System and method for predicting laboratory proficiency testing readiness

Abstract

A system and method are provided for predicting a clinical diagnostic laboratory's readiness to meet regulatory proficiency testing (PT) standards, such as the CLIA 2024 regulatory standards. The system obtains laboratory performance data including Lab Mean, Lab Standard Deviation (SD), and Peer Mean for a set of analytes, and models expected result distributions. Using regulatory acceptance limits, the system computes the probability of individual result failure, PT event failure, and regulatory sanction based on binomial models. The system may generate graphical outputs such as distribution overlays and risk contour plots, enabling laboratories to proactively evaluate and improve PT performance. The system may be deployed as a standalone tool or integrated into a laboratory quality management environment.

Description

SYSTEM AND METHOD FOR PREDICTING LABORATORY PROFICIENCY TESTING READINESSCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 675,942, filed on July 26, 2024, the disclosure of which is hereby incorporated herein in its entirety by reference.BACKGROUND OF THE INVENTION

[0002] The present invention relates generally to the field of clinical diagnostics, and more particularly to systems and methods for evaluating and predicting a clinical diagnostic laboratory’s readiness to meet updated proficiency testing performance criteria under regulatory guidelines.

[0003] In clinical laboratory practice, laboratories are typically required to participate in industry and / or regulatory proficiency testing (PT) programs to ensure their ability to accurately and reliably test patient specimens. These programs usually evaluate a laboratory’ s performance using unknown samples, with the results of the laboratory’s testing assessed against peer group performance and defined quality specifications. For example, regulatory bodies such as the Centers for Medicare & Medicaid Services (CMS) implement proficiency testing standards under the Clinical Laboratory Improvement Amendments (CLIA), which establish performance thresholds for a broad set of analytes used in clinical diagnostic laboratories.

[0004] Recent industry regulatory changes, such as updates to the existing CLIA 2024 Proficiency Testing standards, significantly revise the performance criteria for clinical laboratories in the United States. For instance, of the over one-hundred analyte categories covered by the regulations, nearly eighty of those now have newly adopted or more stringentrequirements than previously. Those new standards were scheduled to be enforced beginning January 1, 2025, with laboratories that fail to meet the new criteria during PT events facing consequences, such as the inability to continue testing specific analytes unless and until corrective actions are taken to bring them into compliance.

[0005] Despite the regulatory impact, many laboratories remain unaware and / or unprepared (or underprepared) for the regulatory changes. While there are many reasons for the changes, one primary reason is that existing quality control tools generally focus on retrospective analysis, i.e., they do not provide any predictive insight or guidance with respect to future PT outcomes, especially in view of changing criteria and regulations. And, while many laboratories are aware of recent (and pending) regulatory changes, there currently is no robust mechanism for individual laboratories to quantify the likelihood of failure in upcoming PT events based on their current operational data and interlaboratory performance.

[0006] Thus, it can be seen that there remains a need in the art for systems and methods that allow laboratories to assess their readiness for updated PT requirements, and to evaluate the statistical risk of PT failure, assess individual analyte performance relative to peer benchmarks, and / or simulate PT outcomes under the revised CLIA 2024 criteria - or under any industry or regulatory criteria the laboratory may be operating under.SUMMARY OF THE INVENTION

[0007] The present invention is directed to systems and methods for predicting a clinical laboratory’s readiness to meet updated proficiency testing (PT) quality specifications, such as those promulgated under the CLIA 2024 regulatory framework. Unlike conventional tools that simply analyze past results or that simply provide generalized QC feedback, the system and method of the present invention integrate real-time inter-laboratory peer data with predictive modeling logic to compute and report predicted performance risks to allow individual laboratories to predict their expected performance and correct any deficienciesbefore being penalized or otherwise impacted for failing to meet readiness requirements. The claimed invention thus improves the functionality of the clinical diagnostic field and the associated laboratory systems by providing real-time, automated, accurate, and scalable simulations of regulatory compliance outcomes that cannot be reliably achieved by human review or by manual statistical calculation.

