System for visualizing and assisting diagnosis decisions according to the context related to infectious diseases

Abstract

A method is provided for visualizing and assisting in making a diagnosis decision according to the context regarding an infectious disease. This method includes receiving information about a sample cartridge from a diagnostic device, the information including location data and risk factors, and the sample cartridge including a plurality of test assays for diagnosing a plurality of infectious diseases. This method further includes selecting a test assay for reporting a diagnosis result, instructing the diagnostic device to execute the test assay from the sample cartridge, receiving a first dataset from the diagnostic device when the test assay is completed, and evaluating the diagnosis result based on the first dataset. A system and a non-transitory computer-readable medium storing instructions for causing the system to implement the above method are also provided.

Description

Technical Field

[0001] Cross - reference to Related Applications

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 354,587, filed on June 22, 2022, the entire disclosure of which is hereby incorporated by reference herein.

[0002] Technical Field

[0002] The present disclosure is directed to networked systems for clinical diagnosis, monitoring, data analysis, and reporting to healthcare institutions. The systems described herein generate a database containing current clinical diagnostic data, analyze previous clinical diagnostic results, report timely to relevant organizations and institutions, and automate the process of using diagnostic results and other location - based information in the context of new diagnoses for infectious diseases.

Background Art

[0003] Background

[0003] As the number of clinical diagnostic tests and the number of patients undergoing such tests increase, the importance and challenges of collecting and storing the resulting data are growing. Technical challenges include storing data for current and future use, ensuring accessibility and management by relevant parties, and ensuring patient privacy and data security. In addition to diagnostic device networks, the current development of Internet resources provides a large amount of geolocation data that may be important for disease diagnosis. In addition to data collection, aggregation, analysis, and access authentication, there is an untapped potential to use previous diagnostic data and other geolocation information in the context of infectious disease diagnosis when diagnostic device networks perform new tests in various locations.

Summary of the Invention

Problems to be Solved by the Invention

[0004]

[0004] Therefore, in order to provide context for the new diagnosis of infectious diseases across various geographical areas, it is desirable to collect, maintain, transmit, analyze, and use clinically important data in a secure manner while protecting privacy. Preferably, these tasks involve minimal human intervention.

Means for Solving the Problem

[0005] Brief Summary

[0005] In a first embodiment, a computer-implemented method includes receiving, from a diagnostic device, information regarding a sample cartridge to be used for a first subject, the information including location data and risk factors, the sample cartridge including a plurality of assay assays for diagnosing a plurality of infectious diseases. The computer-implemented method includes selecting, based on the location data and risk factors, an assay assay for reporting a diagnostic result; instructing the diagnostic device to perform the assay assay from the sample cartridge; receiving, from the diagnostic device, a first data set when the assay assay is completed; and evaluating the diagnostic result based on the first data set.

[0006]

[0006] In a second embodiment, a computer-implemented method includes providing, to a remote server, information regarding a sample cartridge to be used for a diagnostic test for a first subject, the information including location data and risk factors, the sample cartridge including a plurality of assay assays for diagnosing a plurality of infectious diseases. The computer-implemented method further includes receiving, from the remote server, a first assay assay selected for reporting a diagnostic result based on the location data and risk factors; causing the diagnostic device to perform the first assay assay from the sample cartridge; and transmitting a first data set to the remote server when the first assay assay is completed.

[0007]

[0007] In the third embodiment, the device includes a memory that stores a plurality of instructions, a communication module configured to communicate with a remote server, and a processor configured to execute instructions to cause the device to perform an operation. The operation includes receiving, from the remote server, an instruction for an assay for diagnosing an infectious disease to be selected for a subject, the instruction being based on at least one of geolocation data indicating the location of the diagnostic device and a previous diagnosis obtained with one or more diagnostic devices communicatively coupled to the remote server. The operation includes associating the assay with a plurality of values to generate a dataset diagnosis, the dataset diagnosis being stored in the memory of the diagnostic device, and the plurality of values being related to one or more of an assay identifier, an assay result, a patient identifier, and a diagnostic device identifier. The operation further includes, in one embodiment, transmitting and storing the dataset diagnosis to the remote server, the remote server generating a report based on the dataset diagnosis from each of the one or more diagnostic devices, and the report being configured to be transmitted to a database accommodated in a database display on a second server or an end-user workstation.

Brief Description of the Drawings

[0008] Brief Description of the Drawings

Figure 1

[0008] FIG. is an exemplary architecture of a system for visualizing and assisting in diagnostic decisions according to the context of an infectious disease according to some embodiments.

Figure 2

[0009] FIG. is a block diagram showing a client device and a server in the architecture of FIG. 1 according to some embodiments.

Figure 3

[0010] FIG. is a diagram showing feedback based on geolocation information associated with an infectious disease in a network according to some embodiments.

Figure 4A

[0011] A diagram showing a screenshot from a web page hosted by a server in a system for visualizing and assisting diagnostic decisions according to the context of an infectious disease, according to some embodiments.

Figure 4B

[0011] A diagram showing a screenshot from a web page hosted by a server in a system for visualizing and assisting diagnostic decisions according to the context of an infectious disease, according to some embodiments.

Figure 4C

[0011] A diagram showing a screenshot from a web page hosted by a server in a system for visualizing and assisting diagnostic decisions according to the context of an infectious disease, according to some embodiments.

Figure 4D

[0011] A diagram showing a screenshot from a web page hosted by a server in a system for visualizing and assisting diagnostic decisions according to the context of an infectious disease, according to some embodiments.

Figure 5

[0012] A bar graph showing the types of tests performed in a network of diagnostic devices, according to some embodiments.

Figure 6

[0013] A diagram showing a table containing data regarding multiple diagnostic tests, patients, and locations in a network of diagnostic devices, according to some embodiments.

Figure 7

[0014] A diagram showing a map containing a sequence indicating the spread of an infectious disease across various parts of a large geographical area over a selected time span, according to some embodiments.

Figure 8A

[0015] A diagram showing a map obtained from a web page hosted by a server in a system for visualizing and assisting diagnostic decisions according to the context of an infectious disease, according to some embodiments.

Figure 8B

Figure 9A

[0016] FIG. is a screenshot of an application installed on a diagnostic device, including a message to the user, according to some embodiments.

Figure 9B

[0016] FIG. is a screenshot of an application installed on a diagnostic device, including a message to the user, according to some embodiments.

Figure 9C

[0016] FIG. is a screenshot of an application installed on a diagnostic device, including a message to the user, according to some embodiments.

Figure 10

[0017] FIG. is a flowchart showing steps in a method for visualizing and assisting in making a diagnostic decision according to a context related to an infectious disease according to some embodiments.

Figure 11

[0018] FIG. is a flowchart showing steps in a method for performing a diagnostic test related to an infectious disease according to some embodiments.

Figure 12

[0019] FIG. is a flowchart showing steps in a method for collecting data for input into an insight engine and a decision support tool according to some embodiments.

Figure 13

[0020] FIG. is a block diagram showing an exemplary computer system capable of implementing the client and network devices of FIG. 1 and the methods of FIGS. 10 - 12.

DETAILED DESCRIPTION OF THE INVENTION

[0009]

[0021] In the figures, elements and steps with the same or similar reference numerals are associated with the same or similar features or procedures, unless explicitly stated otherwise.

[0010] Detailed Description

[0022] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to one of ordinary skill in the art that embodiments of the present disclosure may be practiced without some of these specific details. For the sake of not obscuring the disclosure, well-known structures and techniques are not shown in detail.

[0011]

[0023] Infectious diseases involve rapidly changing local scenarios that clinicians need to track, interpret, and adjust medical practices accordingly. In current practice, clinicians' training and experience, as well as up-to-date knowledge outside of clinical settings, are utilized to identify possible trends and scenarios. This approach may not work well when the infectious disease is (1) spreading rapidly, (2) locally occurring and different from regional trends, (3) affecting a transient group or a subpopulation such as the elderly who do not receive adequate medical services or are underrepresented, and / or (4) difficult to differentiate symptoms because the clinical symptoms overlap or are similar to other diseases (such as COVID or influenza).

[0012]

[0024] Typically, clinicians are tasked with evaluating a patient's symptoms, suggesting appropriate diagnostic inputs, capturing and incorporating them, and making appropriate clinical recommendations in a form that requires a large number of inputs beyond those generated by the patient. This increases the responsibility of the clinician and / or their organization and raises the risk of serious consequences due to human error.

[0013]

[0025] As an example, one of the relatively difficult scenarios that clinicians face is the diagnosis of an undiagnosed patient presenting with symptoms that typically reflect a systemic immune response, often known as "influenza-like illness" (ILI). Symptoms of ILI can include fever above about 100°F (about 38°C), cough or sore throat, and symptoms such as fatigue, body aches, headache, loss of appetite, and nausea. These characteristic symptoms appear similarly in many underlying pathologies, including influenza, COVID-19, RSV, streptococcus, and other viral or bacterial pathogens. However, influenza accounts for only 35 - 45% of ILI cases, even during peak seasons, and many other viral infections can also present as "influenza-like". ILI is of particular concern, especially among the elderly living in nursing homes, as a cause of infectious disease outbreaks and a frequent cause of hospitalization. In some situations, West Nile virus and other retroviral infections can start as febrile ILI. SARS, MERS, and other fungal infections can also cause or start with ILI. Additionally, ILI is associated with severe infectious diseases in mammals such as pigs, horses, cows, and livestock in general.

[0014]

[0026] Therefore, clinicians may be required to draw on their experience to identify discriminative features, compare with local infectious disease situations, recommend further tests or diagnoses, diagnose patients, and propose treatment pathways.

