System and method for quality control of radiology reports

Abstract

The invention provides a system and method for quality control in radiology reports. It collects data from reports, medical scan images, and questionnaires within a Safety, Quality, Efficiency, and Productivity (SQEP) framework. Scores for safety, quality, productivity, and efficiency are computed for radiologists and departments using weighted parameters, peer reviews, and audit outcomes. A consolidated SQEP score evaluates overall performance. The system identifies deviations in report quality through trend analysis and correlations, providing corrective feedback and training recommendations to radiologists. By addressing errors proactively, the system enhances diagnostic accuracy, operational efficiency, and compliance with quality standards. This comprehensive approach ensures improved performance and reliability in radiology departments while maintaining high-quality patient care.

Description

SYSTEM AND METHOD FOR QUALITY CONTROL OF RADIOLOGY REPORTSFIELD

[0001] The present invention relates to medical auditing systems and more particularly to a system and method for quality control in radiology reports.BACKGROUND

[0002] Radiology plays a critical role in modern healthcare, providing essential diagnostic information for patient treatment and care. Accurate radiology reports are crucial for correct diagnoses and timely interventions, which directly impact patient wellbeing. However, current systems for auditing radiology reports face significant challenges, including inconsistency in report quality, inefficiency in review processes, limited data analysis capabilities, and a reliance on subjective assessment methods. These limitations can lead to variations in diagnostic accuracy, overlooked errors, and delays in addressing underlying health issues.

[0003] One primary goal in radiology is to improve patient outcomes by ensuring that exams are performed accurately, minimizing risks, and adhering to regulatory standards. Achieving these goals requires a rigorous auditing system that assesses radiology reports with a high level of precision and consistency.

[0004] Existing methods for auditing radiology reports typically involve manual reviews by radiologists or quality assurance personnel, with some facilities utilizing software to assist in compliance tracking and audit management. However, these approaches often lack the necessary standardization and structured metrics required for comprehensive evaluation, leading to variability and potential gaps in report quality. For instance, without standardized criteria, inconsistencies in reporting can result in misdiagnoses, putting patient safety at risk.LEAR_001

[0005] Furthermore, traditional auditing processes are often time-consuming and may not thoroughly address all critical aspects of a radiology report, resulting in an oversight of safety, quality, productivity, and efficiency concerns. Manual reviews also limit data analysis capabilities, making it challenging to identify trends or areas for improvement, thus impeding continuous quality enhancement.

[0006] There is, therefore, a need for a systematic and efficient auditing system and method that incorporates standardized metrics to evaluate radiology reports comprehensively. The system and method must address the deficiencies of existing methods, by providing a robust framework for auditing medical reports such as radiology reports, in order to enhance diagnostic accuracy and patient care.SUMMARY

[0007] The following summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, example embodiments, and features described, further aspects, example embodiments, and features, will become apparent by reference to the drawings and the following detailed description.

[0008] Briefly, according to an example embodiment, a structured auditing system, hereinafter referred to as the system, and method, aimed at improving the accuracy and reliability of radiology reports is disclosed. The method leverages four key metrics such as Safety, Quality, Productivity, and Efficiency, hereinafter referred to as (SQEP) metrics, to provide a comprehensive framework for reviewing radiology reports. By systematically applying the SQEP metrics, the system ensures a consistent and thorough evaluation of each report, facilitating a standardized scoring approach and efficient data collection. This structured process not only identifies areas for improvement in radiology reporting but also supports trend analysis, enabling ongoing quality enhancement and more accurate, reliable diagnostic outcomes.

[0009] According to an example embodiment, radiology cases are selected based on predefined criteria, ensuring a representative sample for review. Further, each case isLEAR_001 analyzed by trained auditors where imaging results are examined, and reports are evaluated against the SQEP metrics. Further, each report is scored across SQEP metrics, with scores aggregated to provide an overall performance rating, highlighting strengths and areas for improvement. Further, auditors compile quantitative and qualitative data, storing it in a database for trend analysis, enabling ongoing monitoring of improvements or persistent issues. Also, detailed feedback is provided to radiologists, fostering a culture of continuous improvement through regular training sessions informed by audit findings. Comprehensive documentation of audit results, including findings, scores, and recommendations, supports individual case improvement and informs broader institutional policies.BRIEF DESCRIPTION OF THE FIGURES

[0010] These and other features, aspects, and advantages of the example embodiments will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0011] FIG. 1 is a block diagram of an environment depicting functioning of a system for auditing radiology reports, according to an example embodiment;

[0012] FIG. 2 is a block diagram of a system for auditing radiology reports, according to an example embodiment;

[0013] FIG. 3 is a flowchart illustrating a method for auditing radiology reports, according to an example embodiment; and

[0014] FIG. 4 is a block diagram of an embodiment of a computing device in which the modules of a system for auditing radiology reports, described herein, are implemented.LEAR_001DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0015] The drawings are to be regarded as being schematic representations and elements illustrated in the drawings are not necessarily shown to scale. Rather, the various elements are represented such that their function and general purpose become apparent to a person skilled in the art. Any connection or coupling between functional blocks, devices, components, or other physical or functional units shown in the drawings or described herein may also be implemented by an indirect connection or coupling. A coupling between components may also be established over a wireless connection. Functional blocks may be implemented in hardware, firmware, software, or a combination thereof.

[0016] Various example embodiments will now be described more fully with reference to the accompanying drawings in which only some example embodiments are shown. Specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments. Example embodiments, however, may be embodied in many alternate forms and should not be construed as limited to only the example embodiments set forth herein.

[0017] Accordingly, while example embodiments are capable of various modifications and alternative forms, example embodiments are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that there is no intent to limit example embodiments to the particular forms disclosed. On the contrary, example embodiments are to cover all modifications, equivalents, and alternatives thereof.Similarly, like numbers refer to like elements throughout the description of the figures.

[0018] Before discussing example embodiments in more detail, it is noted that some example embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations as sequential processes, many of the operations may be performed in parallel, concurrently or simultaneously. In addition, the order of operations may be re-arranged. The processes may be terminated when theirLEAR_001 operations are completed but may also have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, subprograms, etc.

[0019] Specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments. Inventive concepts may, however, be embodied in many alternate forms and should not be construed as limited to only the example embodiments set forth herein.

[0020] It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term "and / or" includes any, and all combinations of one or more of the associated listed items. The phrase "at least one of" has the same meaning as "and / or".

[0021] Further, although the terms first, second, etc. may be used herein to describe various elements, components, regions, layers and / or sections, it should be understood that these elements, components, regions, layers and / or sections should not be limited by these terms. These terms are used only to distinguish one element, component, region, layer, or section from another region, layer, or section. Thus, a first element, component, region, layer, or section discussed below could be termed a second element, component, region, layer, or section without departing from the scope of inventive concepts.