[0008] In one exemplary embodiment, the system is implemented in conjunction with a clinical diagnostic laboratory system comprising specialized clinical diagnostic analyzer measurement instruments, control material sampling hardware, interlaboratory databases, and a dedicated user interface. The system acquires laboratory mean values (lab mean), peer group mean values, and laboratory standard deviations (lab SD) from internal interlaboratory data sources. These values are automatically processed in real-time using software modules configured to apply CLIA 2024 acceptance criteria (or any other industry or regulatory standard being implemented), generally as defined by analyte-specific target ranges based on a peer mean and compute the probability of noncompliance across multiple regulatory thresholds.

[0009] The system is operable to transform raw QC data from multiple lab instruments into actionable compliance suggestions using rules and thresholds defined by regulatory or industry criteria. In preferred embodiments, the system models whether specific laboratories will likely produce results that would be deemed failing under real -world PT review conditions. Furthermore, the system uses integrated statistical logic, including normal and binomial distribution modeling, to compute sample-level failure risk, event-level failure risk (for example, failing two of five PT samples), and sanction risk (for example, failing two out of three PT events for an analyte). These non-generic computations are specifically calibrated to the structure and enforcement logic of the CLIA 2024 program, or in alternative embodiments may be adapted to any regulatory or industry regulations and logic.

[0010] In another aspect, the invention includes graphical simulation capabilities that render risk contour graphs and distribution plots in real time. These visual outputs, which are dynamically generated using current lab data, visually depict to users how variations in the laboratory’s mean or standard deviation may influence the probability of regulatory failure. By integrating interactive visual risk modeling into a laboratory’ s workflow, the claimed invention provides an immediate real-time and tangible improvement over existing compliance assessment methods, which typically rely on static reports or retrospective evaluation.

[0011] In some embodiments, the invention may be embodied as a standalone diagnostic support tool, implemented in R, MATLAB, Python, or other laboratory programming languages, and is operable through a secure interface connected to a laboratory’s internal data base or data collection tools. In operation, the readiness prediction system does not require manual user input from laboratory personnel, instead, it retrieves data directly from designated interlaboratory databases and outputs reports (such as a CLIA 2024 PT Readiness Report) that summarizes risk across multiple analytes and control levels. The generated reports may be used by laboratory support and compliance personnel to address any high-risk conditions before testing errors occur.

[0012] The system thus improves the technological field of clinical diagnostics by enabling predictive risk modeling that is tied to physical measurement processes and configured to simulate real-world regulatory enforcement. The system enhances diagnostic instrument utility, reduces the risk of erroneous results, and minimizes unnecessary retesting. Because the computations and evaluations are based on real-time data and mapped to the structure of actual PT enforcement programs, the invention provides a practical application that goes beyond abstract analysis or mental processes.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Embodiments of the present invention will be described in greater detail in the following detailed description of the invention with reference to the accompanying drawings that form a part hereof, in which:

[0014] FIG. 1 is a block diagram of a system for predicting laboratory proficiency testing readiness in accordance with exemplary embodiment of the present invention.

[0015] FIG. 2 is a representation in table form of an ALT (U / L) readiness analysis example for CLIA 2024 PT performance. The figure illustrates the laboratory’ s mean and SD values for two control levels, along with computed risks of sample failure, event failure, and sanction under the CLIA 2024 criteria.

[0016] FIG. 3 is a graphical depiction of the distribution of laboratory results overlaid with the CLIA 2024 PT acceptance range. The figure visually represents the probability of producing results outside of acceptable limits.

[0017] FIG. 4 is a contour plot illustrating the computed risk of failing a CLIA 2024 PT event. The graph displays combinations of laboratory mean and SD values that correspond to increasing levels of failure probability, allowing laboratories to assess how changes in bias or imprecision may impact their event-level risk.

[0018] FIG. 5 is a contour plot illustrating the computed risk of regulatory sanctions due to repeated PT event failure(s). The graph uses the same axes as in FIG. 4 but evaluates the probability of failing two out of three consecutive PT events, as defined under the CLIA 2024 enforcement rules.

[0019] FIG. 6 depicts an exemplary CLIA 2024 PT Readiness Report. The figure presents a multi-analyte report that includes computed risk scores and a tiered row of graphs for each analyte and control level.