[0015]

[0027] In some cases, without knowledge of the spread of infectious diseases in the neighboring area, tests may be inappropriately prescribed for diagnosis. Many diagnostic tests have predictive power that results in a significant number of false positives or false negatives when the test is not appropriately applied at the population level. For example, consider a diagnostic test with a sensitivity and specificity of 90% in a population of 2000 people. The positive and negative predictive values of the test change as the prevalence of the disease changes.

[0016]

[0028] (A) When the disease prevalence is 0%, the test generates 10 false positives per 100 patients tested.

[0017]

[0029] (B) When the disease prevalence is 100%, the test will miss 10 infected patients per 100 patients with the disease.

[0018]

[0030] Clinicians who are unaware of the local infectious disease trends may inaccurately diagnose patients as positive or negative based on incorrect or faulty test results, leading to inadequate or inappropriate treatment. Furthermore, without discriminative features, multiple tests may be required, as a result, patients may be waiting for days before receiving appropriate intervention. This may cause patients to fall out of the window of efficacy for certain antiviral drug options and limit treatment options.

[0019]

[0031] The current development of network technology has made it possible to distribute a large number of medical and non-medical devices and sensors over a wide geographical area and provide a fairly continuous stream of large amounts of data. Furthermore, the wide availability of geolocation information across multiple networks such as social networks, service networks, and publishing media networks enables the rapid identification, mapping, and prediction of infectious diseases or health conditions spreading across one or more geographical areas.

[0020]

[0032] In the field of disease diagnosis, one of the important challenges is to reduce the number of false positive or false negative test results. False positives can impose an excessive burden, both financially and physiologically, on patients who do not require treatment, and can also significantly distort the population health statistics of the region. False negatives can lead to adverse effects on patients, including additional costs due to delayed treatment, and associated liability issues for healthcare providers (such as staff and medical institutions). In addition, many diagnostic devices include cartridges configured to provide tests for multiple analytes in the same workflow. Therefore, when a subject visits a clinic or testing site, it can be difficult to evaluate which tests are more likely to yield useful results, unless there is a specific reason to select a particular test. Thus, in the absence of better decision-making factors, clinicians may decide to perform multiple tests, which increases the likelihood of false positives and generates additional costs for healthcare providers or payers. In another scenario, the symptoms of two different diseases may be very similar, and clinicians may decide to perform two assay tests with great care, which also generates additional costs.

[0021]

[0033] To address the above technical problems in the field of networked diagnostic devices and medical diagnosis, a system is provided that collects and analyzes real-time population data, environmental data, disease prevalence data, and epidemiological data to generate hyper-local pre- and post-test probabilities. As an additional benefit, non-linear regression and artificial intelligence algorithms are provided that use the collected data to create context-specific insights and guidance regarding diagnostic devices.

[0022]

[0034] In some embodiments, the system can direct healthcare providers to prescribe appropriate diagnostic tests, select optimal treatments or treatment pathways, and / or manage aspects of operations and cost management. The end users of these insights can be clinicians, administrators, or biomedical engineers. Also, rather than the results being shared directly with the user, the insights may be provided in some form to employees of the medical device vendor, who can use them to guide user support and training.

[0023]

[0035] Some embodiments may include a foundational data platform and related applications or solutions that are fed by a unique dataset as described above. The data platform acquires geographically specific test results from its own diagnostic devices and a network of third - party sources, incorporates additional non - medical data sources, and normalizes the resulting up - to - date derived insights for use by downstream applications. Downstream applications installed on client devices communicatively coupled to the servers and databases within the data platform use this data to create patient - specific context - appropriate insights and guidance across clinical and operational use cases.

[0024]

[0036] Insights context - specific to diagnosis are delivered via multiple channels such as the diagnostic device itself in use, point - of - care or laboratory equipment, or on individual digital interfaces via electronic health records (EHRs), laboratory information systems (LISs), user portals, mobile applications, or may be delivered via push notifications by email, messaging, or text messages.

[0025]

[0037] Some of the advantages provided by the embodiments disclosed herein include faster and more timely diagnostic evaluations. Clinicians often do not have enough time to examine patients and make appropriate diagnoses. Clinicians may also be required to secure time to keep up with the latest information on the spread and trends of local diseases. Therefore, the embodiments disclosed herein enable clinicians to perform all of these tasks in a timely manner. Also, supplementing the clinician's decision-making and providing context to that decision compensates for the shortage of skilled experts. Staff turnover in the clinical setting and the lack of desired skill levels can result in insufficient real-time diagnostic capabilities for patients, and even lead to serious consequences in the event of an error.

[0026]

[0038] Additional advantages provided by the embodiments disclosed herein include cost reduction in the treatment of infectious diseases. Therefore, the embodiments disclosed herein eliminate unnecessary diagnostic tests or diagnostic times, thereby reducing the costs of clinical operations and payers.

[0027]

[0039] Data collected using the network architecture disclosed herein may be useful in designing effective population screening strategies for infectious diseases. For example, areas with low disease prevalence that produce relatively high levels of false positives can be identified, screening in those areas can be reduced, and inappropriate resource waste can be avoided. Currently, to keep up with the latest information on regional trends, clinicians rely on clinical associations, federal or state public health organizations, newsletters, social media, or peer-to-peer conversations. In the embodiments disclosed herein, by appropriately injecting information in a timely manner in the patient treatment pathway, the knowledge of clinical experts can be updated on the latest events and developments. Employees of healthcare providers can seamlessly stay up-to-date, update protocols, or send staff an email when disease trends are rising and / or falling. Accordingly, the embodiments disclosed herein enable rapid response to changes and quantification of trends, as well as the ability to turn information into action at the point of care.

[0028]

[0040] FIG. 1 shows an exemplary architecture 10 of a system for visualizing and assisting in making diagnostic decisions according to the context related to an infectious disease according to some embodiments. Architecture 10 includes servers 130A, 130B, and 130C (hereinafter collectively referred to as "server 130" in this specification), databases 152A, 152B, and 152C (hereinafter collectively referred to as "database 152" in this specification), client devices 110A-1 and 110A-2 ("client device 110A"), 110-B-1 and 110B-2 ("client device 110B"), and 110C-1 and 110C-2 ("client device 110C"). Hereinafter in this specification, client devices 110A, 110B, and 110C are collectively referred to as "client device 110". Server 130, database 152, and client device 110 are communicatively coupled via a clinical network 150A, a social network 150B, and a media network 150C (hereinafter collectively referred to as "network 150" in this specification).

[0029]

[0041] Clinical network 150A may include server 130A, database 152A, diagnostic device 110A-1, and other computers and desktop devices 110A-2. The clinical network may group clinical facilities, hospitals, examination sites, medical providers, and medical staff. In some embodiments, server 130A and database 152A may be hosted by a government agency that collects, updates, and reports data, progress, and outlooks on infectious diseases. Social network 150B includes any type of social networking service where users of mobile devices such as tablet 110B-1 and mobile phone 110B-2 communicate with each other and exchange messages (such as health-related comments and symptoms) hosted by server 130B and stored in database 152B. The information processed by server 130B and stored in database 152B is publicly available and can be searched to evaluate the status of infectious diseases within a given geographic area and collected by any one of servers 130. Media network 150C may include server 130C and database 152C that support and host browsing applications on mobile devices 110C-1, laptops 110C-2, etc. Thus, media network 150C may include general network traffic such as web searches, mobile location information, and purchase information. In some embodiments, media network 150C may also include weather channel news, and thus server 130C may handle data for predicting weather conditions in the target geographic area, which is important in the context of the progress of infectious diseases in that geographic area.

[0030]

[0042] One of the many servers 130 and client devices 110 may include memory storing instructions that, when executed by a processor, cause the servers 130 and client devices 110 to perform at least some of the steps in the methods disclosed herein. In some embodiments, the architecture 10 is configured to track diagnostic test results inherited by the client device 110. When receiving diagnostic test data from the client device 110, one or more servers 130 may analyze the data to reach a diagnostic result, which is stored in the database 152 along with the raw data collected from the client device 110. The client device 110A in the clinical network 150A may include a personal home diagnostic kit that a user (e.g., a patient or the general public) can purchase at a pharmacy or clinic or order online. For example, in some embodiments, the user can order a test cartridge and disposable components for sample collection, use a personal mobile device to collect test results (e.g., photos or videos) from the cartridge, and upload them to the database 152 for analysis. Additionally, the client device 110A in the clinical network 150A may include diagnostic equipment handled by qualified medical personnel at a clinic.

[0031]

[0043] Therefore, database 152 may include a data platform that runs on client device 110 and enhances downstream applications hosted by server 130. The data stored in database 152A may include diagnostic data generated from a unique source on client device 110A (e.g., a diagnostic device having its own software application). The diagnostic data may include test results (positive / negative), patient demographics (such as age, gender, and zip code), and related metadata. The data stored in database 152C may include third-party data such as public health data, web-based symptom checkers, academic databases, social media, payers, providers, or sources of device manufacturers. Other data stored in and retrieved from database 152C in media network 150C may include data such as disease trends, electronically reported patient symptoms, outcome measurements, digital metrics (e.g., web traffic or search volume related to selected keywords and phrases), and device information or outputs (e.g., readings from a thermometer coupled to the network, number of allergens and pollen, weather forecasts, etc.). The patient information in architecture 10 and database 152 is safe and not personally identifiable information (PII) as it does not include direct personal information from consumers (e.g., address, phone number, social security number, etc.).

