Systems and methods for dynamic monitoring of drug and antimicrobial resistance trends - Patents.com

Abstract

A system for selecting a treatment regimen for a particular patient is provided. A first data store includes a first plurality of patient records for a first plurality of patients, and a second data store includes effectiveness rates of a plurality of treatment regimens for a plurality of infectious diseases and infectious disease-causing pathogens. A first infectious disease and a first prescribed treatment regimen are identified for the particular patient. A second plurality of patient records are generated by identifying patient records associated with a diagnosis or treatment of the first infectious disease in the first plurality of patient records in the first data store. The effectiveness rate for the first infectious disease from the second data store is added to the second plurality of patient records. The added records can be used to generate a dynamic model to determine a likelihood estimate that the first treatment regimen is an appropriate treatment regimen for treating the first infectious disease.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 62 / 981,439, filed February 25, 2020, which is incorporated herein by reference in its entirety.

[0002] Embodiments of the present disclosure relate to systems and methods that provide dynamic situational awareness of resistance rates of specific pathogens, success rates of specific antibiotic regimens to treat drug-resistant pathogens, and susceptibility rates of related pathogens to new antibiotic regimens on a patient-specific, facility-specific, and region-specific basis. The systems and methods of the present disclosure include data processing, including database and file management, and a database system for optimizing treatment protocols and improving patient outcomes based on real-time data on resistance rates, treatment success rates, and susceptibility rates, and treatment prognosis analysis. [Background technology]

[0003] The rate at which antibiotic-resistant infections are growing and spreading continues at an alarming pace. Approximately 2.8 million antibiotic-resistant infections occur in the United States each year, resulting in 35,000 deaths. Although progress has been made toward understanding and containing the spread of antibiotic-resistant infections (or so-called "superbugs"), the threat of untreatable infectious diseases caused by bacteria and microorganisms that can spread rapidly continues to grow. Drug resistance is commonly associated with the misuse and overuse of antibiotics and other microbial treatments, which cause bacteria to evolve into treatment-resistant strains. Addressing antibiotic resistance is critical to ensuring that life-saving medications are administered effectively and in a timely manner, thereby improving patient outcomes. Summary of the Invention

[0004] The systems, methods, and devices described herein each have several aspects, no single one of which is solely responsible for its desirable attributes. Without limiting the scope of the disclosure, some non-limiting features are briefly described below.

[0005] Embodiments of the present disclosure relate to a database system (also referred to herein as a "system") for merging and analyzing data from separate and changing data sets, and dynamically generating and applying customized models based on the merged data. The electronic database of the present disclosure allows for the storage and retrieval of digital data records. The data records in such a database can be automatically updated and processed electronically in real time without human intervention.

[0006] In one aspect, a system for selecting a treatment regimen for a particular patient is provided, the system including: a first data store including a first plurality of patient records for a first plurality of patients; a second data store including effectiveness rates of a plurality of treatment regimens against a plurality of infectious diseases and infectious pathogens; and a hardware processor configured to execute computer-executable instructions to receive from a user an indication of a first infection in the particular patient and a first treatment regimen prescribed to treat the first infection; generate a first database including a second plurality of patient records by identifying patient records associated with a diagnosis or treatment of the first infection in the first plurality of patient records in the first data store; generate a second database including the second plurality of patient records from the second data store with the effectiveness rate for the first infection; and generate a dynamic model configured to determine a likelihood estimate that the first treatment regimen is an appropriate treatment regimen for treating the first infection. The dynamic model is configured to identify a first effectiveness rate of a first treatment regimen for treating the first infectious disease based on the second database and to identify a second effectiveness rate of a second treatment regimen for treating the first infectious disease from the second database. The hardware processor is further configured to execute computer-executable instructions to generate a first alert to a user if the identified first effectiveness rate is less than a first threshold level or the identified second effectiveness rate is greater than a second threshold level. The first data store can continuously store additional patient records. The hardware processor can be further configured to dynamically and automatically execute computer-executable instructions to update the first database and the second database based on patient records associated with the first infectious disease among the additional patient records and to update the first effectiveness rate and the second effectiveness rate.

[0007] The computer-executable instructions can be further configured to receive from the user an indication of a medical facility, and the second plurality of patient records identified by the computer-executable instructions are patient records associated with the first infectious disease and the indicated medical facility.

[0008] The computer-executable instructions can be further configured to receive from the user an indication of a geographic region, and the second plurality of patient records identified by the computer-executable program instructions are patient records associated with the first infectious disease and the indicated geographic region.

[0009] The computer-executable instructions can be further configured to receive an indication from a user indicating a time period, and the second plurality of patient records identified by the computer-executable instructions are patient records associated with a diagnosis or treatment of the first infectious disease within the indicated time period.

[0010] The computer-executable instructions can be configured to filter the second plurality of patient records with the efficacy rate attached to include patient records associated with at least one of a particular treatment regimen of the plurality of treatment regimens, an infection related to the first infection, a geographic region, and a medical facility.

[0011] The computer-executable instructions can be further configured to generate a second alert to the user if, after updating the first effectiveness rate and the second effectiveness rate, the first effectiveness rate falls below a first threshold or the second effectiveness rate exceeds a second threshold.

[0012] In another aspect, a computer-implemented method is provided for selecting a treatment regimen for a particular patient using a first data store including a first plurality of patient records for a first plurality of patients and a second data store including effectiveness rates of a plurality of treatment regimens for a plurality of infectious diseases and infectious disease-causing pathogens. The computer-implemented method includes, under control of one or more processors, receiving an indication of the first infection for the particular patient and a first treatment regimen prescribed to treat the first infection, generating a first database including a second plurality of patient records by identifying patient records associated with a diagnosis or treatment of the first infection in the first plurality of patient records in the first data store, generating a second database including the second plurality of patient records with the effectiveness rates for the first infection added from the second data store, and generating a dynamic model configured to determine a likelihood estimate that the first treatment regimen is an appropriate treatment regimen for treating the first infection. The dynamic model is further configured to identify a first effectiveness rate of the first therapeutic regimen for treating the first infectious disease based on the second database, and to identify a second effectiveness rate of the second therapeutic regimen for treating the first infectious disease from the second database. The computer-implemented method further includes generating, under control of the one or more processors, a first alert if the identified first effectiveness rate is less than a first threshold level or the identified second effectiveness rate is greater than a second threshold level.

[0013] The first data store can continuously store additional patient records, and the computer-implemented method can further include updating the first database and the second database based on a patient record associated with the first infectious disease among the additional patient records, and updating the first effectiveness rate and the second effectiveness rate.

[0014] The computer-implemented method can further include receiving an indication of a medical facility, wherein the second plurality of patient records are patient records associated with the first infection and the indicated medical facility.

[0015] The computer-implemented method can further include receiving an indication of a geographic region, wherein the second plurality of patient records are patient records associated with the first infectious disease and the indicated geographic region.

[0016] The computer-implemented method may further include receiving an indication of a time period, wherein the second plurality of patient records are patient records associated with a diagnosis or treatment of the first infectious disease within the indicated time period.

[0017] The computer-implemented method may further include filtering the second plurality of patient records with the attached efficacy rate to include patient records associated with at least one of a particular treatment regimen of the plurality of treatment regimens, an infection related to the first infection, a geographic region, and a medical facility.

[0018] The computer-implemented method may further include generating a second alert if, after updating the first effectiveness rate and the second effectiveness rate, the first effectiveness rate falls below a first threshold or the second effectiveness rate exceeds a second threshold.

[0019] Other embodiments of the present disclosure are described below in connection with the appended claims, which may serve as a further summary of the present disclosure.

[0020] In various embodiments, a computer system is disclosed that includes one or more hardware computer processors in communication with one or more non-transitory computer-readable storage devices, the one or more hardware computer processors configured to execute a plurality of computer-executable instructions to cause the computer system to perform operations including one or more aspects of the above-described embodiments (including one or more aspects of the appended claims).

[0021] In various embodiments, computer-implemented methods are disclosed in which one or more aspects of the above-described embodiments (including one or more aspects of the appended claims) are implemented and / or performed under the control of one or more hardware computing devices configured with certain computer-executable instructions.

[0022] In various embodiments, a computer-readable storage medium is disclosed that stores software instructions, which, in response to execution by a computing system having one or more hardware processors, configure the computing system to perform operations including one or more aspects of the above-described embodiments (including one or more aspects of the appended claims).

[0023] Although specific embodiments and examples are described below, the subject matter of the present invention extends beyond the specifically disclosed embodiments to other alternative embodiments and / or uses, modifications, and equivalents thereof. Accordingly, the scope of this application is not limited by any of the specific embodiments described below. For example, in any method or process disclosed herein, the acts or operations of the method or process may be performed in any suitable order and are not necessarily limited to any particular order disclosed. While various operations may be described sequentially as multiple separate operations to facilitate understanding of a particular embodiment, the order of description should not be construed to imply that those operations are order-dependent. Furthermore, the structures, systems, and / or devices described herein may be implemented as integrated or separate components. For purposes of comparing various embodiments, certain aspects and advantages of these embodiments will be described. Not necessarily all such aspects or advantages will be realized by any particular embodiment. Thus, for example, various embodiments may be implemented to achieve or optimize one advantage or group of advantages taught herein without necessarily achieving other aspects or advantages that may also be taught or suggested herein.

[0024] These and other features, aspects, and advantages of the present technology are described below in connection with various embodiments and with reference to the accompanying drawings, although the illustrated embodiments are by way of example only and are not intended to be limiting.

[0025] The figures and corresponding descriptions illustrated herein may use examples including patients, doctors, other caregivers such as nurses, pharmacists, and microbiologists, medications, diseases and illnesses, and corresponding entities and records, although these entities and records may be substituted with other entities and records. [Brief explanation of the drawings]

[0026] [Figure 1A] FIG. 1 is a block or data flow diagram illustrating a medication database system for providing electronic notifications regarding database records, according to one embodiment. [Figure 1B] 1B shows exemplary tables and diagrams that can be implemented in the data flow diagram of the medical treatment database system of FIG. 1A, according to one embodiment. [Figure 1C] 1B shows exemplary tables and diagrams that can be implemented in the data flow diagram of the medical treatment database system of FIG. 1A, according to one embodiment. [Figure 1D] 1B shows exemplary tables and diagrams that can be implemented in the data flow diagram of the medical treatment database system of FIG. 1A, according to one embodiment. [Figure 1E] 1B shows exemplary tables and diagrams that can be implemented in the data flow diagram of the medical treatment database system of FIG. 1A, according to one embodiment. [Figure 1F] 1B shows exemplary tables and diagrams that can be implemented in the data flow diagram of the medical treatment database system of FIG. 1A, according to one embodiment. [Figure 1G] 1B shows exemplary tables and diagrams that can be implemented in the data flow diagram of the medical treatment database system of FIG. 1A, according to one embodiment. [Figure 1H] 1B shows exemplary tables and diagrams that can be implemented in the data flow diagram of the medical treatment database system of FIG. 1A, according to one embodiment. [Figure 1I] 1B shows exemplary tables and diagrams that can be implemented in the data flow diagram of the medical treatment database system of FIG. 1A, according to one embodiment. [Figure 2] FIG. 1 illustrates an example block or data flow diagram of a treatment regimen database system, referred to as a dynamic system, according to one embodiment of the present disclosure. [Figure 3] 3 is an embodiment of a communication flow diagram illustrating communications exchanged between various components of the system of FIG. 2 to provide electronic notifications regarding database records, according to one embodiment. [Figure 4]FIG. 3 illustrates overlapping levels of insight available from the system of FIG. 2, according to one embodiment. [Figure 5] FIG. 3 is a block diagram illustrating one possible configuration of the system of FIG. 2 that can dynamically generate and apply models to track drug effectiveness and track conditions to identify potential drug resistance conditions based on information from one or more databases, according to one embodiment. [Figure 6] 3 is a block diagram corresponding to one aspect of the dynamic system and / or hardware and / or software components of an example embodiment of the system of FIG. 2. DETAILED DESCRIPTION OF THE INVENTION

[0027] The disclosed systems and methods advantageously implement antimicrobial stewardship and antimicrobial resistance (AMR) goals at various levels, including patient-specific, facility-specific, and region-specific levels. The disclosed systems and methods provide dynamic, real-time insight and situational awareness to medical staff regarding medication and antimicrobial resistance trends at three core levels: region-specific insight and situational awareness, facility-specific insight and situational awareness, and patient-specific insight and situational awareness.

[0028] Physicians, nurses, pharmacists, medical staff, healthcare administrators, healthcare researchers, and other entities involved in the diagnosis and treatment of infectious diseases (hereafter referred to as healthcare professionals) understand the public health threat of drug-resistant infections and that the misuse and abuse of certain medications can increase such resistance. Healthcare professionals may receive updated drug efficacy information, but only at very infrequent intervals (e.g., every 5–7 years) and based on aggregate data across wide time frames and geographic areas. Furthermore, when diagnosing patients and prescribing medications, physicians and other clinicians may not be aware of which available treatment regimens, including medication types and dosages, may be appropriate or inappropriate (e.g., which treatment regimens have been empirically and clinically proven to resolve infections and which have not).

