass storage device.

In one embodiment, although the operating system, the application programs, and the program data are typically contained within the main memory , some or ail of them may be physically located on different computing systems and may be accessed remotely, e.g., via the network. In a further embodiment, while the operating system, the application programs, and the program data are typically contained within the main memory, these elements are not necessarily all completely contained in the same physical device at the same time, and may even reside in the virtual memory of other computing systems.

In a further embodiment, the system interface units support communication with a variety of storage and I/O devices hi a further embodiment, the mass storage interface unit supports the attachment of one or more mass storage devices, which are typically rotating magnetic disk drive storage devices, although they could alternatively be other devices, including arrays of disk drives configured to appear as a single large storage device to a host and/or archival storage media, such as hard disk dri ves, tape (e.g., mini-DV), writable compact disks (e.g., CD-R and CD-RW), digital versatile disks (e.g , DVD, DVD-R, DVD+R, DVD+RW, DVD-RAM), holography storage systems, blue laser disks, IBM Millipede devices and the like.

In a further embodiment, the tenninal/display interface is used to directly connect one or more display units to the computing system. In a further embodiment, the display units may be non-mteliigent (i.e., dumb) terminals, such as a cathode ray tube, or may themselves be fully programmable workstations used to allow IT administrators and users to communicate with the computing system or the lab instrument itself. In a further embodiment, however, note that while the interface is provided to support communication with one or more displays, the computing systems does not necessarily require a display because all needed interaction with users and other processes may occur via network interface.

In a further embodiment, the computing system with multiple attached terminals, such as might be typical of a multi-user“mainframe” computer system. In such a case, the actual number of attached devices is typically a larger number. In a further embodiment, the computing systems may alternatively be a single-user system, typically containing only a single user display and keyboard input, or might be a server or similar device which has little or no direct user interface, but receives requests from other computer systems (clients). In other embodiments, the computing systems may be implemented as a personal computer, portable computer, laptop or notebook computer, PDA (Personal Digital Assistant), tablet computer, pocket computer, telephone, pager, automobile, teleconferencing system, appliance, or any other appropriate type of electronic device.

In a further embodiment, the network may be any suitable network or combination of networks and may support any appropriate protocol suitable for communication of data and/or code to/from multiple computing systems. Accordingly, in a further embodiment, tire network interfaces can be any device that facilitates such communication, regardless of whether the network connection is made using present day analog and/or digital techniques or via some networking mechanism of the future. In a further embodiment, suitable communication media include, but are not limited to, networks implemented using one or more of the IEEE (Institute of Electrical and Electronics Engineers) 802.3x“Ethernet” specification: cellular transmission networks; and wireless networks implemented one of the IEEE 802.1 lx, IEEE 802.16, General Packet Radio Service (“GPRS”), FRS (Family Radio Service), or Bluetooth specifications. Those skilled in the art will appreciate that many different network and transport protocols can be used to implement the communication medium. In a further embodiment, the Transmission Control Protocol/Tntemet Protocol ("TCP/IP") suite contains suitable network and transport protocols.

The embodiments described with reference to Figures 1-3 generally may also use a client-server network architecture. These embodiments are desirable because the clients can utilize the services without either computer system requiring knowledge of the working details about the other. However, those skilled the art will appreciate that other network architectures are within the scope of the present invention. Examples of other suitable network architectures include peer-to-peer architectures, grid architectures, and multi-tier architectures. Accordingly, the terms web server and client computer should not be construed to limit the invention to client-server network architectures.

In a further embodiment, the computing system may be operating on different operating systems such Linux, Windows, iOS etc. However, those skilled in the art will appreciate that the methods, systems, and apparatuses of the present disclosure apply equally to any computing system and operating system combination, regardless of whether one or both of the computer systems are complicated multi user computing apparatuses, a single w'orkstations, lap-top computers, mobile telephones, personal digital assistants ("PDAs"), video game systems, or the like.

Although the present disclosure has been described in detail with reference to certain examples thereof, it may be also embodied in other specific forms without departing from the essential spirit or attributes thereof. For example, those skilled in the art will appreciate that the present disclosure is capable of being distributed as a program product in a variety of forms, and applies equally regardless of the particular type of tangible, computer-readable signal bearing medium used to actually cany_' out the distribution. Examples of suitable tangible, computer-readable signal bearing media include, but are not limited to: (i) non -writable storage media (e.g., read only memory devices ("ROM"), CD-ROM disks readable by a CD drive, and Digital Versatile Disks ("DVDs") readable by a DVD drive); (ii) waitable storage media (e.g., floppy disks readable by a diskette drive, CD-R and CD-RW disks readable by a CD drive, random access memory ("RAM ), and hard disk drives); and (iii) communications media (e.g., computer networks, such as those implemented using“Infmiband” or IEEE 802.3x“Ethernet’ specifications; telephone networks, including cellular transmission networks; and wireless networks, such as those implemented using the IEEE 802.1 lx, IEEE 802.16, General Packet Radio Service (“GPRS”), Family Radio Service ("FRS"), and Bluetooth specifications). Those skilled in the art will appreciate that these embodiments specifically include computer software downi-loaded over the Internet.

