Systems and methods for generating pharmaceutical compound analysis results with a subject-specific quantity parameter

Abstract

A system (106) and a method for generating pharmaceutical compound analysis results with a subject-specific quantity parameter are disclosed. The system (106) includes a memory (202) and at least one processor (206) operatively coupled to the memory. The at least one processor is configured to receive subject-specific clinical information from a user device (102). The at least one processor is configured to generate the pharmaceutical compound analysis results with the subject-specific quantity parameter based on the received subject- specific clinical information.

Description

SYSTEMS AND METHODS FOR GENERATING PHARMACEUTICAL COMPOUND ANALYSIS RESULTS WITH A SUBJECT-SPECIFIC QUANTITY PARAMETER FIELD OF THE INVENTION

[0001] The present disclosure generally relates to clinical decision support systems. More particularly, the present disclosure relates to systems and methods for generating pharmaceutical compound analysis results with a subject- specific quantity parameter.BACKGROUND

[0002] The information in this section merely provides background information related to the present disclosure and may not constitute prior art(s) for the present disclosure.

[0003] Currently, healthcare professionals, such as doctors, encounter many challenges while selecting optimal antimicrobial therapies. One of the main challenges is information overload. Particularly, the healthcare professionals can become overwhelmed, especially in busy clinical settings, with frequent updates on antibiotic effectiveness, emerging resistances, drug interactions, and new therapies. Additionally, research papers or guidelines associated with medical practices are often dense and complex, making it difficult for the healthcare professionals to quickly apply information contained therein to specific patient situations. Further, even when such information is available, finding precise guidance for a particular case can be time-consuming and distracting from patient care, ultimately impacting the quality of treatment.

[0004] Complex treatment decisions also pose significant challenges for the healthcare professionals while selecting the optimal antibiotic therapies. Choosing the most appropriate antibiotic requires consideration of various factors, including suspected site of infection, patient characteristics such as allergies and renal function, severity of illness, and potential drug interactions. Balancing the aforementioned elements demands considerable time and cognitive effort. Further, an absence of immediate diagnostic confirmation often forces healthcare professionals to make decisions with uncertainty, creating pressure to depend on broad- spectrum pharmaceutical compounds, even when narrower, less potent options might be more suitable. Concerns about treatment failure or the development of severe infections can result in overprescription of the broad-spectrum pharmaceutical compounds as a precaution, thereby contributing to other complications.

[0005] Furthermore, variability in local resistance patterns adds another layer of complexity to selecting optimal antibiotic therapies. For instance, antimicrobial resistance (AMR) patterns can vary greatly, even within the same country or region. Regional guidelines may not always accurately represent pathogen sensitivities specific to a particular hospital or community. Local antibiograms may be unavailable, outdated, or hard to access, which also hinders tailored- antibiotic selection. Further, resistance patterns evolve over time, and even with access to antibiograms, the healthcare professionals may find that standard empiric therapies become less effective due to emerging resistance.

[0006] The above-mentioned challenges contribute to inappropriate antimicrobial use, which leads to the rise of antibiotic resistance and poor patient outcomes. Improper use of the pharmaceutical compounds promotes the emergence and spread of antibiotic-resistant bacteria, making it more difficult to treat common infections and leading to higher morbidity and mortality rates. Further, unnecessary use of the pharmaceutical compounds exposes patients to side effects such as allergic reactions, treatment failures, AMR, Clostridium difficile infections, and disruption of gut flora. Furthermore, the AMR and adverse drug events (ADEs) result in longer hospital stays, the need for additional diagnostic tests, and the use of more costly broad- spectrum pharmaceutical compounds, which place a significant strain on healthcare resources.

[0007] Therefore, there is a need for systems and methods to optimize antibiotic prescribing practices to ensure effective treatment, reduce the risk of AMR, and improve the patient outcomes.SUMMARY

[0008] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended for determining the scope of the invention.

[0009] In accordance with an embodiment of the present disclosure, a system for generating pharmaceutical compound analysis results with a subject-specific quantity parameter is disclosed. The system includes a memory and at least one processor operatively coupled to the memory. The at least one processor is configured to receive subject-specific clinical information from a user device. The at least one processor is configured to generate thepharmaceutical compound analysis results with the subject-specific quantity parameter based on the received subject-specific clinical information.

[0010] In accordance with another embodiment of the present disclosure, a method for generating pharmaceutical compound analysis results with a subject- specific quantity parameter is disclosed. The method includes receiving, by at least one processor, subjectspecific clinical information from a user device. The method includes generating, by the at least one processor, the pharmaceutical compound analysis results with the subject-specific quantity parameter based on the received subject- specific clinical information.

[0011] To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail, with the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] These and other features, aspects, and advantages of the present invention 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:

[0013] Figure 1 illustrates an environment for establishing a system for generating pharmaceutical compound analysis results with a subject- specific quantity parameter, in accordance with an embodiment of the present disclosure;

[0014] Figure 2 illustrates a block diagram of the system for generating the pharmaceutical compound analysis results with the subject-specific quantity parameters, in accordance with an embodiment of the present disclosure;

[0015] Figure 3 illustrates a block diagram of the database of the system, in accordance with an embodiment of the present disclosure;

[0016] Figure 4 illustrates a block diagram of one or more modules of the system, in accordance with an embodiment of the present disclosure;

[0017] Figure 5 illustrates a block diagram depicting an exemplary model framework environment for providing data inputs to the system, in accordance with an embodiment of the present disclosure;

[0018] Figure 6 illustrates a block diagram of an exemplary antimicrobial recommendation generation process performed by system, in accordance with an embodiment of the present disclosure;

[0019] Figure 7A and Figure 7B illustrate a flowchart depicting a method for generating the pharmaceutical compound analysis results with the subject- specific quantity parameters, in accordance with an embodiment of the present disclosure;

[0020] Figure 8A, Figure 8B, and Figure 8C illustrate a flowchart depicting a method of user authentication and subject selection within the system, in accordance with an embodiment of the present disclosure; and

[0021] Figure 9 illustrates a flowchart depicting a method for generating pharmaceutical compound analysis results with a subject- specific quantity parameter, in accordance with an embodiment of the present disclosure.

[0022] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale.

[0023] Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.DETAILED DESCRIPTION OF FIGURES

[0024] For the purpose of promoting an understanding of the principles of the present disclosure, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the present disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the present disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the present disclosure relates.

[0025] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the present disclosure and are not intended to be restrictive thereof.

[0026] Whether or not a certain feature or element was limited to being used only once, it may still be referred to as “one or more features” or “one or more elements” or “at least one feature” or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element do not preclude there being none of that feature or element, unless otherwise specified by limiting language including, but not limited to, “there needs to be one or more...” or “one or more elements is required.”

[0027] Reference is made herein to some “embodiments.” It should be understood that an embodiment is an example of a possible implementation of any features and / or elements of the present disclosure. Some embodiments have been described for the purpose of explaining one or more of the potential ways in which the specific features and / or elements of the proposed disclosure fulfil the requirements of uniqueness, utility, and non-obviousness.

[0028] Use of the phrases and / or terms including, but not limited to, “a first embodiment,” “a further embodiment,” “an alternate embodiment,” “one embodiment,” “an embodiment,” “multiple embodiments,” “some embodiments,” “other embodiments,” “further embodiment”, “furthermore embodiment”, “additional embodiment” or other variants thereof do not necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and / or elements described in connection with one or more embodiments may be found in one embodiment, or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments. Although one or more features and / or elements may be described herein in the context of only a single embodiment, or in the context of more than one embodiment, or in the context of all embodiments, the features and / or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any features and / or elements described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment.