[0029] Additionally, physicians may evaluate treatment regimens based on a patient's presenting symptoms before an infection is diagnosed. In some cases, physicians may not be fully aware of a patient's previous infections and treatment information. For example, a patient being diagnosed by a physician may have traveled to the physician's medical facility from a different geographic area, or may have acquired an infection while traveling or visiting a different geographic area. Therefore, physicians may have insufficient or inaccurate information regarding the source and type of pathogen causing the infection (and, in particular, whether the pathogen is resistant or susceptible to which treatment regimen). As a result, physicians may diagnose infections and prescribe treatment regimens based on the geographic area of ​​the physician's facility and the information the physician can obtain from the patient at that time. Therefore, physicians may rely on diagnostic test results (e.g., blood tests, cultures, bacterial identification, and susceptibility tests), which can be time-consuming and may delay the information the physician needs to accurately diagnose an infectious disease and prescribe an appropriate treatment regimen. Thus, a physician may not know whether the pathogen causing the infection originates from a geographic area that is more or less resistant to treatment regimens than in other regions, whether a particular patient has a history of infections caused by the same pathogen, whether a particular patient has a history of resistance to treatments for that pathogen (for reasons related or unrelated to the pathogen), and / or whether the physician's previous diagnoses and prescriptions for the same or similar infections have been successfully resolved, all of which may be useful in determining what treatment regimen the physician should prescribe for the patient.

[0030] To accurately diagnose infectious diseases and prescribe effective treatment regimens, healthcare professionals need to receive accurate information dynamically and in real time. Information received after a patient has been diagnosed and a treatment regimen prescribed is not as useful as information received at the time of diagnosis and before the treatment regimen is prescribed. Furthermore, providing information about the frequency with which physicians prescribe effective treatment regimens can be useful in identifying antibiotic misuse or abuse, allowing physicians to adapt their practice to improve patient care and identifying ways in which physicians can adapt to improve patient care.

[0031] A physician prescribing an inappropriate treatment regimen may not know how to correct or improve his or her standard of practice. The physician may not be aware that there are different options for medications whose effectiveness rates have changed since the physician last surveyed or received an update on effectiveness rates. Furthermore, when treating a particular current patient, the physician may not know whether the selected treatment regimen successfully resolved infections in previous patients (e.g., whether administration of the prescribed type and dosage of medication resulted in the physician's previous patient recovering from the infection). For example, the physician may remember that a previous patient presented with infection X and that the physician prescribed treatment regimen A, but the physician may not know whether the prescribed treatment regimen resolved infection X in that previous patient. Thus, a physician may prescribe treatment regimen A to a current patient presenting with symptoms of infection X without knowing whether treatment regimen A is effective against infection X or whether there are other factors to consider (e.g., the current patient has a history of resistance to treatment with drug A, the current patient contracted infection X in a geographic area known to harbor strains of infection X that are resistant to drug A, etc.). Additionally, there will inevitably be overlap of resources in the process of diagnosing (or re-diagnosing) the infection and where it lies on the clinical spectrum: partially treated, nearly treated, or treatment failed. Reducing resource overlap increases the overall economic value proposition and helps expedite patient safety and treatment.

[0032] Embodiments of the disclosed systems and methods provide healthcare professionals with dynamically updated information during the diagnostic and / or prescribing phases of a patient's care, allowing physicians to adjust and adapt prescribed treatments to improve treatment for a particular patient's current situation while taking into account recent past treatments. The disclosed systems and methods use real-time or near-real-time data inputs from various data stores and use these data inputs to specifically focus on the current patient and their specific needs and circumstances. The data inputs relate to facility-specific and region-specific insights in addition to patient-specific insights. As a non-limiting example, if a patient contracts a pathogen while traveling abroad, the systems and methods can analyze records of similar infections caused by the same pathogen in that foreign country to take outcome-dependent variables (e.g., drug resistance, different strains of the pathogen, etc.) into account when prescribing a treatment regimen.

[0033] Additionally, physicians often do not know which tests to order to identify a particular patient's infectious pathogen. For example, testing to determine whether a particular infectious pathogen is resistant to a particular treatment regimen may be more useful if the patient has a history of resistance to conventional treatments for that or similar pathogens. However, if a physician is informed that a first patient may be infected with a drug-resistant strain of pathogen A and a second patient is infected with a non-drug-resistant strain of pathogen A, the physician can treat the first patient with a different treatment regimen than that prescribed for the second patient by, for example, ordering additional blood cultures from the first patient to test for drug resistance to other antibiotics, thereby avoiding the use of valuable, limited blood testing resources to test for drug resistance in the second patient. Furthermore, physicians may prescribe different treatment regimens for the first and second patients based on identified or suspected drug resistance. Dynamic Update Parameters Overview

[0034] Implementations of the present disclosure address the aforementioned deficiencies in information flow and situational awareness in infectious disease treatment. The systems and methods described herein provide dynamically updated region-, facility-, and patient-specific insights and analysis in five parameters directly related to antimicrobial stewardship and diagnostic resource stewardship. While references are made throughout this disclosure to "physicians" or "specific physicians," it should be understood that embodiments of the systems and methods described herein provide dynamic insights and situational awareness to any healthcare professional involved in a patient's care. Parameter 1: Resistance rate of a specific pathogen or infectious disease

[0035] In a first step, the systems and methods of the present disclosure use the dynamically updated data to notify healthcare professionals when resistance rates for a particular pathogen or infectious disease are high at that particular participant's facility and in that particular participant's geographic region within a defined time frame. Insight into this parameter can be based on data collected over a time frame selected by the physician, or a time frame set by the physician's facility, a regulatory agency, or any other entity. In one non-limiting example, the time frame is a quarter or three-month period ending on the date the physician requested or received the dynamically updated data. Other example time frames include the previous day, week, two weeks, month, two months, six months, and year. It should be understood that any suitable time frame can be implemented in embodiments of the present disclosure. This data can be provided to the physician in various forms. In one non-limiting example, where the time frame is the previous quarter, the physician identifies an antibiotic treatment regimen that the physician is considering prescribing for a particular patient.

[0036] In response to receiving the identified antibiotic, the disclosed system and method can display to the physician a quarterly heat map of relevant or highly active multidrug-resistant (MDR) pathogens treatable with the selected antibiotic. Examples of MDR pathogens include, but are not limited to, carbapenem-resistant Enterobacteriaceae (CRE) and Acinetobacter baumannii (ACB). The disclosed system and method can display a quarterly heat map of urine, skin / wound, or respiratory cultures that are positive for relevant or highly active MDR pathogens. Upon receiving one or both of these types of quarterly heat maps, the physician is dynamically informed of how a particular antibiotic candidate is more prevalent than other antibiotic candidates for treating a particular infection caused by a resistant pathogen, compared to the physician's facility's resistance records, such as a hospital or outpatient clinic. Parameter 2: Relevant antibiotic use (e.g., ordered, filled, and / or administered) in a specific physician's geographic region and facility

[0037] The systems and methods of the present disclosure use dynamically updated data to inform healthcare professionals about how much of the relevant antibiotic is being used to treat the specific pathogen identified in Parameter 1 above in their specific geographic region and their specific facility. It should be understood that information about relevant antibiotic use can include information about antibiotics ordered, filled, and / or administered in their geographic region or facility. In one non-limiting example, embodiments of the present disclosure inform physicians of research articles evaluating the relevance of antibiotics used to treat a specifically identified resistance type. For example, in the case of Carb NS / CRE, the systems and methods described herein display research articles evaluating the relevance of the latest antibiotics, such as Xerava, Vabomere, Avycaz, Zemdri, Zerbaxa, and colistin, for treating Carb NS / CRE. The systems and methods of the present disclosure can evaluate and display other relevant information, such as, but not limited to, likely or common complications associated with the use of the relevant antibiotic in a particular physician's geographic region and facility. For example, insights can be provided to physicians regarding the percentage of patients prescribed a colistin-based treatment regimen who developed renal dysfunction. As another non-limiting example, insights can be provided to physicians regarding antibiotic combination regimens based on the antibiotics selected in parameter 1 above.

[0038] Finally, treatment intent influences physician prescribing patterns, which may also vary by geographic region and local cultural customs. Furthermore, super-specialists often "override" the prescriptions of inpatient hospital physicians or general practitioners. Thus, the amount, strength, and quantity of prescribed and administered medications may differ. Systems and methods that use analysis of either or both prescribed and administered medications (or medications filled in outpatient settings) may be preferentially used in embodiments of the present disclosure, depending on the type and purpose of the analysis.

[0039] This parametric insight can be based on data collected over a time frame selected by the physician or set by the physician's facility, a regulatory agency, or any other entity. In one non-limiting example, the time frame is the quarter ending on the date the physician requested or received the dynamically updated data. Other example time frames include the previous day, the previous week, the previous two weeks, the previous month, the previous two months, the previous six months, and the previous year. It should be understood that any suitable time frame can be implemented in embodiments of the present disclosure. Parameter 3: Prevalence of inappropriate antibiotic use in a particular physician's geographic region and facility

[0040] The systems and methods of the present disclosure use dynamically updated data to inform a healthcare professional of the frequency with which the antibiotic selected in parameter 1 above is being inappropriately selected for the treatment of resistant pathogens in the healthcare professional's geographic region and in the healthcare professional's facility. In one non-limiting example, embodiments of the present disclosure dynamically inform a physician of the overall or local (e.g., in the physician's geographic region and in the physician's facility) ineffective empirical treatment (IET) rate for the antibiotic selected in parameter 1 above over the most recent quarter. In another non-limiting example, embodiments of the present disclosure dynamically inform a physician of the overall or local (e.g., in the physician's geographic region and in the physician's facility) ineffective empirical treatment (IET) rate for common infections over the most recent quarter. Examples of common infections include, but are not limited to, SSSI, bacteremia, CAP, HCAP, HAP / VAP, and urinary tract infections (UTIs).

[0041] This parametric insight can be based on data collected over a time frame selected by the physician or set by the physician's facility, a regulatory agency, or any other entity. In one non-limiting example, the time frame is the quarter ending on the date the physician requested or received the dynamically updated data. Other example time frames include the previous day, the previous week, the previous two weeks, the previous month, the previous two months, the previous six months, and the previous year. It should be understood that any suitable time frame can be implemented in embodiments of the present disclosure. Parameter 4: Current antibiotics and current antibiotic susceptibility testing for relevant pathogens in a particular physician's geographic region and facility

[0042] The disclosed systems and methods use dynamically updated data to inform healthcare professionals of how frequently current commercially available antibiotic treatment regimens are being tested for the relevant pathogens identified in parameters 2 and 3 above. The dynamically updated data can include information regarding current antibiotic susceptibility in a particular geographic region of the participant and in a particular facility of the participant. Advantageously, embodiments of the present disclosure can determine susceptibility rates using real-world data, what types of patient testing is being performed using real-world data, and whether testing is being performed on hospitalized patients who may require such testing.

[0043] In one non-limiting example, the disclosed systems and methods evaluate the susceptibility rates and minimum inhibitory concentration (MIC) distributions of test pathogens to the specific antibiotics identified in Parameter 1 above, both overall and by source. Examples of modern antibiotics not included in traditional routine testing microbiology panels include Xerava, Vabomere, Avycaz, Zemdri, and / or Zerbaxa. In another non-limiting example, the disclosed systems and methods evaluate quarterly trends in testing of modern antibiotics. In yet another example, the disclosed systems and methods evaluate national and local resistance rates for testing of these selected antibiotics against overall and source-specific resistance rates associated with specific resistance types. This information can include, for example, resistance rate heat maps for specific resistance types. Embodiments of the present disclosure can evaluate these insights to aid in the selection of candidate antibiotics identified in Parameter 1 above.

[0044] This parametric insight can be based on data collected over a time frame selected by the physician or set by the physician's facility, a regulatory agency, or any other entity. In one non-limiting example, the time frame is the quarter ending on the date the physician requested or received the dynamically updated data. Other example time frames include the previous day, the previous week, the previous two weeks, the previous month, the previous two months, the previous six months, and the previous year. It should be understood that any suitable time frame can be implemented in embodiments of the present disclosure. Parameter 5: Identifying patient-specific risk factors for specific infections and resistance types

[0045] The disclosed systems and methods use dynamically updated data to inform healthcare professionals of patient risk factors for specific infections and resistance types identified in parameters 1-4 above. Implementations of the present disclosure can create risk scores for patient populations that enable healthcare professionals to differentiate specific pathogen types by infection (e.g., SSI, CAP, HCAP). For example, the risk score can take into account risk factors associated with specific resistances or pathogen types. In another example, the risk score can take into account mixed resistances or pathogen types associated with these risk factors. Implementations of the present disclosure can create tiered models to guide the need for coverage of specific mixed resistances or pathogen types. The dynamically updated data can include information about current antibiotic susceptibility in a particular geographic region of a participant and in a particular facility of a participant. Advantageously, embodiments of the present disclosure can determine susceptibility rates using real-world data, what types of patient tests are being performed using real-world data, and whether tests are being performed on hospitalized patients who may require such tests.

[0046] This parametric insight can be based on data collected over a time frame selected by the physician or set by the physician's facility, a regulatory agency, or any other entity. In one non-limiting example, the time frame is the quarter ending on the date the physician requested or received the dynamically updated data. Other example time frames include the previous day, the previous week, the previous two weeks, the previous month, the previous two months, the previous six months, and the previous year. It should be understood that any suitable time frame can be implemented in embodiments of the present disclosure.

