Systems and methods of artificial intelligence usage tracking

Abstract

Disclosed herein are systems and methods of tracking usage of non-disposable medical products such as a single-use protective barriers, shields, and related dispensers for medical equipment. The systems and methods include AI / ML models, systems, and methods of usage patterns of healthcare providers based on QR code scan data to predict compliance infection control and other healthcare protocols, predict contact tracing patterns for a disease such as a hospital-acquired infection. The systems and methods include integration of health records into AI / ML models or systems to predict infection-related outcomes at a patient level.

Description

Aty Dkt No.: 52243-705601SYSTEMS AND METHODS OF ARTIFICIAL INTELLIGENCE USAGE TRACKINGCROSS-REFERENCE

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 701,512 filed September 30, 2024, which is incorporated by reference herein in its entirety.BACKGROUND

[0002] Single-use medical products and devices are used in the medical field for a variety of reasons, including infection control and efficient cost management. Single-use medical products, such as disposable medical products, can include single-use coverings of reusable medical products and equipment. Disposable medical products can also include medical products disposed of after multiple uses, such as dispensers of single-use medical products such as disposable medical products. Single-use medical products, such as disposable medical products, can be stored in individual patient rooms, patient homes, as well as in communal locations in a healthcare setting. Disposable medical products can be stored in dispensers that are not disposable. Single-use medical products, such as disposable medical products, can be designed in a variety of shapes and configurations to function for a variety of different healthcare applications. Single-use medical products, such as disposable medical products, can also be used to avoid the time and expense of repeat cleaning and sterilization that reusable medical products require. Although single-use medical products and devices, such as disposable medical products, have a variety of benefits, it is challenging to track these products for compliance with usage protocols, for example as a metric of infection control protocol compliance.SUMMARY

[0003] Applicant has recognized the non-limiting issue relating to compliance of healthcare providers and personnel with predetermined and dynamic protocols directed to use and disposal of single-use medical products, such as disposable medical products and associated dispensers. Applicant has recognized and appreciated the difficulty in tracking healthcare personnel use of single-use medical supplies. Applicant has recognized that tracking of data related to healthcare personnel use of single-use medical supplies can be utilized to determine compliance with infection prevention protocols and single-use medical supply use protocols. Additionally, Applicant has recognized non-limiting issues relating to efficient inventory management of single-use medical supplies. Applicant has recognized and appreciated that QR-code based tracking of single-use medical supply utilization as disclosedAty Dkt No.: 52243-705601 and claimed herein can be linked to healthcare personnel and patient identification, as well as location and time, so as to provide a comprehensive data profile for tracking of infection protocol adherence, hospital-acquired infection (HAI) contact tracing, and infection control at a healthcare system level.

[0004] Additionally, Applicant has recognized and appreciated that QR-code based tracking of single-use medical supply usage patterns addresses issues of improper supply chain management and reducing wastage. Applicant has also recognized and appreciated that QR-code based tracking of single-use medical supply usage patterns can be integrated with patient electronic health record (EHR) data, electronic medical record (EMR) data, customer relationship management (CRM) system data, application program interface (API) bridge data, or any combination thereof, to model hospital-acquired infection (HAI) spread, or stethoscope-acquired infection (SAI) spread, or both, and predictively generate reduction strategies. Applicant has recognized that this generation of HAI spread reduction strategies, SAI spread reduction strategies, or both, can be applied to, for example, stethoscope usage by health practitioners. Applicant has recognized that the stethoscope usage can involve utilization of single use medical products, such as disposable medical products and associated dispensers, with stethoscopes for HIA spread reduction, SAI spread reduction, or both.

[0005] Additionally, Applicant has recognized and appreciated that artificial intelligence systems and methods can be trained, designed, and utilized to optimize designs including development of optimized shapes or materials, or both for shields or barriers. The designed shields or barriers may be single-use, for example disposable shields or barriers.

[0006] In some embodiments, the QR code scan information comprises information relating to a recorded time point.

[0007] In some embodiments, the QR code scan information comprises information relating to an identification of the medical provider.

[0008] In some embodiments, the QR code scan information comprises information relating to an identification of a patient associated with the medical provider.

[0009] In some embodiments, the QR code scan information comprises temporal information.

[0010] In some embodiments, the temporal information is associated with the medical provider.

[0011] In some embodiments, the temporal information is associated with a patient.

[0012] In some embodiments, the patient is associated with the medical provider.

[0013] In some embodiments, the one or more sensors comprise one or more QR code scanning sensors.Aty Dkt No.: 52243-705601

[0014] In some embodiments, the one or more sensors comprise a camera.

[0015] In some embodiments, the one or more sensors are comprised within a smartphone.

[0016] In some embodiments, the one or more sensors are affixed to a surface.

[0017] In some embodiments, the surface comprises a wall of a building.

[0018] In some embodiments, the method further comprises parsing the one or more EHR databases to detect one or more health records associated with the medical provider.

[0019] In some embodiments, the method further comprises parsing the one or more EHR databases to detect one or more health records associated with at least a subset of the plurality of time points.

[0020] In some embodiments, the method further comprises retrieving health information associated with one or more health events.

[0021] In some embodiments, the one or more health events comprise an interaction between the medical provider and a patient.