[0020] FIGS. 7A, 7B, and 7C is a close-up view of the Level 1 ALT readiness graphs as depicted in FIG. 6. The figure shows the individual laboratory’ s result distribution, the PT event failure contour, and the sanction risk contour for Level 1, with each component reflecting performance against CLIA 2024 criteria.

[0021] FIGS. 8A, 8B, and 8C is a close-up view of the Level 2 ALT readiness graphs corresponding to FIG. 6. The figure includes the same graph types as FIGS. 7 A, 7B, 7C but illustrates a different laboratory mean and SD scenario, demonstrating how risk metrics shift across control levels.

[0022] FIG. 9 is a graphical depiction of laboratory bias and imprecision in relation to CLIA 2024 PT acceptance limits. The figure illustrates how the laboratory mean and SD may affect the probability of result rejection by modifying the location and spread of the expected result distribution.

[0023] FIG. 10 is a contour plot demonstrating how a one-unit adjustment in the laboratory mean, while keeping the laboratory SD constant, substantially reduces the risk of failing a PT event. The figure provides an example of how clinical diagnostic laboratories can use the system of the present invention to evaluate the impact of calibration changes on their readiness results.DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0024] Systems and methods for predicting laboratory proficiency testing (PT) readiness in accordance with exemplary embodiments of the present invention are described herein. While the invention will be described in detail with reference to specific implementations and illustrative examples, it should be understood that the invention is not limited to the specific arrangements shown, but rather encompasses all variations that fall within the scope of the appended claims.

[0025] One skilled in the art will appreciate that a variety of configurations may be implemented in accordance with the present invention. As used herein, the terms “clinical diagnostic analyzer”, “analyzer”, “instrument”, and variations thereof may be used to refer to the specialized diagnostic device for testing analytes, patient specimens, and the like to determine various characteristics of the analyte.

[0026] The present invention is directed to systems and methods for predicting laboratory readiness under the CLIA 2024 proficiency testing (PT) framework, and may equally be used for predicting readiness under any industry or regulatory framework. In general, the system accepts laboratory performance data, such as laboratory mean and standard deviation values calculated from routine quality control operations and compares those values to peer mean data derived from interlaboratory comparison programs. Using these inputs, the system models expected result distributions, evaluates performance against analyte-specific acceptance criteria, and computes the probability of PT failure (or potential sanctions) using statistical methods. The system may be deployed as a standalone application, a web-based interface, or integrated into an existing laboratory software infrastructure.

[0027] The invention provides numerical and graphical outputs that summarize failure risk at the individual sample level, the PT event level, and the regulatory sanction level. These outputs enable individual laboratories and groups of laboratories to assess their operational readiness, simulate performance improvements, and plan corrective actions in advance of formal PT events. The invention also supports continuous monitoring in the context of evolving regulatory standards, providing a tool for proactive risk management rather than simple retrospective analysis. The following description details the architecture, functionality, and use of the system of the present invention in a clinical diagnostic environment, with reference to exemplary figures.

[0028] Looking first to FIG. 1, a clinical diagnostic laboratory environment for implementing a system and method for laboratory proficiency testing in accordance with an exemplary embodiment of the present invention is depicted generally by the numeral 100. The system 100 generally includes a plurality of clinical diagnostic analyzers 110a, 110b, 110c, 1 lOn and a server 112 in communication with a database 114. The group of clinical diagnostic analyzers 110a, 110b, 110c, 11 On are in communication with network 116, which facilitates the transmission of instructions, information, and data between each clinical diagnostic analyzer 110a, 110b, 110c, HOn and the server 112, as well as between each of the clinical diagnostic analyzers 110a, 110b, 110c, 11 On and any of the other diagnostic analyzers, or between any combination of clinical diagnostic analyzers and / or the server. It should be understood that the number of analyzers may be any number from 1 to n, such as groups of 2, 6, 10, or 100 analyzers.

[0029] Network 116 may be any local area network (LAN), wide area network (WAN), ad-hoc network, or other network configuration known in the art, or combinations thereof. F or example, in the exemplary embodiment depicted in FIG. 1, network 116 may include a LAN allowing communication between the clinical diagnostic analyzers 110a, 110b, 110c, HOn, such as in a single laboratory setting having multiple clinical diagnostic analyzers, an may also include a WAN, such as the Internet or other wide area network, allowing communication between the LAN and the server 112 and / or between the clinical diagnostic analyzers and the server.

