Systems and methods for diagnostic laboratory staffing

Abstract

In some embodiments, a staffing recommendation tool for a diagnostic laboratory is provided that includes a processor and a memory coupled to the processor. The memory includes computer program instructions that, when executed by the processor, cause the processor to receive local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; access a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff-related data; employ the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and output the staffing recommendations for the local diagnostic laboratory. Numerous other aspects are provided.

Description

SYSTEMS AND METHODS FOR DIAGNOSTIC LABORATORY STAFFINGCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims benefit under 35 USC § 119(e) of U.S. Provisional Patent Application No. 63 / 548,886, filed on February 2, 2024, the disclosure of which is hereby incorporated by reference herein in its entirety.FIELD

[0002] The present application relates to diagnostic laboratories and more particularly to systems and methods for diagnostic laboratory staffing.BACKGROUND

[0003] Diagnostic laboratories play a critical role in healthcare, providing essential services such as medical testing and diagnostics. Ensuring efficient and cost-effective staffing is crucial for maintaining high-quality patient care while managing operational expenses.

[0004] Current methods for staffing a diagnostic laboratory typically assess the expected number of tasks to be performed, the equipment available, the time required for each task to be performed at each piece of equipment, the time constraints associated with each task (e.g., how quickly each task must be performed), and the type of staff required for each task, among other factors. Based on this information, relatively straightforward calculations may be performed to determine how many and what type of staff are required for the diagnostic laboratory.

[0005] While the above approach may be employed to guide staffing decisions, a more accurate staff recommendation approach is desired.SUMMARY

[0006] In some embodiments, a staffing recommendation tool for a diagnostic laboratory is provided that includes a processor and a memory coupled to the processor. The memory includes computer program instructions that, when executed by the processor, cause the processor to receive local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; access a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff-related data; employ the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and output the staffing recommendations for the localdiagnostic laboratory.

[0007] In some embodiments, a method of creating staffing recommendations for a diagnostic laboratory includes receiving, via a processor, local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; accessing, via the processor, a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff-related data; employing, via the processor, the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and outputting the staffing recommendations for the local diagnostic laboratory.

[0008] In some embodiments, a non-transitory computer-readable medium is provided for storing a set of instructions for creating staffing recommendations for diagnostic laboratories, the set of instructions comprising one or more instructions that, when executed by one or more processors of a device, cause the device to receive local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; access a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff- related data; employ the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and output the staffing recommendations for the local diagnostic laboratory.

[0009] Other features and aspects of the present invention will become more fully apparent from the following detailed description, the appended claims, and the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG. 1A illustrates a flow diagram of a method of providing staffing recommendations for a local diagnostic laboratory using a staffing tool in accordance with embodiments provided herein.

[0011] FIG. 1 B illustrates an example embodiment of the staffing tool of FIG. 1 A in accordance with one or more embodiments provided herein.

[0012] FIG. 2 illustrates a method of creating staffing recommendations for a diagnostic laboratory in accordance with embodiments provided herein.

[0013] FIG. 3A illustrates an example of the user interface of FIG. 1B displaying staffing recommendations in accordance with embodiments provided herein.

[0014] FIG. 3B illustrates example matching information for a plurality of externaldiagnostic laboratories in accordance with embodiments provided herein.

[0015] FIG. 3C illustrates an example of the user interface of FIG. 1 B displaying matching scores for a plurality of external diagnostic laboratories relevant to the local diagnostic laboratory of FIG. 1A in accordance with embodiments provided herein.DETAILED DESCRIPTION

[0016] Independent of the grammatical term usage, individuals with male, female or other gender identities are included within the term.

[0017] As stated above, current approaches for generating staffing recommendations for diagnostic laboratories rely primarily on user information such as the expected number of tasks (e.g., workload), the equipment available, the time required for each task to be performed at each piece of equipment, the time constraints associated with each task, the type of staff required for each task, etc.

[0018] Embodiments provided herein include systems and methods for a comprehensive, data-driven solution for staffing recommendations that factor in the workload, equipment, and specific tasks performed by staff in diagnostic laboratories. In addition to user-provided data (also referred to as “local staff-related data”) for the diagnostic laboratory for which staffing recommendations are to be provided (referred to as the “local diagnostic laboratory”), systems and methods provided herein employ staffing information for similar diagnostic laboratories (e.g., stored in a global database and referred to as “external diagnostic laboratories”) to recommend a more optimal staffing structure. Such staffing information from one or more external diagnostic laboratories may be referred to herein as “external staff-related data.”