[0047] 1A is a block diagram or data flow diagram illustrating an example method for providing dynamically updated region-specific, facility-specific, and patient-specific insights and analysis of the parameters described above, according to an embodiment of the present disclosure. The flowchart of method 10 may be performed by any device described herein, such as computing system 102 or dynamic system 103 of system 100, described in more detail below. Depending on the embodiment, one or more of the steps / blocks of method 10 may be omitted, combined with another step / block, or additional steps / blocks may be added to method 10.

[0048] At block 12, the method includes receiving input from a user (such as, but not limited to, a physician, hospital administrator, or other medical professional). For example, the user may be a physician evaluating a patient suffering from an illness caused by a pathogen. The input from the physician may include a specific antibiotic the physician is considering prescribing and / or a selection of pathogens the physician suspects or confirms as causing the illness, such as a bacterial infection. In some embodiments, the user provides input that includes both the pathogen and the antibiotic (e.g., if the physician is treating or planning to treat the selected pathogen with the selected antibiotic). In some embodiments, the input includes only the selected pathogen (e.g., if the physician is unsure which antibiotic to use to treat the selected pathogen). In some embodiments, the input includes only the selected antibiotic (e.g., if the physician is unsure which specific pathogen is causing the infection but knows what antibiotic to use to generally treat the patient's symptoms).

[0049] Based on the selected antibiotic and / or pathogen, method 10 proceeds to block 14, where a resistance rate associated with the selected antibiotic and / or pathogen is identified. For example, method 10 identifies, retrieves, or views information (e.g., one or more of research papers, reports, culture information, heat maps, resistance records, etc.) from one or more databases (e.g., second data store 108, described in more detail below). An example of a research paper is shown in FIG. 1B. In one non-limiting embodiment, a physician can evaluate this research paper and other appropriate research papers to determine whether, in the most recent quarter, the resistance rate to the selected antibiotic and / or pathogen is high in the physician's geographic area, such as Region 2, or in the physician's facility, such as an acute care facility in Region 2. In the example research paper, the physician can determine that Region 2, the physician's geographic area, has a significantly higher non-susceptibility rate (17.1%) for Carb-NS Pseudomonas aeruginosa than Regions 1, 3, and 4. Method 10 may retrieve this information and store it locally, for example, in a database of system 100.

[0050] For example, based on the selected antibiotic, method 10 obtains an associated heat map. An example of a heat map is shown in FIG. 1C. In this example, the heat shows the geographic prevalence (per 1,000 hospitalized patients) of urine isolates of a particular pathogen, pathogen X, in hospitalized patients. An example of a pathogen is ESBL-ENT (Extended-Spectrum β-Lactamase Enterobacteriaceae). Using this heat map, a physician can determine that the prevalence of pathogen X in urine isolates in the physician's specific geographic region, Southern California, is higher compared to other geographic regions. More details are provided in the description related to parameter 1 above. Method 10 may also obtain a heat map associated with the selected pathogen and / or the selected antibiotic. In another example, when a physician provides both the selected antibiotic and the selected pathogen, method 10 identifies a heat map for the selected pathogen and / or the selected antibiotic, or for only one or the other. The heat map may show drug resistance information for the selected antibiotic and / or the selected pathogen. For example, a heat map specific to a selected antibiotic may show drug resistance of the selected antibiotic for pathogens commonly treated with the selected antibiotic in a particular geographic region or facility during or over a particular time period. A heat map for both the selected antibiotic and the selected pathogen may show drug resistance (e.g., as determined by urine, skin / wound, or respiratory culture) of the selected pathogen for the selected antibiotic in a particular geographic region or facility during or over a particular time period. In some embodiments, a user also selects a particular geographic region or facility and / or a particular time period as input to system 100. In some embodiments, the particular geographic region or facility and / or the particular time period are predetermined. The predetermined determination may be a fixed predetermined determination based, for example, on the user's location, facility, or a parameter detectable by the system. The predetermined determination may be a dynamic predetermined determination based on one or more parameters detectable by the system.For example, if the user is a mobile clinic user, the system may identify a geographic area within a threshold distance (e.g., 50 miles (80 km) or 100 miles (160 km)) from the current location of the mobile device being used by the mobile clinic user. Based on the obtained information, method 10 may determine how common the selected antibiotic candidate is for treating the selected pathogen, taking into account any drug resistance characteristics of the selected pathogen. In some embodiments, at block 14, method 10 also identifies a particular resistance type based on the selected antibiotic and / or pathogen.

[0051] After various data are acquired in block 14, method 10 may identify the appropriate antibiotic use for the treatment of the drug-resistant pathogen in block 16. In some embodiments, the user selects a pathogen, and if the selected pathogen is not drug-resistant, this step / block of method 10 can be omitted or skipped (by system 100 or the user). In some embodiments, the selected pathogen is drug-resistant, and system 100 identifies the appropriate antibiotic use to treat the selected pathogen. The identified antibiotic may include the selected antibiotic or an antibiotic commonly used to treat the selected drug-resistant pathogen. Block 16 may include obtaining information, for example, specialized research papers, on the selected antibiotic or an antibiotic commonly used to treat the selected pathogen that is drug-resistant. In some embodiments, the obtained information further includes information regarding complications or side effects of a particular antibiotic, treatment regimens including multiple antibiotics, etc. More details regarding block 16 are provided in the discussion associated with parameter 2 above. The obtained information may pertain to a particular geographic area or facility and may be for a selected pathogen (e.g., as determined by urine, skin / wound, or respiratory cultures) for a selected antibiotic during or over a particular time period. In some embodiments, the user also selects the particular geographic area or facility and / or the particular time period as input to method 10. In some embodiments, the particular geographic area or facility and / or the particular time period are predetermined. The predetermined determination may be a fixed predetermined determination based, for example, on the user's location, the facility, or a parameter detectable by the system. The predetermined determination may be a dynamic predetermined determination based on one or more parameters detectable by the system. For example, if the user is a mobile clinic user, the system may identify a geographic area within a threshold distance (e.g., 50 miles (80 km) or 100 miles (160 km)) from the current location of a mobile device being used by the mobile clinic user.

[0052] An example of a research paper is shown in Figure ID. In one non-limiting embodiment, a physician can evaluate this research paper to determine how much relevant antibiotic use is occurring in the physician's geographic region and / or in the physician's facility to treat the particular resistant pathogen identified in block 12. Using the research paper shown in Figure ID, for example, the physician can determine the cumulative high-risk antibiotic use (sorted by total high-risk antibiotic use) for four classes of antibiotics: Class A, Class B, Class C, and Class D. Examples of antibiotic classes can include cephalosporins, fluoroquinolones, carbapenems, and lincosamides. The physician can determine, for example, that cumulative antibiotic use of Class D antibiotics is particularly high in the physician's hospital.

[0053] In block 18, method 10 determines details of relevant inappropriate antibiotic use for the treatment of drug-resistant pathogens, and this information can be used to inform a user of how frequently the selected antibiotic (or another antibiotic) is inappropriately selected for the treatment of the selected pathogen. For example, method 10 obtains (e.g., from one or more databases) information regarding effective treatment rates. For example, method 10 identifies and / or receives an effective treatment rate (in a particular geographic region or facility during a particular time period) for the selected antibiotic against the selected drug-resistant pathogen. Alternatively or additionally, method 10 identifies and / or receives an effective treatment rate for the selected antibiotic against common drug-resistant pathogens for which the antibiotic is commonly applied. Alternatively or additionally, method 10 identifies and / or receives an effective treatment rate for one or more antibiotics commonly used against the selected drug-resistant pathogen. Details of block 18 are described in the description related to parameter 3 above. The obtained information may pertain to a particular geographic region or facility and for the selected pathogen against the selected antibiotic during or over a particular time period. In some embodiments, the user also selects a particular geographic area or facility and / or a particular time period as input to system 100. In some embodiments, the particular geographic area or facility and / or the particular time period are predetermined. The predetermined determination may be a fixed predetermined determination based, for example, on the user's location, the facility, or a parameter detectable by the system. The predetermined determination may be a dynamic predetermined determination based on one or more parameters detectable by the system. For example, if the user is a mobile clinic user, the system may identify a geographic area within a threshold distance (e.g., 50 miles (80 km) or 100 miles (160 km)) from the current location of a mobile device being used by the mobile clinic user.

[0054] Examples of research articles are shown in Figures 1E, 1F, and 1G. In one non-limiting example, a physician can determine the frequency of inappropriate antibiotic use in the physician's geographic region and / or institution for a particular infection / resistance type. For example, a physician can evaluate appropriate and inappropriate antibiotic use as shown in Figure 1E (Initial Empirical Antibiotic Therapy for Complicated Urinary Tract Infections (cUTIs) Caused by Pathogen Y) and Figure 1F (Clinical Characteristics of Patients with cUTIs Caused by Pathogen Y). An example of a pathogen includes Enterobacteriaceae. In another non-limiting example shown in Figure 1G, a physician can evaluate inappropriate empirical treatment (IET) by pathogen category for antibiotics A through S. Examples of antibiotics include vancomycin-IV, piperacillin / tazobactam-IV, clindamycin-IV, cefepime-IV, ceftriaxone-IV, meropenem-IV, levofloxacin-IV, cefazolin-IV, ampicillin / sulbactam-IV, daptomycin-IV, linezolid-IV, ciprofloxacin-IV, ceftaroline-IV, sulfamethoxazole / trimethoprim-oral, ertapenem-IV, doxycycline-oral, ciprofloxacin-oral, metronidazole-IV, and levofloxacin-oral.

[0055] At block 20, method 10 may identify one or more available antibiotics for treating the selected pathogen and / or for replacing the selected antibiotic. In some embodiments, such identification includes receiving and / or analyzing information related to available antibiotics. For example, method 10 may receive and evaluate susceptibility rates between pathogens (e.g., the selected pathogen or related drug-resistant pathogen) and antibiotics (e.g., the selected antibiotic or an antibiotic commonly used to treat the selected pathogen or related drug-resistant pathogen). In some embodiments, the information relates to testing of new antibiotics or trends and / or rates of testing (e.g., new treatment regimens being tested). More details about block 20 are provided in the discussion related to parameter 4 above. The information obtained may pertain to a particular geographic region or facility and for a particular time period or over a particular time period for the selected pathogen against the selected antibiotic. In some embodiments, the user also selects a particular geographic region or facility and / or a particular time period as input to system 100. In some embodiments, the particular geographic region or facility and / or the particular time period are predetermined. The predetermined determination may be a fixed predetermined determination based, for example, on the user's location, facility, or parameters detectable by the system. The predetermined determination may be a dynamic predetermined determination based on one or more parameters detectable by the system. For example, if the user is a mobile clinic user, the system may identify a geographic area within a threshold distance (e.g., 50 miles (80 km) or 100 miles (160 km)) from the current location of a mobile device being used by the mobile clinic user.

[0056] An example of a research paper is shown in Figure 1H. In one non-limiting embodiment, a physician can evaluate this research to determine how frequently current commercially available antibiotics are tested against the pathogen selected in block 12. Using the research paper shown in Figure 1H, for example, a physician can evaluate the source distribution of non-overlapping PSA (Pseudomonas aeruginosa) isolates tested against a new antibiotic, in this case C / T (ceftolozane / tazobactam).

[0057] At block 22, method 10 may identify patient risk factors for the selected pathogen and / or antibiotic or the determined specific infection type. In some embodiments, the patient risk factors include, for example, a risk score for a patient population. The risk factors may enable identification or analysis by pathogen type or resistance type, or identification of the pathogen type or resistance type based on the risk factors. For example, method 10 may use the risk factors to identify a specific resistance or pathogen type (or mixed resistance or pathogen type) with specific (e.g., user-selected or predetermined) risk factors. Furthermore, method 10 may generate one or more models (e.g., dynamic models) to identify a specific mixed resistance or pathogen type and / or specific antibiotic to use against the selected pathogen. Block 22 is described in more detail in the discussion related to parameter 5 above. The obtained information may pertain to a specific geographic region or facility and for a specific time period or over a specific time period for the selected pathogen against the selected antibiotic. In some embodiments, the user also selects a specific geographic region or facility and / or a specific time period as input to system 100. In some embodiments, the particular geographic area or facility and / or the particular time period are predetermined. The predetermined determination may be a fixed predetermined determination based, for example, on the user's location, the facility, or a parameter detectable by the system. The predetermined determination may be a dynamic predetermined determination based on one or more parameters detectable by the system. For example, if the user is a mobile clinic user, the system may identify a geographic area within a threshold distance (e.g., 50 miles (80 km) or 100 miles (160 km)) from the current location of a mobile device being used by the mobile clinic user.

[0058] An example of a research paper is shown in Figure 1I. In one non-limiting embodiment, a physician can evaluate this and related research papers to identify patient risk factors for specific infection types (e.g., skin and skin structure infections (SSSIs), urinary tract infections (UTIs), pneumonia bacteremia, CAP, HCAP, HAP / VAP) for the pathogen selected in block 12. Using the research paper shown in Figure 1I, for example, a physician can evaluate anti-Pseudomonal beta-lactams prescribed as empirical treatment in hospitalized patients with post-index Pseudomonas aeruginosa (PSA) infection by index PSA infection non-susceptibility (NS) status. Examples of antibiotics include anti-Pseudomonal beta-lactams, carbapenems (Carbs), extended-spectrum cephalosporins (ESC2), and piperacillin / tazobactam (TZP).