[0022] In some embodiments, the one or more health events comprise a treatment of a patient, wherein the treatment is provided by the medical provider.

[0023] In some embodiments, the one or more health events comprise a diagnosis of a subject, wherein the subject had previously interacted with the medical provided before the diagnosis.

[0024] In some embodiments, the compliant action comprises an interaction of the medical provider with a digital check-in model.

[0025] In some embodiments, the check-in model is associated with one or more steps of the protocol.

[0026] In some embodiments, the one or more steps of the protocol comprises utilizing a device as directed by the protocol.

[0027] In some embodiments, the device is a cover or shield.

[0028] In some embodiments, the cover or shield comprises a cover or shield for a stethoscope.

[0029] In some embodiments, the one or more steps of the protocol comprises obtaining the cover or shield.

[0030] In some embodiments, the one or more steps of the protocol comprises placing the cover or shield to cover at least a portion of the stethoscope.

[0031] In some embodiments, the compliant action comprises receiving information relating to the medical provider accessing a code.Aty Dkt No.: 52243-705601

[0032] In some embodiments, the accessing the code is associated with one or more steps of the protocol.

[0033] In some embodiments, the one or more steps of the protocol comprises utilizing a device as directed by the protocol.

[0034] In some embodiments, the device is a cover or shield.

[0035] In some embodiments, the cover or shield comprises a cover or shield for a stethoscope.

[0036] In some embodiments, the one or more steps of the protocol comprises obtaining the cover or shield.

[0037] In some embodiments, the one or more steps of the protocol comprises placing the cover or shield to cover at least a portion of the stethoscope.

[0038] In some embodiments, the noncompliant action comprises not completing a digital check-in protocol of a digital check-in model.

[0039] In some embodiments, the noncompliant action comprises not entering or confirming a code within a time period or at a predetermined time checkpoint.

[0040] In some embodiments, the time period comprises a predetermined time period.

[0041] In some embodiments, the predetermined time period comprises about between 2 minutes and 30 minutes after completion of a previous element of the protocol.

[0042] In some embodiments, the time period comprises a dynamic time period.

[0043] In some embodiments, the dynamic time period comprises a time period between which the medical provider begins an interaction with a first patient, and begins an interaction with a second patient.

[0044] In some embodiments, the interaction with the first patient comprises the medical provider generating information relating to the first patient.

[0045] In some embodiments, the interaction with the second patient comprises the medical provider generating information relating to the second patient.

[0046] In some embodiments, the information comprises medical notes concerning the first patient or the second patient.

[0047] In some embodiments, the compliance of the medical provider with the protocol is determined based at least in part on determining a number of the compliant actions or a number of the noncompliant actions.

[0048] In some embodiments, the compliance of the medical provider with the protocol is determined based at least in part on determining a ratio of an amount of the compliant actions to an amount of the noncompliant actions.Aty Dkt No.: 52243-705601

[0049] In some embodiments, the compliance of the medical provider with the protocol is determined at one or more individual time points of the plurality of time points.

[0050] In some embodiments, at least a subset of the plurality of time points is associated with a subject having a disease.

[0051] In some embodiments, the disease comprises an infectious disease.

[0052] In some embodiments, the method further comprises determining a likelihood of the medical provider transmitting a disease based at least in part on the compliance of the medical provider with the protocol for one or more patients.

[0053] Disclosed herein, in some embodiments are systems for assessing compliance with a usage protocol for a non-reusable medical product, the systems comprising: one or more processors configured to receive a plurality of QR code scan data inputs, wherein the QR code scan data inputs are each associated with a recorded time point data and a medical provider ID data; a matching module communicatively coupled with an EHR database, wherein the matching module is configured to match one or more data elements associated with each of the plurality of QR code scan data inputs with one or more data elements of a record of the EHR database; a predictive compliance module configured to predict compliance of a medical provider associated with the medical provider ID data based on usage patterns of the non-reusable medical product by the medical provider associated with the medical provider ID data, wherein the usage patterns are based on the output of the matching module, wherein the output of the matching module comprises matching the recorded time point data with one or more data elements of the record of the EHR database; an output module configured to output the compliance prediction from the predictive compliance module, wherein the compliance prediction output comprises a predicted compliance report for the medical provider.

[0054] In another embodiment, disclosed herein are methods for tracking usage of a non- reusable medical product, the methods comprising: receiving data associated with a QR code scan; retrieving a provider ID from a database using the QR code scan data; retrieving from a remote database over a network, patient health record data, patient medical record data, or patient monitoring data; matching the provider ID to a provider ID of the patient health record data, the patient medical record data, or the patient monitoring data; predictively matching a time of the QR code scan data with a time of a health event recorded in the patient health record data, the patient medical record data, or the patient monitoring data; generating, using an artificial intelligence or machine learning model, one or more patterns comprising a provider usage pattern, a hospital-acquired infection pattern, or a stethoscope-acquiredAty Dkt No.: 52243-705601 infection pattern; generating infection-related output comprising one or more predictions associated with the one or more patterns.BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which:

[0056] FIG. 1 illustrates a non-limiting example of a workflow comprising a predictive compliance system and an HER database for matching healthcare provider and time of patient treatment, and integrating QR scan data to predict healthcare provider compliance with infection prevention protocols.