[0022] Spatial and functional relationships between elements (for example, between modules) are described using various terms, including “connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitly described as being “direct,” when a relationship between first and second elements is described in the above disclosure, that relationship encompasses a direct relationship where no other intervening elements are present between the first and second elements, and also an indirect relationship where oneLEAR_001 or more intervening elements are present (either spatially or functionally) between the first and second elements. In contrast, when an element is referred to as being "directly” connected, engaged, interfaced, or coupled to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (e.g., "between," versus "directly between," "adjacent," versus "directly adjacent," etc.).

[0023] The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms "a," "an," and "the," are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the terms “and / or” and “at least one of’ include any and all combinations of one or more of the associated listed items. It will be understood that the terms "comprises," "comprising," "includes," and / or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, elements, components, and / or groups thereof.

[0024] It should also be noted that in some alternative implementations, the functions / acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may in fact be executed substantially concurrently or may sometimes be executed in the reverse order, depending upon the functionality / acts involved.

[0025] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skills in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.LEAR_001

[0026] Spatially relative terms, such as “beneath”, “below”, “lower”, “above”, “upper”, and the like, may be used herein for ease of description to describe one element or feature’s relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in ‘addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as “below”, or “beneath” other elements or features would then be oriented “above” the other elements or features. Thus, term such as “below” may encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein are interpreted accordingly.

[0027] Portions of the example embodiments and corresponding detailed description may be presented in terms of software, or algorithms and symbolic representations of operation on data bits within a computer memory. These descriptions and representations are the ones by which those of ordinary skill in the art effectively convey the substance of their work to others of ordinary skill in the art. An algorithm, as the term is used here, and as it is used generally, is conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of optical, electrical, or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0028] Typically, a system for auditing radiology reports is disclosed herein, where protocols from the aviation industry are utilized as a framework to develop a questionnaire for auditing radiology reports, ensuring accuracy and monitoring parameters critical to achieving correct diagnoses. A feedback mechanism is incorporated within the questionnaire to facilitate the rectification of anomalies detected in the reports. Examples ofLEAR_001 quality control protocols applied in the aviation industry, which are adapted in the proposed system, are described further below.

[0029] In aviation, checklists are employed for pre-flight, in-flight, and post-flight tasks to ensure the completion of critical actions. Tasks such as system checks, fuel level verification, and safety measures are systematically addressed. Similarly, in the disclosed system, a checklist is employed for ensuring the comprehensive preparation and review of radiology reports, including key aspects such as patient demographics, clinical history, imaging modalities used, and interpretation of findings.

[0030] In aviation, redundancy systems, such as dual-engine configurations and backup navigation systems, are implemented to mitigate failures. Actions taken by personnel are cross-checked to ensure accuracy. Correspondingly, in the system, radiology reports are subjected to double-reading by two independent radiologists to minimize diagnostic errors and ensure consistency.

[0031] In aviation, incident reporting systems, such as the Aviation Safety Reporting System (ASRS), are employed to enable the anonymous reporting of errors and nearmisses, which are analyzed for corrective actions. In the system, a similar mechanism is implemented, allowing radiologists to report errors in interpretation or technical quality anonymously, thereby facilitating targeted training and protocol improvements.

[0032] Standard operating procedures are established in aviation to guide personnel through standardized responses for routine and emergency scenarios, ensuring consistency and reducing human error. In the system, SOPs are defined for the preparation of radiology reports, imaging techniques, and discrepancy resolution, with periodic updates to incorporate advancements in technology and knowledge.

[0033] Simulation training is utilized in aviation to prepare personnel for emergency scenarios, such as engine failure or adverse weather conditions, enhancing decision-making and procedural adherence. In the system, simulated diagnostic exercises are employed toLEAR_001 enable radiologists to practice identifying rare or complex conditions, thereby refining accuracy in report interpretation.

[0034] In aviation, flight data recorders continuously monitor aircraft performance, with data being analyzed for trends and anomalies. Similarly, in the system, mechanisms are employed to monitor diagnostic accuracy rates, report turnaround times, and patient outcomes, enabling data-driven insights to improve reporting standards.

[0035] In aviation, feedback is provided by maintenance crews, air traffic control, and pilots regarding operational and equipment issues. A structured feedback mechanism is incorporated in the system to enable clinicians to provide insights on report accuracy and highlight areas requiring improvement.

[0036] In aviation, training is provided to address human factors such as fatigue, communication, and teamwork to minimize errors. In the system, training modules are provided for radiologists and technicians to manage stress, enhance peer communication, and maintain focus during report preparation and review. By employing such protocols, the accuracy and reliability of radiology reports are enhanced, thereby improving diagnostic outcomes and reducing the likelihood of errors.

[0037] In an example embodiment a systematic SQEP framework is disclosed, to objectively assess radiology and medical reports based on Safety, Quality, Efficiency, and Productivity. It utilizes data collection, trend analysis, and DICOM tag-based parameters to identify recurring issues and monitor improvements over time. A comprehensive audit questionnaire evaluates key parameters within the SQEP categories, assigning scores based on relevance, while separate scoring mechanisms analyze the contributions of individual radiologists and departments. The system incorporates a formal feedback loop to enhance diagnostic accuracy, reporting quality, and workflows, and employs performance benchmarking using star ratings and predefined weightages to ensure consistent and objective evaluations across institutions. Detailed working is explained hereinbelow with reference to the figures.LEAR_001

[0038] FIG. 1 is a block diagram of an environment 100 depicting a system (e.g. 102a) for auditing or evaluating radiology reports, in an embodiment. The environment 100 includes a plurality of radiology departments 140a-n, a plurality of systems 102a-102n, a network 114, and a server system 104. The system 102a includes an input / output interface 110 that communicates with an input / output device 112, where a user 114 (e.g. a peer reviewer) can access the system 102a via the input / output device 112. The input / output interface 110 also communicates with a communication interface 142 of the radiology department 140a.

[0039] The communication interface 142 facilitates communication with existing information systems such as a Hospital Information System (HIS) 122, a Radiology Information System (RIS) 124, a radiographic device(s) 128 which uses, among others, a computed radiography (CR) cassette, direct radiography (DR), or a CR / DR plate reader, and a Picture Archiving and Communication System (PACS) 126, and conforms with the relevant standards, such as the Digital Imaging and Communications in Medicine (DICOM) standard, DICOM Structured Reporting (SR) standard. While each system (e.g. 102a) is shown to interface with only one radiology department (140a) (e.g. 120a interfaces with 140a), it is understood that each of the plurality of systems (e.g. 120a), can be configured to interface with more than one radiology department also.