[0019] In some embodiments, a staffing recommendation tool (also referred to as a “staffing tool”) may be provided for making staffing recommendations for a local diagnostic laboratory. A user may provide data (local staff-related data) to the staffing tool, such as workload, laboratory schedule, equipment mix, task definitions, roles, and / or job categories. The staffing tool may compute staffing recommendations based on the local staff-related data and also determine if there are one or more matching diagnostic laboratories within the global database based on the local staff-related data. If there are matching diagnostics laboratories within the global database, the staffing tool may determine staffing recommendations based on these matching diagnostic laboratories. In one or more embodiments, the staffing tool may display both the staffing recommendations computed based on the local staff-related data and any staffing recommendations determined based on matching diagnostic laboratories (thus employing both task allocation and peer-based benchmarking).

[0020] Workload may include a weekly worklist, specifying the number of samples andtests the laboratory expects to handle. A laboratory schedule may include who is in the laboratory and available to perform required tasks (e.g., time-sensitive tasks). An equipment mix may include the equipment in the laboratory. In some embodiments, this may be inferred from the worklist and supplemented by user input.

[0021] Task definitions may include common laboratory tasks alongside equipment and workstation-specific tasks for the laboratory. In some embodiments, the staffing tool may include and / or otherwise communicate with a database of common laboratory tasks alongside equipment and workstation-specific tasks to represent a complete picture of labor to be accounted for. Additionally, in some embodiments, the staffing tool may allow users to define custom tasks performed by different roles within the laboratory and specify the typical duration for each task. Roles may include a collection of related tasks to be performed in a given time period (e.g., per day). In some embodiments, the staffing tool may allow a user to define different roles to complete tasks. Some roles may be restricted to the job categories that may perform them. Job categories may include job experience, training, cost, or the like.

[0022] The staffing tool may employ the above local staff-related data as well as staffing information from external diagnostic laboratories (e.g., peer diagnostic laboratories) to improve staffing recommendations. For example, the staffing tool may distinguish and optimize staffing recommendations for the type and / or cost of staff required in addition to the number of staff based on both task allocation and peer-based benchmarking. These and other embodiments of the invention are described below with reference to FIGS. 1 A-3C.

[0023] FIG. 1 A illustrates a flow diagram 100 of a method of providing staffing recommendations 102 for a local diagnostic laboratory 104 using a staffing tool 106 in accordance with embodiments provided herein. With reference to FIG. 1A, local staff-related data 108 from local diagnostic laboratory 104 is provided to staffing tool 106. Staffing tool 106 analyzes the local staff-related data 108, as well as external staff-related data 110 (e.g., stored in a global database 112), and provides the staffing recommendations 102 for local diagnostic laboratory 104.

[0024] As described further below, global database 112 may include staff-related data (e.g., external staff-related data 110) from a plurality of other diagnostic laboratories (e.g., external diagnostic laboratories 114a-n). In general, external diagnostic laboratories 114a-n may be other diagnostic laboratories located anywhere throughout the world. Some of these external diagnostic laboratories may be relevant to local diagnostic laboratory 104 (e.g., because the external diagnostic laboratories have similar staffing requirements due to having similar equipment, processes, workloads, laboratory type, etc.).

[0025] FIG. 1B illustrates an example embodiment of staffing tool 106 of FIG. 1A in accordance with one or more embodiments provided herein. With reference to FIG. 1B, staffing tool 106 includes a processor 116 coupled to a memory 118. Memory 118 may storelocal staff-related information (e.g., local staff-related information 108) relevant to determining staffing recommendations for local diagnostic laboratory 104. For example, in some embodiments, local staff-related information 108 may include workload information 120, laboratory schedule information 122, equipment mix information 124, task definition information 126, role information 128, job category information 130, or the like, as described further below. Other types and / or amounts of information may be employed. In some embodiments memory 118 may also store external staff-related information 110 within global database 112. In other embodiments, this information may be stored elsewhere (e.g., outside memory 118 either locally or remotely).

[0026] Memory 118 may also include one or more program(s) 132, such as computer executable instructions and / or code, for carrying out the methods described herein when executed by processor 116. As described further below, in one or more embodiments, program(s) 132 may include computer program instructions that, when executed by processor 116, cause processor 116 to receive local staff-related data 108 for local diagnostic laboratory 104, access global database 112 to obtain external staff-related data 110 for one or more other diagnostic laboratories relevant to local staff-related data 108 and local diagnostic laboratory 104, and employ the local staff-related data and the external staff- related data to determine staffing recommendations for local diagnostic laboratory 104. In some embodiments, processor 116 may receive information via a user interface 134 (e.g., a touchscreen or other display, keyboard, microphone, etc.) and / or output information to a user (e.g., staffing recommendations) via user interface 134.