[0029] Any particular and all details set forth herein are used in the context of some embodiments and therefore should not necessarily be taken as limiting factors to the proposed disclosure.

[0030] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub- systems or additional elements or additional structures or additional components.

[0031] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

[0032] Figure 1 illustrates an environment 100 for an implementation of a system 106 for generating the pharmaceutical compound analysis results with a subject-specific quantity parameter, in accordance with an embodiment of the present disclosure. The environment 100 may represent a technical and operational setting that establishes an interaction context between one or more subjects (referred to as the subject for the sake of brevity) from whom subject-specific clinical information originates, and one or more healthcare professionals (referred to as the healthcare professionals for the sake of brevity) responsible for accessing, reviewing, and acting upon generating the pharmaceutical compound analysis results.

[0033] The environment 100 may include a user device 102, a cloud server 104, and a network 108. The user device 102 may be operated by the healthcare professional in a healthcare entity. The healthcare professional may include, but is not limited to, a doctor, such as a doctor, a physician, a nurse, or a physician assistant, involved in prescribing and administering antimicrobials, and the like. The healthcare entity may refer to an organisation, institution, or operational unit that generates, manages, stores, or provides access to the subject-specific clinical information. The network 108 may include one or more communication networks such as the Internet, local area networks, wide area networks, or combinations thereof. The user device 102 and the cloud server 104 may be connected through the network 108. The network 108 facilitates communication between the user device 102 and the cloud server 104.

[0034] In an embodiment, the healthcare entity may include, but is not limited to, a hospital, a multi- speciality or single- speciality clinic, an outpatient care center, a diagnostic laboratory, a microbiology laboratory, a pathology center, a pharmacy, an antimicrobial stewardshipdepartment, a public or private healthcare network, a research institution, or a health information management system.

[0035] In an embodiment, the healthcare entity may include an electronic health record (EHR) system, a laboratory information system (LIS), a hospital information system (HIS), or a regional data repository operated by healthcare providers or healthcare administrators. For example, a hospital microbiology laboratory may act as the healthcare entity by transmitting microorganism culture results and susceptibility reports to the system 106, while a clinical facility may act as the healthcare entity by providing subject physiological parameters and indication identifiers through an integrated information system.

[0036] In an embodiment, the user device 102 may be configured to interact with the system 106 via the network 108 for, but not limited to, submitting the subject- specific clinical information and generating the pharmaceutical compound analysis results, which leads to a technical effect of enabling structured, real-time acquisition and centralized computational processing of subject-specific clinical information to generate standardized, consistent, and reproducible analytical outputs with reduced dependence on manual data interpretation and user-specific variability. The user device 102 may include, but is not limited to, a mobile computing device such as a smartphone or tablet, a personal computing device such as a laptop or desktop computer, a tablet, a smart watch, a workstation or terminal deployed in a healthcare or laboratory environment, or any computing system capable of executing a webbased or native software application.

[0037] The subject- specific clinical information may include, but is not limited to, a clinical indication identifier such as suspected site of infection, associated with the subject, or a patient, or a human being under clinical trial. Additionally, the subject-specific clinical information may include, but is not limited to, subject physiological parameters, microbiology culture data of the subject, one or more susceptibility reports (referred to as susceptibility reports for the sake of brevity) of the subject, and the like. The susceptibility reports may refer to structured data artifacts that represent microbiological susceptibility assessment results associated with the subjects or a defined microorganism dataset, and which are received by the system 106 as part of the subject-specific clinical information.

[0038] Further, the pharmaceutical compound analysis results may be generated on the user device 102 based on the received subject- specific clinical information. The pharmaceuticalcompound analysis results may represent data-driven analytical information rather than any medical diagnosis or therapeutic action and are generated to facilitate systematic evaluation of one or more pharmaceutical compounds (referred to as pharmaceutical compounds for the sake of brevity) within a computational framework. The pharmaceutical compounds may include, but are not limited to, antibiotics, drugs, medicines, and the like. The pharmaceutical compound analysis results may refer to evidence-based antimicrobial recommendations and clinical recommendations. The pharmaceutical compound analysis results may include, but are not limited to, identifiers of the pharmaceutical compounds selected from a pharmaceutical compounds master list, one or more confidence-weighted susceptibility indicators derived from microorganism susceptibility data, the subject-specific quantity parameter associated with each identified pharmaceutical compound, ranking or prioritization metadata based on predefined computational rules, explanatory metadata indicating which decision matrices or physiological parameters contributed to the result generation, and the like. Table 1 below depicts pharmaceutical compounds.

[0039] The pharmaceutical compounds master list may include a list of the pharmaceutical compounds that may be approved for prescription against one or more indications (referred toas indications for the sake of brevity) or infections of the subject. The microorganism susceptibility data may indicate the pharmaceutical compounds are susceptible, resistant, or intermediate against one or more microorganisms (referred to as microorganisms for the sake of brevity). The microorganisms may include one or more pathogens. The subject- specific quantity parameter may include a computed administration quantity value generated by the system 106 based on a data-model that receives as inputs one or more physiological parameters selected from age, weight, sex, and renal function, and outputs a parameterized quantity value for each of the pharmaceutical compounds.

[0040] The ranking or prioritization metadata may refer to system-generated data attributes that indicate the relative ordering or precedence of identified pharmaceutical compounds within the pharmaceutical compound analysis results. In an embodiment, the ranking or prioritization metadata may include numerical scores, categorical priority levels, tier identifiers, or ordered list positions assigned to each pharmaceutical compound. The technical purpose of the metadata is to enable consistent, reproducible structuring of analytical outputs by the system 106, such that the pharmaceutical compound analysis results reflect an internally coherent ordering derived from computational logic rather than subjective user assessment or manual interpretation.

[0041] The cloud server 104 may host the system 106 configured to generate the pharmaceutical compound analysis results with the subject-specific quantity parameters. In an embodiment, the system 106 may reside within the cloud server 104 and may perform processing operations related to receiving subject- specific clinical information, identifying the pharmaceutical compounds, and generating the pharmaceutical compound analysis results.

[0042] The user device 102 may connect to the network 108 through a communication pathway. The cloud server 104 may be configured to connect to the network 108 through a separate communication pathway, enabling bidirectional data exchange between the cloud server 104 and the user device 102. In some cases, data transmitted from the user device 102 may traverse the network 108 to reach the cloud server 104, and data transmitted from the cloud server 104 may traverse the network 108 to reach the user device 102. The system 106 within the cloud server 104 may receive the subject-specific clinical information from the user device 102 via the network 108 and may transmit the pharmaceutical compound analysis results to the user device 102 via the network 108. The system 106 is explained in further detail in conjunction with Figures 2-5 in the forthcoming paragraphs.

[0043] Figure 2 illustrates a block diagram of system 106 for generating the pharmaceutical compound analysis results with the subject-specific quantity parameters, in accordance with an embodiment of the present disclosure. The system 106 may include a memory 202, a processor 206, and modules 208. The memory 202 may include a database 204, which stores structured data used for generating the pharmaceutical compound analysis results, including master lists and one or more decision matrices (referred to as decision matrices for the sake of brevity). The processor 206 may be operatively coupled to the memory 202 and may be configured to execute the various functional modules of the system 106.