[0040] The radiology departments (140a- 140b) can be independent medical imaging centers or be integrated within a hospital or medical center. Typically, the radiology department (140a- 140b) are part of an empaneled list of medical centers to which services of plurality of systems (102a-102n) are provided. Services of the plurality of systems 102a- 102n can be availed on a subscription basis or a one-time basis. The details of the radiology departments 140a- 140n and a type of service availed, can be handled by a subscription module 106 of the server system 104. The radiology departments 140a-140n that avail of the service can be included within an empaneled list of medical centers and stored in a storage module 108 of the server 104.LEAR_001

[0041] As shown, the plurality of systems 102a-102n are connected to the server 104 over a network 114. The server 104 can include a subscription model 106, and a storage module 108. The subscription model 106 can be an application that monitors the usage of services provided by the plurality of systems 102a-102n to the plurality of radiology department 140a- 140n. For example, the services of evaluation of radiology reports generated by the radiology department 140a can be billed at a subscription fee of 6000 USD per month. The subscription model 106 shall monitor whether payment for the subscription has been made by the radiology department 140a on time and shall send alerts for delay in payments. The subscription model 106 can also be configured to temporarily halt services of a system (e.g. system 102a), due to delay in payment, or be configured to permanently discontinue services, when a corresponding radiology department (e.g. radiology department 140a) delists itself form the empaneled list of medical centers.

[0042] In an alternate embodiment, the usage of services can be monitored by an application installed within the plurality of systems 102a- 102n. In such cases, dependency on the server 104, and communication between the server 104 and a system (e.g. 102a or 102b) for monitoring usage of services by the system (e.g. 102a or 102b) can be minimized. The services that are offered by the plurality of systems 102a-102n of the present invention are evaluating radiology reports. Basically, one or more radiology reports, generated by a radiology department (e.g. 140a), are processed for a peer review by the system (e.g. 102a) of the present invention. The peer review can be done by the user 116, who can be a medical trained professional. Incase an error is detected in the radiology report, the error can be highlighted by the system 102a, based on inputs provided by the user 116. The error is further communicated by the system 102a, to the radiology department 140a, via the communication interface 142. Upon receipt of the error, the radiology department 140a, can take measures to rectify the error and provide an error-free radiology report. Hence, the plurality of systems 102a-102n, perform the task of evaluation of radiology reports, to ensure error-free radiology reports are provided to patients. Such radiology reports have a high diagnostic accuracy and help in accurate treatment of theLEAR_001 patients. Detailed working of the system 102a ( system 200 of FIG. 2) is explained further with respect to FIG. 2.

[0043] FIG. 2 is a block diagram of a system 200 for evaluating radiology reports, according to an example embodiment. The system 200 is representational of the plurality of systems 102a- 102n, however for sake of clarity system 200 is made with reference to system 102a that communicates with radiology department 140a. The system 200 includes an interface 112, a data collection module 202, a scoring module 204, a prediction module 206, an auditing module 210, a feedback module 208, and a memory 212. These modules can be program applications running on a processor to provide various functions. The modules can use data and information stored in the memory 212 of the system 200.

[0044] The data collection module 202 gathers information pertaining to radiology reports of a radiology department from various sources. The data collection module 202 collects data from a plurality of radiology reports, including metadata extracted from DICOM tags, a plurality of medical scan images corresponding to each radiology report, and responses to an audit questionnaire, also referred to as a first questionnaire. The audit questionnaire comprises a set of questions categorized into one or more categories of a Safety, Quality, Efficiency, and Productivity (SQEP) framework. For example, the questions can be associated with a safety category and / or a quality category, depending on the impact of the data retrieved from the question on the safety and quality of the radiology report respectively. Additionally, the data collection module 202 gathers responses to a second questionnaire focused on the functioning of the radiology department (e.g. 140a).

[0045] The system 200 (or 102a), also includes a scoring module 204, designed to calculate specific performance metrics. The scoring module 204 calculates a safety score and a quality score for each radiologist and a safety score and a quality score for the radiology department (e.g. 140a) based on responses to the first questionnaire, the weightage of the responses, and the percentage of cases audited during an audit cycle. An example of the audit questionnaire or the first questionnaire is shown below in Table 1:LEAR_001LEAR_001Table 1: Audit Questionnaire

[0046] Typically, each response to each of the 17 questions of the audit questionnaire of Table 1 is marked out of 200. A response can either belong to the safety category or the quality category, depending on whether the parameter relates to a performance concerning safety or quality of the SQEP framework. The each response is further sub-divided into a radiologist sub-category and a radiology department sub-category. A score of 100 is allotted to the radiologist sub-category and a score of 100 is allotted to the radiology department sub-category. This helps in assessing a performance of the radiology department with respect to safety or quality, and likewise for individual radiologist. The audit questionnaire categorization and scoring technique is illustrated further in Table 2, as shown below:LEAR_001Table 2: Audit questionnaire categorization and scoring technique

[0047] Further, the score derived from the Table 1 and Table 2, is derived with another parameter for safety which is percentage of total cases audited during an audit cycle. A scoring of the percentage of cases audited is shown in Table 3 below:LEAR_001Table 3: Total percentage of Cases audited and Scoring

[0048] Sub-categorization and the weightage of each question regarding the safety score and the quality score as applicable is further defined in Table 4 below:LEAR_001Table 4: Weightage of individual scores for the safety score and the quality score for each sub-categoryLEAR_001

[0049] In an embodiment, the scoring module 204 analyzes DICOM tags associated with the plurality of radiology reports to extract additional parameters contributing to the safety score and the quality score of the each radiology report and the safety score and the quality score for the radiology department 140a. In this embodiment, the scoring module (204) performs an advanced analysis of the metadata stored within DICOM (Digital Imaging and Communications in Medicine) tags associated with the plurality of radiology reports. DICOM tags are standardized metadata fields embedded in medical imaging files that store crucial information about the scan, patient, imaging parameters, and equipment used.

[0050] The scoring module 204 uses the DICOM tags to extract additional parameters that are relevant to assessing both the safety and quality of radiology practices. These parameters might include imaging protocol details, equipment information, image acquisition parameters, timestamp and workflow data, and patient data consistency. Imaging Protocol Details include information about the imaging protocols used, such as the radiation dose administered during the scan, which can influence the safety score by indicating adherence to dose optimization guidelines. Equipment Information includes data about the imaging equipment, including its make, model, and calibration status. This ensures the equipment is operating within safety and quality standards. Image Acquisition Parameters includes details such as resolution, slice thickness, or imaging sequences, which impact the diagnostic quality of the scan. Poor acquisition parameters might lower the quality score. Timestamp and workflow data includes metadata capturing timestamps of when images were captured, processed, and reviewed. This helps evaluate adherence to Turn Around Time (TAT) standards, contributing to both safety and efficiency. Patient Data Consistency includes cross-verification of patient identification tags to ensure that the reports and scans are correctly attributed, reducing risks of misdiagnosis and increasing safety.

[0051] By integrating these extracted parameters into calculations, the scoring module 204 ensures a comprehensive assessment of safety and quality for each radiology report. For instance, adherence to established safety protocols, like ALARA (As Low AsLEAR_001Reasonably Achievable) for radiation exposure, could enhance the safety score. Similarly, optimal imaging settings and compliance with diagnostic standards would positively influence the quality score. This approach enables a more detailed and objective evaluation of both individual radiology reports and the overall performance of the radiology department, ensuring accurate and actionable insights for continuous improvement.