[0030] It should be understood that the configurations depicted in FIG. 1 is exemplary, and not limiting, and that the invention as described herein may be embodied in a single clinical diagnostic analyzer, in a group of clinical diagnostic analyzers co-located in a single laboratory or facility, and in group of clinical diagnostic analyzers that are geographically dispersed to allow a group of thus interconnected clinical diagnostic analyzers to each test a single analyte.

[0031] For example, multiple systems 100, each comprising one or more clinical diagnostic analyzers and servers may be located in a single laboratory, or in multiple laboratories dispersed across a facility or across the globe, all in communication via a WAN. It should be further understood that the present invention may be embodied in a single clinical diagnostic analyzer, or in a group of clinical diagnostic analyzers in communication with each other over a LAN or WAN, without a server or servers. These and other variations and embodiments will be apparent to those skilled in the art.

[0032] Server 112 preferably includes a processor 118, memory 120, and logic and control circuitry 122, all in communication with each other. Server 112 may be any server, server system, computer, or computer system known in the art, preferably configured to communicate instructions and data between the server 112 and the network, and / or to any device connected to the network, and to store and retrieve data and information to and from the database 114. Processor 118 may be any microprocessor, controller, or plurality of such devices known in the art. Processor 118 preferably runs a server operating system such as a Linux based, Windows based, or other server operating system known in the art. Preferably, the processor 118 is configured to control the operation of the server 112 in conjunction with the operating system, allowing the server to communicate with the database 114 and the network 116 and / or with devices connected to the network, such as the clinical diagnostic analyzers 110a, 110b, 110c, 1 lOn. In some embodiments the server may control the operation of the clinical diagnostic analyzers, for example allowing operation of the analyzers during specific time periods, collecting data from the analyzers for storage in the database 114, transferring data to the analyzers for viewing and / or analysis, collecting test data from the analyzers, and providing data, instructions or prompts to the analyzers either individually or in groups.

[0033] Memory 120 may be volatile or non-volatile memory and is used to store data and information associated with the operation of the server as well as data for transmission to and from the server. For example, the memory stores the server operating system for execution by the processor 118 and may also store data associated with the clinical diagnostic analyzers 110a, 110b, 110c, 11 On in communication with the server 112 over the network 116. In some embodiments the memory 120 on the server may be used as a supplement to, or in place of, the database 114.

[0034] The database 114 is preferably used to store control information relating to the operation of the server 112 and the operation and control of the clinical diagnostic analyzers 110a, 110b, 110c, 11 On, and may also be used to store data relating to the processing of samples by the clinical diagnostic analyzers. For example, the database may contain instructions or programming for execution by a processor on a clinical diagnostic analyzer, or for execution on the server, or may store data related to the number of samples processed, the frequency of testing, the results of analysis performed on the analyzer, as well as data relating to the samples themselves, such as tracking information, lot numbers, sample size, sample weight, percentage of sample remaining, and the like. Preferably, the database 114 includes non-volatile storage such as hard drives, solid state memory, and combinations thereof.

[0035] Logic and control circuitry 122 provides interface circuitry to allow the processor and memory to communicate, and to provide other operational functionality to the server, such as facilitating data communications to and from the network 116.

[0036] As shown in FIG. 1, the system architecture may include one or more functional modules implemented by executable software instructions stored in memory 120 and executed by processor 118. These modules may include, for example, a data acquisition module, a statistical modeling module, and a reporting module, each of which performs specific computational or input / output functions as described in the present disclosure. In someembodiments, the system may also include a peer data module, implemented in software, configured to retrieve peer group mean values from interlaboratory comparison databases.

[0037] The term “module” as used herein refers to a logical grouping of software instructions or routines that perform a defined function when executed by a processor. For example, the statistical modeling module is configured to perform probability calculations using normal and binomial distributions based on laboratory and peer group performance data. The data acquisition module retrieves laboratory mean, laboratory standard deviation, and peer mean values. The reporting module is configured to generate numerical and graphical outputs including readiness scores and contour plots. In some embodiments, these modules may operate as discrete services or integrated subcomponents within a broader software platform deployed on the server.