[0032]

[0044] Server 130 may include any device having an appropriate processor, memory, and communication function for hosting a history log, a diagnostic database, and a healthcare provider host. The healthcare provider host may be accessible by a plurality of client devices 110 via network 150. In some embodiments, server 130 may include a social network host or a network service provider such as a search engine. Client device 110 may include, for example, a diagnostic device, a desktop computer, a mobile computer, a tablet computer (including, for example, an e-book reader), a mobile device (such as a smartphone or PDA), or any other device having an appropriate processor, memory, and communication function for accessing one or more servers 130 via network 150. In some embodiments, client device 110 may include a Bluetooth wireless or Near Field Communication (NFC) transmitter device and application, enabling the client device to communicate directly with another device in its vicinity, such as a device at a Point of Sale (POS) in a retail store. Network 150 may include, for example, any one or more of a Local Area Network (LAN), a Wide Area Network (WAN), and the Internet. Further, network 150 may include any one or more of network topologies, including but not limited to a bus network, a star network, a ring network, a mesh network, a star-bus network, and a tree or hierarchical network.

[0033]

[0045] Figure 2 is a block diagram 20 showing a client device 210 and a server 230 in an architecture for providing context - aware geolocation information in a network architecture (e.g., architecture 10) of a diagnostic device for detecting infectious diseases, according to some embodiments. The client device 210 may include any of the following: a diagnostic device, a mobile device, a computer (e.g., desktop, laptop, palm device), a wearable device worn on a user's body, a virtual reality / augmented reality headset or wearable device, or any combination thereof. For example, in some embodiments, the diagnostic device may autonomously couple to the network 250 or may be paired to a mobile device by a user. The user of the client device 210 may include a physician, a nurse, a laboratory manager, a laboratory technician, a patient, a vendor, and (e.g., at home) general people who handle the diagnostic device. The client device 210 and the server 230 are communicatively coupled to each other via the network 250 by communication modules 218 - 1 and 218 - 2 (hereinafter collectively referred to as "communication module 218" in this specification). The communication module 218 is configured to interface with the network 250 to transmit and receive information such as data, requests, responses, and commands with other devices on the network 250. In some embodiments, the communication module 218 may be, for example, a modem or an Ethernet card. The client device 210 may be coupled to an input device 214 and an output device 216. The input device 214 may include a keyboard, a mouse, a pointer, or even a touch - screen display that a user (e.g., a consumer) can use to interact with the client device. Similarly, the output device 216 may include a display and a speaker, using which a user can obtain results from the client device 210. In some embodiments, the input device 214 includes a sample - transport cartridge for diagnostic tests. The sample - transport cartridge may include one or more assays for examining a plurality of analytes along individual media tracks.The input device 214 may also include a light source configured to illuminate the sample transport cartridge and excite emission (e.g., fluorescence) or absorption from one or more target positions within the media track. The output device 216 may include a camera configured to capture an image or video of the sample transport cartridge and emission or absorption at the target positions. The client device 210 may provide data packets 225 to the server 230. In embodiments where the client device 210 is a mobile device or a desktop, the input device 214 and the output device 216 may include the diagnostic device itself. In some embodiments, the input device 214 and the output device 216 can also directly receive digital test requests from the device via an EHR, LIS, or other clinical information system, or via a direct connection to a clinical user interface. In embodiments where the client device 210 is the diagnostic device, the input device 214 and the output device 216 may be a display for a GUI screen for inputting a sample intake module.

[0034]

[0046] According to some embodiments, data packet 225 may include an image or video of luminescence or absorption at a target position within a sample transport cartridge. Data packet 225 may also include geolocation information associated with the location of client device 210. In some embodiments, data packet 225 includes a query for obtaining an infection data rate at the location of client device 210 from database 252. Data packet 225 may also include probability scores, derived insights, clinical decision-making guidelines / rules, error messages, and troubleshooting at all times before, during, or after the execution of the test. Data packet 225 may also include test results (e.g., positive or negative, or levels of quantified analyte). Data packet 225 can also be kept consistent with existing languages, and the diagnostic device simply obtains an image of the sample or raw diagnostic results and then transmits data packet 225 to the cloud to generate test results with a cloud-based application. When client device 210 is one of a plurality of diagnostic devices within the test network, data packet 225 joins a group of similar data packets 225 transmitted from each diagnostic test device to server 230 for processing by insight engine 234.

[0035]

[0047] Client device 210 and server 230 may each include processors 212-1 and 212-2, and memories 220-1 and 220-2 (hereinafter collectively referred to as "processor 212" and "memory 220" in this specification). Memory 220 may store instructions that, when executed by processor 212, cause server 230 and device 210 to at least partially implement some of the procedures in the methods disclosed herein.

[0036]

[0048] Processor 212 may be configured to perform normalization and standardization of data packet 225, regardless of the type or specific details of client device 210 and the current format of application 222. In some embodiments, data packet 225 may include geolocation information that links the diagnostic results to a specific area, in some cases a postal code or geofenced latitude / longitude.

[0037]

[0049] In some embodiments, server 230 may provide data packet 227 to client device 210. Data packet 227 may include software and update programs for application 222 that are executed on client device 210 and hosted by server 230. In some embodiments, data packet 227 may be provided as an infectious disease warning sent from server 230 to one or more client devices 210 within network 250, in response to a request by application 222, or based on a schedule, and may include an epidemiological report regarding the local area of client device 210. Data packet 227 may also include a probability score, derived insights, clinical decision-making guidelines / rules, insights and recommendations, error messages, and troubleshooting. In some embodiments, clinical insights / recommendations may be generated by insight engine 234, and client device 210 may function only as an agent to receive user examination requests and then access server 230 to consider the proposed test samples in relation to the current risk disease types. Data packets 225 and 227 may include real-time data and predictive data. Predictive data may include algorithm-based predictions regarding future trends and may be used to influence the output. In some embodiments, data packets 225 and 227 may include a registry of known diagnostic tests and their associated performance including sensitivity and specificity.

[0038]

[0050] In some embodiments, server 230 may install and host application 222 on memory 220-1 of client device 210 via application layer interface (API) 215. API 215 may provide operation management functions processed by insight engine 234 and network management engine 236 in memory 220-2 to a user of client device 210 (e.g., a doctor, pharmacist, laboratory manager, administrator, quality control personnel, etc.). API 215 may access specific calls and services for memory 220-2. In some embodiments, client device 210 may initiate a call to API 215 with a specific trigger for updated information such as the spread of a disease, decision guidelines, or customer support.

[0039]

[0051] Application 222 may be a diagnostic assay application configured to perform diagnostic tests on client device 210. Thus, application 222 may be configured to display instructions to a user (e.g., a medical professional at a clinic or a patient at home) on display (e.g., an output device) 216. In that regard, application 222 may display to the user the corrective actions required to continue the diagnostic test. Application 222 may also request input from the user, such as metadata (name, date, location, symptoms, and desired tests), before, during, or after the performance of the diagnostic test.

[0040]

[0052] Application 222 may include a graphical user interface (GUI) coupled to output device 216. Application 222 can be a location identification application that locates point-of-care or laboratory devices (e.g., geographical coordinate systems, postal codes, addresses, buildings, floor numbers, and room numbers, etc.). Application 222 may include communication to the user regarding pre-examination procedures, such as display of information according to the context regarding pre-test probability and recommendations regarding clinical or operational appropriateness of the requested test. Application 222 may also provide post-examination communication to the user. Some post-examination communication may include post-test probability displayed with the test results and operational or clinical guidance, such as the need for reflex testing using molecular diagnostics or higher performance diagnostics. In some embodiments, Application 222 may periodically query Server 230 and / or Database 252 to obtain updated information regarding the local progression of infectious diseases or epidemiological reports in that area. This ensures the user that tests performed on Client Device 210 are used for the diagnosis of infectious diseases spreading in that area.

[0041]

[0053] Data capture engine 232 controls and manages the collection of data (e.g., data packets 225) from multiple client devices 210 via network 250. Insight engine 234 may include clinical decision-making and support tool 240, location identification tool 242, and statistical tool 244.

[0042]

[0054] The clinical decision-making and support tool 240 includes an engine focused on guiding the selection of appropriate tests, interpreting tests, recommending additional tests, recommending treatment pathways, and requesting additional information during the process to support the recommendations. In some embodiments, the clinical decision-making and support tool 240 includes an engine focused on operations and logistics. For example, the clinical decision-making and support tool 240 may determine which tests may be in demand, evaluate inventory levels at an account, evaluate inventory levels across different hospitals within the same network to suggest balancing, identify users who are constantly deviating from best practices, and may alert administrators about such cases.

[0043]

[0055] The clinical decision-making and support tool 240 may prevent the client device 210 from prescribing a particular diagnostic test or may warn the user based on the requested test evaluation, the prevalence of infectious diseases, and the location information provided by the location tool 242. For example, if influenza is not prevalent at the location where the test was requested, the clinical decision-making and support tool 240 may prevent the client device 210 from performing an influenza test. Instead, the clinical decision-making and support tool 240 may recommend a different (e.g., more appropriate or more likely to yield effective results) type of test (e.g., molecular or antigen) based on the prevalence, disease risk, and known test performance for the transmission rate. The administrative user of the client device 210 (e.g., a diagnostic device) may have options regarding how much to limit the workflow based on the disease prevalence. For example, the client device 210 may prevent a laboratory technician from performing a test when there is no prevalence situation. In some embodiments, the clinical decision-making and support tool 240 provides specific guidance regarding the appropriate test for the subject based on the clinical notes and natural language processing and / or the subject's medical history based on the symptoms selected via the form, and provides the proposed diagnostic test in combination with context-specific disease data. In some embodiments, the clinical decision-making and support tool 240 may cause the client device 210 to display a message such as "Based on the provided subject medical history, the prevalence of the disease in the area, and other data, it is expected that this subject has a 76% chance of testing positive for RSV. To continue with the test, please click here for confirmation."