[0052] Furthermore, the scoring module 204 calculates a productivity score and an efficiency score. In an embodiment, the productivity score is determined by comparing performance metrics with predefined benchmarks. The predefined benchmarks can be subjective or objective parameters, applicable to both the sub-categories viz. the radiologist sub-category and the radiology department sub-category. The productivity score can be evaluated as per Table 5 shown below:Table 5: Productivity scoring for each sub-category

[0053] Further, in an embodiment, the efficiency score is calculated based on adherence to a predefined Turn Around Time (TAT). The efficiency can also be marked out of 200 just like the productivity score. The TAT of the radiologist and the radiology department is looked into in determining the efficiency score. The TAT and the efficiency scoring technique is shown in Table 6 below:LEAR_001Table 6: Efficiency scoring for each sub-category

[0054] Typically, the safety score is calculated by considering an interpretability of a referral, an adequacy of a clinical history of a patient, an understandability of clinical questions, an appropriateness of communication of critical findings, a conclusion of a corresponding radiology report, recommendations for additional clinical management, a language accuracy of the corresponding radiology report, and a percentage of cases audited and weighted during an audit cycle. The quality score is calculated by considering a diagnostic quality of images; availability of additional images, a relevance of prior images for comparison, a reference of the previous images in a corresponding radiology report, an appropriateness of procedural techniques, an examination quality, presence of differential diagnosis in the corresponding radiology report, an adequacy of a conclusion of the radiology report, and availability of relevant recommendations for appropriate imaging. The productivity score is determined based on one or more of subjective and objective assessments of a radiologist and the radiology departmental, and the efficiency score is calculated based on an adherence to a predefined Turn Around Time (TAT) applicable for generating a corresponding radiology report.LEAR_001

[0055] These individual scores are then aggregated using predefined weighted criteria to generate a SQEP score for each radiologist and an overall SQEP score for the radiology department. Typically, a case corresponds to a medical condition of a patient and includes a set of radiology reports, corresponding to the medical condition. In an example, the output of the audit questionnaire (Table 1) is used for populating the values for each parameter. Typically, each question represents a parameter of evaluation in the SQEP framework. Further, an output of the percent of cases to be audited is obtained from the values of Table 2. In a preferred embodiment, 100% of the cases are audited. Further, the safety score and the quality score for the radiologist and the radiology department are obtained separately by using the weighted distribution provided in Table 1 and 3.

[0056] The productivity score for the radiologist and the radiology department is obtained from Table 5. Similarly the efficiency score for the radiologist and the radiology department is obtained from Table 6. An overall SQEP score for the radiology department is obtained by using the weighted average as shown in Table 7 below:Table 7: Weightage aggregation of each component to obtain the overall SQEP score

[0057] The overall SQEP score is further analyzed and compared against predefined thresholds to provide a star rating to the radiology department (e.g. 140a). The comparison with predefined thresholds and associated star ratings are provided further in Table 8 below:LEAR_001Table 8: Predefined Thresholds for the Overall SQEP score and associated star rating

[0058] A poor rating of less than 3 indicates serious issues in the safety, quality, productivity and efficiency of the radiology department (E.g. 140a). It throws light on inefficiencies, and lack of diagnostic accuracy in the radiology reports generated by such radiology department. An exact area of the radiology department that requires attention or a causative factor that resulted in such poor rating, is identified from values of the individual parameters that make up the safety score, the productivity score, the quality score and the efficiency score of each of the sub-category.

[0059] The prediction module 206 is included to analyze trends and anticipate potential issues. The prediction module 206 identifies one or more trends by correlating the SQEP scores of individual radiologists and the radiology department (e.g. 140a) with the data extracted from the plurality of radiology reports. Based on the one or more identified trends, the prediction module 206 predicts the likelihood of errors occurring in future operations. For example, the prediction module 206 can identify a trend or a functional deviation, in a set of radiology reports, when a SQEP score for the one or more radiologists and corresponding overall SQEP score is deviates from an acceptable limit.LEAR_001

[0060] In an embodiment, the prediction module 206 correlates the trend or the deviation to one or more problems comprising of a psychological or personal problem of the radiologist that produced the set of radiology reports, a functional inefficiency, a lack of expertise of the radiologist in a specific area of radiology, a working schedule of the radiology department, based on a time or periodicity of occurrence of the deviation. The prediction module 206, further includes actions to alleviate the one or more problems within a feedback. The feedback module 208 then provides the feedback related to the one or more radiologists and the radiology department to the radiology department (e.g. 140a) with a view to enhance a diagnostic accuracy and quality of subsequent radiology reports, where the feedback comprises actions to alleviate the trend or resolve the functional deviation.

[0061] For instance, consider a scenario involving a radiologist, Mr. A, who demonstrates a sudden decline in performance during a specific time period — say, from March 15, 2024, to a current date, April 15, 2024. This decline manifests as an increase in Turn Around Time (TAT) for report completion and a noticeable reduction in diagnostic accuracy, as identified through responses to the audit questionnaire and corroborated by data extracted from DICOM tags and related sources. The prediction module (206) is programmed to detect such deviations by analyzing the SQEP score of Mr. A during this period. A sudden drop in the SQEP score is flagged as an anomaly, prompting further investigation.

[0062] The prediction module 206 analyzes potential causes of this deviation, linking it to possible personal or psychological issues faced by Mr. A. Instead of immediately attributing the drop in performance to professional incompetence, the system provides a feedback to the radiology department (e.g., 140a) recommending a more empathetic approach. It suggests that the department engage with Mr. A to understand his personal well-being and, if necessary, grant him a leave of absence until the underlying issue is resolved. This approach helps mitigate the risk of diagnostic inaccuracies that could arise from Mr. A's compromised state during this time.LEAR_001

[0063] By adopting such measures, the system 200 ensures that medical professionals, including radiologists, doctors, or practitioners, are not dismissed solely due to temporary lapses in efficiency. Instead, they are supported through mentorship and interventions aimed at improving their personal well-being. This not only fosters a supportive organizational culture but also aids in retaining valuable professionals within the institution.

[0064] Furthermore, early detection of such trends helps prevent potential major diagnostic errors in the future, which could otherwise result in significant reputational damage and financial losses for the radiology department. By proactively addressing issues, the system contributes to maintaining high standards of diagnostic accuracy while promoting employee welfare and organizational stability.

[0065] Lastly, the feedback module 208 is configured to facilitate continuous improvement. The feedback module 208 is configured to track a causative factor that resulted in an identified trend and suggest one or more corrective measures to address the causative factor in order to eliminate the occurrence of the error. This feedback module 208 tracks causative factors contributing to the one or more identified trends and suggests corrective measures to address the causative factors, thereby working to eliminate the occurrence of errors in the radiology workflow.