[0057] FIG. 2 illustrates a non-limiting example of a workflow comprising an AI / ML model or system capable of receiving various data types including hospital-acquired infection (HAI) or stethoscope-acquired infection (SAI) data, and provider activity, generating various models based on the data, and outputting alerts or predictions concerning infection, cost, and inventory related output.

[0058] FIG. 3 illustrates an exemplary non-limiting table comprising various capability categories for the AI / ML systems disclosed herein, coupled with associated functionality of the AI / ML systems involved in accomplishing the capability.

[0059] FIG. 4A illustrates an exemplary non-limiting embodiment of a displayed hospital-acquired infection (HAI) cost savings calculator input table.

[0060] FIG. 4B illustrates an exemplary non-limiting embodiment of a displayed result table for a cost savings model involving HAIs of various types.

[0061] FIG 5A illustrates an exemplary non-limiting embodiment of a displayed stethoscope-acquired infection (SAI) input table.

[0062] FIG. 5B illustrates an exemplary non-limiting embodiment of a displayed result table for a cost savings model involving SAI’s of various types.

[0063] FIG. 6A illustrates an exemplary non-limiting embodiment of a displayed healthcare provider office-acquired infection input table.

[0064] FIG 6B illustrates an exemplary non-limiting embodiment of a displayed result table for a cost savings model involving provider-office-acquired infections of various types.

[0065] FIG. 7A illustrates an exemplary non-limiting embodiment of a displayed nurse, EMT, first responder, and ambulance-acquired infection input table.Aty Dkt No.: 52243-705601

[0066] FIG 7B illustrates an exemplary non-limiting embodiment of a displayed result table for a cost savings model involving nurse, EMT, first responder, and ambulance-acquired infections of various types.

[0067] FIG. 8 illustrates a non-limiting embodiment of a checkbox display that may be integrated into a patient’s EHR or EMR involving the barrier or shield usage by a healthcare practitioner.

[0068] FIG. 9 illustrates a non-limiting example of a computing device; in this case, a device with one or more processors, memory, storage, and a network interface, per one or more embodiments herein.

[0069] FIG. 10A illustrates one non-limiting exemplary embodiment of the barrier or shield disclosed herein.

[0070] FIG. 10B illustrates one non-limiting example of a dispenser for the exemplary single-use medical products, such as disposable medical products.DETAILED DESCRIPTION

[0071] While preferable embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.Terms and Definitions

[0072] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0073] As used herein, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0074] As used herein, the term “about” in some cases refers to an amount that is approximately the stated amount, in some cases near the stated amount by 10%, 5%, or 1%, including increments therein, and in some cases, in reference to a percentage, refers to an amount that is greater or less the stated percentage by 10%, 5%, or 1%, including increments therein.

[0075] As used herein, the phrases “at least one,” “one or more,” and “and / or” are open- ended expressions that are both conjunctive and disjunctive in operation. For example, eachAty Dkt No.: 52243-705601 of the expressions “at least one of A, B and C,” “at least one of A, B, or C,” “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together. As used herein, the phrase “at most three” can mean less than one, one, two, or three.

[0076] Reference throughout this specification to “some embodiments,” “further embodiments,” or “a particular embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in some embodiments,” or “in further embodiments,” or “in a particular embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments

[0077] The terms "subject," "individual," and "patient" may be used interchangeably and refer to humans, as well as non-human mammals (e.g., non-human primates, canines, equines, felines, porcines, bovines, ungulates, lagomorphs, rodents, and the like). In various embodiments, the subject can be a human (e.g., adult male, adult female, adolescent male, adolescent female, male child, female child) under the care of a physician or other health worker in a hospital, as an outpatient, or other clinical context. In certain embodiments, the subject may not be under the care or prescription of a physician or other health worker. In some embodiments, the subject may be under the care of a dental professional.

[0078] As used herein, “treatment” or “treating” refers to an approach for obtaining beneficial or desired results with respect to a disease, disorder, or medical condition including, but not limited to, a therapeutic benefit and / or a prophylactic benefit. In certain embodiments, treatment or treating involves administering a therapeutic to a subject. A therapeutic benefit may include the eradication or amelioration of the underlying disorder being treated. Also, a therapeutic benefit may be achieved with the eradication or amelioration of one or more of the physiological symptoms associated with the underlying disorder, such as observing an improvement in the subject, notwithstanding that the subject may still be afflicted with the underlying disorder.