[0038] Turning now to FIG. 2, an exemplary ALT (alanine aminotransferase) CLIA 2024 PT readiness analysis chart / table is depicted. The table includes a tabular display of two separate control levels used by a laboratory to assess performance for ALT testing. Each level is characterized by a laboratory mean (lab mean), laboratory standard deviation (lab SD), and a peer mean, all derived from an interlaboratory peer group dataset. As seen in the table, the CLIA 2024 acceptance limits are defined for ALT as either 15% or 6 U7L (units per liter), whichever is greater. The table shows the input values and resulting computed risk metrics, i.e., the probability of producing a result outside the CLIA 2024 acceptance range, the probability of failing a PT event (defined as two or more failed samples out of five), and the probability of incurring a regulatory sanction (defined as failing two out of three consecutive PT events). These computations are performed automatically and in real-time by the system using statistical modeling and are presented to the laboratory as part of a readiness analysis. As shown in Level 2 of the table, the laboratory mean is lower than the peer group mean,consistent with a scenario in which the laboratory exhibits negative bias relative to the interlaboratory average.

[0039] As used herein, the statistical distribution model comprises at least a normal distribution for modeling the expected variability of individual test results and one or more binomial distributions for modeling the probability of failure across multiple samples or events.

[0040] Turning to FIG. 3, the system may further generate a graph showing the laboratory’s result distribution overlaid with the CLIA 2024 acceptance limits. The acceptance limits are derived from the peer mean in accordance with the regulatory specification. The laboratory’s result distribution is modeled using the laboratory mean (lab mean) and the laboratory standard deviation (lab SD) as the center and the spread of a normal distribution, respectively. As seen in the graph, the area of the distribution that lies outside the acceptance range is shaded, visually indicating the probability of rejection / failure under the CLIA 2024 criteria. In operation, the readiness testing system of the present invention may dynamically update the graphical visualization as the laboratory performance metrics change. For example, a reduction in lab SD or a shift in lab mean toward the peer mean would result in a narrower shaded region, representing a lower probability of noncompliance.

[0041] In operation, the system and method of the present invention system applies real-time standard statistical methods and calculations. For example, if the lab mean is 27 and the peer mean is 30, and the lab SD is 1.8, the system first computes the CLIA 2024 acceptance range. For example, if the applicable range is defined as + / - 5 U / L, then the lower and upper limits are 25 and 35, respectively. The system then uses the cumulative distribution function (CDF) for the normal distribution to calculate the probability that a randomly generated result from the laboratory’s distribution will fall below 25 or above 35. Thus, for example, the probability of producing a result below 25 may be approximately 4.8%, while the probabilityof exceeding 35 may be negligible. Thus, in that case, the system calculates a combined risk of result rejection of approximately 4.8%. Note that while the numerical example provided here corresponds to the Level 1 data set (Lab Mean = 27, Peer Mean = 30, SD = 1.8), FIG. 3 itself illustrates the corresponding graphical output for the Level 2 scenario.

[0042] Turning now to FIG. 4, a contour plot generated by the system and method of the present invention maps the probability of failing a PT event as a function of laboratory bias and imprecision. Laboratory bias is typically defined as the difference between the lab mean and the peer mean, while laboratory imprecision is represented by the lab SD. The contour lines on the depicted plot represent increasing levels of PT event failure risk, thus enabling laboratories to visualize their current risk profile and identify paths toward improvement. The graphical output aids in simulating the impact of adjustments to either the lab mean or the lab SD allows the laboratory to make informed decisions regarding its quality control efforts and in meeting regulatory requirements. Although FIG. 4 depicts the Level 2 case (Lab Mean = 46, Peer Mean = 50), the described computation method is equally applicable to the Level 1 example discussed above.

[0043] To calculate PT event failure risk, the system preferably applies a binomial distribution using the previously determined result rejection probability. For example, if the sample failure risk is 4.8%, the system models the probability of failing two or more out of five PT samples. Using binomial distribution logic, this results in a PT event failure risk of approximately 2.1%. This value reflects the combined likelihood of two, three, four, or five failed results out of five total samples.