[0066] The auditing module 210, facilitates a peer review on the each radiology report, and a set of medical scan images associated with the each radiology report. For instance, the auditing module 210 projects the each radiology report and the associated set of medical scan images on the input / output device 112, that can be viewed by the user 116. The user 116 reads the each radiology report and observed the associated set of medical scan images and provides inputs on the accuracy of the findings in the each radiology report. The feedback of the user 116 is then provided via the input / output interface 110 to the auditing module 210. Upon receiving the inputs, the auditing module 210 determines an accuracy score of the each radiology report based on a comparison of findings of theLEAR_001 peer review and the each radiology report; and integrates the accuracy score within the safety score of the each radiology report.

[0067] FIG. 3 is a flowchart 300 illustrating a method for auditing radiology reports provided by a plurality of radiologists of a radiology department, according to an example embodiment.

[0068] At 302, data is collected from a plurality of radiology reports, where the reports are provided by the plurality of radiologists, a plurality of medical scan images (e.g. Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI) scan, Xray, Ultrasound and the like), where a radiology report is generally associated with one or more medical scan image of a patient. Typically, each radiologist is involved in the generation of at least one radiology report independently or in consultation with other radiologists. The data is further collected from responses to a first questionnaire, where each question is assigned to one or more categories of a Safety, Quality, Efficiency and Productivity (SQEP) framework, and responses to a second questionnaire where each question is related to one of clinical management, working schedule, infrastructure of the radiology department, training sessions given to the radiologists working in the radiology department and the functioning of the radiology department.

[0069] At 304, a safety score, and a quality score is calculated for each radiologist and the radiology department is based on responses to the first questionnaire, weighted parameters, and a percentage of cases audited during an audit cycle. The safety score is calculated by considering an interpretability of a referral, an adequacy of a clinical history of a patient, an understandability of a clinical questions, an appropriateness of communication of critical findings, a conclusion of a corresponding radiology report, recommendations for additional clinical management, a language accuracy of the corresponding radiology report, a percentage of cases audited and weighted based on the audit cycle. In an embodiment, a peer review is performed on the each radiology report and a set of medical scan images associated with the each radiology report. The peer review can be conducted by specialized external medical professionals, hired to review the radiologyLEAR_001 reports and validate it by reviewing the medical scans associated with the each radiology report. Further, based on a comparison of findings of the peer review and the each radiology report received from the radiology department, an accuracy score is determined. For example, if upon a peer review it is observed that a radiology report matches more than 95% with the findings of the peer review, then the accuracy score of 5 (meaning excellent accuracy) is provided, on the other hand if it matched below 20% an accuracy score of 1 (meaning poor accuracy) is given. The accuracy score is then integrated within the safety score of the each radiology report.

[0070] The quality score is calculated by considering a diagnostic quality of images; availability of additional images, a relevance of prior images for comparison, a reference of the previous images in a corresponding radiology report, an appropriateness of procedural techniques, an examination quality, presence of differential diagnosis in the corresponding radiology report, an adequacy of a conclusion of the radiology report, and availability of relevant recommendations for appropriate imaging. In an embodiment, DICOM tags associated with the plurality of radiology reports are analyzed, to extract additional parameters contributing to the safety score and the quality score of the each radiology report and the radiology department.

[0071] At 304, a productivity score and an efficiency score is calculated for each radiologist and the radiology department based on responses to the first questionnaire, weighted parameters, and a percentage of cases audited during an audit cycle. The productivity score is determined based on one or more of subjective and objective assessments of a radiologist and the radiology department. The efficiency score is calculated based on an adherence to a predefined Turn Around Time (TAT) applicable for generating a corresponding radiology report.

[0072] At 306, a SQEP score is generated for the each radiologist and an overall SQEP score is generated for the radiology department by applying a predefined weighted aggregation of the safety score, the quality score, the efficiency score and the productivity score of the radiology department.LEAR_001

[0073] At 308, one or more deviation in a radiology report, based on a peer review of the radiology report, a correlation between the SQEP score for the each radiologist, the overall SQEP score for the radiology department, and the data collected. Further, one or more trends are identified based on a series of deviations occurring in subsequent radiology reports, a correlation between the SQEP score for a radiologist associated with the subsequent radiology reports the overall SQEP score for the radiology department, and the data collected from the subsequent radiology reports. An occurrence of an error in the future based on the one or more trends is predicted, by a prediction module. In order to avoid such an occurrence of the error, one or more factors that resulted in the one or more trends is tracked, and one or more corrective measures to address the one or more factors is suggested by a feedback module. This helps to eliminate the occurrence of the error.

[0074] For example, consider a scenario where a peer review is conducted on a set of medical scan images taken by a radiologist, Mr. B, between 4 PM and 6 PM, along with the corresponding radiology reports prepared by him. During this review, it is observed that the diagnostic accuracy for a specific body region, such as the head, is suboptimal and below expected standards. This observation highlights a potential issue in Mr. B’s performance during this specific time frame.

[0075] The prediction module (206), an artificial intelligence (Al) component configured to identify trends, detects this pattern. It associates the poor diagnostic accuracy with a recurring trend during the 4 PM to 6 PM window and flags it for further analysis. This trend is then investigated by tracking it to one or more causative factors that might contribute to the observed issue.

[0076] The feedback module (208), another Al component trained on a vast set of historical data and various combinations of datasets, is employed to analyze Mr. B’s performance further. Historical data reveals that Mr. B demonstrates high diagnostic accuracy for head scans during the 10 AM to 4 PM period, a time when he frequently consults with other radiologists available during those hours. However, in the 4 PM to 6 PM window, Mr. B prepares radiology reports independently without consultation. WhileLEAR_001 he maintains accuracy for scans of other body parts, such as the knee, ankle, elbow, shoulder, abdomen, and hip, his accuracy specifically for head region scans declines significantly.

[0077] The feedback module 208 concludes that Mr. B may lack sufficient training or expertise in diagnosing head region scans without additional support. Further, the feedback module 208 recommends that Mr. B undergo specialized training or mentoring focused on diagnostic techniques for head scans. By addressing this gap, the feedback module 208 aims to help Mr. B improve his performance and avoid future errors.

[0078] This functionality highlights the intelligent design of the feedback module (208) as an Al-driven system capable of deriving insights from diverse datasets and tracking causative factors linked to trends identified by the prediction module (206). The prediction module, in this embodiment, operates as a rule-based Al system configured to detect trends by applying predefined rules to patterns in data, such as scheduling practices and performance metrics in the radiology department (e.g., 140a). Together, these modules enable the system to proactively identify, analyze, and address performance issues, ensuring enhanced diagnostic accuracy and overall efficiency in radiology workflows.

[0079] At 310, correction of the one or more deviations in the radiology report are facilitated, via a feedback given by the feedback module. The feedback includes one or more corrective suggestions for a set of radiologists associated with the radiology report and the radiology department.

[0080] In an embodiment, a trend or a functional deviation is identified in a set of radiology reports, when a SQEP score for the one or more radiologists and corresponding overall SQEP score is below an acceptable limit. The trend or the functional deviation is correlated to one or more problems, such as a psychological or personal problem of the radiologist, a functional inefficiency, a lack of expertise in a specific area of radiology, and a working schedule of the radiology department, based on a time or periodicity of occurrence of the functional deviation. A feedback related to the one or more radiologistsLEAR_001 and the radiology department to enhance a diagnostic accuracy and quality of subsequent radiology reports is provided. Typically, the feedback includes one or more actions that need to be executed by the radiology department, to alleviate the trend or the functional deviation, or to avoid occurrence of the one or more problems.