[0027] In one or more embodiments, processor 116 may be a computational resource such as, but not limited to, a microprocessor, a microcontroller, an embedded microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA) configured to perform as a microcontroller, or the like. Additionally, in some embodiments, multiple processors may be employed.

[0028] Memory 118 may be any suitable type of memory, such as, but not limited to, one or more of a volatile memory and / or a non-volatile memory. In one or more embodiments, memory 118 may be a non-transitory memory (e.g., a hard drive, a solid-state drive, a flash drive, another non-transitory computer-readable medium, etc.).

[0029] Memory 118 may have a plurality of instructions stored therein that, when executed by processor 116, cause processor 116 to perform various actions specified by one or more of the stored instructions. Memory 118 may include multiple memory units and / or types of memory. In some embodiments, all or a portion of memory 118 may be external memory and / or remote from processor 116. For example, global database 112 with external staff-related information 110 may be stored in an external memory 118E which may be local or remote (e.g., a remote memory accessible via a network such as cloud-basedmemory / storage). In other embodiments, global database 112 may be local (e.g., within memory 118) and synchronized with external data (e.g., updated periodically).

[0030] FIG. 2 illustrates a method 200 of creating staffing recommendations for a diagnostic laboratory in accordance with embodiments provided herein. With reference to FIG. 2, in block 202, method 200 includes receiving, via a processor, local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory. For example, a user may provide local staff-related data 108 for local diagnostic laboratory 104 (e.g., employing user interface 134 controlled by processor 116 of staffing tool 106) of FIGS. 1A and 1 B. In some embodiments, workload may include a weekly worklist, specifying the number of samples and tests local diagnostic laboratory 104 expects to handle. A laboratory schedule may include who is in the laboratory and available to perform required tasks (e.g., time-sensitive tasks). The available equipment (e.g., equipment mix) may include the equipment in local diagnostic laboratory 104 that is in service and / or otherwise available. In some embodiments, this may be inferred from the worklist and supplemented by user input (e.g., via user interface 134).

[0031] Task to be performed (e.g., task definitions) may include common laboratory tasks alongside equipment and workstation-specific tasks for local diagnostic laboratory 104. In some embodiments, staffing tool 106 may include and / or otherwise communicate with a database (e.g., stored in memory 118 and / or external memory 118E of FIG. 1 B) of common laboratory tasks alongside equipment and workstation-specific tasks to represent a complete picture of labor to be accounted for. Additionally, in some embodiments, staffing tool 106 may allow users to define custom tasks performed by different roles within local diagnostic laboratory 104 and specify the typical duration for each task. Roles may include a collection of related tasks to be performed in a given time period (e.g., per day). In some embodiments, staffing tool 106 may allow a user to define different roles to complete tasks. Some roles may be restricted to the job categories that may perform them. Job categories may include job experience, training, cost, or the like. Additional and / or alternative local staff- related data may be provided to staffing tool 106.

[0032] In some embodiments, memory 118 may include one or more instructions (e.g., within program(s) 132) that, when executed by processor 116 of staffing tool 106, cause staffing tool 106 to prompt a user, such as via user interface 134, to provide a number of samples and tests local diagnostic laboratory 104 will process, a work schedule for local diagnostic laboratory 104, a laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for local diagnostic laboratory 104.

[0033] In block 204, method 200 includes accessing, via the processor, a global database to obtain external staff-related data for one or more other diagnostic laboratoriesrelevant to the local staff- related data. For example, staffing tool 106 may access external staff-related data 110 stored in global database 112 (e.g., stored in local memory 118 and / or external memory 118E as shown in FIG. 1B). In some embodiments, external staff-related data 110 may be stored on a remote server accessible via a network such as an intranet, the Internet, or the like.

[0034] In general, through use of global database 112, external staff-related data 110 may be obtained from one or more external diagnostic laboratories (e.g., external diagnostic laboratories 114a-n) that are unrelated or otherwise unaffiliated with local diagnostic laboratory 104. For example, staffing information from tens, hundreds, or even thousands or more of diagnostic laboratories may be compiled within global database 112. Thus, external staff-related data 110 may include staff-related data from diagnostic laboratories having a wide range of workloads, laboratory schedules, available equipment, tasks to be performed, staff roles, and job categories.