[0044] Looking now to FIG. 5, a second contour plot depicts predicted probability of regulatory sanction due to repeated PT event failure. Using the same axes as FIG. 4, the contour plot of FIG. 5 evaluates risk at the sanction level, that is, the likelihood of failing two out of three PT events based on the laboratory’s current performance metrics. The sanctionrisk is computed by applying binomial probability to the previously calculated PT event failure rate. For instance, if the event failure risk is 2.1%, the system uses a second binomial model to compute the probability of failing two of three PT events, resulting in a sanction risk of approximately 0.1%. As with the event risk contour, the laboratory’s current laboratory mean and laboratory SD are superimposed onto the graph, giving users an indication of where their laboratory operations fall as compared to the compliance thresholds. As with FIG. 4, the contour plot shown in FIG. 5 is based on the Level 2 scenario, while the numerical event failure probability discussed above is calculated using Level 1 values.

[0045] FIG. 6 is a comprehensive CLIA 2024 PT Readiness Report generated by the system and method of the present invention. As seen in the report of FIG. 6, for each analyte tested both summary and detailed readiness metrics are displayed. Computed probabilities for result rejection, PT event failure, and sanction risk for each analyte are listed. The report further presents a series of graphs corresponding to each control level used in the laboratory’s quality control protocol, including the lab result distribution graph, PT event failure contour, and sanction risk contour. The multi-level report presentation as in FIG. 6 allows the laboratory to evaluate readiness for each analyte and control level independently, with risk metrics based on the actual statistical performance of the laboratory. As used herein, a graphical output refers to any system-generated visual representation, including but not limited to normal distribution plots, risk contour graphs, and shaded overlays illustrating predicted failure probabilities for a given analyte or control level.

[0046] Looking to FIGS. 7A, 7B, and 7C, a close-up view of the graphical outputs corresponding to Level 1 of FIG. 6 is depicted. The lab results distribution graph shows the relationship between the lab mean, lab SD, and the acceptance limits derived from the peer mean. The PT event risk contour illustrates where the laboratory’s Level 1 metrics place it within the risk field, while the sanction risk contour provides a similar view with respect to thecumulative risk of regulatory action. This allows laboratory personnel to better assess whether failure risk is driven more by measurement bias or by variability.

[0047] Similarly, FIGS. 8A, 8B, and 8C shows the graphical outputs for Level 2 of the data as depicted in FIG. 6. The structure and layout in FIGS. 8A, 8B, 8C mirrors that of FIGS. 7A, 7B, 7C, but as can be seen the underlying lab mean and lab SD differ, thus resulting in a different risk profile. In this case, the Level 2 data demonstrates greater deviation from the peer mean and a larger standard deviation than the Level 1 data did, thus resulting in higher predicted risks across all categories. The graphs as depicted in FIGS. 6, 7A, 7B, 7C, and 8A, 8B, 8C thus provide a visual representation of the statistical models applied by the system and method of the present invention, and allow laboratories to understand and visualize risk behavior across multiple concentration levels.

[0048] Turning to FIG. 9, the relationship between laboratory bias and imprecision is depicted. As can be seen, the graph of FIG. 9 shows how the central location of the result distribution (a determined by the lab mean) and its spread (as determined by the lab SD) influence the probability of producing unacceptable results. The figure visually separates the effects of bias and imprecision, thus allowing a user to diagnose which performance attribute is contributing more heavily to observed failure risks. This differentiation allows the laboratory to properly apply and target corrections and interventions that may improve the laboratory’s performance.

[0049] Finally, FIG 10 depicts a practical example of how the system and method of the present invention may be used to simulate the impact of laboratory adjustments and how they affect regulatory compliance. As seen in the contour plot of FIG. 10, the failure risk associated with a lab mean of 46 and a lab SD of 3.0, with a peer mean of 50, is depicted. At these values, the system calculates a PT event failure risk of 11.5%. The plot also shows the effect of shifting the lab mean to 47, which results in a significantly lower failure risk. Thisexample thus demonstrates how the system may be used not only to assess risk, but also to proactively plan and / or model performance based on proposed changes in the laboratory performance (e.g., changing the lab mean from 46 to 47).