[0044] In an embodiment, the system 106 may be configured to generate the pharmaceutical compound analysis results based on the indications, the microorganisms, and the pharmaceutical compounds available for prescription. In an embodiment, the system 106 may be configured to assist the healthcare professionals in making evidence-based decisions regarding antimicrobial therapy based on a specific microorganism, available pharmaceutical compound options, the indications, and prevalent resistance patterns as per guidelines established by one or more medical regulatory bodies (referred to as regulatory bodies for the sake of brevity). In an embodiment, the system 106 may be configured to provide guidance for selecting appropriate pharmaceutical compounds for common infectious diseases encountered in clinical practices.

[0045] In an embodiment, the system 106 may be implemented as a web-browser based application. In another embodiment, the system 106 may be implemented as a software application installed on the user device 102 of installation of a software-based application. The web-browser based application and the software application and associated devices may be available to the healthcare professionals at one or more clinics, hospitals, or any remote locations for providing the pharmaceutical compound analysis results, as discussed throughout the present disclosure.

[0046] In an embodiment, the one or more modules 208 may include a subject- specific clinical information receiving module 210, a pharmaceutical compounds identifying module 212, a pharmaceutical analysis result generating module 214, a pharmaceutical compounds filtering module 216, and a subject-specific quantity parameter determining module 218.

[0047] In an embodiment, the memory 202 may be communicatively coupled to the one or more processor(s) 206. In one embodiment, the memory 202 may be configured to store data, instructions executable by the processor(s) 206. In one embodiment, the memory 202 maycommunicate via a bus within the system 106. The memory 202 may include, but is not limited to, a non-transitory computer-readable storage media, such as various types of volatile and non-volatile storage media including, but not limited to, random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one example, the memory 202 may include a cache or random-access memory for the processor(s) 206. In alternative examples, the memory 202 is separate from the processor(s) 206, such as a cache memory of a processor, the system memory, or other memory. In an embodiment, the memory 202 may be an external storage device or database for storing data. In an embodiment, the memory 202 may be operable to store instructions executable by the processor(s) 206. The functions, acts, or tasks illustrated in the figures or described may be performed by the programmed processor(s) 206 for executing the instructions stored in the memory 202. The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor, or processing strategy, and may be performed by software, hardware, integrated circuits, firmware, micro-code, and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing, and the like. Further, the memory 202 may include an operating system for performing one or more tasks of the system 106, as performed by a generic operating system in the communications domain.

[0048] The modules 208 may include several functional components that work together to generate the pharmaceutical compound analysis results. The subject- specific clinical information receiving module 210 may be configured to receive the subject-specific clinical information from the user device 102.

[0049] In an embodiment, the pharmaceutical compounds identifying module 212 may be configured to identify the pharmaceutical compounds based on the received subject-specific clinical information and the decision matrices. The decision matrices may indicate one or more relationships (referred to as relationships for the sake of brevity) among one or more biological conditions (referred to as biological conditions for the sake of brevity), the microorganisms, and the pharmaceutical compounds.

[0050] In an embodiment, the bioglocal conditions may correspond to classified condition identifiers, such as suspected infection categories, affected organ systems, or condition types derived from predefined indication taxonomies stored in the system database. The biologicalconditions may operate as intermediate nodes within the decision matrices, facilitating computational mapping between subject-specific clinical inputs and reference datasets. For example, a biological condition may be represented as a condition identifier associated with a particular anatomical system or clinical scenario category, which the system 106 uses to retrieve related microorganism profiles or compound associations without performing any act of diagnosis or treatment selection.

[0051] In an embodiment, to generate the pharmaceutical compound analysis results with the subject-specific quantity parameter, the system 106 may be configured to employ the decision matrices. The decision matrices may include a microorganism-condition matrix. The microorganism-condition matrix may indicate the relationships between the microorganisms and the biological conditions. The microorganism-condition matrix may encode the relationships between structured, system-interpretable context, such as organ-system categories or condition identifiers, and the microorganisms, enabling the system 106 to logically infer a microorganism set relevant to the received subject-specific clinical information. Further, the decision matrices may include a compound-microorganism matrix. The compound-microorganism matrix may indicate the relationships between the pharmaceutical compounds and the microorganisms.

[0052] The compound-microorganism matrix may include the microorganism susceptibility data. Furthermore, the one or more decision matrices may include a compound-condition matrix. The compound-condition matrix may indicate the relationships between the pharmaceutical compounds and the biological conditions.

[0053] Further, the pharmaceutical compounds filtering module 216 may be configured to filter the identified pharmaceutical compounds based on one or more susceptibility patterns from the microorganism susceptibility data. The susceptibility patterns may refer to structured, system-interpretable data representations that characterize the relationships between the microorganisms and the pharmaceutical compounds based on the microorganism susceptibility data. The susceptibility patterns may be derived from microbiology culture data, susceptibility reports, and / or aggregated antibiogram datasets and encode computationally usable indicators reflecting whether a given microorganism exhibits susceptibility, resistance, or intermediate responsiveness to a corresponding pharmaceutical compound.

[0054] In an embodiment, the pharmaceutical compounds filtering module 216 may be configured to output prevalence statistics for the microorganisms based on antibiogram data.The the pharmaceutical compounds filtering module 216 may be configured to integrate the antibiogram data that indicates susceptibility of the microorganisms to the pharmaceutical compounds. The prevalence statistics may represent quantitative, system-computed measures that indicate the relative occurrence, distribution, or dominance of the microorganisms within a dataset associated with the user device 102. The antibiogram data may be received from the user device 102 and may reflect local, regional, or institutional resistance patterns.

[0055] Further, the pharmaceutical compounds filtering module 216 may be configured to receive a selection of at least one suspected microorganism from the user device 102 based on the output prevalence statistics. The suspected microorganism may correspond to one or more microorganism identifiers associated with relatively higher prevalence values, confidence-weighted occurrence scores, or relevance metrics within the outputted prevalence statistics. Furthermore, the pharmaceutical compounds filtering module 216 may be configured to filter the pharmaceutical compounds based on the received selection of at least one suspected microorganism.

[0056] The pharmaceutical analysis result generating module 214 may be configured to generate the pharmaceutical compound analysis results based on filtering the identified pharmaceutical compounds based on one or more susceptibility patterns from the microorganism susceptibility data. The microorganism susceptibility data may include, but are not limited to, the antibiogram data, and the like.

[0057] In an embodiment, the pharmaceutical analysis result generating module 214 may be configured to generate the pharmaceutical compound analysis results according to an antimicrobial stewardship classification framework. The antimicrobial stewardship classification framework may categorize the pharmaceutical compounds into a plurality of groups based on recommended usage priority.

[0058] In an embodiment, the subject-specific quantity parameter determining module 218 may be configured to determine the subject-specific quantity parameter for the pharmaceutical compounds in the generated pharmaceutical compound analysis results based on the physiological parameters. In an embodiment, the subject- specific quantity parameter determining module 218 may be configured to determine the subject- specific quantity parameter using a physiological-parameter-based quantity adjustment. The physiological-parameter-based quantity adjustment may include a renal function-based dosage calculation.

[0059] In an embodiment, the pharmaceutical analysis result generating module 214 may be configured to generate the pharmaceutical compound analysis results with the determined subject-specific quantity parameter based on the determined subject-specific quantity parameter.