[0079] As used herein, “single-use medical products” comprise disposable medical products. “Single-use medical products” comprise single-use stethoscope coverings. “Singleuse medical products” comprise healthcare equipment coverings. “Single-use medical products” comprise disposable gloves. “Single-use medical products” comprise face masks, gloves, surgical gowns, shoe covers, disposable syringes, disposable needles, disposable lancets, disposable IV kits, disposable catheters, disposable scalpels, disposable surgicalAty Dkt No.: 52243-705601 blades, disposable suture materials, gauze, bandages, adhesive strips, disposable thermometers, tongue depressors, urine and stool specimen collection containers, disposable speculums, disposable forceps, disposable suction catheters, disposable bedpans, disposable urinals, disposable washcloths, adult diapers, sterile saline solutions for wound cleaning, disposable medical scissors, alcohol prep pads, disposable tracheostomy tubes, disposable oxygen masks, disposable spirometers, disposable umbilical cord clamps, disposable suction bulbs, petri dishes, pipettes, and test tubes.Examples of Machine Learning Techniques

[0080] As disclosed throughout, in some cases, the systems, the methods, the computer- readable media, and the techniques disclosed herein may implement one or more machine learning techniques. In some cases, ML may generally involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. ML may include a ML model (which may include, for example, a ML algorithm). Machine learning, whether analytical or statistical in nature, may provide deductive or abductive inference based on real or simulated data. The ML model may be a trained model. ML techniques may comprise one or more supervised, semi-supervised, self-supervised, or unsupervised ML techniques. For example, an ML model (e.g., the machine learning model described herein) may be a trained model that is trained through supervised learning (e.g., various parameters are determined as weights or scaling factors). ML may comprise one or more of regression analysis, regularization, classification, dimensionality reduction, ensemble learning, meta learning, association rule learning, cluster analysis, anomaly detection, deep learning, or ultradeep learning. ML may comprise: k-means, k-means clustering, k-nearest neighbors, learning vector quantization, linear regression, non-linear regression, least squares regression, partial least squares regression, logistic regression, stepwise regression, multivariate adaptive regression splines, ridge regression, principal component regression, least absolute shrinkage and selection operation (LASSO), least angle regression, canonical correlation analysis, factor analysis, independent component analysis, linear discriminant analysis, multidimensional scaling, non-negative matrix factorization, principal components analysis, principal coordinates analysis, projection pursuit, Sammon mapping, t-distributed stochastic neighbor embedding, AdaBoosting, boosting, gradient boosting, bootstrap aggregation, ensemble averaging, decision trees, conditional decision trees, boosted decision trees, gradient boosted decision trees, random forests, stacked generalization, Bayesian networks, Bayesian belief networks, naive Bayes, Gaussian naive Bayes, multinomial naive Bayes, hidden Markov models, hierarchical hidden Markov models, support vector machines, encoders, decoders, auto-encoders, stacked auto-encoders, perceptrons, multi-layer perceptrons, artificial neuralAty Dkt No.: 52243-705601 networks, feedforward neural networks, convolutional neural networks, recurrent neural networks, residual neural networks, physics-informed neural networks, long short-term memory, deep belief networks, deep Boltzmann machines, deep convolutional neural networks, deep recurrent neural networks, large language models, transformer models, vision transformers, or generative adversarial networks.Examples of Decision Trees and Random Forests

[0081] As described above, the machine learning model may implement a decision tree. A decision tree may be a supervised ML algorithm that can be applied to both regression and classification problems. For example, a decision tree may grow from a root (base condition), and when it meets a condition (internal node / feature), it may split into multiple branches. The end of the branch that does not split anymore may be an outcome (leaf). A decision tree can be generated using a training dataset set according to the following operations: (A) starting from a root node (the entire dataset), the algorithm may split the dataset in two branches using a decision rule or branching criterion; (B) each of these two branches may generate a new child node; (C) for each new child node, the branching process may be repeated until the dataset cannot be split any further; (D) each branching criterion may be chosen to maximize information gain (e.g., a quantification of how much a branching criterion reduces a quantification of how mixed the labels are in the children nodes). The labels may be the data or the classification that is predicted by the decision tree.

[0082] A random forest regression is an extension of the decision tree model that tends to yield more robust predictions by stretching the use of the training dataset partition. Whereas a decision tree may make a single pass through the data, a random forest regression may bootstrap 50% of the data (e.g., with replacement) and build many trees. Rather than using all explanatory variables as candidates for splitting, a random subset of candidate variables may be used for splitting, which may enable trees that have different data and different variables (hence the term random). The predictions from the trees, which may be collectively referred to as the “forest,” may then be averaged to produce a final prediction. Many trees (e.g., ten trees, fifty trees, one hundred trees, one thousand trees, etc.) may be included in a random forest model, with a number (e.g., 3, 6, 10, etc.) of terms sampled per split, a minimum of number (e.g., 1, 2, 4, 10, etc.) of splits per tree, and a minimum split size (e.g., 16, 32, 64, 128, 256, etc.). Random forests may be trained in a similar way as decision trees. Specifically, training a random forest may include the following operations: (A) randomly select k features from the total number of features; (B) create a decision tree from these k features using the same operations as for generating a decision tree; and (C) repeat the previous two operations until a target number of trees is created.Aty Dkt No.: 52243-705601

[0083] As disclosed, a random forest classifier, which may comprise a plurality of decision trees where the output prediction may be the mode of the predicted classifications of the individual trees, can be helpful in reducing overfitting to training dataset. In some cases, an ensemble of decision trees can be constructed using a random subset of features at each split or decision node. The Gini criterion may be employed, in some cases, to choose the best partition, where decision nodes having the lowest calculated Gini impurity index are selected. The Gini impurity can be used, in some cases, as a criterion to find informative features based on which the splits in each decision tree may be constructed.