[0059] In some embodiments, method 10 generates a customizable output to the user at block 24. In some embodiments, the output to the user is an interactive report or notification to provide the user with various information obtained by method 10, for example, as described in connection with FIG. 1A and the parameters above. The report generated by method 10 may include, for example, the selected antibiotic (or an alternative antibiotic if a more effective alternative is identified by method 10 for use against the selected pathogen). In some embodiments, the report includes the obtained heat map, report, and other related information. Thus, block 24 may generate output based on the output of the various blocks and parameters associated with the processing of method 10 and associated with the above description.

[0060] In some embodiments, a user may interact with method 10 via a user interface. Thus, a user may have the option to select which steps of method 10 to perform based on input provided to system 100 implementing method 10. For example, if a user determines that a particular antibiotic will be used in a treatment regimen, the user may choose to skip identifying a new antibiotic. Example of a database system that provides dynamic update parameters

[0061] Embodiments of the present disclosure may include a database system (also referred to herein as a “system”) for dynamically generating electronic notifications (also referred to herein as “notifications” or “alerts”) to various healthcare professionals, e.g., using one or more customized models to incorporate data from remote, possibly disparate database systems and provide dynamically updated data related to any one or combination of parameters 1-5 listed above. Subsets of data from the disparate database systems may be processed, merged, and further filtered to meet requirements and criteria selected by the healthcare professionals. Furthermore, various filters, including geographic filters, facility type filters, treatment regimen filters, pathogen filters, resistance type filters, and / or infection type filters, may be applied to aggregate and / or analyze the merged subsets of data into datasets that can be used to dynamically generate electronic notifications without straining available processing power or memory storage, as may occur when overly granular datasets are used. Aggregating merged subsets of data may also help improve levels of accuracy that may otherwise be compromised when multiple datasets are merged.

[0062] Embodiments of the present disclosure may also enable a system and / or method to perform such processing, merging, filtering, aggregation, and / or alert generation in a time-efficient manner as data is added to the database and / or individual participant requirements (e.g., selected filters) may vary.

[0063] The systems and methods may rely on data feeds from multiple databases, one or more of which contain data in different formats and / or pertaining to different information and / or geographic regions. Dynamically generated, customized alerts may be generated based on various customized models and rules for processing the data and generating outputs based on that data. Processing the data may include, for example, aggregating, filtering, merging, comparing, and / or refining the data in the databases, and automatically generating new data files and / or databases. term

[0064] To facilitate understanding of the systems and methods described herein, several terms are explained below. The terms explained below and other terms used herein should be interpreted broadly to include the information described, the ordinary and customary meaning of those terms, and / or other connoted meanings of each term. Therefore, the following explanations do not limit the meaning of these terms, but merely provide exemplary explanations.

[0065] Data store: includes any computer-readable storage medium and / or device (or collection of data storage media and / or devices). Examples of data stores include, but are not limited to, optical disks (e.g., CD-ROMs and DVD-ROMs), magnetic disks (e.g., hard disks, floppy disks, etc.), and memory circuits (e.g., solid-state drives and random access memory (“RAM”). Another example of a data store is a hosted storage environment (commonly referred to as “cloud” storage) that includes a collection of physical data storage devices that are remotely accessible and can be rapidly provisioned as needed.

[0066] Database: includes any data structure (and / or combination of data structures) for storing and / or organizing data, including, but not limited to, relational databases (e.g., Oracle databases and MySQL databases), non-relational databases (e.g., NoSQL databases), in-memory databases, spreadsheets, comma-separated value ("CSV") files, extensible markup language ("XML") files, TeXT ("TXT") files, flat files, spreadsheet files, and / or any other commonly used or proprietary format for data storage. Databases are typically stored in one or more data stores. Accordingly, each database referred to herein (e.g., in the description and / or drawings of this application) should be understood to be stored in one or more data stores.

[0067] Database Record and / or Record: Includes one or more related data items stored in a database. The one or more related database items that make up a record may be related within the database, for example, by a common key value and / or a common index value.

[0068] Electronic Notification, Notification, and / or Alert: Includes electronic notification of the results of calculations, analyses, and / or other processing of records. A notification may indicate the results of calculations, analyses, and / or other processing of records, for example, to a user (e.g., a healthcare professional). A notification may be sent electronically and may cause one or more processes to be initiated, as described herein.

[0069] Users and / or Healthcare Professionals: Includes entities that provide input (e.g., requests) to the system and / or entities that use a device to receive event notifications, notifications, or alerts (e.g., users who are interested in receiving notifications). Non-limiting examples of users include doctors, nurses, pharmacists, medical staff, medical administrators, medical researchers, and regulatory agencies. Example Operation of a Dynamic System According to the Present Disclosure

[0070] FIG. 2 illustrates an example block or data flow diagram of a treatment regimen database system, referred to as dynamic system 103 or system 103, according to one embodiment of the present disclosure. System 103 uses data records in various databases to dynamically generate electronic notifications regarding, for example, treatment regimen effectiveness rates, antibiotic use and infection rates by geographic region and facility, resistance heat maps, and other information related to parameters 1-5 above. Depending on the implementation, one or more of the blocks in FIG. 1A may be optional, additional blocks may be added, and / or blocks may be reordered. As shown, system 103 is part of system 100, which is described in detail with respect to FIG. 5.

[0071] The system 103 includes first filter 112 information, filtered records 114, merged internal database records 116, filter 118, dynamic model 120, and generated notifications. The first filter 112 is configured to filter records in a first data store 104. In some embodiments, the first data store 104 stores information about a patient population. The first filter 112 can include content selected by a user (e.g., selected antibiotics and / or selected pathogens). The dynamic system 103 can use the first filter 112 to identify several records in the first data store 104 that fit, match, or are otherwise based on the filter content and to filter and perform calculations for a particular subset of the patient population. The database records in the first data store 104 can represent patients and can include the patient's name, address (including city, county, state, country, and zip code), past medical history, past treatment regimens (including information about the effectiveness of past treatment regimens), current medical conditions including suspected or confirmed infections, known health problems or medical conditions, and currently prescribed treatment regimens. In some embodiments, the first filter 112 includes instructions for applying or performing one or more specific filtering functions or calculations on the database records stored in the first data store 104 by the system. The system 103 can query the first data store 104 for a specific set of data and perform calculations on that data (based on the first filter 112) to automatically generate a set of filtered records 114. Non-limiting examples of such instructions and / or filters and / or calculations are described below. The system may determine that the first filter 112 includes instructions to perform a count of the number of people with records in the first data store 104 who reside within a particular geographic area, such as, for example, a specified geographic area, zip code, zip code+4 area, city, county, state, set of five particular zip codes, or any other suitable geographic region.

[0072] For example, a first filter 112 may be applied to records in the first data store 104 to generate or identify records in the first data store 104 that are relevant to the first filter 112 or that remain after application of the first filter 112. In one non-limiting embodiment, the first data store 104 includes medical records of patients admitted to or treated at a particular medical facility (such as a particular hospital or outpatient facility) or who reside in a particular geographic area, such as a county or state. Continuing with this non-limiting example, the first filter 112 may include one or more of a particular infectious disease, a particular infectious disease-causing pathogen, a particular strain of an infectious disease-causing pathogen, a treatment regimen prescribed for one or more patients (including type and dosage information of medication, such as antibiotics), or a particular city within a state. By applying the first filter 112 to the records in the first data store 104, the dynamic system 103 generates a first set of filtered records 114 that includes only records from the first data store 104 that match the user-selected parameters of the first filter 112. Thus, if the first data store 104 includes all patient records in a particular state and the first filter 112 includes a filter for methicillin-resistant Staphylococcus aureus (MRSA) infections, the resulting filtered records 114 will include only records from the data store 104 that relate to patients in that particular state who are or have been infected with MRSA.

[0073] In some embodiments, the second data store 108 may include records relating to the effectiveness of treatment regimens against particular infectious diseases or infectious disease-causing pathogens, and in some embodiments, the records may include details such as specific geographic regions, different infectious disease strains, different patient categories, different treatment regimens, etc.

[0074] The filtered records 114, as described above, include records from the first data store 104 that meet the criteria of the first filter 112. In some embodiments, the dynamic system 103 combines treatment regimen efficacy data from the second data store 108 with the filter records 114 to generate a combined or merged internal database record 116. Such a combination of records may include filtered records 114 further supplemented with efficacy information from the second data store 108. Thus, the records in the merged internal database record 116 may include, for each patient record in the merged internal database record 116, details for each patient, including (among other information) the patient's current infections and current treatment regimen supplemented with efficacy information from the second data store 108. The patient details may also include information regarding the patient's past infections and past treatment regimens for treating those past infections. In one non-limiting example, patient records for patients infected with MRSA indicating treatment with trimethoprim-sulfamethoxazole (TMP-SMX) are augmented with the established efficacy rate of TMP-SMX against MRSA, using information dynamically updated based on real-world data aggregated over the most recent quarter. In embodiments of the present disclosure, this augmented information is specific to the geographic area in which the patient resides or became infected and / or the facility in which the patient is being treated.

[0075] In some embodiments, the merged internal database record 116 includes one or more (or all) items included in the corresponding database record in the first data store 104, along with additional information from the second data store 108. The dynamic system 103 may dynamically add information from the second data store 108 to the filtered record 114 (e.g., when new data or records are received in the first data store 104 or the second data store 108, or periodically, e.g., daily, weekly, monthly, bimonthly, semi-annually, yearly, every 28 days, or any other suitable schedule). In some embodiments, the dynamic system 103 may filter and merge records from the first data store 104 and the second data store 108 when the information is requested by a user of the dynamic system 103.

[0076] Advantageously, storing such filtered records and merging the filtered records with efficacy information can speed subsequent processing by the dynamic system 103, such as dynamic generation of custom models and / or additional filtering or merging operations. For example, as described below, by storing a subset of patient records associated with a treatment regimen or infection, such as patients infected with MRSA and treated with intravenous vancomycin, in real time or periodically, the system can more quickly analyze and merge those records with other data sets organized at one or more levels when a user request is received. Indexing is one way to provide faster access to data. Factors that affect the indexing process and the resources required for indexing include the number of records indexed, the number of fields included in the index, and the size of the data stored in the fields included in the index. Subsets of information can be indexed in detail to speed future record retrieval and analysis while using resource-efficient indexes.

[0077] In some embodiments, one or more of the first data store 104 and the second data store 108 may be updated, for example, daily, weekly, monthly, bimonthly, semi-annually, annually, every 28 days, or any other type of schedule. In some embodiments, the first data store and / or the second data store 108 may be updated on demand or when a certain threshold of changes is detected (e.g., 2% of the records have been updated, 0.8% of certain fields of the records have been updated, etc.).

[0078] In some embodiments, the merged internal database record 116 is stored in a file separate from the first data store 104 and / or the second data store 108, while in other embodiments, the merged internal database record 116 is stored in one of the first data store 104 and the second data store 108. In some embodiments, storing the merged internal database record 116 separately may have advantages, such as when the set of accessed data is a smaller data set compared to the complete set of records in the first data store 104 and / or the second data store 108, or as another example, when the merged internal database record 116 is stored as a file or in a system that is more easily readable and accessible than the first data store 104 and / or the second data store 108. In some embodiments, the merged internal database record 116 is stored locally within the dynamic system 103, while in other embodiments, the merged internal database record 116 is stored remotely from the dynamic system 103, such as in an external database or second data store. If separate from the second data store 108, the merged internal database record 116 can be processed by the dynamic system 103 without the need for further communication with the first data store 104 or the second data store 108 to process the merged internal database record 116.

[0079] In the next step of this example process, the dynamic system 103 may then apply a filter 118 to the merged internal database records 116. This may occur in real time or periodically, as described above. In some embodiments, the filter 118 includes one or more criteria stored in or accessed by the dynamic system 103. The filter 118 may be used to focus insights on a particular treatment regimen, infection, geographic region, etc. In some embodiments, the subset of data generated by applying the filter 118 is stored as a database separate from the merged internal database records 116, while in other embodiments, the filtered subset of data is stored in a temporary storage location, such as RAM or buffer memory, that is accessed for use by the dynamic model 120, which is described in more detail below.

[0080] In some embodiments, when a filter 118 is applied, one or more criteria of the filter 118 are based on user input. For example, a user may use a computing device to interface with the dynamic system 103 and request information regarding the effectiveness of a particular treatment regimen for a particular infection or a particular pathogen in a particular geographic area. It should be understood that in some cases, the particular infection or pathogen has not yet been definitively identified, and insights may be provided to the user based on the patient's symptoms. In some embodiments, additional information, such as a date of interest, is provided by the user as a data parameter. For example, the user may provide instructions to filter results outside of the six-month period prior to the current date to ensure the information used for analysis and processing is timely and relevant.