[0044]

[0056] In some embodiments, the client device 210 may issue a warning, which is displayed as a pop-up on the display 216 and can also be disabled. In some embodiments, the user of the client device 210 can indicate the spread of a disease for an inspection in the device log for future analysis (e.g., transferred to the database 252). In some embodiments, when the local prevalence is below a preselected threshold for an infectious disease that may be more prevalent elsewhere, the clinical decision-making and support tool 240 may have a "safety net" such that local-level inspections are maintained (e.g., every fifth inspection request from the client device 210 is approved). More generally, the clinical decision-making and support tool 240 assists the user of the client device 210 (e.g., physicians, nurses, pharmacists, general people) in making clinical decisions. In some embodiments, the clinical decision-making and support tool 240 guides an appropriate clinical workflow for a patient (e.g., the subject of a diagnostic test performed on the client device 210). For example, the clinical decision-making and support tool 240 may evaluate a patient's history (e.g., obtained from the database 252), symptoms, and other inputs. The clinical decision-making and support tool 240 may also score the pre-test probability and propose appropriate diagnostic tests, sampling sites, and other desirable inputs. Further, the clinical decision-making and support tool 240 may provide post-test results, compare them (in combination with the statistical tool 244) to the post-test probability, and recommend additional diagnostic tests. The clinical decision-making and support tool 240 interprets diagnostic results, patient information data, and (in combination with the location information tool 242) location information and recommends an appropriate treatment pathway. In some embodiments, the clinical decision-making and support tool 240 relays inspection results to the database 252. The clinical decision-making and support tool 240 provides assistance to the user of the client device 210 (e.g., for the general consumer, user, or dependent family members) based on the location information obtained by the location tool 242.In some embodiments, the clinical decision-making and support tool 240 pushes notifications of purchase recommendations for tests, including discounts and purchase incentives, by transmitting a message (e.g., an email, chat, etc.) to the client device 210 when the disease trend increases or is predicted to increase beyond a certain threshold. The clinical decision-making and support tool 240 may communicate with the client device 210 in the context of diagnostic text or, more generally, in the context of the occurrence or monitoring of infectious diseases (regardless of whether the user of the client device 210 plans to perform a test). Thus, the clinical decision-making and support tool 240 may transmit a message to the client device 210 for symptom confirmation, interpretation of the local spread of infectious diseases, and proposal of possible test or treatment pathways based on the pre-test probability. In some embodiments, the clinical decision-making and support tool 240 may direct the user to a virtual care flow based on test results and a higher probability of a positive diagnosis. In some embodiments, the clinical decision-making and support tool 240 includes a virtual assistant that interacts with the user in real time. For example, in some embodiments, the clinical decision-making and support tool 240 may guide the user of the client device 210 to a virtual reality room for a one-on-one support session.

[0045]

[0057] The location-specific tool 242 handles location information via a companion mobile application (e.g., application 222) within an administrator portal embedded in an EHR server (e.g., server 230) or a diagnostic device (e.g., client device 210). The clinical decision-making and support tool 240 may cooperate with the location-specific tool 242 to detect that a user of the client device 210 is entering or leaving an area where a particular infectious disease is spreading. Accordingly, the clinical decision-making and support tool 240 may send a message to the user that a diagnostic test is recommended. Further, the clinical decision-making and support tool 240 may schedule a calendar and provide a reminder to the user of the client device 210 to undergo a diagnostic test. In some embodiments, the clinical decision-making and support tool 240 may cooperate with a statistical tool 244 to find a pre-test probability rate. When the pre-test probability rate exceeds a preselected threshold, the clinical decision-making and support tool 240 generates a prescription for a diagnostic test, either synchronously or asynchronously.

[0046]

[0058] The statistical tool 244 performs statistical operations based on historical data (e.g., EHRs within the database 252) and other data collected from network resources (e.g., location data, progression of infectious diseases, etc.). The statistical tool 244 performs mathematical analyses such as the mean, variance, standard deviation, and higher-order moments of a distribution, histograms, and fitting to probability functions. Further, in cooperation with the clinical decision-making and support tool 240, the statistical tool 244 may use the post-test probability to interpret test results, correct for false positives, predict geographical future disease trends, and guide event planning and screening requirements.

[0047]

[0059] In some embodiments, the network management engine 236 controls data input 225 related to web traffic via network 250, and this data input 255 may not be directly associated with diseases, diagnoses, or even technical medical data, but may provide insights and guidance regarding disease diagnosis and treatment. Some of the inputs handled by the network management engine 236 may include population data (e.g., age, gender, socioeconomic information), social and environmental information, third-party clinical information, social networks, and the like. In such cases, the client device 210 may include a mobile phone or other network computer through which the user communicates with network 250. For example, in some embodiments, the user of client device 210 may execute a search query for a drug or pharmacy, or an item to relieve cough symptoms, or may pose questions about specific symptoms or conditions on a social network. All of this information is collected in data packets 225 and may be captured or selected by the network management engine 236.

[0048]

[0060] Figure 3 shows feedback 300 based on geolocation information 342 associated with an infectious disease within a network, according to some embodiments. The application 322 displays user geolocation information 342 on the mobile device based on statistical data 344-1, 344-2, 344-3, 344-4, and 344-5 (collectively referred to herein as "statistical data 344"). The statistical data 344 are details of tests and relative ratios conducted for various demographic segments (e.g., by age).

[0049]

[0061] Application 322 highlights who has a disease, but does not necessarily highlight the probability of a positive or negative test. Application 322 can be a consumer-facing application for population health guidance that can be accessed by a user via a mobile device, desktop, or any other networked computer. Application 322 can provide the user with recommendations on which at-home tests to obtain or apply, and can also provide links to virtual or in-store test providers.

[0050]

[0062] FIGS. 4A - 4D show various screenshots 400A, 400B, 400C, and 400D (hereinafter collectively referred to as "screenshots 400") from a web page 422 hosted by a server within a system for visualizing and assisting in making diagnostic decisions according to the context of an infectious disease, according to some embodiments. The screenshots 400 are visual representations of various products of the Insight Engine, which are pushed to user applications running on client devices (e.g., the Insight Engine 234 that hosts applications 222 and 322, and client device 210). The Insight Engine generates the screenshots 400 by automating visualization and analysis, and then converts them into point-of-care context-sensitive recommendations. The screenshots 400 can represent management tools viewable by biomedical engineers and laboratory managers other than clinicians.

[0051]

[0063] The web page 422 includes a menu 410, and the user can select from various tools such as filters, dates, assays (e.g., test assays available for a selected infectious disease), result types, organizations / facilities, locations, operators, postal codes, serial numbers (e.g., the serial number of a diagnostic device, etc.). The various tabs may include patient tests 425, instrument report data 427, and notifications 429.

[0052]

[0064] Screenshot 400B shows patient test 425 including lists of test assays 426-1 (SARS antigen) and 426-2 (FLU+SARS) (hereinafter collectively referred to as "test assay 426" in this specification).

[0053]

[0065] Screenshot 400C shows equipment report data 427 including a list of facilities 428.

[0054]

[0066] Screenshot 400D shows notification 429 including firmware update information 430 and other elements that can be downloaded by a user to a diagnostic device or service station.

[0055]

[0067] FIG. 5 shows a bar graph 500 showing the number 502 of test types 501 implemented in a network of diagnostic devices according to some embodiments. The test types 501 may include patient diagnosis 505, quality control 507, and calibration tests 509.

[0056]

[0068] FIG. 6 shows a table 600 including data regarding a plurality of diagnostic tests, patients, and locations in a network of diagnostic devices (see networks 150 and 250) according to some embodiments. Table 600 may be selected from a menu 610 on a website (e.g., menu 410 on website 422). Each row of table 600 is associated with a diagnostic test performed at a given location and time with respect to a specified array and a specified result.

[0057]

[0069] Table 600 includes columns 620-1 (execution date), 620-2 (storage date), 620-3 (facility name), 620-4 (country), 620-5 (state), 620-6 (county), 620-7 (organization), 620-8 (result type), 620-9 (assay), 620-10 (result), and 620-11 (patient), which are collectively referred to as "column 620".

[0058]

[0070] Figure 7 shows a map 700 including a sequence 710 that depicts the spread of an infectious disease across various parts within a wide geographical area over a selected time span, according to some embodiments. The sequence 710, when played back, is an animated sequence of frames showing the geographical progression of the infectious disease across multiple hotspots. Each circle 720 within the map 700 is centered on a city, town, or region, and its diameter indicates, at the center, the total number of positive diagnoses of the disease. In some embodiments, when the disease increases in adjacent areas, multiple circles may overlap, providing a further graphical representation of the infection density of the disease.

[0059]

[0071] Figures 8A - 8B are maps 800A and 800B (hereinafter collectively referred to as "map 800") obtained from a web page (e.g., web page 422) hosted by a server within a system to visualize and assist in making diagnostic decisions according to the context regarding an infectious disease, according to some embodiments.

[0060]

[0072] The map 800 includes a key 810 showing various features. The color code indicates the percentage of positive cases within each area. The circles 820 (20 - 100%), 822 (16 - 19%), 824 (11 - 15%), and 826 (6 - 20%) are centered on county seats. The map 800 includes indicators regarding counties and facilities.