[0084] In some cases, each decision tree of a random forest may comprise one or more decision nodes, where each decision node specifies a predicate condition. For example, decision node may predicate the condition that, for a given dataset, the outcome to an question is a specific outcome. At each decision node, a decision tree can be split based on whether the predicate condition attached to the decision node holds true, leading to various prediction nodes. Each prediction node can comprise output values that represent “votes” for one or more of the classifications or conditions being evaluated by the assessment model. At prediction time, a “vote” can be taken over all of the decision trees, and the majority vote (or mode of the predicted classifications) can be output as the predicted classification.

[0085] In some cases, when the dataset being queried in the assessment model reaches a “leaf’, or a final prediction node with no further downstream splits, the output values of the leaf can be output as the votes for the particular decision tree. Since a random forest model comprises a plurality of decision trees, the final votes across all trees in the forest can be summed to yield the final votes and the corresponding classification of the subject. A large number of decision trees can help reduce overfitting of the assessment model to the training dataset, by reducing the variance of each individual decision tree. For example, an assessment model can comprise, for example, at least about 3 decision trees, at least about 5 decision trees, at least about 10 decision trees, at least about 20 decision trees, at least about 50 decision trees, at least about 100 decision trees, etc.QR Code Generation and Placement

[0086] In some embodiments, a system can comprise a unique QR code associated with one or more single-use medical products, or the packaging thereof. In some embodiments, the system can further comprise one or more processors configured to perform operations comprising generating a unique QR code for one or more single-use medical products, the packaging thereof, or the dispenser thereof. In some embodiments, QR codes can be unique.Aty Dkt No.: 52243-705601In some embodiments, QR codes can be generated using error correction algorithms. In some embodiments, QR codes can be printed, embossed, debossed, or any combination thereof. In some cases, the QR codes can be printed, embossed, debossed, or any combination thereof onto the single-use or disposable products. In some embodiments, the QR codes can be printed, embossed, debossed, or any combination thereof on the packaging of single-use medical products, for example disposable medical products. In some embodiments, the QR codes can comprise a manufacturer’s bar code. In some embodiments, the QR codes can be printed, embossed, debossed, or any combination thereof on a dispenser of single-use medical products such as disposable medical products. In some embodiments, the QR code application can be selected based on durability. In some embodiments, the QR code application can be selected based on security needs. In some embodiments, the QR code can be modified in size. In some embodiments, the QR code can be modified in size to facilitate scanning. In some embodiments, the QR code can be modified in position. In some embodiments, the QR code can be modified in position to facilitate scanning.

[0087] In some embodiments, the one or more processors can be further configured to perform operations comprising linking the unique QR code to one or more pieces of identifying data. In some embodiments, the QR code can function as a digital fingerprint for the single-use medical product. In some embodiments, the identifying data can comprise manufacturing data. In some embodiments, the manufacturing data can comprise production batch, lot number, expiration date, or number of single-use medical supplies per package unit, or any combination thereof. In some embodiments, the packaging can comprise a box, a dual box, a box comprising one or more barriers, dispenser tubes, or dispenser rolls, or any combination thereof. In some embodiments, the number of single-use medical supplies per package unit can be determined by input data linked to practice locations. In some embodiments, the package unit can comprise a box, a dual box, a box comprising one or more barriers, dispenser tubes, or dispenser rolls, or any combination thereof. Dispensers can be comprised of materials such as disposable materials or non-disposable materials. Dispensers can be comprised of materials such as plastic, paper, metal, rubber, glass, latex, silicon, fabric, ceramics, polymers, wood, nylon, leather, carbon fiber, or any other material, or any combination thereof.

[0088] In some embodiments, the one or more processors can be further configured to perform operations comprising implementing error correction. In some embodiments, implementing error correction can comprise modifying a level of error correction. In some embodiments, modifying the level of error correction can be performed in response to input to the system indicating damage to the packaging of the single-use medical products. In someAty Dkt No.: 52243-705601 embodiments, modifying the level of error correction can be performed in response to input to the system indicating the unique QR code is at least partially obscured during handling or storage.

[0089] In some embodiments, the one or more processors can comprise a design module configured to perform operations comprising designing a unique QR code. In some embodiments, designing a unique QR code can comprise determining one or more design elements comprising one or more of an optimal QR code size, an optimal QR code placement, or whether to pair additional visual cues with the QR code. In some embodiments, the visual cues can comprise arrows or color coding. In some embodiments, the arrows or color coding can be configured to inform users of proper scanning orientation.

[0090] In some embodiments, designing the unique QR can comprise modifying a previously generated QR code to achieve the one or more design elements. In some embodiments, an optimal QR code placement can comprise placing the QR code on a packaging label or a package roll. In some embodiments, designing a unique QR code can further comprise integrating the QR code design into an existing packaging design. In some embodiments, the design module can be further configured to design the unique QR code based on input comprising QR code design requests from one or more healthcare entities. The design requests can comprise a requested QR code size, a requested QR code placement, or a request to pair additional visual cues with the QR code. In some embodiments, the design module can be further configured to design the unique QR code placement that can facilitates scanning of the QR code before accessing the single-use medical products.