[0050] With the system and method for laboratory proficiency testing readiness set forth as described above, the implementation of the invention within a clinical laboratory setting will now be described. A laboratory seeking to evaluate its readiness under the CLIA 2024 PT requirements using the present invention begins by extracting quality control data from its existing analyzers or laboratory information systems. Specifically, for each analyte and control level, the lab mean and lab SD are computed from routine QC runs. These lab mean and SD values are paired with corresponding peer mean values obtained from an interlaboratory peer group comparison programs.

[0051] The laboratory then inputs this data into the system of the present invention, which may be deployed as a standalone software tool, a web-based platform, or an integrated module within an existing clinical diagnostic analyzer or QC management suite. Using the lab mean, lab SD, and peer mean values, the system retrieves the relevant CLIA 2024 PT acceptance criteria (or other desired industry or regulatory criteria) and applies these parameters to compute the acceptable result range. Using the lab mean and the lab SD, the system defines a normal distribution and calculates the probability that a randomly drawn result from this distribution would fall outside the specified acceptance limits. The calculated probability forms the basis for sample-level risk assessment.

[0052] Next, using the sample-level failure probability, the system applies a binomial distribution to model the probability of failing two or more of five samples in a PT event. This PT event failure probability is then further evaluated using another binomial distribution to estimate the risk of failing two out of three PT events (which under CLIA results in a regulatorysanction). These computations are carried out automatically by the system, using preprogrammed statistical models.

[0053] The system outputs these results in both numerical and graphical form, such as depicted in the FIGs discussed above. The numerical data include computed probabilities for each analyte and level, the graphical outputs may include result distribution plots, PT event failure contours, and sanction risk contours as discussed above and as depicted in the FIGs. These various outputs may be compiled into a readiness report organized by analyte, with each analyte receiving a summary table and a row of graphs for each control level.

[0054] In practice, laboratories may use these reports to identify analytes at elevated risk of noncompliance, to assess whether risk is primarily attributable to bias or imprecision, and to evaluate the likely impact of proposed process improvements. The system and method of the claimed invention thus allows laboratory staff to simulate alternative configurations, such as adjusting calibration settings or adopting more consistent handling protocols and immediately observe the resulting impact on PT readiness. The system and method as claimed are particularly useful for compliance with upcoming or already implemented regulatory changes, by modeling future PT event outcomes based on current performance data the system allows a laboratory to continuously assess its readiness under ever-changing industry and regulatory standards.

[0055] The systems and methods described herein enable clinical diagnostic laboratories to predict their readiness to meet proficiency testing (PT) requirements, such as those set forth under the CLIA 2024 regulatory framework. By integrating real-time interlaboratory peer data with predictive statistical modeling, the invention allows laboratories to evaluate expected compliance outcomes in advance of regulatory review. Unlike conventional retrospective tools, the disclosed system operates prospectively, transforminglaboratory quality control data into risk predictions using analyte-specific criteria and statistical logic aligned with regulatory enforcement structures.

[0056] As can be seen, the system and method of the present invention is well-suited for allowing individual and groups of clinical diagnostic laboratories to predict and assess their readiness for new and pending regulatory and industry compliance schemes. In exemplary embodiments, the system automatically acquires laboratory means, peer means, and laboratory standard deviations from internal databases and computes probabilities of result rejection, PT event failure, and regulatory sanction using normal and binomial distribution analysis. These computations are dynamically rendered into numerical and graphical outputs, such as contour plots and distribution overlay plots, that illustrate how variations in laboratory bias or imprecision influence compliance risk. In preferred embodiments, these capabilities are embedded in laboratory systems and operate without manual intervention, providing real-time views of potential deficiencies in meeting new regulation standards.

[0057] In various embodiments, the system and method may be implemented as standalone software modules or may be integrated into existing diagnostic and laboratory platforms, configured to generate readiness reports across multiple analytes and control levels. These reports allow laboratory personnel to proactively identify high-risk areas and improve testing practices before compliance failures occur. By providing real-time, automated, and scalable simulations of various regulatory outcomes, the system further improves the functionality of clinical diagnostic instruments and supports more reliable laboratory operations. The claimed invention thus provides a practical, technological improvement in the field of laboratory quality control, enabling real-time compliance assessment beyond human review or generic statistical tools.