[0061]

[0073] Figures 9A - 9C are screenshots 900A, 900B, 900C (hereinafter collectively referred to as "screenshot 900") of an application 922 installed on a diagnostic device, including messages 927A, 927B, 927C (hereinafter collectively referred to as "message 927") to the user. Each message 927 includes a set of options, and the user can accept (951) or invalidate (952A, 952) any of the options. Screenshot 900 or a variant consistent with the present disclosure may also be present in a prescription step (rather than an in - progress test or execution step), in which case, an appropriate prescribing clinician (typically a physician, but also a PA, pharmacist, and CNP, etc.) is guided regarding the appropriate test. Screenshot 900 may appear as a pop - up in a HER application (e.g., application 922), or as part of a hospital follow - up packaging, on an input screen at a nurse's station, or printed in a patient chart as part of a laboratory manager's request check / QC check.

[0062]

[0074] Message 927A may include an initial message (e.g., "Welcome! Based on today's data and subject information, a list of recommended tests (in order of relevance) will be displayed"). The recommended tests (hereinafter collectively referred to as "recommended test 926" in this specification) may be as follows: 1) Coronavirus (positive rate 22%, 926-1), 2) Influenza A (positive rate 18%, 926-2), 3) Respiratory Syncytial Virus (RSV, positive rate 17%, 926-3), and 4) Lyme disease. The user may invalidate all proposals of 952A, and in the case of invalidating 952A, may enter another test 925 to be performed. The recommended test 926 is selected based on the geographical location of the diagnostic device and the trend and likelihood of positive results for any one of the recommended infectious diseases. Further, certain initial data that the user may enter into the diagnostic device at login, such as demographic data (e.g., age, gender, and occupation), may also be important. Other important subject information may include initial tests such as body temperature, descriptions of symptoms, or even a general visual analysis of the subject (such as bloodshot eyes or skin rashes).

[0063]

[0075] Message 927B may include text (not limited but optionally "The test you selected has a 95% chance of being negative. Please select one of the following recommendations.") provided when the threshold confidence level indicates a negative result (or positive result) before the completion of the diagnostic assay. The recommendations (hereinafter collectively referred to as "recommendations 936" in this specification) may include the following: 1) Ignore and perform the remaining tests (936-1), 2) Select an alternative test (936-2), 3) Stop immediately and transmit the current test results (926-3), and 4) Consider the recommended data (936-4).

[0064]

[0076] Message 927C may contain the following text provided after the assay is completed or after any of the options from Message 927B are accepted: "The test you selected has been completed. Please select one of the following recommendations" (hereinafter collectively referred to as "Recommendation 946" in this specification). The recommendations may include any one or more of the following: 1) stopping the test and transmitting the results (946-1), 2) starting a new test (946-2), 3) considering the recommended data (946-3).

[0065]

[0077] Figure 10 is a flowchart showing the steps in a method 1000 for visualizing and assisting in making a diagnostic decision according to a context related to an infectious disease, according to some embodiments. Method 1000 may be at least partially implemented by any one of a plurality of servers in cooperation with one or more client devices and databases communicatively coupled via a network, as disclosed herein (see client devices 110 and 210, servers 130 and 230, databases 152 and 252, and networks 150 and 250). For example, at least some of the steps in method 1000 may be implemented by one component within an architecture (see architectures 10 and 20), the component including a browser for accessing a website for an insight engine that processes logic to evaluate and contextualize the occurrence of an infectious disease and code for an application that executes on a mobile device, and a network management engine (e.g., insight engine 234 and network management engine 236). In some embodiments, the insight engine may include, as disclosed herein, a clinical decision-making and support tool, a location identification tool, and a statistical tool (see clinical decision-making and support tool 240, location identification tool 242, and statistical tool 244). In some embodiments, one or more servers may also include an application layer (e.g., application layer 215) for hosting and handling applications installed on client devices, thereby enabling third-party users to access the disease analysis engine. Accordingly, at least some of the steps in method 1000 may be implemented by a processor that executes commands stored in the memory of one of the server or client devices, or accessible by at least one of the server or client devices (e.g., processor 212 and memory 220). Further, in some embodiments, at least some of the steps in method 1000 may be implemented in a temporally overlapping, substantially simultaneous, or different order than the order shown in method 1000.Furthermore, a method consistent with some embodiments disclosed herein may include at least one of the steps in method 1000, but not all of them.

[0066]

[0078] Step 1002 includes receiving, from a diagnostic device, information regarding a sample cartridge to be used for a first subject, the information including location data and risk factors regarding at least one of a plurality of infectious diseases, the sample cartridge including a plurality of test assays for diagnosing infectious diseases. In some embodiments, step 1002 includes receiving, from a search engine, data associated with a search frequency for selected keywords related to infectious diseases within an area associated with the location data. In some embodiments, step 1002 includes providing, to the diagnostic device, an update regarding an application interface for running the diagnostic device, based on the information regarding the sample cartridge and an identifier of the diagnostic device. In some embodiments, step 1002 includes receiving, via clinical notes and natural language processing, a patient history including symptoms selected via a form filled out by the subject or a clinician.

[0067]

[0079] Step 1004 includes selecting a test assay for reporting a diagnostic result based on the location data and the risk factors. In some embodiments, step 1004 includes providing specific guidance regarding appropriate tests to be performed to the subject or a clinician. In some embodiments, step 1004 includes determining a false positive probability exceeding a preselected threshold for a diagnostic result regarding the test assay. In some embodiments, step 1004 includes preventing the diagnostic device from performing a test assay based on the location data and the subject's symptoms. In some embodiments, step 1004 includes determining the prevalence of an infectious disease associated with the location data and the test assay. In some embodiments, step 1004 includes communicating a request to execute a test assay from a sample cartridge within the diagnostic device to a client device associated with the location data.

[0068]

[0080] Step 1006 includes instructing the diagnostic device to perform an assay from the sample cartridge. It should be understood that the one or more assays in the sample cartridge may be assays in any format capable of determining the presence or absence of an analyte in a sample from a patient or subject. Exemplary and non-limiting assays include immunoassays, including lateral flow immunoassays and enzyme-linked immunosorbent assays, and molecular assays for the detection of genetic material, such as single molecule detection methods using biosensors or detection of nucleic acids (DNA and / or RNA) using amplification techniques such as polymerase chain reaction (PCR) amplification or isothermal amplification.

[0069]

[0081] Step 1008 includes receiving a first dataset from the diagnostic device when the assay is complete.

[0070]

[0082] Step 1010 includes evaluating the diagnostic results based on the first dataset. In some embodiments, step 1010 includes obtaining a second dataset associated with a completed assay on a second subject with a verified diagnostic result from a database and comparing the first dataset with the second dataset. In some embodiments, step 1010 includes providing a virtual assistant for the user based on the diagnostic results. If the initial test result is negative and there are multiple high-risk diseases, step 1010 includes providing the user with a recommendation to test for the next likely analyte.

[0071]

[0083] In some embodiments, step 1010 may include displaying recommendations to the user of the diagnostic device based on the diagnostic results. The recommendations may include optimal diagnostic techniques (e.g., alternative or complementary diagnostics that may be available to the subject based on the initial diagnostic results). In some embodiments, step 1010 may include recommendations for reflex testing for the subject, monitoring of the subject (e.g., further testing at regular schedules), physical examinations, and consideration of the subject's health history. In some embodiments, step 1010 may be triggered based on a low disease probability despite a positive result, or may be triggered when the user performs two diagnoses on the same analyte with a low probability for the subject and a positive result is obtained in both instances. In some embodiments, step 1010 includes displaying a treatment pathway for a first subject when the diagnostic result is positive for an infectious disease.

[0072]

[0084] FIG. 11 is a flowchart showing steps in a method 1100 for performing a diagnostic test for an infectious disease according to some embodiments. The method 1100 can be at least partially implemented by any one of a plurality of servers in cooperation with one or more client devices and databases communicatively coupled via a network (see client devices 110 and 210, servers 130 and 230, databases 152 and 252, and networks 150 and 250). For example, at least some of the steps in the method 1100 may be performed by one component within an architecture (see architectures 10 and 20), the component including a browser for accessing a website for an insight engine that processes logic to evaluate and contextualize the occurrence of an infectious disease and code for an application that executes on a mobile device, and a network management engine (e.g., insight engine 234 and network management engine 236). In some embodiments, the insight engine may include, as disclosed herein, a clinical decision-making tool, a location identification tool, and a consumer support tool (see clinical decision-making and support tool 240, location identification tool 242, and statistical tool 244). In some embodiments, one or more servers may also include an application layer (e.g., application layer 215) for hosting and handling applications installed on client devices, thereby enabling third-party users to access a disease analysis engine. Accordingly, at least some of the steps in the method 1100 may be performed by a processor that executes commands stored in the memory of one of the server or client device, or accessible by at least one of the server or client device (e.g., processor 212 and memory 220). Further, in some embodiments, at least some of the steps in the method 1100 may be performed in a temporally overlapping, substantially simultaneous, or different order than the order shown in method 1000.Furthermore, methods consistent with some embodiments disclosed herein may include, but not necessarily include, at least one of the steps in method 1100.

[0073]

[0085] Step 1102 includes providing information about a sample cartridge to be used for a diagnostic test on a first subject to a remote server, the information including location data and risk factors related to at least one of a plurality of infectious diseases, and the sample cartridge including a plurality of test assays for diagnosing infectious diseases. In some embodiments, step 1102 includes requesting an epidemiological report related to the infectious disease associated with the location data from the remote server.

[0074]

[0086] Step 1104 includes receiving from the remote server a first test assay selected for reporting a diagnostic result based on the location data and risk factors.