[0091] In some embodiments, the system can further comprise a printing device. In some embodiments, the printing device can print the unique QR code. In some embodiments, the printing device can print the unique QR code on packaging of a single-use medical product or a plurality of single-use medical devices. In some embodiments, the printing device can print the unique QR so that the unique QR code is integrated into the existing packaging. In some embodiments, the printing device can print both the unique QR code and the associated packaging design. In some embodiments, the associated packaging design can comprise supplementary information. The supplementary information can comprise usage instructions, lot numbers, or other relevant details.

[0092] In some embodiments, the printing device can emboss the unique QR code on an individual single-use medical product. In some embodiments, the printing device can emboss the unique QR code on a packaging of an individual single-use medical product. In some embodiments, the printing device can emboss the unique QR code on a packaging of a plurality of single-use medical products. In some embodiments, the printing device canAty Dkt No.: 52243-705601 deboss the unique QR code on an individual single-use medical product. In some embodiments, the printing device can deboss the unique QR code on a packaging of an individual single-use medical product. In some embodiments, the printing device can deboss the unique QR code on a packaging of a plurality of single-use medical products. In some embodiments, the one or more processors of the system can be further configured to determine whether the unique QR code should be printed, embossed, or debossed based at least in part on data related to a required durability value of the single-use medical product or a packaging thereof. In some embodiments, the one or more processors of the system can be further configured to determine whether the unique QR code should be printed, embossed, or debossed using materials resistant to wear and tear, moisture, or cleaning agents, or any combination thereof.

[0093] In some embodiments, the one or more processors of the system can be further configured to determine whether the unique QR code should be printed, embossed, or debossed based at least in part on data related to values quantifying a request for or predicted need to prevent alteration or removal of the QR code. In some embodiments, the one or more processors of the system can be further configured to determine whether the unique QR code should be printed, embossed, or debossed based at least in part on data related to values quantifying a predicted need or request to improve the aesthetic of single-use medical products or the packaging thereof. In some embodiments, the one or more processors of the system can be further configured to determine whether the unique QR code should be printed, embossed, or debossed based at least in part on data related to manufacturing costs. In some embodiments, the one or more processors of the system can be further configured to determine whether the unique QR code should be printed, embossed, or debossed based at least in part on data related to the need for specialized scanning equipment.

[0094] In some embodiments, the one or more processors of the system can further comprise a quality control module. In some embodiments, the quality control module can be configured to generate one or more quality control checks. In some embodiments, the one or more quality control checks detect legibility of the unique QR code, whether appropriate data is linked with the unique QR code, or both. In some embodiments, the quality control module can be further configured to output an alert or notification if a legibility quantification value or linked data quantification value, or both, are below a predetermined threshold value.

[0095] In some embodiments, a method can comprise generating a unique QR code. In some embodiments, a unique QR code can be assigned to each single-use medical product. In some embodiment, a unique QR code can be assigned to a packaging of a plurality of singlemedical products. In some embodiments, a unique QR code can be assigned to a dispenserAty Dkt No.: 52243-705601 package that dispenses a plurality of single-use medical products. In some embodiments, the unique QR code can be assigned during manufacturing of a plurality of single-use medical products or associated packaging.Scanning Device and Software Systems

[0096] In some embodiments, the system can further comprise one or more scanning devices or a scanning software system, or both. In some embodiments, the scanning devices can be customizable scanning devices. In some embodiments, the scanning devices can comprise one or more of smartphones, tablets, or dedicated handheld scanners. In some embodiments, the scanning devices can comprise dedicated handheld scanners. In some embodiments, the dedicated handheld scanners can be used in high-traffic locations. In some embodiments, the scanning devices can be devices readily available to healthcare professionals. In some embodiments, the scanning devices can be devices capable of efficiently scanning QR codes. In some embodiments, the scanning devices can be devices capable of efficiently scanning QR codes in various lighting conditions. In some embodiments, the one or more scanning devices are configured to be deactivated or activated by users for maintenance or replacement.

[0097] In some embodiments, the scanning software system can comprise a graphic user interface. In some embodiments, the scanning software system can comprise mobile application software. In some embodiments, the scanning software system can be configured to perform operations comprising accurately reading and decoding QR codes. In some embodiments, the scanning software system can be configured to perform operations comprising recording scan data to a memory. In some embodiments, scan data can comprise one or more of dispenser roll identification data, location data, timestamp data, and user identification data. In some embodiments, the scanning software system can be configured to perform operations comprising reading each of standard printed QR codes, embossed QR codes, and debossed QR codes.

[0098] In some embodiments, customizable scanning devices can be used to detect the QR codes. In some embodiments, the customizable scanning devices can comprise one or more of smartphones, tablets, dedicated handheld scanners, scanner stations, portable scanners, stationary scanners, wearable scanners, wireless scanners, or kiosk scanners, or any combination thereof. In some embodiments, the scanners can be operatively coupled to or utilize software for reading QR codes. In some embodiments, the software can be configured to detect and read QR codes in non-ideal lighting conditions. In some cases, the non-ideal lighting conditions can be low light, uneven light, bright light, colored light, shadows, or any combination thereof. In some embodiments, the software can be configured to record the scanAty Dkt No.: 52243-705601 data. In some embodiments, the software can be configured to record the scan data locally. In some embodiments, the software can be communicatively coupled with a database over a network. In some embodiments, the database can be an HER or EMR database. In some embodiments, the database can be a HIPAA-compliant database. In some embodiments, the software can send and receive data across a network from the database. In some embodiments, the QR code can be configured to facilitate scanning of it from a remote location.