[0058] While the present invention has been described and illustrated hereinabove with reference to various exemplary embodiments, it should be understood that variousmodifications could be made to these embodiments without departing from the scope of the invention. Therefore, the invention is not to be limited to the exemplary embodiments described and illustrated hereinabove, except insofar as such limitations are included in the following claims.

Claims

CLAIMSWhat is claimed and desired to be secured by Letters Patent is as follows:

1. A system for predicting laboratory proficiency testing readiness, comprising: a processor; a memory operably coupled to the processor; a data acquisition module configured to receive laboratory quality control data comprising a laboratory mean and a laboratory standard deviation for a specified analyte; a peer data module configured to obtain a peer group mean for the specified analyte; a statistical modeling module configured to compute a probability of result rejection by applying a statistical distribution model to the laboratory mean and laboratory standard deviation; and a reporting module configured to generate a readiness report indicating the probability of result rejection.

2. The system of claim 1, wherein the risk modeling engine is further configured to compute a probability of failing a proficiency testing event by applying a binomial distribution to the computed probability of result rejection.

3. The system of claim 2, wherein the statistical modeling module is further configured to compute a probability of regulatory sanction by modeling the likelihood of failing two out of three proficiency testing events based on the computed probability of event failure.

4. The system of claim 1, wherein the reporting module is further configured to produce a graphical output comprising a normal distribution curve overlaid with regulatory acceptance limits.

5. The system of claim 4, wherein the graphical output further comprises a contour plot indicating regions of event failure probability as a function of laboratory mean and standard deviation.

6. The system of claim 4, wherein the graphical output further comprises a contour plot indicating regions of sanction risk based on consecutive event failure probabilities.

7. The system of claim 1, wherein the peer data module is configured to retrieve the peer group mean from an interlaboratory peer comparison system.

8. The system of claim 1, wherein the readiness report is configured to present results for a plurality of analytes, each associated with at least one control level.

9. A method for assessing readiness for regulatory proficiency testing in a clinical laboratory, comprising: obtaining a laboratory mean and a laboratory standard deviation for an analyte; obtaining a peer group mean for the analyte; computing regulatory acceptance limits based on the peer group mean and predefined criteria; modeling a probability distribution using the laboratory mean and standard deviation; computing a probability that a result drawn from the modeled distribution falls outside the acceptance limits.

10. The method of claim 9, further comprising computing a probability of failing a proficiency testing event using a binomial distribution and the probability of result rejection.

11. The method of claim 10, further comprising computing a probability of regulatory sanction by modeling the likelihood of failing two out of three proficiency testing events using the computed event failure probability.

12. The method of claim 9, further comprising generating a graphical output including a normal distribution curve and acceptance limits for the analyte.

13. The method of claim 12, further comprising generating a contour plot of event failure probability over a range of laboratory mean and standard deviation values.

14. The method of claim 13, further comprising generating a contour plot of regulatory sanction probability over the same range.

15. The method of claim 9, further comprising compiling a readiness report comprising numerical risk scores and graphical outputs for a plurality of analytes.

16. The method of claim 9, wherein the peer group mean is retrieved from an interlaboratory peer comparison system.

17. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause a computing system to: receive laboratory performance data comprising a laboratory mean and laboratory standard deviation for an analyte; retrieve a peer group mean for the analyte; compute regulatory acceptance limits based on the peer group mean;apply a statistical model to compute a probability that a result falls outside the acceptance limits.

18. The non-transitory computer-readable medium of claim 17, further comprising instructions to compute a probability of failing a proficiency testing event using a binomial model based on the probability of result rejection.

19. The non-transitory computer-readable medium of claim 18, further comprising instructions to compute a probability of regulatory sanction based on repeated event failure.

20. The non-transitory computer-readable medium of claim 17, further comprising instructions to generate a readiness report comprising a result distribution graph, an event failure risk contour, and a sanction risk contour.

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