[0035] In some embodiments, obtaining external staff-related data from one or more diagnostic laboratories relevant to local diagnostic laboratory 104 may include obtaining external staff-related data from at least one peer diagnostic laboratory. For example, a peer diagnostic laboratory may be selected (e.g., by staffing tool 106) based on at least one of laboratory type, equipment, laboratory schedule, workload information, staff information, etc., being similar to that of local diagnostic laboratory 104.

[0036] In one or more embodiments, the data (e.g., external staff-related data 110) within global database 112 may be populated by gathering all of the relevant data (e.g., local staff-related data 108 such as equipment, tasks, worklist, schedule, etc.) from diagnostic laboratories during regular customer visits / engagements (e.g. laboratory “health checks”), in response to a customer’s workflow, equipment, and / or volume changes, etc. In some embodiments, a processor of each diagnostic laboratory may communicate (e.g., automatically) local staffing changes within the diagnostic laboratory to global database 112.

[0037] In block 206, method 200 includes employing, via the processor, the local staff- related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory. For example, processor 116 may execute computer program instructions (e.g., one or more programs 132) to analyze local staff-related data 108 provided for local diagnostic laboratory 104 and external staff-related data 110 obtained from global database 112 to determine staffing recommendations for local diagnostic laboratory 104.

[0038] In one or more embodiments, staffing tool 106 may process user-provided data (e.g., local staff-related data 108) and determine staffing recommendations using task allocation and peer-based benchmarking. Task allocation may include employing staffing tool 106 to organize tasks, roles, and time requirements so as to recommend the number offull-time and / or part-time staff members required to efficiently complete all tasks based on local staff-related data 108 provided for local diagnostic laboratory 104. For example, processor 116 may analyze the expected number of tasks to be performed, the equipment available, the time required for each task to be performed at each piece of equipment, the time constraints associated with each task (e.g., how quickly each task must be performed), the type of staff required for each task, etc., to determine staffing recommendations.

[0039] In some embodiments, an arithmetic / algorithmic approach may be employed to determine staffing recommendations based on local staff-related data 108 (e.g., task-based staffing). For example, local staff-related data 108 may include workstation information such as the instruments, modules, or laboratory processes employed, task information associated with each workstation such as task duration, task schedule, and task frequency, role information such as who can perform each task, etc. The workstation, task, and role information may be used to determine all tasks to be performed and how many people are required to perform the scheduled tasks during a given time period (e.g., a workday).

[0040] Additionally, by accessing external staff-related data 110 within global database 112, staffing tool 106 may provide staffing recommendations based on the staffing structure of similar peer or cohort laboratories (e.g., one or more of external diagnostic laboratories 114a-n). In some embodiments, laboratory matching may be performed along a number of distinguishing vectors (e.g., type of lab, equipment mix, staff roles, staff tasks, worklists, headcount, etc.) to identify a number of similar laboratories. In one or more embodiments, a matching score may be produced for each identified laboratory and a user may select from a number of possible matches to obtain staffing recommendations. This feature offers a comparative analysis and may help laboratories align their staffing with industry standards.

[0041] Thus, staffing tool 106 may employ local staff-related data 108 from local diagnostic laboratory 104 and external staff-related data 110 (from global database 112) to identify laboratories that perform a similar amount and type of work. Further, staffing tool 106 may employ staffing plans from such laboratories to create a staffing proposal for local diagnostic laboratory 104. For example, in some embodiments, a laboratory similarity vector may be defined that includes information about laboratory type (e.g., hospital laboratory, reference laboratory, etc.), laboratory location (e.g., geographic information), patient demographics (e.g., human, animal, adult, pediatric, etc.), production schedule (e.g., 9AM to 5PM weekdays, 24 hours 7 days per week, etc.), primary testing disciplines, or the like. Further, a work similarity vector may be defined that includes equipment used (e.g., preprocessing, post processing, and / or analytical equipment), whether and / or type of automation used, number of samples processed per time period (e.g., daily, weekly, etc.), test density (e.g., number of tests per sample), test mix (e.g., types of tests per sample), sample types (e.g., blood, plasma, urine, fecal, etc.), system software and / or process used,requirements (e.g., regulatory requirements, quality requirements, etc.), or the like. In some embodiments, staffing tool 106 may examine staffing information from external laboratories (stored as external staff-related data 110 in global database 112) having a laboratory similarity vector and / or work similarity vector that is comparable to that of local diagnostic laboratory 104. This may include, for example, identifying external diagnostic laboratories that have laboratory similarity vectors and / or work similarity vectors that are the same as, share several features of, or have features within a predetermined percentage of those of the laboratory similarity vector and / or work similarity vector of local diagnostic laboratory 104 (e.g., the same or a similar laboratory type, location, production schedule, primary testing discipline, equipment, automation, number of samples processed, test density, sample types, system software and / or process used, requirements, etc.).