[0081] In some embodiments, the dynamic system 103 (e.g., via a dynamic model generator) may dynamically generate and apply a dynamic model 120 (e.g., as a custom model or custom modeling algorithm) that groups the filtered subset after applying the filter 118 to determine a prediction or likelihood estimate of an appropriate treatment regimen for treating the current infection. The dynamic system 103 may use the various forms of data described above to generate the dynamic model 120. Using the example described above, the dynamic system 103 may obtain a filtered set of patient records for a particular infection or pathogen, a particular treatment regimen, and a particular geographic region to dynamically generate a dynamic model 120 that determines the treatment regimen with the highest efficacy rate (or a higher efficacy rate than other candidate treatment regimens) for treating the current infection by optimizing the likelihood that the selected treatment regimen will have the highest (or a sufficiently high) efficacy against that target infection in a given time period, a given geographic region, etc. In some embodiments, the dynamic model 120 considers the different parameters 1-5 described above and generates a report including risk factors and / or a risk score that is output to a user. The dynamic model 120 may be dynamic in that the model itself is continually updated with additional information that may affect the outputs produced by the dynamic model 120 .

[0082] In some embodiments, the dynamic system 103 may generate and / or identify an effectiveness rate threshold to be used in generating recommendations. For example, if a treatment regimen has an effectiveness rate below a first threshold, the particular treatment regimen may not be recommended by the system 100. In one non-limiting example, if the effectiveness rate of a treatment regimen is below a first threshold of 90%, the particular treatment regimen may not be recommended by the system 100. Alternatively or additionally, if the effectiveness rate is above a second threshold, a second (e.g., newer) treatment regimen may be recommended over a first (older or previously selected) treatment regimen, even if the first treatment regimen has an acceptable effectiveness rate (e.g., above the first threshold). In one non-limiting example, if the effectiveness rate is greater than a second threshold of 80%, a second (e.g., newer) treatment regimen is recommended over a first (older or previously selected) treatment regimen, even if the effectiveness rate of the first treatment regimen exceeds the first threshold of 90%. It should be understood that these examples of first and second thresholds are not limiting and that embodiments of the present disclosure may be suitably implemented using other acceptable first and second thresholds.

[0083] For example, a user may seek insight and situational awareness regarding the optimal (most appropriate) treatment regimen for treating MRSA in a particular geographic region. The user may filter the patient records in the first data store 104 (e.g., via a first filter 112 that includes the infection MRSA) to generate filtered records 114 that include only patient records of patients who have been exposed to MRSA (in the user's particular geographic region, the particular geographic region where the patient resides, the particular geographic region where the patient contracted the infection, the user's particular facility, or any combination thereof) and the corresponding treatment regimens used to treat the MRSA in those cases. The dynamic system 103 generates merged internal database records 116 by applying efficacy rates from the second data store 108 to the filtered records 114. The dynamic system 103 may then further filter the merged internal database records 116 (e.g., based on one or more of a geographic area, a period of interest, etc.). The resulting subset of records may then be used to generate a custom model that is used to identify an optimal (most appropriate) treatment regimen for treating the MRSA infection based on the current patient's medical information, the records in the subset of records, and the merged internal database records 116 or filtered records 114. As discussed above, embodiments of the present disclosure include a dynamic model 120 that takes into account patient-specific information such as, but not limited to, past exposure to the same pathogen causing that particular patient's current infection, past infections of that particular patient that are confirmed or likely to have been caused by the same pathogen, and past resistance to treatment of that same infection (using the same or different treatment regimen as the user is currently considering for the treatment of the current infection).

[0084] The dynamic modeling described herein (performed by the dynamic model 120) can enable updating efficacy rate information for various treatment regimens in light of diagnosed infections and providing alerts or notifications of recommended treatment regimens without overburdening the healthcare facility's computing systems. For example, embodiments of the present disclosure can avoid overburdening the healthcare facility's computing systems by focusing on parameters identified by the system (or a user of the system) rather than unrelated parameters and providing insights related to those parameters. For example, insights for a particular geographic area or time frame can be created without having to process data for other unrelated geographic areas or time frames. As another example, embodiments of the present disclosure can avoid overburdening the healthcare facility's computing systems by dynamically generating patient and efficacy rate information based on data updated in real time or near real time, thereby reducing the amount of memory required by the computing system. As described above, one or more of the first data store 104 and the second data store 108 can be updated as frequently as daily, weekly, or monthly, or on any other suitable schedule.

[0085] The dynamic system 103 can also dynamically or periodically (e.g., daily, weekly, monthly, bimonthly, quarterly, semi-annually, etc.) generate output files or notifications 122 and send notification alerts or notification packages. The generated notification packages or alerts may include digital and / or electronic messages. The notification packages may include indicators indicating the user's selected treatment regimen for treating the patient's current infection. The notification packages may include indicators indicating that the user should access the dynamic system 103 to review records from various data stores and databases associated with the dynamic system 103. The notifications may be sent to the user who provided the filter criteria and / or to any other recipients indicated by the user or notification package. Additionally, the notification packages may be delivered via any suitable mode. Using the above example, the dynamic system 103 may send a notification package containing information regarding an optimal treatment regimen for treating an infection in a given geographic area based on efficacy data obtained over a defined period of time.

[0086] In some embodiments, such notification alerts (also referred to herein as notifications 122) may include a notification recommending against prescribing a particular treatment regimen, along with a recommendation to prescribe an alternative treatment regimen. Dynamic modeling as described herein may enable the dynamic system 103 to generate updated effectiveness rates by monitoring and aggregating patient records and analyzing those records to identify factors that improve or decrease effectiveness rates. Dynamic modeling may consider various geographic factors, time periods, etc. to determine updated effectiveness rates.

[0087] In some embodiments, the alert and / or notification is automatically sent to a user-operated device associated with the corresponding notification. The alert and / or notification can be sent to the computing device 106, for example, at the time the alert and / or notification is generated or at some predetermined time after the alert and / or notification is generated, as described below with reference to FIGS. 3 and 5. Once received by the computing device 106, the alert and / or notification can cause the computing device 106 to display the alert and / or notification by launching an application (e.g., a browser, a mobile application, etc.) on the computing device 106. For example, receipt of the alert and / or notification may automatically launch an application on the computing device 106, such as a messaging application (including, but not limited to, an SMS or MMS messaging application), a standalone application (e.g., a user's messaging application), or a browser, to display the information contained in the alert and / or notification. If the computing device 106 is offline when the alert and / or notification is sent, the application may be automatically launched when the computing device 106 is online so that the alert and / or notification is displayed. As another example, receipt of the alert and / or notification may cause a browser to open and redirect to a login page generated by system 100 so that the user can log in to system 100 and view the alert and / or notification. Alternatively, the alert and / or notification may include a URL for a web page (or other online information) associated with the alert and / or notification such that when computing device 106 receives the alert, a browser (or other application) is automatically launched and the URL included in the alert and / or notification is accessed over the Internet.

[0088] In some embodiments, the alerts and / or notifications may be automatically routed directly to an interactive user interface where they may be viewed and / or evaluated by a user, e.g., a physician or administrator. In another example, the alerts and / or notifications may be automatically routed directly to a printer device where they may be printed into a report for viewing by a user. In another example, the alerts and / or notifications may be automatically routed directly to an electronic work queue device so that information from the notification can be automatically displayed to a user and, optionally, used to automatically contact (e.g., dial a phone number) an interested party identified in the notification (such as, but not limited to, a supervisor or hospital administrator). In yet other examples, the alerts and / or notifications may be automatically routed as input to an external system (e.g., fed into a pharmacy prescription management system, a hospital patient management system, or a health insurance customer relationship management system).

[0089] In some embodiments, if the infection for which a treatment regimen is prescribed is more resistant than a given threshold (e.g., if there is more than 50% resistance in a geographic area, resulting in an effectiveness rate of the treatment regimen being less than 50%), notification 122 may indicate or recommend to the physician not to prescribe the treatment regimen originally selected by the physician. Alternatively, or in addition, if a different treatment regimen is more effective (i.e., has lower drug resistance) than the selected treatment regimen, notification 122 may provide a recommendation to the physician to prescribe the more effective treatment regimen. For example, if the different treatment regimen has an effectiveness rate higher than a second threshold (e.g., an effectiveness rate of more than 70% for the infection in the area or facility), notification 122 may recommend a different treatment regimen than the originally selected treatment regimen.

[0090] In various implementations, the first data store 104, the second data store 108, the filtered records 114, the merged internal database records 116, and / or any combination of these or other databases and / or data storage devices of the system may be combined and / or separated into additional databases, and / or subsets of records produced after application of the filter 118 may be merged into one or more common databases or separated into different databases.

[0091] 3 is an exemplary communication flow diagram 300 illustrating communications between various components of system 100 of FIG. 2 for providing electronic notifications regarding database records, according to one embodiment of the present disclosure. Communication flow diagram 300 illustrates several components of system 100. The communication flow of this non-limiting embodiment involves computing device 106, data stores 104 and 108, and dynamic system 103. Although not shown, each of computing device 106, data stores 104 and 108, and dynamic system 103 can be coupled via a network, such as network 110 of FIG. 5, described in more detail below. Thus, each of the illustrated components can be configured to communicate, for example, via a network connection.

[0092] In one embodiment, the computing device 106 is configured to receive user input 302. The computing device 106 that receives the user input 302 may be the computing device 102, described below with reference to FIG. 5. In some embodiments, the user input 302 may be one or more of a selected antibiotic and a selected pathogen. Additionally, the user input 302 may include geographic constraints and / or a time period or duration of interest. The user input 302 may be provided all at once or at different times or intervals. Similarly, the data stores 104 and 108 may be configured to receive or retrieve various types of information, including patient information and / or health records, pathogen information (including resistance information, susceptibility information, etc.), antibiotic information (including resistance information, susceptibility information, testing information, etc.), heat maps, and drug resistance research papers. In some embodiments, the patient information and health records are stored in the first data store 104, and the remaining information is stored in the second data store 108.

[0093] Based on the received user input 302, the dynamic system 103 may generate a user request 304 for the dynamic system 103. The user request 304 for the dynamic system 103 may include one or more of a selected antibiotic, a selected pathogen, a specific geographic area, and a specific period or time frame. When the computing device 102 receives the user input 302, the computing device 102 may generate the user request 304 based on the received user input 302.

[0094] The dynamic system 103 receives the user request 304 and generates one or more requests for targeted insights based on the user request 304. For example, the targeted insight request may include a request for insights for a specific patient, a specific facility, and / or a specific geography. Additionally or alternatively, the insight request 306 may include a request for insights regarding a selected antibiotic and / or a selected pathogen over a specific period or time frame.

[0095] Based on the received insight request 306, the data stores 104 and 108 may retrieve and / or access data. For example, the first and second data stores 104 and 108 may retrieve or access patient records, pathogen information, antibiotic information, heat maps, drug resistance research papers, information, and evidence, etc. At 308, the data stores 104 and 108 may provide the retrieved or accessed data to the dynamic system 103. In some embodiments, the relevant information provided to the dynamic system 103 from the data stores 104 and 108 includes patient records or information from the patient records, pathogen information (e.g., pathogen drug resistance and similar information), details about the selected antibiotic, heat maps (e.g., heat maps previously generated by the dynamic system 103 or system 100), and other relevant information. The dynamic system 103 may generate and / or update the dynamic model 310 using the information from the user request 304 and the relevant information 308. The dynamic model 310 uses the user request 304 and related information 308 to generate analysis or additional information regarding the best antibiotic and / or treatment regimen for treatment of the selected pathogen. In some embodiments, the best antibiotic may be the selected antibiotic or a different antibiotic. The dynamic model 310 may analyze the received request and related information 308 to identify information regarding the effectiveness of the selected antibiotic against the selected pathogen, which may include drug resistance information.

[0096] After the dynamic model is generated or updated at 310, the dynamic system 103 may provide a report to the user at 312. In some embodiments, the report may include one or more pieces of information used by the dynamic system 103 to generate the recommended antibiotic and / or treatment regimen and associated risk factors (as described further herein). The report 312 may also include heat maps, pathogen information, patient information, and other information from the data stores 104 and / or 108 and the analysis generated by the dynamic system 103 related to the recommended antibiotic and / or treatment regimen and the corresponding selection. The report 312 may include all of this information to allow the user to view any information relevant to the recommendation.

[0097] FIG. 4 illustrates a diagram 400 of overlapping levels of insights available to a user of the system 100 of FIG. 2 , according to one embodiment of the present disclosure. For example, diagram 400 illustrates multiple levels of insights that the system 100 can provide. A first level, a patient-specific insight level 402, may include insights specific to a particular patient. Thus, based on various inputs (e.g., as shown in FIG. 4 ), the system 100 may identify one or more specific patient insights based on the inputs and deliver those insights to the user. In one non-limiting example, the system 100 may identify whether a particular patient under examination or consideration is currently infected with or has previously been infected with a particular pathogen. In another non-limiting example, the system 100 may identify whether a particular patient under examination or consideration has health risks associated with a particular treatment regimen. In yet another non-limiting example, the system 100 may identify whether a particular patient has previously been treated with or is currently being treated using a particular treatment regimen. As detailed above, the system 100 can advantageously convey these and other insights to the user during optimal time frames within the patient care timeline.

[0098] The second level, corresponding to the next largest circle in diagram 400, represents facility-specific insights 404, which represent insights for a particular facility. Facility-specific insights may include insights for patients being treated at that particular facility. For example, system 100 may identify patients being tested for, treated for, exposed to, etc., a particular pathogen. Thus, facility-specific insights 404 for that particular facility essentially include patient-specific insights 402 for patients being treated at that particular facility.