[0081] The modules of the system (200) for evaluating radiology reports described herein are implemented in computing devices. One example of a computing device (400) is described below in FIG.4. The computing device includes one or more processor (402), one or more computer-readable RAMs (404) and one or more computer-readable ROMs (406) on one or more buses (408). Further, a computing device (400) includes a tangible storage device (410) that may be used to execute operating systems (420) and the system (200). The various modules of the system (200) may be stored in tangible storage device (410). Both, the operating system (420) and the system (200) are executed by processor (402) via one or more respective RAMs (404) (which typically include cache memory). The execution of the operating system (420) and / or the system (200) by the processor (402), configures the processor (402) as a special purpose processor configured to carry out the functionalities of the operation system (420) and / or the system (200) as described above.

[0082] Examples of storage devices (410) include semiconductor storage devices such as ROM, EPROM, flash memory or any other computer-readable tangible storage device that may store a computer program and digital information.

[0083] Computing device also includes a R / W drive or interface (414) to read from and write to one or more portable computer-readable tangible storage devices (428) such as a CD-ROM, DVD, memory stick or semiconductor storage device. Further, network adapters or interfaces (412) such as a TCP / IP adapter cards, wireless Wi-Fi interface cards, or 3G or 4G wireless interface cards or other wired or wireless communication links are also included in computing device.LEAR_001

[0084] In one example embodiment, the system (200) may be stored in tangible storage device (410) and may be downloaded from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and network adapter or interface (412).

[0085] Computing device further includes device drivers (416) to interface with input and output devices. The input and output devices may include a computer display monitor (418), a keyboard (424), a keypad, a touch screen, a computer mouse (426), and / or some other suitable input device.

[0086] It will be understood by those within the art that, in general, terms used herein, are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present.

[0087] The disclosed system 200 offers several technical advantages, primarily in terms of improving the overall quality control and operational efficiency of medical reporting within hospitals. By auditing radiology reports and associated medical scan images, the system ensures higher accuracy, safety, and quality in medical diagnostics. It aggregates data from multiple sources, such as reports, peer reviews, and departmental questionnaires, and generates comprehensive scores that assess the performance of individual radiologists and the department as a whole. This holistic evaluation allows for the identification of trends and deviations, which can then be addressed proactively, reducing the risk of recurring errors.

[0088] A significant advantage of such a system is its ability to predict errors using artificial intelligence (Al). The Al-driven prediction module analyses patterns and trends in past reports, allowing it to foresee potential errors before they occur. This predictiveLEAR_001 capability is especially valuable in identifying recurring issues, such as time-based performance drops or specific diagnostic challenges, and can alert relevant stakeholders to take corrective actions before the issues impact patient care. The system tracks causative factors for these trends, providing valuable insights into underlying problems, such as a radiologist's working hours, training gaps, or the department's operational inefficiencies.

[0089] By utilizing Al, the system not only detects errors but also learns from data over time, continuously refining its predictions and recommendations. This ongoing improvement ensures that the system remains effective and adaptable to the evolving needs of the department. Additionally, Al helps ensure that corrective actions are more precise and personalized, targeting specific areas of concern and optimizing departmental workflows. Ultimately, such a quality control system minimizes errors, improves diagnostic accuracy, and enhances the overall efficiency of the medical reporting process.

[0090] The disclosed method for auditing and evaluating radiology reports to ensure quality control offers several technical advantages that significantly enhance the overall performance and efficiency of radiology departments. By systematically collecting data from a variety of sources, including radiology reports, medical scan images, and responses to detailed questionnaires, the method ensures a comprehensive evaluation of the radiology department. The questionnaires, which are linked to a Safety, Quality, Efficiency, and Productivity (SQEP) framework, provide a structured approach to categorizing and analyzing different aspects of radiology operations. This multi-faceted data collection process allows for a thorough assessment of not only the quality of radiology reports but also the operational factors impacting the department's performance.

[0091] One of the key advantages of this approach is the calculation of performance scores based on safety, quality, productivity, and efficiency metrics. By incorporating weighted parameters and audit cycles, the method ensures objective and data-driven evaluations of both individual radiologists and the department as a whole. The safety score, for example, is validated through a peer review process, which enhances the accuracy of the assessment by incorporating external expert opinions. This added layer of validationLEAR_001 ensures that the safety score is grounded in real-world accuracy, as it is based on both the radiology reports and the associated medical scans.

[0092] The integration of artificial intelligence (Al) in the prediction module 206 and the feedback module 208, further enhances the technical advantages of the disclosed method. The prediction module identifies patterns and trends in radiology reports, allowing for proactive error detection and intervention. By correlating deviations across multiple reports and identifying recurring trends, the Al system can predict potential future errors and suggest corrective actions. The feedback module 208 then analyzes these trends and provides targeted recommendations, such as additional training for radiologists or adjustments to working schedules, to address identified gaps in performance. This proactive approach not only prevents errors but also helps maintain high diagnostic accuracy and quality.

[0093] In terms of diagnostic quality, the method evaluates multiple parameters such as image quality, procedural techniques, and the appropriateness of differential diagnoses. By considering a broad range of factors, the system ensures that radiologists provide accurate, comprehensive, and actionable reports. Additionally, the method’s use of DICOM tag analysis allows for the extraction of even more granular data, which contributes to both the safety and quality scoring of radiology reports. This enhances the department's ability to identify subtle deficiencies in diagnostic practices that might otherwise go unnoticed.

[0094] Another significant advantage is an ability to track and improve productivity and efficiency within the radiology department. By calculating efficiency scores based on adherence to predefined turnaround times (TAT) for report generation, the system promotes timely reporting and better management of workloads. The productivity score incorporates both subjective and objective assessments of individual radiologists, which allows for a more holistic evaluation of their performance. This, in turn, helps to streamline processes and align radiologists' work with departmental goals, ensuring that operational efficiency is maintained without sacrificing the quality of the reports.LEAR_001

[0095] The feedback module 208 plays a critical role in ensuring continuous improvement. It analyzes trends in radiology reports and identifies functional deviations, which are then correlated with specific issues such as personal or psychological factors affecting a radiologist, departmental inefficiencies, or lack of expertise in certain areas. By tracking these deviations, the system can provide actionable feedback that is tailored to the individual or the department, ensuring that corrective measures are implemented to address underlying causes. For example, if a radiologist is consistently underperforming during a particular time window, the system may suggest additional training or mentoring during that period to improve performance.

[0096] Finally, an adaptability and scalability of the disclosed method make it suitable for a wide range of radiology departments, regardless of size or imaging modality. The system can be customized to accommodate different workflows and data inputs, allowing it to be easily implemented in various clinical settings. Additionally, the Al-driven modules can continuously learn and adapt to new data, improving their predictive capabilities and corrective recommendations over time. This ensures that the system remains effective in identifying and addressing performance issues as they arise, supporting the ongoing improvement of radiology department operations and patient care outcomes.