[0075]

[0087] Step 1106 includes causing a first test assay from the sample cartridge to be executed on a diagnostic device. In some embodiments, step 1106 includes executing a plurality of test assays within the sample cartridge and storing a plurality of results from the test assays in a local memory of the diagnostic device. In some embodiments, step 1106 includes instructing the diagnostic device to collect an image of the first test assay upon completion and receiving the image of the first test assay from the diagnostic device.

[0076]

[0088] Step 1108 includes transmitting a first dataset to the remote server when the first test assay is completed.

[0077]

[0089] Step 1108 includes receiving from the remote server recommendations for a user of the diagnostic device based on the diagnostic results. In some embodiments, step 1108 includes receiving a request from the remote server for providing test results from a second test assay.

[0078]

[0090] FIG. 12 is a flowchart showing steps in a method 1200 for collecting data for input into an insight engine and a decision support tool (e.g., insight engine 234 and clinical decision and support tool 240) according to some embodiments. Method 1200 may be at least partially implemented by any one of a plurality of servers in cooperation with one or more client devices and databases communicatively coupled via a network, as disclosed herein (see client devices 110 and 210, servers 130 and 230, databases 152 and 252, and networks 150 and 250). For example, at least some of the steps in method 1200 may be implemented by one component within an architecture (see architectures 10 and 20), the component including a browser for accessing a website for an insight engine that processes logic to evaluate and contextualize the occurrence of an infectious disease and code for an application that executes on a mobile device, and a network management engine (e.g., insight engine 234 and network management engine 236). In some embodiments, the insight engine may include a clinical decision tool, a location identification tool, and a consumer support tool, as disclosed herein (see clinical decision and support tool 240, location identification tool 242, and statistical tool 244). In some embodiments, one or more servers may also include an application layer (e.g., application layer 215) for hosting and handling applications installed on client devices, thereby enabling third-party users to access the disease analysis engine. Accordingly, at least some of the steps in method 1200 may be implemented by a processor that executes commands stored in the memory of one of the servers or client devices or accessible by at least one of the servers or client devices (e.g., processor 212 and memory 220).Furthermore, in some embodiments, at least some of the steps in method 1200 may be performed out of the order shown in method 1200, overlapping in time, or substantially simultaneously. Further, methods consistent with some embodiments disclosed herein may include, but need not include, all of the steps in method 1200.

[0079]

[0091] Step 1202 includes receiving, at a server, information associated with at least one of a plurality of infectious diseases within a geographic area from a first device. In some embodiments, step 1202 includes storing information from the first device in a database. In some embodiments, step 1202 includes receiving from the first device data that is not related to an infectious disease but is important for insights for decision-making support regarding one or more infectious diseases. For example, step 1202 may include receiving mobile phone mobility data, school attendance rates by school or zip code, or allergen intensity data in a particular area, which may be important for calculating infection probabilities and infection rates for a population / sub-population. In some embodiments, step 1202 may include receiving high-resolution patient-specific information when requested. For example, step 1202 may include deriving determinants of health metrics such as socioeconomic information, which may indicate that in some individuals, the risk of a more severe outcome for a particular type of disease is higher.

[0080]

[0092] Step 1204 includes normalizing the information to determine values for standardized parameters.

[0081]

[0093] Step 1206 includes determining insights regarding one of the infectious disease situations (current or future) based on values related to the standardized parameters. In some embodiments, step 1206 also includes determining clinical or operational decision support related to at least one of the infectious diseases. In some embodiments, step 1206 includes determining the probability that the disease affects a particular aggregated portion of the population (as a result, decision support for best practices is obtained). In some embodiments, step 1206 includes determining the probability and severity that the disease affects the subject at the individual level (as a result, user-specific decision support is obtained).

[0082]

[0094] Step 1208 includes transmitting to a second device a test selected to be performed on the subject based on insights regarding the progression of the infectious disease. In some embodiments, step 1208 includes receiving test results from the second device and updating the database with the test results. In some embodiments, step 1208 includes providing a firmware update to the second device in real time or according to a preselected schedule. In some embodiments, step 1208 includes receiving an update of preliminary test results from the second device and providing to the second device a recommendation regarding a second test for the subject based on the preliminary test results. In some embodiments, step 1208 includes receiving a negative test result regarding the subject from the second device and transmitting a request to the second device to re-run the selected test until a positive test result is obtained or the probability of a true negative test result is higher than a preselected threshold. In some embodiments, step 1208 further includes transmitting the test results to a third-party server (e.g., an EHR or government database).

[0083]

[0095] FIG. 13 is a block diagram showing an exemplary computer system that can implement the client and network devices of FIG. 1 and the methods of FIGS. 10 - 12. In certain embodiments, computer system 1300 may be implemented using hardware, or a combination of software and hardware, within a dedicated network device, or integrated within another entity, or distributed among multiple entities.

[0084]

[0096] Computer system 1300 (e.g., client devices 110 and 210, and servers 130 and 230) includes a bus 1308 or other communication mechanism for communicating information, and a processor 1302 coupled to bus 1308 for processing information. By way of example, computer system 1300 may be implemented using one or more processors 1302. Processor 1302 may be a general-purpose microprocessor, a microcontroller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a state machine, gate logic, discrete hardware components, or any other suitable entity capable of performing calculations or other operations on information.

[0085]

[0097] In addition to hardware, computer system 1300 can include code that creates an execution environment for a target computer program, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. This code is stored in a memory 1304, such as random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable PROM (EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable storage device. The memory 1304 is coupled to a bus 1308 and stores information and instructions to be executed by a processor 1302. The processor 1302 and the memory 1304 can be complemented by a dedicated logic circuit configuration or incorporated into a dedicated logic circuit configuration.

[0086]

[0098] The commands are stored in the memory 1304 and may be implemented in one or more modules of computer program instructions encoded on a computer-readable medium for execution by, for example, the computer system 1300 or for controlling the operation of the computer system 1300, and may follow any method well known to those skilled in the art, including but not limited to computer languages such as data-oriented languages (e.g., SQL, dBase), system languages (e.g., C, Objective-C, C++, Assembly), architecture languages (e.g., Java,.NET), application languages (e.g., PHP, Ruby, Perl, Python), etc. The instructions may also be implemented in computer languages such as array languages, aspect-oriented languages, assembly languages, authoring languages, command-line interface languages, compilation languages, parallel languages, bracket languages, dataflow languages, data-structured languages, declarative languages, esoteric languages, extension languages, fourth-generation languages, functional languages, interactive mode languages, interpreter-type languages, iterative languages, list-based languages, simple languages, logic-based languages, machine languages, macro languages, metaprogramming languages, multi-paradigm languages, numerical analysis, non-English-based languages, object-oriented class-based languages, object-oriented prototype-based languages, offside rule languages, procedural languages, self-reflective languages, rule-based languages, script languages, stack-based languages, synchronous languages, syntax processing languages, visual languages, Wirth languages, and xml-based languages. The memory 1304 may also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by the processor 1302.

[0087]

[0099] The computer programs discussed in this specification do not necessarily correspond to files within a file system. The program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), a single file dedicated to the program in question, or multiple associated files (e.g., files that store one or more modules, subprograms, or portions of code). The computer program can be deployed so as to be executed on one computer, or on multiple computers that are on one site or distributed across multiple sites and interconnected by a communication network. The processes and logical flows described in this specification can be implemented by one or more programmable processors executing one or more computer programs to perform functions by operating on input data to produce output.

[0088]

[0100] Computer system 1300 further includes a data storage device 1306, such as a magnetic disk or an optical disk, coupled to bus 1308 for storing information and instructions. Computer system 1300 may be connected to various devices via input / output module 1310. Input / output module 1310 may be any input / output module. Exemplary input / output module 1310 includes a data port such as a USB port. Input / output module 1310 is configured to connect to communication module 1312. Exemplary communication module 1312 includes a network interface card such as an Ethernet card and a modem. In certain aspects, input / output module 1310 is configured to connect to a plurality of devices such as input device 1314 and / or output device 1316. Exemplary input device 1314 includes a keyboard and a pointing device, such as a mouse or a trackball, whereby a consumer can provide input to computer system 1300. Other types of input devices 1314, such as a tactile input device, a visual input device, an auditory input device, or a brain-computer interface device, may also be used to provide interaction with the consumer. For example, the feedback provided to the consumer may be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and the input from the consumer may be received in any form, such as acoustic, voice, tactile, or electroencephalogram input. Exemplary output device 1316 includes a display device such as an LCD (liquid crystal display) monitor for displaying information to the user. [[ID=…]] [[ID=…]]

[0089] [[ID=…]] [[ID=…]]

[0101] According to one aspect of the present disclosure, the client device 110 and the server 130 can be implemented using the computer system 1300 in response to the processor 1302 executing one or more sequences of one or more instructions included in the memory 1304. Such instructions may be read into the memory 1304 from another machine-readable medium such as the data storage device 1306. By executing the instruction sequence included in the main memory 1304, the processor 1302 performs the process steps described herein. One or more processors in a multiprocessing configuration may also be employed to execute the instruction sequence included in the memory 1304. In an alternative aspect, a hardwired circuit configuration may be used instead of or in combination with software instructions to implement the various aspects of the present disclosure. Accordingly, the aspects of the present disclosure are not limited to any particular combination of hardware circuit configurations and software.

[0090]

[0102] Various aspects of the subject matter described in this specification can be implemented in a computing system that includes back-end components (such as data network devices), or middleware components (such as application network devices), or front-end components (such as client computers having a graphical consumer interface or a web browser through which a consumer can interact with an implementation of the subject matter described in this specification), or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, such as a communication network. The communication network (such as network 150) can include, for example, any one or more of a LAN, a WAN, and the Internet. Further, the communication network can include any one or more of network topologies, including but not limited to, for example, a bus network, a star network, a ring network, a mesh network, a star-bus network, or a tree or hierarchical network. The communication module can be, for example, a modem or an Ethernet card.