[0042] Referring again to FIG. 2, in block 208, method 200 may include outputting staffing recommendations for the local diagnostic laboratory. FIG. 3A illustrates an example of user interface 134 of FIG. 1 B displaying staffing recommendations in accordance with embodiments provided herein. In addition to (and / or in place of) matching scores, staffing tool 106 may output staffing recommendations such as task-based staffing recommendations 300a (e.g., computed staffing recommendations based on local staff- related data 108) and, when available, peer-based staffing recommendations 300b determined from one or more matching diagnostic laboratories within global database 112 (e.g., such matches determined based on local staff-related data 108 of local diagnostic laboratory 104). That is, in some embodiments, staffing recommendations may be based on both calculations performed on local staff-related data 108 from local diagnostic laboratory 104 (e.g., task-based staffing recommendations 300a) as well as based on information from external diagnostic laboratories identified by examining global database 112 for laboratories that are similar to local diagnostic laboratory 104 (e.g., peer-based staffing recommendations 300b). As stated, peer-based staffing recommendations 300b may be based on laboratory similarity and work similarity, for example.

[0043] FIG. 3B illustrates example matching information for a plurality of external diagnostic laboratories in accordance with embodiments provided herein. For example, processor 116 may examine external staff-related information 110 within global database 112, determine matching scores, identify external laboratories having high matching scores (e.g., Lab 10, Lab 4, Lab 6, and Lab 15), and provide details regarding each matching laboratory. Other numbers of matching laboratories and / or other information may be provided.

[0044] In some embodiments, user interface 134 may provide staffing recommendations from external staff-related information 110 that update and / or override the calculated staffing plan based on local staff-related data 108 alone (e.g., using anarithmetic / algorithmic approach). Further, in some embodiments, global database 112 may store actual and calculated head counts / roles (e.g., head counts / roles calculated based using an arithmetic / algorithmic approach with only local staff-related data 108). In one or more embodiments, user interface 134 may ask users how they feel about their current actual staffing (e.g., just right, under-staffed, excess capacity, role mismatches, etc.). This additional information may be included within global database 112 and provided to users as part of the information displayed for matching laboratories (e.g., in addition to the information shown in FIG. 3B). Further, such information may be employed by processor 116 to adjust algorithmic calculations to be more accurate.

[0045] FIG. 3C illustrates an example of user interface 134 of FIG. 1 B displaying matching scores 302a-d for a plurality of external diagnostic laboratories (e.g., Lab 4, Lab 6, Lab 10, and Lab 15) relevant to local diagnostic laboratory 104 in accordance with embodiments provided herein. In the embodiment of FIG. 3C, four matching scores 302a-d are shown and matching scores may range from 0-10, with 10 being a perfect match. Other numbers, ranges, and / or types of matching scores may be employed.

[0046] Each matching score 302a-d may be based on any relevant factors. In some embodiments, matching scores 302a-d may be calculated based on a laboratory similarity vector 304 and a work similarity vector 306 for each relevant external diagnostic laboratory (example vectors are shown for Lab 15 in FIG. 3C). For example, a sub-score may be determined for laboratory similarity vector 304 and a sub-score may be determined for work similarity vector 306 and the two sub-scores may be combined (e.g., added) to form a matching score. User interface 134 may display matching scores 302a-d of the identified external diagnostic laboratories (e.g., Labs 4, 6, 10, and 15). Thus, in at least some embodiments, staffing tool 106 may determine a matching score for (and / or based on) at least one external diagnostic laboratory and may output the matching score. Example staffing recommendations 308a-d are also shown.

[0047] Staffing recommendations may be developed for local diagnostic laboratory 104 from a plurality of external diagnostic laboratories (e.g., peer diagnostic laboratories identified as being similar to local diagnostic laboratory 104 by having high matching scores). For example, the closest matching features of relevant external diagnostic laboratories may be used to formulate staffing recommendations for local diagnostic laboratory 104 such as by combining, averaging, interpolating between, or otherwise basing staffing recommendations on the staffing employed by external diagnostic laboratories. In some embodiments, a first peer diagnostic laboratory may be identified that has a larger staff and larger workload than local diagnostic laboratory 104 and a second peer diagnostic laboratory may be identified that has a smaller staff and smaller workload than local diagnostic laboratory 104. An average of the number of staff members of the larger peer diagnosticlaboratory and the smaller peer diagnostic laboratory may be provided as a staffing recommendation for local diagnostic laboratory 104. Likewise, interpolation between the staff size of the larger and smaller peer laboratories may be used to determine staffing recommendations for local diagnostic laboratory 104.