[0099] In some embodiments, the QR code scan information may comprise information relating to a recorded time point. The QR code scan information may comprise information relating to an identification of the medical provider. The QR code scan information may comprise information relating to an identification of a patient associated with the medical provider. The QR code scan information may comprise temporal information. The temporal information may be associated with the medical provider. The temporal information may be associated with a patient. The patient may be associated with the medical provider. The one or more sensors may comprise one or more QR code scanning sensors. The one or more sensors may comprise a camera. The one or more sensors may be comprised within a smartphone. The one or more sensors may be affixed to a surface. The surface may comprise a wall of a building. The method further may comprise parsing the one or more EHR databases to detect one or more health records associated with the medical provider. The method further may comprise parsing the one or more EHR databases to detect one or more health records associated with at least a subset of the plurality of time points. The method further may comprise retrieving health information associated with one or more health events. The one or more health events may comprise an interaction between the medical provider and a patient. The one or more health events may comprise a treatment of a patient, wherein the treatment may be provided by the medical provider. The one or more health events may comprise a diagnosis of a subject, wherein the subject had previously interacted with the medical provided before the diagnosis. The compliant action may comprise an interaction of the medical provider with a digital check-in model. The check-in model may be associated with one or more steps of the protocol. The one or more steps of the protocol may comprise utilizing a device as directed by the protocol. The device may be a cover or shield. The cover or shield may comprise a cover or shield for a stethoscope. The one or more steps of the protocol may comprise obtaining the cover or shield. The one or more steps of the protocol may comprise placing the cover or shield to cover at least a portion of the stethoscope. The compliant action may comprise receiving information relating to the medical provider accessing a code. The accessing the code may be associated with one or more steps of theAty Dkt No.: 52243-705601 protocol. The one or more steps of the protocol may comprise utilizing a device as directed by the protocol. The device may be a cover or shield. The cover or shield may comprise a cover or shield for a stethoscope. The one or more steps of the protocol may comprise obtaining the cover or shield. The one or more steps of the protocol may comprise placing the cover or shield to cover at least a portion of the stethoscope.

[0100] In some embodiments, the shield or barrier may be fitted for an auscultation device. In some embodiments, the shield or barrier can comprise a stethoscope shield or barrier. In some embodiments, the shield or barrier may be fitted for a stethoscope. In some embodiments, the shield or barrier may be fitted for an acoustic stethoscope. In some embodiments, the acoustic stethoscope may be a single-head acoustic stethoscope, dual-head acoustic stethoscope, triple-head acoustic stethoscope, or non-tunable acoustic stethoscope. In some embodiments, the shield or barrier may be fitted for an Al enabled stethoscope. In some embodiments, the shield or barrier may be fitted for an amplifying stethoscope. In some embodiments, the shield or barrier may be fitted for a cardiology stethoscope. In some embodiments, the cardiology stethoscope may be a high-fidelity cardiology stethoscope, tunable diaphragm stethoscope, or digital cardiology stethoscope. In some embodiments, the shield or barrier may be fitted for an analog stethoscope. In some embodiments, the shield or barrier may be fitted for a digital stethoscope. In some embodiments, the digital stethoscope may be a basic digital stethoscope, digital stethoscope with noise-cancellation, wireless / Bluetooth digital stethoscope, digital stethoscope with visual display, or a cardiology digital stethoscope. In some embodiments, the shield or barrier may be fitted for a digitalization stethoscope. In some embodiments, the shield or barrier may be fitted for a doppler stethoscope. In some embodiments, the shield or barrier may be fitted for an EKGZECG (electrocardiogram) enabled stethoscope. In some embodiments, the shield or barrier may be fitted for an electronic stethoscope. In some embodiments, the shield or barrier may be fitted for a fetal, or obstetrical, stethoscope. In some embodiments, the fetal stethoscope may be a pinard horn stethoscope, fetal doppler stethoscope, electronic fetal stethoscope, or fetal monitor with a built-in stethoscope. In some embodiments, the shield or barrier may be fitted for a flexible endoscope stethoscope. In some embodiments, the shield or barrier may be fitted for an infant stethoscope. In some embodiments, the shield or barrier may be fitted for a neonatal stethoscope. In some embodiments, the shield or barrier may be fitted for a pediatric stethoscope. In some embodiments, the pediatric stethoscope may be a pediatric acoustic stethoscope or electronic pediatric stethoscope. In some embodiments, the shield or barrier may be fitted for a phonocardiograph. In some embodiments, the shield or barrier may be fitted for a recording stethoscope. In some embodiments, the shield or barrierAty Dkt No.: 52243-705601 may be fitted for a surgical stethoscope, such as a surgical acoustic stethoscope or intraoperative stethoscope. In some embodiments, the shield or barrier may be fitted for a teaching stethoscope. In some embodiments, the shield or barrier may be fitted for a traditional stethoscope. In some embodiments, the shield or barrier may be fitted for a veterinary stethoscope. In some embodiments, the veterinary stethoscope may be a veterinary acoustic stethoscope, veterinary digital stethoscope, or specialized equine stethoscope. In some embodiments, the shield or barrier may be fitted for a wired stethoscope. In some embodiments, the shield or barrier may be fitted for a wireless stethoscope. In some embodiments, the shield or barrier may be fitted for an ergonomic stethoscope, such as an ergonomic acoustic stethoscope or an ergonomic digital stethoscope. In some embodiments, the shield or barrier may be fitted for a single-use, or disposable, acoustic stethoscope or a stethoscope with another barrier. In some embodiments, the shield or barrier may be fitted for a telemedicine stethoscope, integrated stethoscope system, or smart stethoscope. In some embodiments, the shield or barrier may be fitted for a stethoscope with a built-in pulse oximeter.