[0097] It will be understood by those within the art that, in general, terms used herein, are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present.

[0098] For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits anyLEAR_001 particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., “a” and / or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, means at least two recitations, or two or more recitations).

[0099] While only certain features of several embodiments have been illustrated, and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of inventive concepts.

[0100] The aforementioned description is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses. The broad teachings of the disclosure may be implemented in a variety of forms. Therefore, while this disclosure includes particular examples, the true scope of the disclosure should not be so limited since other modifications will become apparent upon a study of the drawings, the specification. It should be understood that one or more steps within a method may be executed in different order (or concurrently) without altering the principles of the present disclosure. Further, although each of the example embodiments is described above as having certain features, any one or more of those features described with respect to any example embodiment of the disclosure may be implemented in and / or combined with features of any of the other embodiments, even if that combination is not explicitly described. In other words, the example embodiments described are not mutually exclusive, and permutations of one or more example embodiments with one another remain within the scope of this disclosure.LEAR_001

[0101] The example embodiment or each example embodiment should not be understood as a limiting / restrictive of inventive concepts. Rather, numerous variations and modifications are possible in the context of the present disclosure, in particular those variants and combinations which may be inferred by the person skilled in the art with regard to achieving the object for example by combination or modification of individual features or elements or method steps that are described in connection with the general or specific part of the description and / or the drawings, and, by way of combinable features, lead to a new subject matter or to new method steps or sequences of method steps, including insofar as they concern production, testing and operating methods. Further, elements and / or features of different example embodiments may be combined with each other and / or substituted for each other within the scope of this disclosure.

[0102] Still further, any one of the above-described and other examples features of example embodiments may be embodied in the form of an apparatus, method, system, computer program, tangible computer readable medium and tangible computer program product. For example, the aforementioned methods may be embodied in the form of a system or device, including, but not limited to, any of the structures for performing the methodology illustrated in the drawings.

[0103] In this application, including the definitions below, the term ‘module’ or the term ‘controller’ may be replaced with the term ‘circuit.’ The term ‘module’ may refer to, be part of, or include processor hardware (shared, dedicated, or group) that executes code and memory hardware (shared, dedicated, or group) that stores code executed by the processor hardware.

[0104] The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules that are connected via interface circuits. For example, multiple modulesLEAR_001 may allow load balancing. In a further example, a server (also known as remote, or cloud) module may accomplish some functionality on behalf of a client module.

[0105] Further, at least one example embodiment relates to a non-transitory computer- readable storage medium comprising electronically readable control information (e.g., computer-readable instructions) stored thereon, configured such that when the storage medium is used in a controller of a magnetic resonance device, at least one example embodiment of the method is carried out.

[0106] Even further, any of the aforementioned methods may be embodied in the form of a program. The program may be stored on a non-transitory computer readable medium, such that when run on a computer device (e.g., a processor), cause the computer-device to perform any one of the aforementioned methods. Thus, the non-transitory, tangible computer readable medium is adapted to store information and is adapted to interact with a data processing facility or computer device to execute the program of any of the above- mentioned embodiments and / or to perform the method of any of the above-mentioned embodiments.

[0107] The computer readable medium or storage medium may be a built-in medium installed inside a computer device’s main body, or a removable medium arranged so that it may be separated from the computer device’s main body. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave), the term computer-readable medium is therefore considered tangible and non-transitory. Non-limiting examples of the non-transitory computer-readable medium include, but are not limited to, rewriteable nonvolatile memory devices (including, for example flash memory devices, erasable programmable read-only memory devices, or a mask read-only memory devices), volatile memory devices (including, for example static random access memory devices or a dynamic random access memory devices), magnetic storage media (including, for example an analog or digital magnetic tape or a hard disk drive), and optical storage media (including, for example a CD, a DVD, or a Blu-ray Disc). Examples of the media with aLEAR_001 built-in rewriteable non-volatile memory, include but are not limited to memory cards, and media with a built-in ROM, including but not limited to ROM cassettes, etc. Furthermore, various information regarding stored images, for example, property information, may be stored in any other form, or it may be provided in other ways.

[0108] The term code, as used above, may include software, firmware, and / or microcode, and may refer to programs, routines, functions, classes, data structures, and / or objects. Shared processor hardware encompasses a single microprocessor that executes some or all code from multiple modules. Group processor hardware encompasses a microprocessor that, in combination with additional microprocessors, executes some or all code from one or more modules. References to multiple microprocessors encompass multiple microprocessors on discrete dies, multiple microprocessors on a single die, multiple cores of a single microprocessor, multiple threads of a single microprocessor, or a combination of the above.

[0109] Shared memory hardware encompasses a single memory device that stores some or all code from multiple modules. Group memory hardware encompasses a memory device that, in combination with other memory devices, stores some or all code from one or more modules.

[0110] The term memory hardware is a subset of the term computer-readable medium. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave), the term computer-readable medium is therefore considered tangible and non- transitory. Non-limiting examples of the non-transitory computer-readable medium include, but are not limited to, rewriteable non-volatile memory devices (including, for example flash memory devices, erasable programmable read-only memory devices, or a mask read-only memory devices), volatile memory devices (including, for example static random access memory devices or a dynamic random access memory devices), magnetic storage media (including, for example an analog or digital magnetic tape or a hard disk drive), and optical storage media (including, for example a CD, a DVD, or a Blu-ray Disc).LEAR_001Examples of the media with a built-in rewriteable non-volatile memory, include, but are not limited to memory cards, and media with a built-in ROM, including but not limited to ROM cassettes, etc. Furthermore, various information regarding stored images, for example, property information, may be stored in any other form, or it may be provided in other ways.

[0111] The apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general-purpose computer to execute one or more particular functions embodied in computer programs. The functional blocks and flowchart elements described above serve as software specifications, which may be translated into the computer programs by the routine work of a skilled technician or programmer.

[0112] The computer programs include processor-executable instructions that are stored on at least one non-transitory computer-readable medium. The computer programs may also include or rely on stored data. The computer programs may encompass a basic input / output system (BIOS) that interacts with hardware of the special purpose computer, device drivers that interact with particular devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, etc.

[0113] The computer programs may include: (i) descriptive text to be parsed, such as HTML (hypertext markup language) or XML (extensible markup language), (ii) assembly code, (iii) object code generated from source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. As examples only, source code may be written using syntax from languages including C, C++, C#, Objective-C, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5, Ada, ASP (active server pages), PHP, Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, and Python®.