[0091]

[0103] Computer system 1300 can include clients and network devices. Typically, clients and network devices are separated from each other and typically interact via a communication network. The relationship between a client and a network device results from computer programs being executed on respective computers and having a client-network device relationship with each other. Computer system 1300 can be, for example, but not limited to, a desktop computer, a laptop computer, or a tablet computer. Computer system 1300 can also be embedded in other devices, such as, for example, but not limited to, a mobile phone, a PDA, a mobile audio player, a global positioning system (GPS) receiver, a video game console, and / or a television set-top box.

[0092]

[0104] As used herein, the terms "machine-readable storage medium" or "computer-readable medium" represent any medium involved in providing instructions to processor 1302 for execution. Such a medium can take many forms including, but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks such as data storage device 1306. Volatile media includes dynamic memory such as memory 1304. Transmission media includes coaxial cables, copper wire, and fiber optics including the wires that form bus 1308. Common forms of machine-readable media include, for example, floppy disks, flexible disks, hard disk drives, magnetic tape, any other magnetic medium, CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH EPROM, any other memory chip or cartridge, or any other medium that can be read by a computer. A machine-readable storage medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter affecting a machine-readable propagated signal, or a combination of one or more of them.

[0093]

[0105] To illustrate the interchangeability of hardware and software, elements such as various exemplary blocks, modules, components, methods, operations, instructions, and algorithms are generally described in terms of their functionality. Whether such functionality is implemented as hardware, software, or a combination of hardware and software depends on the particular application and design constraints imposed on the overall system. Those of ordinary skill in the art can implement the described functionality in various ways for each particular application.

[0094]

[0106] As used herein, the phrase "at least one" with respect to a series of elements modifies the entire list rather than each individual element in the list, along with the term "and" or "or" used to separate the elements. The phrase "at least one" does not require the selection of at least one element; rather, this phrase is meant to include at least one of any one of the elements, and / or at least one of any combination of the elements, and / or at least one of each of the elements. By way of example, the phrases "at least one of A, B, and C" or "at least one of A, B, or C" each represent only A, only B, or only C; any combination of A, B, and C; and / or at least one of each of A, B, and C.

[0095]

[0107] As used herein, the term "exemplary" is used to mean "an example, instance, or illustration". Any embodiment described as "exemplary" herein should not necessarily be construed as preferred or advantageous over other embodiments. The terms one aspect, aspect, another aspect, some aspects, one or more aspects, one implementation, implementation, another implementation, some implementations, one or more implementations, one embodiment, embodiment, another embodiment, some embodiments, one or more embodiments, one configuration, configuration, another configuration, some configurations, one or more configurations, the subject technology, the disclosure, the present disclosure, and other variations of those terms are for convenience and do not imply that the disclosure related to such terms is essential to the subject technology or applies to all configurations of the subject technology. The disclosure related to such terms may apply to all configurations, or one or more configurations. The disclosure related to such terms may provide one or more examples. Phrases such as one aspect or some aspects may represent one or more aspects, and vice versa. This also applies to the other terms described above.

[0096]

[0108] References to elements in the singular are intended to mean one or more unless otherwise specified. Masculine pronouns (e.g., his) include feminine and neuter pronouns (e.g., her and its), and vice versa. The term "some" represents one or more. Headings and subheadings shown in underlined and / or italic are used for convenience only, do not limit the subject technology, and are not to be referred to in connection with the interpretation of the description of the subject technology. Relative terms such as "first" and "second" may be used to distinguish one entity or action from another entity or action, and do not necessarily require or imply any actual such relationship or order between such entities or actions. All structural and functional equivalents of the various elements of the disclosed configurations, known or later to become known to those of ordinary skill in the art, are hereby expressly incorporated by reference and are intended to be embraced by the subject technology. Further, regardless of whether explicitly recited in the foregoing description, the subject matter disclosed herein is not intended to be dedicated to the public. Except where an element is expressly recited using the phrase "means for" or, in the case of a method claim, using the phrase "step for", claim elements should not be construed under the provisions of 35 U.S.C. § 112, ¶ 6.

[0097]

[0109] Embodiments of the present disclosure include the following.

[0098]

[0110] Embodiment I: A computer-implemented method includes receiving, from a diagnostic device, information regarding a sample cartridge to be used for a first subject, the information including location data and risk factors for one of a plurality of infectious diseases, the sample cartridge including a plurality of test assays for determining the presence or absence of an analyte associated with a disease or disorder such as an infectious disease and / or for diagnosing an infectious disease. The computer-implemented method further includes selecting, based on the location data and risk factors, a test assay for reporting a diagnostic result; instructing the diagnostic device to perform the test assay from the sample cartridge; receiving, from the diagnostic device, a first data set when the test assay is completed; and evaluating the diagnostic result based on the first data set.

[0099]

[0111] Embodiment II: A computer-implemented method includes providing, to a remote server, information regarding a sample cartridge to be used for a diagnostic test for a first subject, the information including location data and risk factors for at least one of a plurality or a number of target infectious diseases, such as an analyte indicating the presence or absence of an infectious disease, the sample cartridge including a plurality or a number of test assays for determining the presence or absence of an analyte associated with a disease or disorder such as an infectious disease and / or for diagnosing an infectious disease. The computer-implemented method further includes receiving, from the remote server, a first test assay selected for reporting a diagnostic result based on the location data and risk factors; causing the diagnostic device to perform the first test assay from the sample cartridge; and transmitting a first data set to the remote server when the first test assay is completed.

[0100]

[0112] Embodiment III: The device includes a memory for storing a plurality of instructions, a communication module configured to communicate with a remote server, and a processor configured to execute the instructions. When executing the instructions, the processor causes the device to receive, from the remote server, an instruction for a test assay to be selected regarding a subject, the test assay being for determining the presence or absence of an analyte associated with a disease or disorder such as an infectious disease and / or for diagnosing an infectious disease, the instruction being based on at least one of geolocation data indicating the location of the diagnostic device and previous diagnoses obtained with one or more diagnostic devices communicatively coupled to the remote server, and to associate the test assay with a plurality of values to generate a dataset diagnosis, the dataset diagnosis being stored in the memory of the diagnostic device, the plurality of values being related to one or more of a test assay identifier, a test assay result, a patient identifier, and / or a diagnostic device identifier, and to transmit the dataset diagnosis to the remote server for storage, the remote server generating a report based on the dataset diagnoses from each of the one or more diagnostic devices, the report being configured to be transmitted to a database accommodated in a database display on a second server or an end-user workstation.

[0101]

[0113] Embodiment IV: A computer-implemented method includes, at a server, receiving information associated with at least one of a plurality of infectious diseases within a geographic area from a first device, normalizing the information to determine values for standardized parameters, determining insights regarding the state of one of the infectious diseases based on the values for the standardized parameters, and transmitting a selected test to be performed on a subject based on the insights regarding the state of one of the infectious diseases to a second device.

[0102]

[0114] Any one of Embodiments I, II, III, and IV may be combined with any one or more of the following elements in any number or permutation.

[0103]

[0115] Further comprising: Element 1, receiving from a search engine data related to the search frequency of selected keywords associated with an infectious disease in an area associated with location data; Element 2, automatically updating a risk factor for one of a plurality of infectious diseases based on social network data; Element 3, further providing to a diagnostic device an update regarding an application interface for executing the diagnostic device based on information regarding a sample cartridge and an identifier of the diagnostic device; Element 4, selecting an assay including determining a false positive probability exceeding a preselected threshold for a diagnostic result regarding the assay; Element 5, further comprising preventing the diagnostic device from executing the assay based on the location data and the risk factor; Element 6, selecting an assay including determining a prevalence of an infectious disease associated with the location data and the assay; Element 7, further comprising communicating to a client device associated with the location data a request to execute an assay from a sample cartridge within the diagnostic device; Element 8, evaluating a diagnostic result including obtaining from a database a second data set associated with a completed assay regarding a second subject having a verified diagnostic result and comparing the first data set with the second data set; Element 9, further comprising providing a virtual assistant for a user based on the diagnostic result; Element 10, transmitting a recommendation to the user including providing a treatment pathway for a first subject when the diagnostic result is positive for an infectious disease.

[0104]

[0116] Element 11 further includes requesting an epidemiological report on an infectious disease associated with location data from a remote server. Element 12, performing a first test assay from a sample cartridge includes performing a plurality of test assays within the sample cartridge and storing a plurality of results from the test assays in the local memory of the diagnostic device. Element 13, performing a first test assay from a sample cartridge includes instructing the diagnostic device to collect an image of the first test assay upon completion and receiving the image of the first test assay from the diagnostic device. Element 14, receiving a medical recommendation includes receiving a request from a remote server to provide a test result from a second test assay.

[0105]

[0117] Element 15 further includes providing geolocation data. Element 16, the communication module is configured to request an epidemiological report on a location associated with the geolocation data from a remote server. Element 17, the communication module is configured to request a pre - test probability of a false - positive result of a dataset diagnosis from a remote server. Element 18, the communication module is configured to receive a post - test communication from a remote server that warns the user to perform a higher - performance diagnosis on the subject.