[0060] The modules 208 may interact with the memory 202 and the database 204 to retrieve reference data, decision matrices, and the antibiogram data during the generation of the pharmaceutical compound analysis results. The processor 206 may coordinate the execution of modules 208 to process the received subject-specific clinical information and generate the pharmaceutical compound analysis results for the healthcare professionals. In some cases, the system 106 may integrate with Hospital Information Systems to receive subject vitals and information, including subject demographics, physiological parameters, laboratory results, and clinical assessments directly from existing healthcare infrastructure.

[0061] In an embodiment, the memory 202 may include a database 204. The database 204 is explained in further detail in conjunction with Figure 3 in the forthcoming paragraphs.

[0062] Figure 3 illustrates a block diagram of the database 204 of the system 106, in accordance with an embodiment of the present disclosure. In an embodiment, the database 204 may store one or more master lists and one or more decision matrices. In an embodiment, the one or more master lists may include an indication master list 302, a microorganism master list 304, and a pharmaceutical compound master list 306. In an embodiment, the one or more decision matrices may include a microorganism-indication matrix 308, a compoundmicroorganism matrix 310, and a compound-indication matrix 312. In an embodiment, the one or more master lists and the one or more decision matrices may assist in generating the evidence-based antimicrobial recommendations. In another embodiment, the database 204 may be available remotely to the system 106.

[0063] In an embodiment, the indication master list 302 may include a list of the indications mentioned, for example, in antimicrobial guidelines established by the medical regulatory bodies. In an embodiment, the indication master list 302 may include a list of diseases that may assist the healthcare professionals, such as doctors, in clinical recommendations for specific infections. In an embodiment, the indication master list 302 may be a standard list. In an embodiment, the indications may be listed in the indication master list 302 in at least two categories -Tier 1 category and Tier 2 category. In an embodiment, the tier 1 category may include broad categories of the indications based at least on affected body systems (such asrespiratory tract infections and urinary tract infections). In an embodiment, the tier 2 category may include specific diseases or the indications within each category (such as appendicitis and pharyngitis).

[0064] In an embodiment, the microorganism master list 304 may include at least one of an entire spectrum of the microorganisms or a list of the microorganisms that are causative of the indications listed in the indication master list 302. In an embodiment, the microorganism master list 304 may be based at least on the guidelines established by the medical regulatory bodies. In an embodiment, the microorganism master list 304 may be updated periodically.

[0065] In an embodiment, the pharmaceutical compounds master list 306 may include the pharmaceutical compounds approved by the medical regulatory bodies. In an embodiment, the pharmaceutical compounds master list 306 may also be a standard list. In an embodiment, the pharmaceutical compounds master list 306 may also include strengths of the pharmaceutical compounds, with each strength being considered as a different pharmaceutical compound in the pharmaceutical compounds master list 306 for purposes of the present disclosure. For example, DrugA 50mg and DrugA lOOmg may be considered as two different pharmaceutical compounds in the pharmaceutical compounds master list 306.

[0066] In an embodiment, the microorganism-indication matrix 308 may include a list of the microorganisms encountered in the clinical practices and a list of at least one of a major infection or a type of the indications categorized by organ system. In an embodiment, the list of the microorganisms may be included in rows in the microorganism-indication matrix 308, and the list of at least one of the major infections or the type of the indications may be included in columns in the microorganism-indication matrix 308. Table 2 below depicts an exemplary microorganism-indication matrix 308.Table 2: Exemplary microorganism-indication matrix

[0067] In an embodiment, the microorganism-indication matrix 308 may assist the healthcare professionals in narrowing down the potential causative microorganisms for a subject and associated symptoms, based on the suspected infection site. In scenarios requiring immediate treatment, the microorganism-indication matrix 308 may assist the healthcare professionals in highlighting the most likely the microorganisms to target.

[0068] In an embodiment, the pharmaceutical compound-microorganism matrix 310 may include a list of the microorganisms and a list of specific pharmaceutical compounds from major classes. In an embodiment, the list of the microorganisms may be included in rows of the pharmaceutical compound-microorganism matrix 310, and the list of the specific pharmaceutical compounds may be included in columns of the pharmaceutical compoundmicroorganism matrix 310. Table 2 below depicts an exemplary pharmaceutical compoundmicroorganism matrix 310. In an embodiment, each cell of the pharmaceutical compoundmicroorganism matrix 310 represents an intersection of a microorganism and a specific antibiotic. For instance, the cells may represent susceptibility to the antibiotic.Table 3: Exemplary pharmaceutical compound-microorganism matrix, where ‘S’ indicates a susceptible antibiotic, ‘R’ represents a resistant antibiotic, ‘I’ represents anintermediate / variable susceptibility, and indicates an antibiotic that is not commonly tested or has no established activity against the corresponding microorganism

[0069] In an embodiment, the pharmaceutical compound-microorganism matrix 310 may assist the healthcare professionals in identifying the microorganisms based on at least one of culture / sensitivity results or suspected microorganism in empiric treatment scenarios. In an embodiment, the pharmaceutical compound-microorganism matrix 310 may assist the healthcare professionals in checking class (susceptible, resistant, etc.) of the pharmaceutical compounds by scanning the rows of the pharmaceutical compound-microorganism matrix. In an embodiment, the pharmaceutical compound-microorganism matrix 310 may be updated regularly based on the antibiogram data or changes in the resistance patterns.

[0070] In an embodiment, the pharmaceutical compound-indication matrix 312 may include a list of the pharmaceutical compounds and a list of the indications or infections. In an embodiment, the list of the pharmaceutical compounds may be included in rows of the pharmaceutical compound-indication matrix 312 and the indications or infections may be included in columns of the pharmaceutical compound-indication matrix 312. In an embodiment, the pharmaceutical compound-indication matrix 312 may link the list of the pharmaceutical compounds to the indications, based, for example, on approval from the medical regulatory bodies. Table 4 below depicts an exemplary pharmaceutical compoundindication matrix 312.Table 4: Exemplary pharmaceutical compound-indication matrix

[0071] In an embodiment, the pharmaceutical compound-indication matrix 312 may assist the healthcare professionals in narrowing down antibiotic choices based on the suspected infection site. In an embodiment, the pharmaceutical compound-indication matrix 312 may assist the healthcare professionals in guiding adjustments once diagnostics identify the microorganism, thereby aiding in de-escalation or escalation of therapy.

[0072] Referring back to Figure 2, in an embodiment, the one or more processors 206 may be operatively coupled to each of the memory 202 and the module(s) 208. The processor(s) 206 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. In one embodiment, the processor(s) 206 may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or both. The processor(s) 206 maybe one or more general processors, Digital Signal Processors (DSPs), application-specific integrated circuits, Field-Programmable Gate Arrays (FPGAs), servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor(s) 206 may execute a software program, such as code generated manually (i.e., programmed) to perform the desired operation. The system 106 may implement various techniques such as, but not limited to, data extraction, Artificial Intelligence (Al), Machine Learning (ML), Deep Learning (DL), and so forth to achieve the desired objective and methods discussed herein.

[0073] The module(s) 208, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The module(s) 208 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and / or any other device or component that manipulates signals based on operational instructions. Further, the module(s) 208 can be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit can comprise a computer, the processor(s) 206, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit can be a general-purpose processor that executes instructions to cause the general-purpose processor to perform the required tasks, or the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the module(s) 208 may be machine-readable instructions (software) that, when executed by a processor / processing unit, perform any of the described functionalities. The module(s) 208 are explained in further detail in conjunction with Figure 4 in the forthcoming paragraphs.