[0099] The third level, corresponding to the next largest circle in diagram 400, represents geography-specific insights 406, which represent insights for a specific geographic region. Geography-specific insights may include facility-specific insights for facilities located within that specific geographic region. For example, system 100 may identify all patients being tested for, treated for, or assessed for exposure to a specific pathogen, which would include any specific patient at any specific facility within that specific geographic region. Thus, geography-specific insights 406 for that specific geographic region essentially include facility-specific insights 404 for any facility within that specific geographic region and patient-specific insights 402 for patients being treated at facilities within that specific geographic region. As described above, these insights can be based on real-time or real-time data aggregated over a specific period, including the period ending with the date of the user's insight request. Thus, embodiments of the disclosed systems and methods can provide dynamic, real-time insights and situational awareness to medical staff at three core levels: geographic region-specific insights and situational awareness, facility-specific insights and situational awareness, and patient-specific insights and situational awareness. Other exemplary implementations of dynamic systems according to this disclosure

[0100] 5 and 6 illustrate other non-limiting embodiments of a dynamic system 103 configured to communicate with a data store in the system 100, according to implementations of the present disclosure. System Overview

[0101] 5 illustrates a block diagram of one possible configuration of the system of FIG. 2 that can dynamically generate and apply models to track medication effectiveness and track conditions to identify possible drug resistance conditions based on information from one or more databases, according to an embodiment of the present disclosure. System 100 can enable a user to dynamically generate models to process records and data obtained from various databases, and the models can generate outputs (e.g., reports and notifications) from the data based on dynamically changing requirements from the user.

[0102] The system 100 of FIG. 5 includes a dynamic system 103 interfacing with a computing device 102, a first data store 104, a second data store 108, other computing devices 106, and a network 110. Additionally, communication links are shown that enable communication between the components of the system 100 over the network 110. While the computing device 102 is shown communicatively coupled to the dynamic system 103 in a local manner (e.g., via a local communication link), the dynamic system 103 may be incorporated into the computing device 102, or vice versa, or may be accessible over the network 110. Also, in some embodiments, one or more of the data stores described herein may be combined into a single data store that is local to the computing device 102 or remote from the computing device 102. In some embodiments, two or more of the above components may be integrated. In some embodiments, one or more of the components may be excluded from the communication system 100, or one or more components not shown in FIG. 5 may be included in the communication system 100. The communication system 100 can be used to implement the systems and methods described herein.

[0103] In some embodiments, the network 110 may include any wired or wireless communication network capable of communicating data and / or information between multiple electronic and / or computing devices. The wireless or wired communication network may employ widely used networking protocols to interconnect nearby devices or systems. Various aspects described herein are applicable to any communication standard, such as the wireless 802.11 protocol. The computing device 102 may include any computing device configured to transmit and receive data and information over the network 110 for medical personnel. The medical personnel may be individuals (e.g., individual doctors or nurses) or institutions, such as, but not limited to, businesses, non-profit organizations, educational institutions, medical facilities, etc. In some embodiments, the computing device 102 may include or have access to one or more databases (e.g., the first data store 104 and the second data store 108) containing various records and information usable to generate customized outputs. In some embodiments, the computing device 102 may be accessible locally and remotely via the network 110. The computing device 102 may create customized outputs based on events associated with the patient (e.g., past infections, past prescribed treatment regimens, health events, and health risks). These events (and corresponding information) may be used to dynamically generate a model of which treatment regimen should be prescribed to ensure maximum effectiveness against a particular infection, a particular pathogen, or a particular drug-resistant strain of a pathogen.

[0104] The first data store 104 may include one or more databases or data stores and may store data related to either historical or current events. Using an example use case, the first data store 104 may include patient records including patient names, contact information, address information, medical information, current and past infections, past prescribed treatment regimens, geographic areas where the patient was infected (or may have been infected based on residential or travel information), and dates the patient was ill. In some embodiments, the first data store 104 may provide data for patients within a particular geographic area defined by a user via one or more computing devices 106 or computing device 102. For example, the first data store 104 may provide details about patients within a geographic area defined by state, county, zip code, or other geographic identifier.

[0105] Computing devices 102, 106 may include any computing device configured to transmit and receive data and information over network 110. In some embodiments, computing device 106 may be configured to perform analysis of the transmitted and received data and information and / or perform one or more actions based on the analysis and / or the transmitted and received data and information. In some embodiments, one or more computing devices 102, 106 may include mobile or stationary computing devices, including, but not limited to, personal computers, server computers, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, microcontrollers, or microcontroller-based systems, programmable consumer electronics products, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and smartphones. Accordingly, users (e.g., physicians, hospital administrators, etc.) described herein may use any of these types of devices to access reports generated based on their respective requests or inquiries. In some embodiments, computing devices 102, 106 may be integrated into a single terminal or device. In some embodiments, computing device 102 is remote from the patient, user, physician, hospital, etc. Computing device 106 may be used by a user to access network 110 and remotely access computing device 102. For example, computing device 102 may be located in a medical facility and accessible by computing device 106.

[0106] The second data store 108 may include one or more databases or data stores, and may store data regarding, for example, the effectiveness rates of treatment regimens for particular infections or pathogens in different geographic regions and for different defined time frames. In an exemplary use case, as described above, the second data store 108 includes a database of effectiveness rates of infections treated using treatment regimens corresponding to infections and / or treatment regimens of patients having records in the first data store 104.

[0107] The dynamic system 103 may process data from the first data store 104 and the second data store 108 and may generate one or more models based on requests or inputs provided by users via the computing device 106 and the computing device 102. The dynamic system 103 may dynamically generate one or more models to be applied to data obtained from one or more of the first data store 104, the second data store 108, or the users (via one or more computing devices 106). In some embodiments, the models may be dynamically generated by the dynamic system 103 as the inputs and data change or on a predetermined schedule. For example, the dynamic system 103 may generate models that change in real time based on inputs received from users (e.g., but not limited to, the type of infection diagnosed, the type of treatment regimen prescribed, the area affected by the infection, and the associated time frame). In some embodiments, the generated models may themselves be dynamically applied to the inputs and data. For example, the model generated by the dynamic system 103 may generate various metrics and data points based on data obtained from the first and second data stores 104 and 108, respectively, and data obtained from the user himself (e.g., the user's selected filters and geographic region). In some cases, the model may dynamically apply one or more rules to ensure that the generated output does not violate any predetermined requirements or standards for data mining, storage, etc. (e.g., to ensure that medical information compliance elements are met). In some embodiments, generating the model may include generating a set of heuristic rules, filters, and / or electronic data screens to determine and / or identify and / or predict which medications will be deemed more likely to meet certain criteria based on current and / or historical data. In some embodiments, the dynamic system 103 may automatically adjust the model to meet a preselected degree of accuracy and / or efficiency.

[0108] In some embodiments, the dynamic system 103 may be adaptable to constantly changing data from the first data store 104, the second data store 108, or from users. For example, input received from users (e.g., via the user interface module 214 or the I / O interfaces and devices 204, described in more detail below) may vary from user to user. Using an example use case, one user (e.g., a first physician) may be interested in the effectiveness rate of patients infected with MRSA when treated with TMP-SMX in a first set of zip codes, another user (e.g., a second physician) may be interested in the effectiveness rate of patients infected with Streptococcus pneumoniae when treated with penicillin in the first set of zip codes, and a third user (e.g., a third physician) may be interested in the effectiveness rate of a third infection when treated with a third medication. Thus, the data retrieved from the first and second data stores 104 and 108 using the filter criteria from these users is likely to be constantly changing. Thus, processing and / or model generation will vary for each user and / or combination of data of interest. Additionally, the data retrieved from the first and second data stores 104 and 108 will likely change over time as records in the data stores are updated, replaced, and / or deleted. In an exemplary use case, different users may require different time periods, different patient parameters, different infections, different treatment regimens, etc., so that patient and medication records are constantly updated. Thus, the dynamic system 103 may dynamically generate models to address constantly changing data and requirements.

[0109] As described in more detail herein, based on a user request, the data retrieved from the first and second data stores 104 and 108, respectively, may be filtered to exclude undesired records. For example, in an exemplary use case, records may be filtered to exclude infections, dates, and / or treatment regimens that are not of interest.

[0110] In various embodiments, large amounts of data are automatically and dynamically calculated interactively in response to user input, and the calculated data is efficiently and concisely presented to the user by the system. Thus, in some embodiments, the data processing and generation of user interfaces described herein is more efficient than traditional data processing and user interface generation in which data and models are not dynamically updated in response to interactive input and are not presented concisely and efficiently to the user.

[0111] Also, as described herein, the system can be configured and / or designed to generate output data and / or information usable to render various interactive user interfaces or reports as described. The output data can be used by system 100 and / or another computer system, device, and / or software program (e.g., a browser program) to render an interactive user interface or report. The interactive user interface or report can be displayed, for example, on an electronic display (including, for example, a touch-enabled display).

[0112] The various embodiments of the interactive dynamic data processing and output generation of the present disclosure are the result of considerable research, development, refinement, iteration, and testing. The result of this significant development is the modeling and output generation described herein, which can provide increased efficiencies and advantages over conventional systems. The interactive dynamic modeling, user interface, and output generation involve improved human-computer and computer-computer interactions that can result in reduced user workload and / or improved predictive analysis, etc. For example, output generation via the interactive user interface described herein can provide an optimized display of time-varying, report-related information, allowing users to access, navigate, evaluate, and assimilate such information more quickly than conventional systems.

[0113] In some embodiments, the output data or reports may be presented in graphical representations, such as charts, spreadsheets, graphs, and other visual representations, where appropriate, to enable users to efficiently review large amounts of data and take advantage of the particularly strong human pattern recognition capabilities associated with visual stimuli. In some embodiments, the system may present aggregate quantities, such as totals, counts, and averages. The system may also use this information to interpolate or extrapolate, e.g., predict, future developments.

[0114] Additionally, the models, data processing, and interactive dynamic user interfaces described herein are made possible by innovations in efficient data processing, modeling, user interface interactions, and underlying systems and components. For example, improved methods are disclosed herein for receiving user inputs, transforming and delivering those inputs to various system components, automatically and dynamically executing complex processes in response to the input delivery, automatically capturing data, automatically interacting with various components and processes of the system, and automatically and dynamically generating reports and updating user interfaces.

[0115] Various embodiments of the present disclosure provide improvements in various technical and technological fields. For example, as described above, existing data storage and processing technologies (including, e.g., memory databases) are limited in various ways (e.g., manual data review is slow, costly, lacks detail, data volumes are excessive, etc.), and various embodiments of the present disclosure provide significant improvements over such technologies. Furthermore, various embodiments of the present disclosure are closely tied to computer technology. Specifically, various embodiments rely on detecting user inputs via a graphical user interface, retrieving data based on those inputs, modeling the data to generate dynamic output based on those user inputs, automatically processing related electronic data, and presenting output information via an interactive graphical user interface or report. These and other features (e.g., processing and analyzing large amounts of electronic data) are closely tied to, enabled by, and would not exist without computer technology. For example, the interactions with data sources and displayed data described below with reference to various embodiments cannot reasonably be performed by humans alone without the computer technology upon which they are implemented. Additionally, implementation of various embodiments of the present disclosure through computer technology enables many of the advantages described herein, including more efficient interaction with and presentation of various types of electronic data. Dynamic System Examples

[0116] Figure 6 is a block diagram corresponding to aspects of the hardware and / or software components of an example embodiment of dynamic system 103 and / or system 100 of Figure 5. As described below with reference to block diagram 200, the hardware and / or software components may be included in any of the devices of system 100 (e.g., computing device 102, computing device 106, or dynamic system 103). These various components as shown may be used to implement the systems and methods described herein.

[0117] In some embodiments, certain modules described below, such as modeling module 215, user interface module 214, or reporting module 216 included in dynamic system 103, may be included in, executed by, or distributed across different and / or multiple devices in system 100. For example, certain user interface functionality described herein may be performed by user interface module 214 on various devices, such as computing device 102 and / or one or more computing devices 106.

[0118] In some embodiments, the various modules described herein may be implemented in either hardware or software. In one embodiment, the various software modules included in the dynamic system 103 may be stored in components of the dynamic system 103 itself (e.g., local memory 206 or mass storage device 210), or in computer-readable storage media or other components separate from and in communication with the dynamic system 103 via the network 110 or other suitable means.

[0119] The dynamic system 103 may include, for example, an IBM, Macintosh, or Linux / Unix-compatible computer, or a server or workstation or mobile computing device running any compatible operating system. In some embodiments, the dynamic system 103 interfaces with a smartphone, personal digital assistant, kiosk, tablet, smartwatch, car console, electronic assistant, media player, or similar electronic computing device. In some embodiments, the dynamic system 103 may include more than one of these devices. In some embodiments, the dynamic system 103 includes one or more central processing units (“CPUs” or processors) 202, I / O interfaces and devices 204, memory 206, modeling module 215, mass storage device 210, multimedia device 212, user interface module 214, reporting module 216, and bus 218.

[0120] The CPU 202 can control the operation of the dynamic system 103. The CPU 202 may also be referred to as a processor. The processor 202 may include or be a component of a processing system implemented using one or more processors. The one or more processors may be implemented using any combination of general-purpose microprocessors, microcontrollers, digital signal processors (“DSPs”), field programmable gate arrays (“FPGAs”), programmable logic devices (“PLDs”), controllers, state machines, gate logic, discrete hardware components, dedicated hardware finite state machines, or any other suitable entities capable of computing or otherwise manipulating information.