[0106]

[0118] Element 19 further includes storing information in a database, receiving inspection results from a second device, and updating the database with the inspection results. Element 20 further includes updating the configuration of the second device in real time. Element 21 further includes updating software commands in the second device. Element 22 further includes updating firmware in the second device. Element 23 further includes providing a firmware update to the second device according to a preselected schedule or logical condition. Element 24 further includes providing the probability of the outcome of a selected inspection to the second device and displaying it on the graphical user interface of the second device. Element 25 further includes receiving, from the second device, a query regarding the pre-inspection probability of the outcome before the selected inspection is completed. Element 26 further includes receiving an update of preliminary inspection results from the second device and providing, based on the preliminary inspection results, a recommendation regarding a second inspection for the subject to the second device. Element 27 further includes receiving a negative inspection result regarding the subject from the second device and transmitting a request to the second device to re-run the selected inspection until an outcome is obtained from either a positive inspection result or a negative inspection result with a high confidence level. Element 28 further includes receiving inspection results from the second device and providing the inspection results to a third-party server. Element 29 further includes transmitting medical recommendations to the user of the diagnostic device based on the diagnostic results. Element 30 further includes receiving medical recommendations based on diagnostic results from a first dataset from a remote server.

[0107]

[0119] This specification includes many details, but these should not be construed as limitations on the scope of what can be described. Rather, they should be construed as descriptions of particular implementations of the subject matter. Specific features described in the context of individual embodiments herein can also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented individually in multiple embodiments or in any suitable sub-combination. Additionally, features may have been described as acting in a particular combination, and have been so stated above, but in some cases, one or more features from the described combination can also be removed from that combination, and the described combination may be directed to a sub-combination or a variation of a sub-combination.

[0108]

[0120] The subject matter of this specification has been described with respect to particular aspects, but other aspects can also be implemented and are within the scope of the following claims. For example, although operations are shown in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or sequentially, or that all of the illustrated operations be performed, in order to achieve a desirable result. The actions recited in the claims can also be performed in a different order and still achieve a desirable result. As an example, the processes shown in the accompanying drawings do not necessarily require the particular order or sequential order shown in order to achieve a desirable result. In certain circumstances, multitasking and parallel processing can be advantageous. Additionally, the separation of the various system components in the aspects described above should not be understood as requiring such separation in all aspects, and the program components and systems described are generally understood to be able to be integrated into a single software product or packaged into multiple software products.

[0109]

[0121] The title of the invention, background, brief description of the drawings, abstract, and the drawings are incorporated herein and provided as illustrative examples of the present disclosure, and are not provided for limiting purposes. This is submitted with the understanding that it is not to be used to limit the scope or meaning of the claims. Further, in the detailed description, the description provides illustrative examples, and it will be understood that various functions are grouped in various implementations for the purpose of streamlining the present disclosure. The methods of the present disclosure should not be construed as reflecting an intention that the described subject matter requires more features than are expressly recited in each claim. Rather, as each claim represents, the subject matter of the present invention is not all of the features of a single disclosed configuration or operation. The claims are incorporated by reference in this application, and each claim stands on its own as a separately recited subject matter.

[0110]

[0122] The claims are not intended to be limited to the aspects described herein, but rather are to be accorded the full scope consistent with the language of the claims and to include all legal equivalents. Nevertheless, no claim is intended to, nor should be construed to, cover subject matter that fails to meet the requirements of the applicable patent law.

Claims

**Claim 1** Receiving, from a diagnostic device, information regarding a sample cartridge to be used for a first subject, the information including location data and risk factors related to one of a plurality of infectious diseases, the sample cartridge including a plurality of test assays for diagnosing the infectious disease; selecting, based on the location data and the risk factors, a test assay for reporting a diagnostic result; instructing the diagnostic device to perform the test assay from the sample cartridge; receiving, from the diagnostic device, a first data set when the test assay is completed; evaluating a diagnostic result based on the first data set A computer-implemented method comprising. **Claim 2** The computer-implemented method according to claim 1, further comprising receiving, from a search engine, data related to a search frequency regarding a selected keyword associated with an infectious disease in an area associated with the location data. **Claim 3** The computer-implemented method according to claim 1, further comprising automatically updating the risk factors related to the one of the plurality of infectious diseases based on social network data. **Claim 4** The computer-implemented method according to claim 1, further comprising providing, to the diagnostic device, an update regarding an application interface for operating the diagnostic device based on the information regarding the sample cartridge and an identifier of the diagnostic device. **Claim 5** The computer-implemented method according to claim 1, wherein selecting a test assay includes determining a false positive probability exceeding a preselected threshold for the diagnostic result regarding the test assay. **Claim 6** The computer-implemented method according to claim 1, further comprising preventing the diagnostic device from performing a test assay based on the location data and the risk factors. **Claim 7** The computer-implemented method according to claim 1, wherein selecting a test assay includes determining a prevalence of an infectious disease associated with the location data and the test assay. **Claim 8** The computer-implemented method according to claim 1, further comprising communicating, to a client device associated with the location data, a request to perform a test assay from a sample cartridge within the diagnostic device. **Claim 9** Evaluating the diagnostic result includes obtaining from a database a second dataset associated with a completed test assay for a second subject having a verified diagnostic result, and comparing the first dataset with the second dataset. The computer-implemented method according to claim 1.

10. The computer-implemented method according to claim 1, further comprising providing a virtual assistant for a user of the diagnostic device based on the diagnostic result.

11. The computer-implemented method according to claim 1, further comprising transmitting a recommendation to a user of the diagnostic device, for example, by providing a treatment pathway for the first subject when the diagnostic result is positive for an infectious disease.

12. Providing information about a sample cartridge to be used for a diagnostic test on a first subject, the information including location data and risk factors for at least one of a plurality of infectious diseases, the sample cartridge including a plurality of test assays for diagnosing the infectious diseases, and Receiving from a remote server a first test assay selected for reporting a diagnostic result based on the location data and the risk factors; Causing the diagnostic device to execute the first test assay from the sample cartridge; Transmitting a first dataset to the remote server when the first test assay is completed A computer-implemented method comprising.

13. The computer-implemented method according to claim 12, further comprising requesting an epidemiological report for an infectious disease associated with the location data from the remote server.

14. Executing the first test assay from the sample cartridge includes executing a plurality of test assays in the sample cartridge and storing a plurality of results from the test assays in a local memory of the diagnostic device. The computer-implemented method according to claim 12.

15. Executing the first test assay from the sample cartridge includes instructing the diagnostic device to collect an image of the first test assay upon completion and receiving the image of the first test assay from the diagnostic device. The computer-implemented method according to claim 12.

16. Further comprising receiving, from the remote server, a medical recommendation based on a diagnostic result from the first dataset. The computer-implemented method according to claim 12.

17. The computer-implemented method according to claim 16, wherein receiving the medical recommendation includes receiving a request from the remote server to provide test results from a second test assay.

18. A memory storing a plurality of instructions; A communication module configured to communicate with a remote server; A processor configured to execute the instructions, the device being caused to: Receive, from the remote server, an instruction for a test assay for diagnosing an infectious disease to be selected for a subject, the instruction being based on at least one of geolocation data indicating the location of a diagnostic device and a previous diagnosis obtained by one or more diagnostic devices communicatively coupled to the remote server; Associate the test assay with a plurality of values to generate a dataset diagnosis, the dataset diagnosis being stored in the memory of the diagnostic device, the plurality of values being related to one or more of a test assay identifier, a test assay result, a patient identifier, and a diagnostic device identifier; Transmit and store the dataset diagnosis to the remote server, the remote server generating a report based on the dataset diagnosis from each of the one or more diagnostic devices, the report being configured to be transmitted to a database stored in a database display on a second server or an end-user workstation; A processor for causing; A device comprising.

19. The device according to claim 18, further comprising providing the geolocation data.

20. The device according to claim 19, wherein the communication module is configured to request from the remote server an epidemiological report regarding a location associated with the geolocation data.

21. The device according to claim 18, wherein the communication module is configured to request from the remote server a pre-test probability of a false positive result of the dataset diagnosis.

22. The device according to claim 18, wherein the communication module is configured to receive, from the remote server, post-examination communication that warns the user to perform a higher-performance diagnosis on the subject.

23. In a server, receiving information associated with at least one of a plurality of infectious diseases within a geographical area from a first device; normalizing the information to determine a value for a standardized parameter; determining an insight regarding the state of one of the infectious diseases based on the value for the standardized parameter; and transmitting a selected examination to be performed on a subject based on the insight regarding the state of one of the infectious diseases to a second device A computer-implemented method comprising:

24. The computer-implemented method according to claim 23, further comprising storing the information in a database, receiving examination results from the second device, and updating the database with the examination results.

25. The computer-implemented method according to claim 23, further comprising updating the configuration of the second device in real time.

26. The computer-implemented method according to claim 23, further comprising updating a software command in the second device.

27. The computer-implemented method according to claim 23, further comprising updating firmware in the second device.

28. The computer-implemented method according to claim 23, further comprising providing a firmware update to the second device according to a preselected schedule or logical condition.

29. The computer-implemented method according to claim 23, further comprising providing a probability of an outcome of the selected examination to the second device and displaying it on a graphical user interface of the second device.

30. The computer-implemented method according to claim 23, further comprising receiving, from the second device, a query regarding a pre-examination probability of an outcome before completion of the selected examination.

31. The computer-implemented method according to claim 23, further comprising receiving an update of preliminary examination results from the second device and providing a recommendation regarding a second examination for the subject to the second device based on the preliminary examination results.

32. Receiving a negative test result regarding the subject from the second device and transmitting a request to the second device to re - execute the selected test until an outcome is obtained from one of a positive test result or a negative test result with a high confidence level, the computer - implemented method according to claim 23.

33. The computer - implemented method according to claim 23, further comprising receiving a test result from the second device and providing the test result to a third - party server.

Images