[0074] Figure 4 illustrates a block diagram of the one or more modules 208 of the system 106, in accordance with an embodiment of the present disclosure. Referring to Figure 4, the one or more modules 208 may include a user interface module 402, an empiric therapy module 404, a culture-guided therapy module 406, an escalation and de-escalation guidance module 408, an alert and reminder module 410, a cost indicator module 412, and a pregnancy and lactation guidance module 414.

[0075] In an embodiment, the user interface module 402 may be configured to allow the healthcare professionals to select the indications from the indication master list. In an embodiment, the indications may be presented in a drop-down menu. In an embodiment, the dropdown menu may first show the indications included in the tier 1 category, followed bythe indications included in the tier 2 category for selection by the healthcare professionals. Thus, the present disclosure provides a streamlined approach that aims to facilitate efficient and accurate indication selection for clinical recommendations.

[0076] In an embodiment, the empiric therapy module 404 may be configured to provide recommendations based on common sites of infection (such as respiratory, gastrointestinal, and skin) and specific disease indications. In an embodiment, the empiric therapy module 404 may be configured to provide guidance and recommendations for the pharmaceutical compounds based on at least one of the guidelines established by the medical regulatory bodies or antibiogram data available at healthcare facilities. In an embodiment, the empiric therapy module 404 may be configured to provide a prioritized list of pharmaceutical compound analysis results to the healthcare professionals. In an embodiment, the list of pharmaceutical compound analysis results may include one or more of first-line options, second-line options, and the pharmaceutical compounds to consider when resistance is a concern. In an embodiment, the empiric therapy module 404 may be configured to allow the healthcare professionals to adjust the recommendations based on the severity of subject’s conditions.

[0077] In an embodiment, the culture-guided therapy module 406 may be configured to provide an indication and microorganism-based antimicrobial suggestion based on a susceptibility report and the guidelines established by the medical regulatory bodies. In an embodiment, the culture-guided therapy module 406 may be configured to display antibiotic susceptibility data. In an embodiment, the antibiotic susceptibility data may be linked either to a regional antibiogram or the antibiogram data of the healthcare facilities.

[0078] In an embodiment, the escalation and de-escalation guidance module 408 may be configured to allow the healthcare professionals to adjust the recommendations based on the severity of the subject’s conditions. In an embodiment, the escalation and de-escalation guidance module 408 may be configured to generate escalation and de-escalation suggestions based on the guidelines established by the medical regulatory bodies.

[0079] In an embodiment, the alert and reminder module 410 may be configured to provide alerts and reminders. In an embodiment, the alerts and reminders may include one or more of critical pharmaceutical compound to pharmaceutical compound interactions, flagging of potential allergy conflicts, and reminders for therapy review.

[0080] In an embodiment, the cost indicator module 412 may be configured to provide cost indications for different pharmaceutical compound analysis results.

[0081] In an embodiment, the pregnancy and lactation guidance module 414 may be configured to provide one or more of warnings or links to resources for antibiotic safety in pregnancy and breastfeeding.

[0082] Figure 5 illustrates a block diagram depicting an exemplary model framework environment 500 for providing data inputs to the system 106, in accordance with an embodiment of the present disclosure. The model framework environment 500 may include one or more sources 504 (referred to as sources for the sake of brevity), a model framework 502, and the system 106.

[0083] As shown in the Figure 5, a model framework 502 may be trained with data received from the sources 504. The data may include, but is not limited to, a list of one or more diseases, a list of one or more microorganisms causative of the one or more diseases, a list of pharmaceutical compounds approved for prescription, and the like. In an embodiment, the sources 504 may include the medical regulatory bodies of a region (e.g., a country), one or more hospitals, one or more laboratories, one or more research paper databases, and so forth. Thereafter, the model framework 502 creates the system 106 for generating the pharmaceutical compound analysis results.

[0084] The sources 504 may provide multiple types of input data to the model framework 502. Each of the data types may flow from the sources 504 to the model framework 502 via separate data paths. The model framework 502 may receive and process the lists of diseases, causative pathogens, and approved pharmaceutical compounds from the source 504 and may provide processed data or outputs to the system 106.

[0085] In an embodiment, the sources 504 may represent one or more data repositories or external databases that contain reference information regarding diseases, the pathogens associated with the diseases, and the pharmaceutical compounds approved for treating infections caused by the pathogens. The model framework 502 may aggregate and structure the received data to support the generation of pharmaceutical compound analysis results by the system 106. The arrangement of the model framework environment 500 may enable the system 106 to access organized reference data for use in identifying the pharmaceutical compounds and generating recommendations based on clinical indications and pathogenassociations. The system 106 is explained in further detail in conjunction with Figures 6-9 in the forthcoming paragraphs.

[0086] Figure 6 illustrates a block diagram of an exemplary antimicrobial recommendation generation process 600 performed by the system 106, in accordance with an embodiment of the present disclosure. As shown in the Figure 6, the system 106 takes one or more of the indications 602, the microorganisms 604, and the pharmaceutical compounds 606 as inputs and generates the pharmaceutical compound analysis results 608 for assisting the healthcare professionals.

[0087] The system 106 is configured to analyze the relationships among the indications 602, the microorganisms 604, and available pharmaceutical compounds 606 to determine appropriate therapeutic options. The output of the system 106 includes antimicrobial recommendations that are generated based on the processing of the input data.

[0088] In an embodiment, to identify the pharmaceutical compounds, the processor 206 may be configured to query the indication master list 402 to identify the indications corresponding to the clinical indication identifier based on the received subject-specific clinical information. The processor 206 may retrieve the microorganisms associated with the identified indications from the microorganism-indication matrix 308. The processor 206 may retrieve the pharmaceutical compounds from the pharmaceutical compound-microorganism matrix 410. The pharmaceutical compounds may be retrieved based on susceptibility of the pharmaceutical compounds to the retrieved microorganisms. The processor 206 may then identify the pharmaceutical compounds 606 for inclusion in the pharmaceutical compound analysis results.

[0089] In an embodiment, the system 106 may be configured to store the antibiogram data in a structured format that associates the microorganism identifiers with pharmaceutical compound identifiers and corresponding susceptibility metrics. The susceptibility metrics may include percentage susceptibility values that indicate the proportion of tested isolates that may be susceptible to a particular pharmaceutical compound.

[0090] In some cases, the processor 206 may be configured to update the microorganism susceptibility data based on the received microbiology culture data and the one or more susceptibility reports from the user device 102. The system 106 may log each new microbiology culture and susceptibility report into the system 106 and may continuously recalculate and generate an up-to-date dynamic hospital antibiogram reflecting the mostcurrent resistance trends. The real-time updating capability may improve empiric antibiotic choice and escalation or de-escalation decisions.

[0091] In an embodiment, the processor 206 may be configured to aggregate the microorganism susceptibility data with the antibiogram data. The antibiogram data may include national-level antibiogram data, which provides susceptibility patterns for the microorganisms across a geographical region. The processor 206 may be configured to determine one or more confidence- weighted susceptibility parameters based on the aggregated microorganism susceptibility data. The confidence- weighted susceptibility parameters may reflect the reliability of susceptibility estimates based on the volume and recency of available data from local and geographical sources.

[0092] In some cases, when hospital-level antibiogram data is not available, the system 106 may utilize the antibiogram data to provide prevalence statistics and susceptibility information. When the hospital-level antibiogram data is available, the system 106 may prioritize the hospital-level data while supplementing with geographical-level data to enhance the confidence weighting of susceptibility parameters. The aforementioned aggregation approach may enable the system 106 to provide the pharmaceutical compound analysis results even in scenarios where local data is limited.