[0121] I / O interface 204 may include a keypad, microphone, touchpad, speaker, and / or display, or any other commonly available input / output ("I / O") devices and interfaces. I / O interface 204 may include any element or component that communicates information to and / or receives input from a user of dynamic system 103 (e.g., a requesting doctor, nurse, hospital administrator, researcher, or other entity). In one embodiment, I / O interface 204 includes one or more display devices, such as a monitor, that enable the visual presentation of data to a consumer. More specifically, the display device provides for the presentation of, for example, GUIs, application software data, websites, web applications, and multimedia presentations.

[0122] In some embodiments, I / O interface 204 may provide a communications interface with various external devices. For example, dynamic system 103 is electronically coupled to network 110 ( FIG. 5 ), which may include one or more of a LAN, a WAN, and / or the Internet. Accordingly, I / O interface 204 includes an interface that enables communication with network 110, for example, via a wired communications port, a wireless communications port, or a combination thereof. Network 110 may enable various computing devices and / or other electronic devices to communicate with each other via wired or wireless communications links.

[0123] Memory 206, including one or both of read-only memory (ROM) and random access memory ("RAM"), may provide instructions and data to processor 202. For example, data received via inputs received by one or more components of dynamic system 103 may be stored in memory 206. A portion of memory 206 may also include non-volatile random access memory ("NVRAM"). Processor 202 typically performs logical and arithmetic operations based on program instructions stored in memory 206. The instructions in memory 206 may be executable to implement methods described herein. In some embodiments, memory 206 may be configured as a database and may store information received via user interface module 214 or I / O interfaces and devices 204.

[0124] The dynamic system 103 may also include a mass storage device 210 for storing software or information (e.g., generated models or acquired data to which the models are applied). Software should be broadly construed to mean any type of instructions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. The instructions may include code (e.g., in source code format, binary code format, executable code format, or any other suitable format of code). The instructions, when executed by one or more processors, cause the processing system to perform the various functions described herein. Thus, the dynamic system 103 may include, for example, hardware, firmware, and software, or any combination thereof. The mass storage device 210 may include a hard drive, a diskette, a solid-state drive, or an optical media storage device. In some embodiments, the mass storage device 210 may be structured so that the data stored therein can be easily manipulated and analyzed.

[0125] As shown in FIG. 6, dynamic system 103 includes modeling module 215. As described herein, modeling module 215 dynamically generates one or more models for processing data obtained from a data store or a user. In some embodiments, modeling module 215 may apply the generated models to the data. In some embodiments, one or more models may be stored in mass storage device 210 or memory 206. In some embodiments, modeling module 215 may be stored in mass storage device 210 or memory 206 as executable software code executed by processor 202. This module or other modules in dynamic system 103 may include components such as hardware and / or software components, object-oriented software components, class and task components, processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. In the embodiment shown in FIG. 6, dynamic system 103 is configured to execute modeling module 215 to perform various methods and / or processes described herein.

[0126] In some embodiments, the reporting module 216 may be configured to generate reports, notifications, or outputs as referenced and described herein. In some embodiments, the reporting module 216 may use information received from the dynamic system 103, the data store of FIG. 5 and / or the computing device 102, or data obtained from the user of the computing device 106 or the computing device 102 to generate a report, notification, or output for a particular physician, nurse, medical administrator, medical researcher, or other healthcare professional. For example, the dynamic system 103 may receive information provided by a physician, nurse, medical administrator, medical researcher, or other healthcare professional via the network 110 that the dynamic system 103 uses to retrieve information from the data store and generate a model for processing the information. In some embodiments, the generated report, notification, or output may include a data file containing patient medical information related to a recommended treatment regimen to be used to treat a particular patient's input infection. In some embodiments, the reporting module 216 may include information received from the user in the generated report, notification, or output. In some embodiments, the reporting module 216 or the processor 202 may generate the notification 122 referenced in FIG.

[0127] The dynamic system 103 also includes a user interface module 214. In some embodiments, the user interface module 214 may be stored on the mass storage device 210 as executable software code executed by the processor 202. In the embodiment shown in Figure 6, the dynamic system 103 may be configured to execute the user interface module 214 to perform various methods and / or processes as described herein.

[0128] The user interface module 214 may be configured to generate and / or operate various types of user interfaces. In some embodiments, the user interface module 214 creates pages, applications, or displays for display in a web browser or computer / mobile application. In some embodiments, the user interface module 214 may provide applications or similar modules for downloading and operating on the computing device 102 and / or computing device 106 through which a user can interface with the dynamic system 103 to obtain desired reports or outputs. In some embodiments, the pages or displays may be specific to a device type, such as a mobile device or desktop web browser, to maximize usability for a particular device. In some embodiments, the user interface module 214 may also interact with customer-side applications, such as a mobile phone application, a standalone desktop application, or a user communication account (e.g., email or SMS messaging), to provide the data necessary to display medication or infectious disease notifications.

[0129] For example, as described herein, the dynamic system 103 may be accessible via a website to certain doctors, nurses, medical administrators, medical researchers, or other medical personnel. In some embodiments, users may choose to receive or not receive any reports or outputs.

[0130] After the dynamic system 103 receives user input (e.g., identified infection, data time frame of interest, geographic region, treatment regimen of interest), the user may view the received information via the I / O interfaces and devices 204 and / or the user interface module 214. After the dynamic system 103 receives information from the data store (e.g., via the I / O interfaces and devices 204 or via the user interface module 214), the processor 202 or the modeling module 215 may store the received input and information in the memory 206 and / or the mass storage device 210. In some embodiments, the information received from the data store may be analyzed and / or manipulated (e.g., filtered or similar processing) by the processor 202 of the dynamic system 103.

[0131] In some embodiments, the processor 202 or the modeling module 215 may generate the dynamic model 120 described above with reference to FIG.

[0132] The various components of dynamic system 103 can be coupled to one another by a bus system 218. Bus system 218 can include a data bus and, for example, a power bus, a control signal bus, and a status signal bus in addition to the data bus. In different embodiments, the bus can be implemented with, for example, Peripheral Component Interconnect ("PCI"), MicroChannel, Small Computer System Interface ("SCSI"), Industry Standard Architecture ("ISA"), and Extended ISA ("EISA") architectures. Additionally, the functionality provided in the components and modules of dynamic system 103 can be combined into fewer components and modules than those shown in FIG. 6 or further separated into additional components and modules. Use Case - Systems and Methods for Using Dynamically Updated Infectious Disease Data

[0133] As briefly described above, system 100 can be used in a variety of environments to perform database updates and dynamically generate custom models. As one non-limiting example, as described above and herein, system 100 can be used to process infectious disease and treatment regimen data. Healthcare professionals involved in patient care (e.g., physicians, pharmacists, nurses, healthcare administrators, and medical researchers) constantly work to maintain the latest best practices. In some cases, this includes maintaining awareness of drug effectiveness against infections and diseases and tracking infections and diseases within a geographic area of ​​interest. Medications are used to treat various causes of infections, diseases, and illnesses, which can develop resistance to these medications. For example, as antibiotics are used to treat many bacterial infections, drug-resistant bacteria are becoming more common worldwide. In some embodiments, such drug resistance may be region-specific. For example, different geographic areas are exposed to different pathogens or prescribe different medications for the treatment of infections and other illnesses. Therefore, different geographic regions may have pathogens with varying levels of drug resistance. Differences in resistance affect the success rate or effectiveness of treatment regimens prescribed to treat infectious diseases. Each medication may have region-, institution-, and patient-specific effectiveness rates for each infection for which it is prescribed. For example, penicillin may have a higher effectiveness rate for some illnesses than for other bacterial infections. Thus, medications may have different effectiveness rates for different illnesses or diseases (e.g., whether the illness or disease is viral, bacterial, fungal, etc.). Similarly, different strains of penicillin may have different effectiveness rates for different penicillin-resistant bacterial infections.

[0134] In some cases, a patient presenting at a medical facility for diagnosis and / or treatment may have been exposed to an infectious disease-causing pathogen in a different geographic region than the medical facility treating the patient, or at a different medical facility within the same geographic region as the current medical facility. For example, a patient may become infected while traveling but begin to show symptoms or become ill only after the patient returns from their trip. As another example, a patient may travel from their home area where they were initially infected to a medical facility in a more populated area where they are diagnosed and / or treated. Embodiments of the present disclosure enable medical personnel currently treating such patients (or interested in disease trends associated with such patients) to consider infection, medication, and efficacy rate information for geographic regions other than the geographic region in which the medical facility is located or to which they are generally exposed. In some embodiments, different patients suffering from the same infectious disease may present to different medical facilities for treatment, and therefore no single medical facility will have all data associated with any infection and / or corresponding treatment in a given geographic region. In some embodiments of the present disclosure, a medical facility may store in a patient record details of an infection associated with the patient and the treatment regimen used to treat the infection, along with the corresponding results of the treatment and the relative date of the infection. Thus, a patient's medical record, such as the medical record stored in the first data store 104, may contain details useful for updating medication effectiveness information for a particular illness at the same medical facility and other medical facilities.

[0135] In some embodiments, physicians, pharmacists, nurses, medical administrators, and medical researchers associated with medical facilities may conduct research studies on efficacy rates to maintain quality control and improve situational awareness regarding prescribing trends. However, such efforts can be time-consuming and generally difficult to perform manually, as constraints and system incompatibilities can make accessing patient records from different medical facilities cumbersome, time-consuming, inefficient, and costly. Such constraints and system incompatibilities make it virtually impossible to analyze resistance and antibiotic use trends using real-time data. Furthermore, aggregating sufficient information to accurately update drug efficacy rates for infectious diseases can require a significant amount of data points, time, and resources (e.g., time-consuming data processing and calculations). Furthermore, the results of such efforts may not be reliable with respect to accurately updating drug efficacy rates and / or uniformly aggregating information from multiple medical facilities. Embodiments of the present disclosure address these shortcomings of conventional systems and methods by dynamically responding to customized user input with real-time insights and situational awareness gleaned from large amounts of real-world data sets collected over a time frame based on the user's request.

[0136] In some embodiments, patient records regarding specific patient, illness, and / or medication details may be available, but the information is stored in segregated, limited-access medical records from which some useful information cannot be aggregated and analyzed with other details to generate up-to-date information (e.g., due to regulations and system incompatibilities). Some patient records may not contain information regarding whether the patient fully recovered from an infection based on the prescribed treatment regimen, or may not contain pertinent details regarding the diagnosed infection or the prescribed treatment regimen. Therefore, medical personnel generally interested in such records may not be able to use information from the patient records and / or determine and / or update such efficacy rates. Also, such parties may not be able to identify the optimal treatment regimen for treating an infection based on a particular patient's record, or they may not recognize that they have selected a treatment regimen that is less optimal than an alternative treatment regimen.

[0137] Embodiments of the present disclosure address these shortcomings by using dynamically updating data records and user-specific filters to generate insights based on information aggregated across a geographic region or facility, and that information has been aggregated over a period spanning recent quarters, months, weeks, or days prior to the user's information request. Use Case - Systems and Methods for Using Risk Scores

[0138] Embodiments of the system 100 according to the present disclosure generate risk scores for specific patients and generate heat maps (and similar data displays) and timely insights regarding changes in infections, drug effectiveness rates, geographic distribution of infections, drug effectiveness rates, and other variables.

[0139] The dynamic system 103 uses current patient information or records (hereinafter, current patient records) in combination with the patient records from the first data store 104 and the efficacy rate information from the second data store 108 to generate a risk score. The risk score may indicate the risk of the patient being diagnosed with a particular infectious disease, the risk that the diagnosed infection is caused by a drug-resistant pathogen, the risk that the current patient is resistant to treatment of a particular pathogen with a particular medication, and / or the risk that the diagnosed infection will respond or not respond to a particular medication. For example, the current patient information or records may indicate particular symptoms (and test results, if available). The dynamic system 103 of the present disclosure may determine the likelihood that the current patient has a particular infectious disease based on a comparison of the current patient's symptoms and test results with known symptoms and test results of infectious diseases and infections present in the same geographic area as the current patient. The dynamic system 103 may also determine a risk score representing the likelihood that the current patient's infection is resistant to a particular medication or class of medications. For example, the dynamic system 103 may use the dynamic model to determine a risk score that a current patient is infected with a drug-resistant strain of an infection. A higher risk score may indicate a higher likelihood that the current patient is infected with a drug-resistant strain. In some embodiments, a higher risk score may additionally or alternatively indicate a higher likelihood that a particular treatment regimen will be successful in treating the infection based on records of similar successful treatment regimens in or near the same geographic area of ​​interest.

[0140] In some embodiments, system 100 identifies risk scores for individual patients, particular demographics of patients, particular geographic regions, and other populations of patients. In some embodiments, system 100 may incorporate the patient's (or corresponding patient population's) past diagnoses, details of the medical facility or facilities associated with those diagnoses, etc. System 100 may determine a risk score representing the likelihood that the current patient will be resistant to treatment with a particular drug based on an analysis of the current patient's past diagnoses, responses to medications, and the current patient's current symptoms and diagnoses.