Claims

We Claim:

1. A method for evaluating radiology reports provided by radiologists of a radiology department, the method comprising: collecting data from a plurality of radiology reports, a plurality of medical scan images, responses to a first questionnaire, wherein each question is assigned to one or more categories of a Safety, Quality, Efficiency and Productivity (SQEP) framework, and responses to a second questionnaire related to functioning of the radiology department; calculating a safety score and a quality score for each radiologist and the radiology department based on one or more of responses to the first questionnaire, and weighted parameters for each question of the first questionnaire and a percentage of cases audited during an audit cycle; calculating a productivity score and an efficiency score for the radiologists and the radiology department based on an assessment of examination appropriateness and a time duration of delivering the each radiology report; generating a SQEP score for the each radiologist and an overall SQEP score for the radiology department by a predefined weighted aggregation of the safety score, the quality score, the efficiency score, and the productivity score; identifying one or more deviations in a radiology report, based on a peer review of the radiology report, a correlation between the SQEP score for the each radiologist, the overall SQEP score for the radiology department, and the collected data; and facilitating correction of the one or more deviations in the radiology report via a feedback comprising one or more corrective suggestions for a set of radiologists associated with the radiology report and the radiology department.LEAR_0012. The method of claim 1, wherein the corrective suggestions is generated based on identifying one or more causative factors responsible for the one or more deviations.

3. The method of claim 1, further comprising: identifying one or more trends based on a series of deviations occurring in subsequent radiology reports, a correlation between the SQEP score for a radiologist associated with the subsequent radiology reports, the overall SQEP score for the radiology department, and the data collected from the subsequent radiology reports; predicting an occurrence of an error based on the one or more trends; tracking one or more factors that resulted in the one or more trends; and suggesting one or more corrective measures to address the one or more factors in order to eliminate the occurrence of the error.

4. The method of claim 1 , wherein the each radiologist is involved in the generation of at least one radiology report independently or in consultation with other radiologists.

5. The method of claim 1, wherein the causative factor is related to one or more of a radiologist, the radiology department, a diagnostic software, a working schedule, and an infrastructure of the radiology department.

6. The method of claim 1, further comprising: performing a peer review on the each radiology report and a set of medical scan images associated with the each radiology report; determining an accuracy score of the each radiology report based on a comparison of findings of the peer review and the each radiology report; and integrating the accuracy score within the safety score of the each radiology report.

7. The method of claim 1, further comprising:LEAR_001 identifying a trend or a functional deviation in a set of radiology reports, when a SQEP score for the one or more radiologists and corresponding overall SQEP score is below an acceptable limit; and providing a feedback related to the one or more radiologists and the radiology department to enhance a diagnostic accuracy and quality of subsequent radiology reports.

8. The method of claim 7, further comprising: correlating the trend or the functional deviation to one or more problems comprising of a psychological or personal problem of the radiologist, a functional inefficiency, a lack of expertise in a specific area of radiology, a working schedule of the radiology department, based on a time or periodicity of occurrence of the functional deviation; and including within the feedback actions to alleviate the one or more problems.

9. The method of claim 1, further comprising: analysing DICOM tags associated with the plurality of radiology reports to extract additional parameters contributing to the safety score and the quality score of the each radiology report and the radiology department.

10. The method of claim 1, wherein the safety score is calculated by considering an interpretability of a referral, an adequacy of a clinical history of a patient, an understandability of a clinical questions, an appropriateness of communication of critical findings, a conclusion of a corresponding radiology report, recommendations for additional clinical management, a language accuracy of the corresponding radiology report, a percentage of cases audited and weighted based on the audit cycle.

11. The method of claim 1 , wherein the quality score is calculated by considering a diagnostic quality of images; availability of additional images, a relevance of priorLEAR_001 images for comparison, a reference of the previous images in a corresponding radiology report, an appropriateness of procedural techniques, an examination quality, presence of differential diagnosis in the corresponding radiology report, an adequacy of a conclusion of the radiology report, and availability of relevant recommendations for appropriate imaging.

12. The method of claim 1, wherein the productivity score is determined based on one or more of subjective and objective assessments of a radiologist and the radiology departmental.

13. The method of claim 1, wherein the efficiency score is calculated based on an adherence to a predefined Turn Around Time (TAT) applicable for generating a corresponding radiology report.

14. A system for evaluating radiology reports provided by radiologists of a radiology department, wherein the system comprises: a data collection module configured to: collect data from a plurality of radiology reports, including metadata from DICOM tags, a plurality of medical scan images corresponding to each radiology report, responses to an audit questionnaire comprising a set of questions categorized into one or more categories of a Safety, Quality, Efficiency and Productivity (SQEP) framework; and responses to a second questionnaire related to functioning of the radiology department; a scoring module configured to: calculate a safety score and a quality score for each radiologist and the radiology department based on responses to the first questionnaire, weightage of the responses, and a percentage of cases audited during an audit cycle;LEAR_001 calculate a productivity score and an efficiency score for radiologists and the radiology department, wherein the productivity score is determined through comparison with predefined benchmarks, and the efficiency score is determined based on an adherence to a predefined Turn Around Time (TAT); and generating a SQEP score for the each radiologist and an overall SQEP score for the radiology department by a predefined weighted aggregation of the safety score, the quality score, the efficiency score and the productivity score; a prediction module configured to: identifying one or more trends based on a correlation between the SQEP score for the each radiologist, the overall SQEP score for the radiology department, and the data from the plurality of radiology reports; and predict an occurrence of an error based on the one or more identified trends; and a feedback module configured to: track a causative factor that resulted in an identified trend; and suggest one or more corrective measures to address the causative factor in order to eliminate the occurrence of the error.

15. The system of claim 14, wherein a case corresponds to a medical condition of a patient and includes a set of radiology reports corresponding to the medical condition.

16. The system of claim 14, further comprising: an auditing module configured to: facilitate a peer review on the each radiology report and a set of medical scan images associated with the each radiology report; determine an accuracy score of the each radiology report based on a comparison of findings of the peer review and the each radiology report; andLEAR_001 integrate the accuracy score within the safety score of the each radiology report.

17. A non-transitory computer-readable storage medium configured with instructions that can be executed by one or more processors to cause the one or more processors to execute the method comprising: collecting data from a plurality of radiology reports, a plurality of medical scan images, responses to a first questionnaire, wherein each question is assigned to one or more categories of a Safety, Quality, Efficiency and Productivity (SQEP) framework, and responses to a second questionnaire related to functioning of the radiology department; calculating a safety score and a quality score for each radiologist and the radiology department based on responses to the first questionnaire, weighted parameters, and a percentage of cases audited during an audit cycle; calculating a productivity score and an efficiency score for radiologists and departments based on an assessment of examination appropriateness and a time duration of delivering the each radiology report; generating a SQEP score for the each radiologist and an overall SQEP score for the radiology department by a predefined weighted aggregation of the safety score, the quality score, the efficiency score, and the productivity score; identifying one or more deviations in a radiology report, based on a peer review of the radiology report, a correlation between the SQEP score for the each radiologist, the overall SQEP score for the radiology department, and the data collected; and facilitating correction of the one or more deviations in the radiology report via a feedback comprising one or more corrective suggestions for a set of radiologists associated with the radiology report and the radiology department.

Images