Embodiments of the present disclosure may also be delivered as part of a service engagement with a client corporation, laboratory information system, hospital system, nonprofit organization, government entity, internal organizational structure, or the like. Aspects of these embodiments may include configuring a computing system to perform, and deploy software, hardware, and services that implement, some or all of tire methods described herein. Aspects of these embodiments may also include analyzing the client’s operations, creating recommendations responsive to the analysis, building systems that implement portions of the recommendations, integrating the systems into existing processes and infrastructure, metering use of the systems, allocating expenses to users of the systems, and billing for use of the systems. Any sen-ice engagement may be directed at providing both the client sendees and the application management sendees may be limited to only application management services, or some combination thereof. Accordingly, these embodiments may further comprise receiving charges from other entities and associating that charge with users of the application m anager.

The various software components illustrated in Figures 1-3 and implementing various embodiments of the disclosure may be implemented in a number of manners, including using various computer software applications, routines, components, programs, objects, modules, data structures, etc., referred to hereinafter as "computer programs," or simply "programs." The computer programs typically comprise one or more instructions that are resident at various times in various memory and storage devices in the computer system, and that, when read and executed by one or more processors in the computing system, cause the computing system to perform steps necessary to execute steps or elements comprising various aspects of an embodiment of the disclosure. The various software components may also be located on different systems. Some embodiments may reside on a computing system and request services from itself or from another computer system. Some embodiments may reside on one or more separate physical devices that are communicatively coupled into a larger, logical computer system .

The accompanying figures and description depicted and described embodiments of the present disclosure, and features and components thereof. Those skilled in the art will appreciate that any particular program nomenclature used in this description was merely for convenience, and thus the present disclosure should not be limited to use solely in any specific application identified and/or implied by such nomenclature. Thus, for example, the routines executed to implement the embodiments of the in vention, whether implemented as part of an operating system or a specific application, component, program, module, object, or sequence of instructions could have been referred to as a "program", "application", "server", or other meaningful nomenclature. Indeed, other alternative hardware and/or software environments may he used without departing from the scope of the invention . Therefore, it is desired that the embodiments described herein be considered in all respects as illustrative, not restrictive, and that reference be made to die appended claims for determining the scope of the invention. Although embodiments of the invention have been described using specific terms, such description is for illustrative purposes only, and it is to be understood that changes and variations may be made without departing from the spirit or scope of the following claims.

Claims

We Claim :

1. A method for real-time tracking of samples in a laboratory, the method comprising

- creating a smart tube, the smart tube comprises a tube comprising a biological sample embedded with a sample identification label, wherein the sample identification label is associated with a patient;

- embedding the tube with a sensor and associating the sensor with the sample identification label for tracking the smart tube real-time in the laboratory and communicating various measurable parameters associated with the patient sample to a laboratory processing device.

2. lire method as claimed in claim 1 , wherein the sensor comprises at least one of a MEMS (micro electromechanical system), a NEMS (nano electromechanical system), and an RFID (radio frequency identifier).

3. The method as claimed in claims 1 , wherein the MEMS is within a range between 0.01 millimeter to 1 millimeter and the NEMS is within a range between 1 nanometer to 100 nanometers.

4. The method as claimed in claim 1, wherein the MEMS and/or the NEMS comprise at least one of a carbon allotrope (having semiconducting properties) or smart dust.

5. The method as claimed in claim 1, wherein the MEMS and/or NEMS is configured to store parameters for tracking the smart tube.

6. The method as claimed in claim 5, wherein the parameters comprises at least one of a patient identification, a volume of the patient sample, a residual volume of the patient sample after a test is performed, a cell layer, a plasma volume of the sample, a temperature of the sample, a serum clarity of the sample, a clot in the sample, a STAT sample, an ER sample for work flow' assignment, a time spent on track, a time spent in an analyzer, a time spent in a storage, a time from collection of a sample to a first time the sample is analyzed, a temperature changes associated with the samples, a volume changes associated with the sample, a location associated with the sample at any given point in time, an identity associated with the sample.

7. The method as claimed in claim 1 , wherein the smart tube is coupled to a hub, and each hub configured to retain information of the smart tubes associated with the hub, and each hub coupled to a laboratory computer.

8. The method as claimed in claim 7, wherein the hub and the laboratory' computer are communicatively coupled to each other, and are configured to bidirectionally exchange information.

9. The method as claimed in claim 6, wherein the hub is configured to track the smart tubes associated with the hub and report the smart tubes within the hub to the laboratory computer.

10. A system comprising a plurality of smart tubes, wherein the smart tubes are coupled to a hub, the system further comprising a plurality of hubs coupled to a laboratory computer, the system configured to perform the method as claimed on any of the claims.