[0048] Staffing tool 106 provides data-driven staffing recommendations for diagnostic laboratories. These recommendations are enhanced by basing staffing recommendations, at least in part, on staffing employed at similar, external diagnostic laboratories, leading to improved operational efficiency and cost effectiveness. Further, such an approach provides alignment with industry best practices through benchmarking. The ability to provide evidence-based staffing recommendations may benefit healthcare institutions and laboratories by improving efficiency, reducing costs, and enhancing patient care.

[0049] In some embodiments, staffing tool 106 may trigger updated staffing recommendations based on customer workflow, equipment, and / or volume changes. Further a consultant may facilitate a staffing plan update in response to customer changes.

[0050] In some embodiments, a non-transitory computer-readable medium such as a hard drive, a solid-state drive, a flash drive, a digital versatile disc (DVD), or the like, may be provided that stores a set of instructions for creating staffing recommendations for diagnostic laboratories. The set of instructions may include one or more instructions that, when executed by one or more processors of a device, such as staffing tool 106 (e.g., a programmed desktop, laptop, or tablet computer), cause the device to receive local staff- related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; access a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff-related data; employ the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and output the staffing recommendations for the local diagnostic laboratory.NON-LIMITING ILLUSTRATIVE EMBODIMENTS

[0051] The following is a list of non-limiting embodiments of inventive concepts disclosed herein:

[0052] An illustrative staffing recommendation tool for a diagnostic laboratory, comprising a processor; and a memory coupled to the processor, the memory including computer program instructions that, when executed by the processor, cause the processor to: receive local staff-related data for a local diagnostic laboratory, wherein the local staff- related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory;access a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff-related data; employ the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and output the staffing recommendations for the local diagnostic laboratory.

[0053] The illustrative staffing recommendation tool of any of the proceeding illustrative embodiments, wherein the memory includes computer program instructions that, when executed by the processor, cause the processor to: compute staffing recommendations based on the local staff-related data; determine if there are one or more matching diagnostic laboratories within the global database based on the local staff-related data and, if so, determine staffing recommendations based on the one or more matching diagnostic laboratories; and display the staffing recommendations computed based on the local staff- related data and any staffing recommendations determined based on the one or more matching diagnostic laboratories.

[0054] The illustrative staffing recommendation tool of any of the proceeding illustrative embodiments, wherein the global database is stored in the memory.

[0055] The illustrative staffing recommendation tool of any of the proceeding illustrative embodiments, wherein the global database is located in an external memory.

[0056] The illustrative staffing recommendation tool of any of the proceeding illustrative embodiments, wherein the external staff-related data is from at least one external diagnostic laboratory.

[0057] The illustrative staffing recommendation tool of any of the proceeding illustrative embodiments, wherein the memory includes computer program instructions that, when executed by the processor, cause the processor to select the at least one external diagnostic laboratory based on at least one of laboratory type, equipment, laboratory schedule, workload information, and staff information.

[0058] The illustrative staffing recommendation tool of any of the proceeding illustrative embodiments, wherein the external staff-related data is from a plurality of external diagnostic laboratories.

[0059] The illustrative staffing recommendation tool of any of the proceeding illustrative embodiments, wherein the memory includes computer program instructions that, when executed by the processor, cause the processor to determine a matching score for the at least one external diagnostic laboratory.

[0060] The illustrative staffing recommendation tool of any of the proceeding illustrative embodiments, wherein the memory includes computer program instructions that, when executed by the processor, cause the processor to output the matching score and staffing data for the at least one external diagnostic laboratory.

[0061] A illustrative method of creating staffing recommendations for a diagnostic laboratory, comprising: receiving, via a processor, local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; accessing, via the processor, a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff-related data; employing, via the processor, the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and outputting the staffing recommendations for the local diagnostic laboratory.

[0062] The illustrative method of any of the proceeding illustrative embodiments, wherein employing, via the processor, the local staff-related data and the external staff- related data to determine staffing recommendations for the local diagnostic laboratory comprises: computing, via the processor, staffing recommendations based on the local staff- related data; and determining, via the processor, if there are one or more matching diagnostic laboratories within the global database based on the local staff-related data and, if so, determining, via the processor, staffing recommendations based on the one or more matching diagnostic laboratories.