[0093] In an embodiment, the system 106 may include a knowledge and rules module that stores clinical rules and therapeutic guidelines used during the generation of pharmaceutical compound analysis results. The knowledge and rules module may store dosing rules that specify dose calculations based on the subject physiological parameters including age, weight, and renal function. In some cases, the dosing rules may include calculations based on the Cockcroft- Gault formula for renal function-based dosage adjustments. The knowledge and rules module may store contraindication rules that identify the pharmaceutical compounds that may be avoided based on subject-specific factors such as allergies, comorbidities, or concurrent medications.

[0094] The system 106 may include a decision engine configured to analyze subject- specific clinical information and generate pharmaceutical compound analysis results. The decision engine may query the decision matrices to identify the pharmaceutical compounds based on the clinical indication identifier received as part of the subject-specific clinical information. The decision engine may retrieve the antibiogram data corresponding to the healthcare entity from which the subject-specific clinical information was received. The decision engine mayfilter the pharmaceutical compounds based on susceptibility metrics from the antibiogram data. The decision engine may apply rules from the knowledge and rules module to further filter and rank the pharmaceutical compounds. The decision engine may generate a ranked list of pharmaceutical compounds that represents the pharmaceutical compound analysis results.

[0095] In an embodiment, the system 106 may calculate subject-specific quantity parameters for each pharmaceutical compound in the pharmaceutical compound analysis results. The subject-specific quantity parameters may include dose amounts, dosing frequencies, and treatment durations calculated based on subject physiological parameters. The system 106 may calculate doses based on subject weight using weight-based dosing formulas stored in the knowledge and rules module. The system 106 may adjust doses based on renal function using creatinine clearance values derived from subject laboratory results. In some cases, the system 106 may incorporate classification data into the pharmaceutical compound analysis results to support antimicrobial stewardship objectives.

[0096] Figure 7A and Figure 7B illustrate a flowchart depicting a method 700 for generating the pharmaceutical compound analysis results with the subject-specific quantity parameters, in accordance with an embodiment of the present disclosure. The method 700 may begin with step 702, where a user logs into the system 106 using one or more biometric credentials. The method 700 may then proceed to step 704, where a subject list is displayed on a user device 102, and a user may be allowed to search subject data by entering a name of the subject or identity of the subject. Following this, the method 700 may move to step 706, where the subject list and the subject details are stored within database 204.

[0097] The method 700 may then reach step 708, which may involve determining whether the subject is critical. Based on the determination at step 708, the method 700 may branch into two paths. If the subject is determined to be critical, the method 700 may proceed along a first path to step 710, where a criticality indicator is displayed on the user device 102 based on condition of the subject. The method 700 may then continue to step 712, where reports and required reports with new symptoms are uploaded and reviewed on the user device 102. The method 700 may then move to step 714, where pharmaceutical compound analysis results are generated based on filtering the identified pharmaceutical compounds based on one or more susceptibility patterns from the microorganism susceptibility data. Following this, the method 700 may proceed to step 716, where the generated pharmaceutical compound analysis results are displayed.

[0098] If the subject is determined to be non-critical at step 708, the method 700 may proceed along a second path to step 718, where a criticality indicator is displayed on the user device 102 based on the condition of the subject. The method 700 may then continue to step 720, where pre-culture reports are uploaded and reviewed on the user device 102. The method 700 may then move to step 722, where the subject- specific quantity parameter for the pharmaceutical compounds in the generated pharmaceutical compound analysis results is determined based on the one or more physiological parameters. Following this, the method 700 may proceed to step 724, where the prescription details are documented in records of the subjects for future reference. The method 700 may then conclude with step 726, where feedback from the healthcare professionals is received for continuous improvement.

[0099] Figure 8A, Figure 8B, and Figure 8C illustrate a flowchart depicting a method 800 of user authentication and subject selection within the system 106, in accordance with an embodiment of the present disclosure. The method 800 may begin with step 802, where a user logs into the system 106 using login credentials. The method 800 may then proceed to step 804, where the system 106 validates the login credentials. If the login credentials are not valid, the method 800 may move to step 806, where the verification process is reinitiated, and the method 800 may return to step 802. If the login credentials are valid, the method 800 may proceed to step 808, where an associated list of hospitals is displayed on the user device 102.

[0100] The method 800 may then move to step 810, where a current hospital is selected from the associated list of hospitals. Following the hospital selection, the method 800 may proceed to step 812, where the system 106 determines whether the user is at the same location as the selected hospital. If the user is not at the same location, the method 800 may move to step 814, where a different location alert is generated. If the user is at the same location, the method 800 may proceed to step 816, where an active subject list of the current hospital is displayed. A connector C may indicate that step 816 may also be reached from other portions of the method 800. The method 800 may then move to step 818, where the system 106 determines whether an existing subject is in the active subject list. If the subject does not exist in the active subject list, the method 800 may proceed to step 820, where a new subject is created or added to the active subject list, and the method 800 may continue to a connector A indicating continuation to another portion of the flowchart. If the subject exists in the active subject list, the method 800 may proceed to step 848, where the subject is selected from the associated list of hospitals. Following step 848, the method 800 may continue to a connector D, indicating continuation to another portion of the flowchart.

[0101] Referring to Figure 8B, the method 800 may continue with processes related to culture report availability and post-culture guidance. The method 800 may proceed to step 850, where the system 106 determines whether a culture report is available. If a culture report is available, the method 800 may proceed to step 852, where post-culture report guidance is selected. The process may then continue to connector E, which links to subsequent steps in the method 800. If a culture report is not available at step 850, the method 800 may proceed to step 854, where the subject is evaluated by a health professional. Following this evaluation, the method 800 may move to step 856, where the system 106 determines whether escalation or de-escalation is required. If escalation or de-escalation is required, the method 800 may proceed to step 858, where antibiotic suggestions based on the antibiogram are displayed to the healthcare professional. If escalation or de-escalation is not required at step 856, the method 800 may proceed to step 860, where the system 106 determines whether extension of treatment is required. If extension of treatment is not required, the method 800 may move to step 862, where a subject evaluation report is saved. If extension of treatment is required at step 860, the method 800 may proceed to step 864, where the prescription duration is extended. Following the extension of prescription duration, the method 800 may move to step 866, where the information is saved. The method 800 may then continue to connector C, which links to subsequent steps. In a parallel process beginning with connector A, the method 800 may proceed to step 822, where subject details are entered. The method 800 may then proceed to step 824, where a selection of systems and indications is generated. The process may then continue to connector B, which links to subsequent steps in the method 800.

[0102] Referring to Figure 8C, the method 800 may continue from connector B with step 826, where the system 106 determines whether a previous culture report is available. If a previous culture report is available, the method 800 may proceed to step 828, where the microorganism name and susceptibility are added. The method 800 may then move to step 830, where antibiotic suggestions based on guidelines are displayed. Following this, the method 800 may reach step 832, where the system 106 determines whether to proceed with the suggested pharmaceutical compounds. If the decision is to proceed with the suggested pharmaceutical compounds, the method 800 may move to step 834, where pharmaceutical compounds are selected from the suggestions. If a previous culture report is not available at step 826, the method 800 may proceed to step 842, where empirical regimen guidance is selected. The method 800 may then move to step 844, where the system 106 determines whether a hospital antibiogram is available. If a hospital antibiogram is available, the method800 may proceed to step 846, where antibiotic suggestions based on the antibiogram are displayed. The process from step 846 may then connect to step 832 for the determination of whether to proceed with the suggested pharmaceutical compounds. If a hospital antibiogram is not available at step 844, the method 800 may connect to step 828 to add the microorganism name and susceptibility. From connector E at step 840, the method 800 may provide an alternative path where a manual search and selection of an antibiotic from a dropdown is performed. The method 800 may then move to step 836, where a prescription is created by adding dosage and duration details. Following this, the method 800 may proceed to step 838, where the prescription is saved, and the process exits to connector C.