[0141] In some embodiments, system 100 may use a scoring system or determined risk score to identify the risk of failure of a treatment regimen or drug therapy. For example, if a medication prescribed to treat an identified infection has a high risk score for drug resistance (meaning the infection is likely resistant to treatment with the prescribed medication), system 100 may identify a higher risk of failure. For example, the scoring system may aggregate the risk score from the infection being resistant to treatment and the risk score that the current patient is resistant to treatment or responding poorly to a particular medication with the risk that the current patient is infected with a drug-resistant pathogen. Embodiments of the present disclosure can generate and communicate these risk scores to medical personnel at key points in patient care, i.e., when medical personnel are first evaluating and weighing treatment regimens based on the patient's presenting symptoms, potentially even before a specific infection is diagnosed.

[0142] Embodiments of the present disclosure also provide useful insights and situational awareness that can lead to improved appropriate use of diagnostic testing resources. Some infectious diseases are difficult, time-consuming, and / or expensive to test for. Furthermore, drug resistance testing may not cover all relevant, available, or appropriate medications, and therefore some drug-resistant infections may not be addressed by a general test panel. Furthermore, even if some degree of drug resistance is known or expected for a particular infection, various dosages of a medication may not be able to be tested for drug-resistant infections. For example, even if a particular strain of MRSA is known to be resistant to a particular dosage level of a particular medication, a user may not know other dosages of that medication that are effective against that strain of MRSA, or dosages that are ineffective or inappropriate for treating that strain of MRSA. Based on all of the records in the first data store 104 and the second data store 108, the dynamic system 103 and / or system 100 of the present disclosure can determine and recommend a specific medication and / or dosage to prescribe based on the patient information in the first data store 104 and the efficacy information in the second data store 108, without the need for a medical professional to order a diagnostic test to determine the specific pathogen causing the patient's infection. In this manner, embodiments of the present disclosure improve the appropriate use of diagnostic testing resources, which are often scarce and expensive.

[0143] Embodiments of the dynamic system 103 and system 100 of the present disclosure can identify when a physician is prescribing an inappropriate or inappropriate treatment regimen to treat a current patient's infection. An inappropriate treatment regimen may include an inappropriate, suboptimal, or ineffective medication (e.g., a medication to which the infection is known to be resistant), an insufficient dosage to overcome the infection's drug resistance, etc. Thus, the dynamic system 103 can identify how successful an individual physician is in prescribing appropriate medications for the infections they are diagnosing, how effective a particular medical facility is in prescribing appropriate medications for the infections they are diagnosing and / or treating, and the amount of new / existing medications prescribed for treatment. In some embodiments, the dynamic system 103 or system 100 dynamically compares values ​​across physicians at a medical facility, across physicians in a geographic region, across physicians in a particular specialty, or any other suitable population as additional patient records are added or updated in the first data store 104 and / or as efficacy information is added or updated in the second data store 108. In some embodiments, dynamic system 103 or system 100 generates reports for individual physicians to indicate how often they inappropriately prescribe medications and to identify instances where the physician can adapt or change their routine prescribing strategy to improve the selection of appropriate treatment regimens earlier in the patient's care (e.g., sending prompts to the physician informing them of inappropriate selection of treatment regimens, sending recommendations to the physician for more optimal treatment regimens, sending recommendations to a pharmacist or testing clinician to intervene earlier in the physician's selected treatment regimen by ordering diagnostic testing of patient samples). Other embodiments

[0144] The foregoing description details specific embodiments of the systems, devices, and methods described herein. However, it should be understood that no matter how detailed the above text may appear, the systems, devices, and methods can be implemented in many ways. Also, as noted above, the use of a particular term when describing a particular feature or aspect of the invention should not be construed to imply that the term is redefined herein to be limited to include any particular characteristics of the feature or aspect of the technology with which it is associated.

[0145] While the foregoing detailed description has shown, described, and pointed out novel features of the present development as applied to various embodiments, it will be understood by those skilled in the art that various omissions, substitutions, and changes may be made in the form and details of the illustrated devices or processes without departing from the spirit of the present development. It should be understood that some features may be used or practiced separately from other features, and therefore the present development may be embodied in a form that does not provide all of the features and advantages described herein. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.

[0146] In general, as used herein, the term "module" refers to a collection of software instructions, or logic embodied in hardware or firmware, possibly having entry and exit points and written in any programming language described herein. Software modules may be compiled and linked into an executable program, installed in a dynamic link library, or written in any interpreted programming language. It should be understood that software modules may be callable by other modules or by themselves, and / or may be invoked in response to detected events or interrupts. Software modules configured to run on a computing device may be provided on a computer-readable medium, such as a compact disc, digital video disc, flash drive, or any other tangible medium. Such software code may be stored in part or in whole in a memory device of an executing computing device, such as dynamic system 103, for execution by the computing device. Software instructions may be embedded in firmware, such as an EPROM. It should also be understood that hardware modules may consist of connected logic units, such as gates and flip-flops, and / or may consist of programmable units, such as a programmable gate array or a processor. The modules described herein are preferably implemented as software modules. These may be represented in hardware or firmware. Generally, a module as described herein refers to a logical module that may be combined with other modules or divided into sub-modules, regardless of its physical organization or storage. A module may be stored on any type of non-transitory computer-readable medium or computer storage device, such as a hard drive, solid-state memory, and / or optical disk.The systems and modules may be transmitted as a generated data signal (e.g., as part of a carrier wave or other analog or digital propagated signal) over a variety of computer-readable transmission media, including wireless and wired / cabled media, and may take various forms (e.g., as part of a single or multiplexed analog signal, or as multiple individual digital packets or frames). The processes and algorithms may be implemented in part or in whole in application-specific circuitry. The results of the processes and process steps of the present disclosure may be stored, persistently or otherwise, in any type of non-transitory computer storage, such as, for example, volatile or non-volatile storage. Those skilled in the art may implement the described functionality in various ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

[0147] A model generally refers to a machine learning construct that can be used to automatically generate a result or outcome. A model may be trained. Training a model generally refers to an automated machine learning process for generating a model that accepts inputs and provides a result or outcome as output. A model may be represented as a data structure that identifies one or more correlation values ​​for a given value. For example, the data structure may include data indicating one or more categories. In such implementations, the model may be indexed to enable efficient lookup and retrieval of categorical values. In other embodiments, a model may be created based on statistical or mathematical properties and / or definitions implemented in executable code without necessarily employing machine learning.

[0148] Machine learning generally refers to an automated process in which received data is analyzed to generate and / or update one or more models. Machine learning may include artificial intelligence such as neural networks, genetic algorithms, and clustering. Machine learning may be performed using a training set of data. The training data can be used to generate a model that best characterizes the feature of interest using the training data. In some implementations, the class of the feature may be identified prior to training. In such cases, the model can be trained to provide an output that most resembles the desired class of feature. In some implementations, no prior knowledge may be available for training data. In such cases, the model may discover new relationships for the provided training data. Such relationships may include similarities between proteins, such as protein function.

[0149] The microprocessor may be any conventional general-purpose single- or multi-chip microprocessor, such as a Pentium® processor, a Pentium® Pro processor, an 8051 processor, a MIPS® processor, a Power PC® processor, or an Alpha® processor. The microprocessor may also be any conventional special-purpose microprocessor, such as a digital signal processor or a graphics processor. Microprocessors typically have conventional address lines, conventional data lines, and one or more conventional control lines.

[0150] The system can be used with a variety of operating systems, such as Linux®, UNIX®, MacOS® or Microsoft Windows®.

[0151] The system control can be written in any conventional programming language, such as C, C++, BASIC, Pascal, .NET (e.g., C#), or Java, and can run under conventional operating systems. C, C++, BASIC, Pascal, Java, and FORTRAN are industry-standard programming languages ​​for which many commercially available compilers are available to create executable code. The system control can also be written using an interpreted language, such as Perl, Python, or Ruby. Other languages, such as PHP and JavaScript, can also be used.

[0152] With respect to the use of substantially any plural and / or singular term herein, those skilled in the art can convert from plural to singular and / or from singular to plural as appropriate depending on the context and / or application. For clarity, various singular / plural permutations may be specified herein.

Claims

1. 1. A system for selecting a treatment regimen for a particular patient, comprising: a first data store containing a first plurality of patient records for a first plurality of patients; a second data store comprising efficacy rates of a plurality of treatment regimens for a plurality of microbial infections; a hardware processor; The hardware processor includes: receiving from a user an indication of a first microbial infection in the particular patient and a first treatment regimen prescribed to treat the first microbial infection; generating a first database including a second plurality of patient records by identifying patient records in the first plurality of patient records in the first data store that are associated with a diagnosis or treatment of the first microbial infection; generating a second database from the second data store including the second plurality of patient records populated with an efficacy rate for the first microbial infection, the efficacy rate indicating a resistance rate to a plurality of treatment regimens for the first microbial infection; configured to execute computer-executable instructions to generate a dynamic model configured to determine a likelihood estimate that the first treatment regimen is an appropriate treatment regimen for treating the first microbial infection; The dynamic model is identifying a first efficacy rate of the first treatment regimen for treating the first microbial infection based on the second database; identifying from the second database a second efficacy rate of a second therapeutic regimen for treating the first microbial infection; generating a first alert when the determined first effectiveness rate is less than a first threshold level or the determined second effectiveness rate is greater than a second threshold level; transmitting the first alert to a computing device over a wireless communication network, the first alert being further configured to launch an application on the computing device to display the first alert; additional patient records are stored consecutively in the first data store; continuously updating efficacy rates of the plurality of treatment regimens for the plurality of microbial infections in the second data store; The hardware processor includes: updating the first database and the second database based on a patient record among the additional patient records associated with the first microbial infection and an efficacy rate for the first microbial infection; The system is further configured to dynamically and automatically execute computer-executable instructions to update the first effectiveness rate and the second effectiveness rate.

2. 2. The system of claim 1, wherein the computer-executable instructions are further configured to receive an indication from a user of a medical facility, and wherein the second plurality of patient records identified by the computer-executable instructions are patient records associated with the first microbial infection and the indicated medical facility.

3. 3. The system of claim 2, wherein the computer-executable instructions are further configured to receive an indication of a geographic region from a user, and the second plurality of patient records identified by the computer-executable instructions are patient records associated with the first microbial infection and the indicated geographic region.

4. 4. The system of claim 3, wherein the computer-executable instructions are further configured to receive an indication from a user of a time period, and the second plurality of patient records identified by the computer-executable instructions are patient records associated with a diagnosis or treatment of the first microbial infection within the indicated time period.

5. 5. The system of claim 4, wherein the computer-executable instructions are configured to filter the second plurality of patient records with attached efficacy rates to include patient records associated with at least one of a particular treatment regimen of a plurality of treatment regimens, an infection related to the first microbial infection, a geographic region, and a medical facility.

6. 6. The system of claim 5, wherein the computer-executable instructions are further configured to generate a second alert to the user if, after updating the first effectiveness rate and the second effectiveness rate, the first effectiveness rate falls below the first threshold level or the second effectiveness rate exceeds the second threshold level.

7. 1. A computer-implemented method for selecting a treatment regimen for a particular patient using a first data store containing a first plurality of patient records for a first plurality of patients and a second data store containing efficacy rates of a plurality of treatment regimens for a plurality of microbial infections, the method comprising: Under the control of one or more processors, receiving an indication of a first microbial infection in the particular patient and a first treatment regimen prescribed to treat the first microbial infection; generating a first database including a second plurality of patient records by identifying patient records in the first plurality of patient records in a first data store that are associated with a diagnosis or treatment of the first microbial infection; generating a second database including the second plurality of patient records populated with efficacy rates for the first microbial infection from the second data store, the efficacy rates indicating resistance rates to a plurality of treatment regimens for the first microbial infection; a dynamic model configured to determine a likelihood estimate that the first treatment regimen is an appropriate treatment regimen for treating the first microbial infection, identifying a first efficacy rate of the first treatment regimen for treating the first microbial infection based on the second database; and further configured to identify from the second database a second efficacy rate of a second treatment regimen for treating the first microbial infection. generating the dynamic model; generating a first alert when the determined first effectiveness rate is less than a first threshold level or the determined second effectiveness rate is greater than a second threshold level; transmitting the first alert to a computing device over a wireless communication network, the first alert invoking an application on the computing device to display the first alert; updating the first database and the second database based on patient records from the additional patient records associated with the first microbial infection, wherein the first data store is continually populated with additional patient records and efficacy rates of a plurality of treatment regimens for a plurality of microbial infections in the second data store are continually updated; updating the first effectiveness rate and the second effectiveness rate.

8. 8. The computer-implemented method of claim 7, further comprising receiving an indication of a medical facility, wherein the second plurality of patient records are patient records associated with the first microbial infection and the indicated medical facility.

9. 10. The computer-implemented method of claim 8, further comprising receiving an indication of a geographic region, wherein the second plurality of patient records are patient records associated with the first microbial infection and the indicated geographic region.

10. 10. The computer-implemented method of claim 9, further comprising receiving an indication of a time period, wherein the second plurality of patient records are patient records associated with a diagnosis or treatment of the first microbial infection within the indicated time period.

11. 11. The computer-implemented method of claim 10, further comprising filtering the second plurality of patient records with attached efficacy rates to include patient records associated with at least one of a particular treatment regimen of a plurality of treatment regimens, an infection related to the first microbial infection, a geographic region, and a medical facility.

12. 12. The computer-implemented method of claim 11, further comprising generating a second alert if, after updating the first effectiveness rate and the second effectiveness rate, the first effectiveness rate falls below the first threshold level or the second effectiveness rate exceeds the second threshold level.

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