[0063] The illustrative method of any of the proceeding illustrative embodiments, wherein outputting the staffing recommendations for the local diagnostic laboratory comprises displaying the staffing recommendations computed based on the local staff- related data and any staffing recommendations determined based on the one or more matching diagnostic laboratories.

[0064] The illustrative method of any of the proceeding illustrative embodiments, wherein the external staff-related data is from at least one external diagnostic laboratory.

[0065] The illustrative method of any of the proceeding illustrative embodiments, further comprising selecting the at least one external diagnostic laboratory based on at least one of laboratory type, equipment, laboratory schedule, workload information, and staff information.

[0066] The illustrative method of any of the proceeding illustrative embodiments, further comprising determining a matching score for the at least one external diagnostic laboratory.

[0067] The illustrative method of any of the proceeding illustrative embodiments, further comprising outputting the matching score and staffing data for the at least one external diagnostic laboratory.

[0068] The illustrative method of any of the proceeding illustrative embodiments, further comprising outputting matching scores and staffing data for a plurality of external diagnostic laboratories.

[0069] A illustrative non-transitory computer-readable medium storing a set ofinstructions for creating staffing recommendations for diagnostic laboratories, the set of instructions comprising one or more instructions that, when executed by one or more processors of a device, cause the device to: receive local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; access a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff- related data; employ the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and output the staffing recommendations for the local diagnostic laboratory.

[0070] The illustrative non-transitory computer-readable medium of any of the proceeding illustrative embodiments, further comprising one or more instructions that, when executed by one or more processors of the device, cause the device to: compute staffing recommendations based on the local staff-related data; determine if there are one or more matching diagnostic laboratories within the global database based on the local staff-related data and, if so, determine staffing recommendations based on the one or more matching diagnostic laboratories; and display the staffing recommendations computed based on the local staff-related data and any staffing recommendations determined based on the one or more matching diagnostic laboratories.

[0071] The illustrative non-transitory computer-readable medium of any of the proceeding illustrative embodiments, wherein the external staff-related data is from at least one external diagnostic laboratory.

[0072] The illustrative non-transitory computer-readable medium of any of the proceeding illustrative embodiments, further comprising one or more instructions that, when executed by one or more processors of the device, cause the device to select the at least one external diagnostic laboratory based on at least one of laboratory type, equipment, laboratory schedule, workload information, and staff information.

[0073] The illustrative non-transitory computer-readable medium of any of the proceeding illustrative embodiments, further comprising one or more instructions that, when executed by one or more processors of the device, cause the device to determine a matching score for the at least one external diagnostic laboratory.

[0074] The illustrative non-transitory computer-readable medium of any of the proceeding illustrative embodiments, further comprising one or more instructions that, when executed by one or more processors of the device, cause the device to output the matching score and staffing data for the at least one external diagnostic laboratory.

[0075] The illustrative non-transitory computer-readable medium of any of the proceeding illustrative embodiments, further comprising one or more instructions that, whenexecuted by one or more processors of the device, cause the device to prompt a user to provide a number of samples and tests the local diagnostic laboratory will process, a work schedule for the local diagnostic laboratory, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory.

[0076] The foregoing description discloses only example embodiments of the invention. Modifications of the above disclosed apparatus and methods which fall within the scope of the invention will be readily apparent to those of ordinary skill in the art.

[0077] Accordingly, while the present invention has been disclosed in connection with example embodiments thereof, it should be understood that other embodiments may fall within the spirit and scope of the invention, as defined by the following claims.

Claims

WHAT IS CLAIMED IS:

1. A staffing recommendation tool for a diagnostic laboratory, comprising: a processor; and a memory coupled to the processor, the memory including computer program instructions that, when executed by the processor, cause the processor to: receive local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; access a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff-related data; employ the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and output the staffing recommendations for the local diagnostic laboratory.

2. The staffing recommendation tool of claim 1, wherein the memory includes computer program instructions that, when executed by the processor, cause the processor to: compute staffing recommendations based on the local staff-related data; determine if there are one or more matching diagnostic laboratories within the global database based on the local staff-related data and, if so, determine staffing recommendations based on the one or more matching diagnostic laboratories; and display the staffing recommendations computed based on the local staff- related data and any staffing recommendations determined based on the one or more matching diagnostic laboratories.