[0103] Figure 9 illustrates a flowchart depicting a method 900 for generating pharmaceutical compound analysis results with a subject- specific quantity parameter, in accordance with an embodiment of the present disclosure. The method 900 may begin with step 902, where subject- specific clinical information is received from the user device 102. The subject-specific clinical information may comprise one or more of a clinical indication identifier associated with a subject, one or more subject physiological parameters, microbiology culture data, and one or more susceptibility reports. In an embodiment, the one or more subject physiological parameters may comprise one or more of age, weight, gender, and renal function.

[0104] The method 900 may then proceed to step 904, where the pharmaceutical compounds are identified based on the received subject- specific clinical information and one or more decision matrices. The decision matrices may indicate one or more relationships among the biological conditions, the microorganisms, and the pharmaceutical compounds. In some cases, identifying the pharmaceutical compounds may comprise querying the indication master list 402 to identify the indications corresponding to the clinical indication identifier, retrieving the microorganisms associated with the identified indications from the pathogenindication matrix 408, and retrieving the pharmaceutical compounds from the drug-pathogen matrix 410 based on susceptibility of the pharmaceutical compounds to the retrieved microorganisms.

[0105] Following the identification of pharmaceutical compounds, the method 900 may move to step 906, where pharmaceutical compound analysis results are generated based on filtering the identified pharmaceutical compounds based on one or more susceptibility patterns from the microorganism susceptibility data. In an embodiment, the method 900 mayinclude updating the microorganism susceptibility data based on the received microbiology culture data and the susceptibility reports from the user device 102. The method 900 may also include aggregating the microorganism susceptibility data with the antibiogram data and determining one or more confidence- weighted susceptibility parameters based on the aggregated microorganism susceptibility data.

[0106] The method 900 may then continue to step 908, where the processor 206 determines the subject- specific quantity parameter for the pharmaceutical compounds in the generated pharmaceutical compound analysis results based on the one or more physiological parameters. In an embodiment, determining the subject- specific quantity parameter may comprise determining the subject-specific quantity parameter for the pharmaceutical compounds in the generated antimicrobial recommendations using a physiological-parameterbased quantity adjustment. The physiological-parameter-based quantity adjustment may include weight-based dosing calculations, age-based dosing considerations, and renal impairment-based dosage adjustments.

[0107] The method 900 may conclude with step 910, where the pharmaceutical compound analysis results are generated with the determined subject-specific quantity parameter. The generated pharmaceutical compound analysis results may include a ranked list of pharmaceutical compounds with associated subject- specific quantity parameters. In some cases, the method 900 may include outputting prevalence statistics for the microorganisms based on the antibiogram data, receiving a selection of the at least one suspected microorganism from the outputted prevalence statistics, and filtering the pharmaceutical compounds based on the received selection of at least one suspected microorganism.

[0108] In an embodiment, the system 106 may include an administration and governance module configured to manage system configuration, user access, and audit logging. The administration and governance module may enable administrators to configure system parameters, including integration settings, rule configurations, and user permissions. The administration and governance module may maintain audit logs that record system activities, including received subject-specific clinical information, generated pharmaceutical compound analysis results, and user interactions. The audit logs may support compliance with healthcare regulations and may enable retrospective analysis of system usage and recommendation patterns.

[0109] The system 106 may integrate with external systems and infrastructure components to support its operation. The system 106 may communicate with Hospital Information Systems through standardized interfaces to receive subject-specific clinical information and to transmit pharmaceutical compound analysis results. The system 106 may communicate with laboratory information systems to receive culture and susceptibility results that may be incorporated into the generation of pharmaceutical compound analysis results. The system 106 may be deployed on a cloud infrastructure that provides scalable computing resources, data storage, and network connectivity.

[0110] In an embodiment, the present disclosure may be implemented at the healthcare facilities, including, but not limited to, hospitals, outpatient clinics, and long-term care facilities, to enhance antibiotic prescribing practices. In an embodiment, the present disclosure may primarily be used by the healthcare professionals, such as physicians, nurses, and physician assistants, involved in prescribing and administering the antimicrobials. In an embodiment, the present disclosure may also be used by pharmacists, infectious disease specialists, and microbiologists (as part of antimicrobial stewardship).

[0111] The system and method for generating the pharmaceutical compound analysis results with subject-specific quantity parameters may provide several advantages and technical effects:The present invention enables real-time updating of the antibiogram data with every new culture and susceptibility report, thereby providing healthcare professionals with the most current resistance patterns for their subject populations. The real-time updating capability addresses limitations of traditional approaches that compile the antibiogram data on a semi-annual or annual basis, during which resistance patterns may shift.The present invention provides subject- specific dosing recommendations that account for individual physiological parameters including age, weight, gender, and renal function. The physiological-parameter-based quantity adjustment may help ensure that pharmaceutical compound dosages are appropriate for each subject, potentially reducing the risk of toxicity from overdosing or treatment failure from underdosing. The integration of the antibiogram data with local hospital-level data enables the present invention to provide evidence-based recommendations even in scenarios where local data is limited. The determination of confidence- weighted susceptibilityparameters based on aggregated data may enhance the reliability of recommendations by accounting for the volume and recency of available susceptibility information. The present invention supports antimicrobial stewardship objectives by incorporating classification frameworks, such as the classification of pharmaceutical compound analysis results. The aforementioned integration promotes appropriate use of antimicrobial agents by guiding the healthcare professionals toward access category pharmaceutical compounds when clinically appropriate, potentially contributing to reduced antimicrobial resistance through more judicious prescribing practices. The decision matrices and master lists stored in the database enable systematic and consistent identification of the pharmaceutical compounds based on clinical indications and pathogen associations. The structured approach to pharmaceutical compound identification reduces reliance on individual recall and clinical experience, potentially improving the consistency of antimicrobial prescribing across healthcare professionals and institutions.The present invention optimizes the antibiotic prescribing practices, leading to a reduction in inappropriate antibiotic use, thereby minimizing the risk of antimicrobial resistance.The present invention also supports the healthcare professionals in making informed decisions regarding antimicrobial prescribing.The present invention further results in improvements in subject outcomes and key performance indicators (KPIs) by ensuring effective and targeted antibiotic therapy. For instance, the present invention results in a decrease in average number (by units) of the pharmaceutical compounds prescribed per admission, an increase in proportion (by units) of the first line pharmaceutical compounds usage per admission, a decrease in proportion (by units) of broad- spectrum pharmaceutical compounds usage per admission, a decrease in proportion (by units) of reserve and watch pharmaceutical compounds usage per admission, an increase in proportion of subjects de-escalated within four days of admission, a decrease in incidence of the antimicrobial resistance (such as methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), extended- spectrum beta-lactamase (ESBL)-producing organisms), and an increase in proportion of the subjects receiving appropriate antibiotic dosing and duration.Further, the present invention results in a reduction in the incidence of healthcare- associated infections (HAIs) (such as ventilator-associated pneumonia (VAP), central line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and surgical site infections (SSI)), a reduction of average length of stay (ALOS), a reduction in readmission rate, and a reduction of in-hospital mortality rate.Additionally, the present invention enhances adherence to the antimicrobial stewardship principles and guidelines, increases confidence of the healthcare professionals and efficiency in antimicrobial decision-making, enhances the reputation of a hospital as a leader in antimicrobial stewardship and subject care excellence, and provides financial optimization through faster bed churning, minimized wastage, and streamlined procurement practices.