3. The staffing recommendation tool of claim 1, wherein the global database is stored in the memory.

4. The staffing recommendation tool of claim 1, wherein the global database is located in an external memory.

5. The staffing recommendation tool of claim 1, wherein the external staff- related data is from at least one external diagnostic laboratory.

6. The staffing recommendation tool of claim 5, wherein the memory includes computer program instructions that, when executed by the processor, cause the processor to select the at least one external diagnostic laboratory based on at least one of laboratory type, equipment, laboratory schedule, workload information, and staff information.

7. The staffing recommendation tool of claim 5, wherein the external staff- related data is from a plurality of external diagnostic laboratories.

8. The staffing recommendation tool of claim 5, wherein the memory includes computer program instructions that, when executed by the processor, cause the processor to determine a matching score for the at least one external diagnostic laboratory.

9. The staffing recommendation tool of claim 8, wherein the memory includes computer program instructions that, when executed by the processor, cause the processor to output the matching score and staffing data for the at least one external diagnostic laboratory.

10. A method of creating staffing recommendations for a diagnostic laboratory, comprising: receiving, via a processor, local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; accessing, via the processor, a global database to obtain external staff- related data for one or more other diagnostic laboratories relevant to the local staff-related data; employing, via the processor, the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and outputting the staffing recommendations for the local diagnostic laboratory.

11. The method of claim 10, wherein employing, via the processor, the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory comprises: computing, via the processor, staffing recommendations based on the local staff-related data; anddetermining, via the processor, if there are one or more matching diagnostic laboratories within the global database based on the local staff-related data and, if so, determining, via the processor, staffing recommendations based on the one or more matching diagnostic laboratories.

12. The method of claim 11 wherein outputting the staffing recommendations for the local diagnostic laboratory comprises displaying the staffing recommendations computed based on the local staff-related data and any staffing recommendations determined based on the one or more matching diagnostic laboratories.

13. The method of claim 10, wherein the external staff-related data is from at least one external diagnostic laboratory.

14. The method of claim 13, further comprising selecting the at least one external diagnostic laboratory based on at least one of laboratory type, equipment, laboratory schedule, workload information, and staff information.

15. The method of claim 13, further comprising determining a matching score for the at least one external diagnostic laboratory.

16. The method of claim 15, further comprising outputting the matching score and staffing data for the at least one external diagnostic laboratory.

17. The method of claim 15, further comprising outputting matching scores and staffing data for a plurality of external diagnostic laboratories.

18. A non-transitory computer-readable medium storing a set of instructions for creating staffing recommendations for diagnostic laboratories, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: receive local staff-related data for a local diagnostic laboratory, wherein the local staff-related data includes at least one of workload, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory; access a global database to obtain external staff-related data for one or more other diagnostic laboratories relevant to the local staff-related data;employ the local staff-related data and the external staff-related data to determine staffing recommendations for the local diagnostic laboratory; and output the staffing recommendations for the local diagnostic laboratory.

19. The non-transitory computer-readable medium of claim 18, further comprising one or more instructions that, when executed by one or more processors of the device, cause the device to: compute staffing recommendations based on the local staff-related data; determine if there are one or more matching diagnostic laboratories within the global database based on the local staff-related data and, if so, determine staffing recommendations based on the one or more matching diagnostic laboratories; and display the staffing recommendations computed based on the local staff- related data and any staffing recommendations determined based on the one or more matching diagnostic laboratories.

20. The non-transitory computer-readable medium of claim 18, wherein the external staff-related data is from at least one external diagnostic laboratory.

21. The non-transitory computer-readable medium of claim 20, further comprising one or more instructions that, when executed by one or more processors of the device, cause the device to select the at least one external diagnostic laboratory based on at least one of laboratory type, equipment, laboratory schedule, workload information, and staff information.

22. The non-transitory computer-readable medium of claim 20, further comprising one or more instructions that, when executed by one or more processors of the device, cause the device to determine a matching score for the at least one external diagnostic laboratory.

23. The non-transitory computer-readable medium of claim 22, further comprising one or more instructions that, when executed by one or more processors of the device, cause the device to output the matching score and staffing data for the at least one external diagnostic laboratory.

24. The non-transitory computer-readable medium of claim 18, further comprising one or more instructions that, when executed by one or more processors of thedevice, cause the device to prompt a user to provide a number of samples and tests the local diagnostic laboratory will process, a work schedule for the local diagnostic laboratory, laboratory schedule, available equipment, tasks to be performed, staff roles, and job categories for the local diagnostic laboratory.

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