[0112] While specific language has been used to describe the present disclosure, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein. The drawings and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment.

Claims

We Claim:

1. A system (106) for generating pharmaceutical compound analysis results with a subject- specific quantity parameter, the system (106) comprising:a memory (202);at least one processor (206) operatively coupled to the memory (202), wherein the at least one processor (206) is configured to:receive subject-specific clinical information from a user device (102); andgenerate the pharmaceutical compound analysis results with the subject- specific quantity parameter based on the received subject-specific clinical information.

2. The system (106) as claimed in claim 1, wherein to generate the pharmaceutical compound analysis results with the subject- specific quantity parameter, the at least one processor (206) is configured to:identify one or more pharmaceutical compounds based on the received subject- specific clinical information and one or more decision matrices;generate the pharmaceutical compound analysis results based on filtering the identified one or more pharmaceutical compounds and one or more susceptibility patterns from microorganism susceptibility data; determine the subject-specific quantity parameter for the one or more pharmaceutical compounds in the generated pharmaceutical compound analysis results using a physiological-parameter-based quantity adjustment based on the one or more physiological parameters; andgenerate the pharmaceutical compound analysis results with the subject- specific quantity parameter.

3. The system (106) as claimed in claim 2, wherein the one or more decision matrices indicate one or more relationships among one or more biological conditions, one or more microorganisms, and the one or more pharmaceutical compounds, wherein the one or more decision matrices comprise one or more of:a microorganism-condition matrix indicates the one or more relationships between the one or more microorganisms and the one or more biological conditions;a compound-microorganism matrix indicates the one or more relationships between the one or more pharmaceutical compounds and the one or more microorganisms, wherein the compound-microorganism matrix comprises microorganism susceptibility data indicating the one or more pharmaceutical compounds are susceptible to the one or more microorganisms; anda compound-condition matrix indicates the one or more relationships between the one or more pharmaceutical compounds and the one or more biological conditions.

4. The system (106) as claimed in claim 1, wherein subject- specific clinical information comprises one or more of a clinical indication identifier associated with a subject, one or more subject physiological parameters, microbiology culture data, and one or more susceptibility reports.

5. The system (106) as claimed in claim 1, wherein the at least one processor (206) is configured to:update microorganism susceptibility data based on the received microbiology culture data and the one or more susceptibility reports from the user device (102);aggregate the microorganism susceptibility data with antibiogram data; anddetermine one or more confidence-weighted susceptibility parameters based on the aggregated microorganism susceptibility data.

6. The system (106) as claimed in claim 1, wherein the one or more subject physiological parameters comprise one or more of age, weight, gender, and renal function.

7. The system (106) as claimed in claim 1, wherein the at least one processor (206) is configured to:query an indication master list to identify one or more indications corresponding to the clinical indication identifier based on the received subject- specific clinical information;retrieve the one or more microorganisms associated with the identified one or more indications from a microorganism-indication matrix; retrieve the one or more pharmaceutical compounds from a pharmaceutical compound-microorganism matrix, wherein the one or more pharmaceutical compounds are retrieved based on susceptibility of the one ormore pharmaceutical compounds to the retrieved one or more microorganisms; andidentify one or more pharmaceutical compounds.

8. The system (106) as claimed in claim 1, wherein the at least one processor (206) is configured to:output prevalence statistics for the one or more microorganisms based on antibiogram data;receive a selection of at least one suspected microorganism from the outputted prevalence statistics; andfilter the one or more pharmaceutical compounds based on the received selection of at least one suspected microorganism.

9. A method for generating pharmaceutical compound analysis results with a subjectspecific quantity parameter, the method comprising:receiving, by at least one processor (206), subject- specific clinical information from a user device (102), wherein subject- specific clinical information comprises one or more of a clinical indication identifier associated with a subject, one or more subject physiological parameters, microbiology culture data, and one or more susceptibility reports; andgenerating, by the at least one processor (206), the pharmaceutical compound analysis results with the subject- specific quantity parameter based on the received subject-specific clinical information.

10. The method as claimed in claim 9, wherein generating the pharmaceutical compound analysis results with the subject-specific quantity parameter comprises:identifying one or more pharmaceutical compounds based on the received subject-specific clinical information and one or more decision matrices;generating the pharmaceutical compound analysis results based on filtering the identified one or more pharmaceutical compounds and one or more susceptibility patterns from microorganism susceptibility data;determining the subject-specific quantity parameter for the one or more pharmaceutical compounds in the generated pharmaceutical compound analysis results using a physiological-parameter-based quantity adjustment based on the one or more physiological parameters; andgenerating the pharmaceutical compound analysis results with the subject- specific quantity parameter.

11. The method as claimed in claim 10, wherein the one or more decision matrices indicate one or more relationships among one or more biological conditions, one or more microorganisms, and the one or more pharmaceutical compounds, wherein the one or more decision matrices comprise one or more of:a microorganism-condition matrix indicates the one or more relationships between the one or more microorganisms and the one or more biological conditions;a compound-microorganism matrix indicates the one or more relationships between the one or more pharmaceutical compounds and the one or more microorganisms, wherein the compound-microorganism matrix comprises microorganism susceptibility data indicating the one or more pharmaceutical compounds are susceptible to the one or more microorganisms; anda compound-condition matrix indicates the one or more relationships between the one or more pharmaceutical compounds and the one or more biological conditions.

12. The method as claimed in claim 9, wherein subject- specific clinical information comprises one or more of a clinical indication identifier associated with a subject, one or more subject physiological parameters, microbiology culture data, and one or more susceptibility reports.

13. The method as claimed in claim 9, comprising:updating, by the at least one processor (206), microorganism susceptibility data based on the received microbiology culture data and the one or more susceptibility reports from the user device (102);aggregating, by the at least one processor (206), the microorganism susceptibility data with antibiogram data; anddetermining one or more confidence-weighted susceptibility parameters based on the aggregated microorganism susceptibility data.

14. The method as claimed in claim 9, wherein the one or more subject physiological parameters comprise one or more of age, weight, gender, and renal function.

15. The method as claimed in claim 9, comprising:querying, by the at least one processor (206), an indication master list to identify one or more indications corresponding to the clinical indication identifier based on the received subject-specific clinical information; retrieving, by the at least one processor (206), the one or more microorganisms associated with the identified one or more indications from a microorganism-indication matrix;retrieving, by the at least one processor (206), the one or more pharmaceutical compounds from a pharmaceutical compound-microorganism matrix, wherein the one or more pharmaceutical compounds are retrieved based on susceptibility of the one or more pharmaceutical compounds to the retrieved one or more microorganisms; andidentifying, by the at least one processor (206), one or more pharmaceutical compounds.

16. The method as claimed in claim 9, comprising:outputting prevalence statistics for the one or more microorganisms based on the antibiogram data;receiving a selection of at least one suspected microorganism from the outputted prevalence statistics; andfiltering the one or more pharmaceutical compounds based on the received selection of at least one suspected microorganism.

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