[0101] In some embodiments, the stethoscope may comprise a smart stethoscope. The smart stethoscope may generate an indication of a disease risk. The smart stethoscope may comprise one or more processors. The smart stethoscope may perform one or more measurements with one or more sensors. The smart stethoscope may analyze results of one or more sensors of the smart stethoscope to determine a disease risk or disease diagnosis.

[0102] The noncompliant action may comprise not completing a digital check-in protocol of a digital check-in model. The noncompliant action may comprise not entering or confirming a code within a time period or at a predetermined time checkpoint. The time period may comprise a predetermined time period. The predetermined time period may comprise about between 2 minutes and 30 minutes after completion of a previous element of the protocol. The time period may comprise a dynamic time period. The dynamic time period may comprise a time period between which the medical provider begins an interaction with a first patient, and begins an interaction with a second patient. The interaction with the first patient may comprise the medical provider generating information relating to the first patient. The interaction with the second patient may comprise the medical provider generating information relating to the second patient. The information may comprise medical notes concerning the first patient or the second patient. The compliance of the medical provider with the protocol may be determined based at least in part on determining a number of the compliant actions or a number of the noncompliant actions. The compliance of the medical provider with the protocol may be determined based at least in part on determining a ratio ofAty Dkt No.: 52243-705601 an amount of the compliant actions to an amount of the noncompliant actions. The compliance of the medical provider with the protocol may be determined at one or more individual time points of the plurality of time points.

[0103] In some embodiments, at least a subset of the plurality of time points may be associated with a subject having a disease. The disease may comprise an infectious disease. The method further may comprise determining a likelihood of the medical provider transmitting a disease based at least in part on the compliance of the medical provider with the protocol for one or more patients.Database and loT Integration

[0104] In some embodiments, the scanning software system can be configured to perform operations comprising transmitting data to a centralized database. In some embodiments, the scanning software system can be configured to perform operations comprising securely transmitting data to a centralized database using one or more security protocols. In some embodiments, the scanning software system can be configured to perform operations comprising forming a secure connection to the centralized database. In some embodiments, the secure connection can be encrypted. In some embodiments, the scanning software system can be configured to communicate with an access control system of a database. In some embodiments, the centralized database can limit access to authorized users. In some embodiments, the scanning software system can be configured to perform real-time transmission of scan data to the centralized database using the secure connection. In some embodiments, the centralized database can be a HIPAA-compliant database. In some embodiments, the centralized database can store scan data, manage scan data, or both. In some embodiments, the scanning software system can be configured to send a request to the centralized database. In some embodiments, the scanning software system can be configured to receive data from the centralized database. In some embodiments, the scanning software is configured to encrypt data during transmission to a database and to encrypt data while stored. In some embodiments, the scanning software is configured to restrict database access to authorized personnel only using one or more authentication and authorization mechanisms. In some embodiments, the centralized database can store and manage scan data. In some embodiments, the centralized database can provide access control. In some embodiments, the access control can limit access to the centralized database to authorized users only.

[0105] In some embodiments, the scanning software system can be integrated with existing clinical workflows. In some embodiments, the scanning software system can be configured to integrate with electronic health records (EHR), electronic medical records (EMR), or customer relationship management systems (CRM), or any combination thereof. InAty Dkt No.: 52243-705601 some embodiments, the scanning software system can be integrated into the internet of things. IN some embodiments, the scanning software system can be integrated with one or more connectable devices. In some embodiments, the connectable devices can comprise sensors. In some embodiments, the sensors can comprise wearable sensors. In some embodiments, the sensors can provide monitoring of a subject. In some embodiments, the sensors can provide data concerning usage and compliance monitoring.

[0106] In some embodiments, the scanning software system can be configured to receive and execute software updates. In some embodiments, the software updates can be updates to the scanning software or updates to the database integration software. In some embodiments, the software updates can be directed to one or more of optimal performance, security, or to incorporate new features or improvements. In some embodiments, the scanning software system can be further configured to receive data concerning existing clinical workflows. In some embodiments, the scanning software system can be further configured to communicate with software, hardware, and databases comprising existing clinical workflows.

[0107] In some embodiments, the scanning software system can be configured to output instructions or training materials to users concerning directions on scanning the box QR code before opening, and using the single-use medical product, for example a ViruShield Stethoscope Barrier™. In some embodiments, the scanning software system can be configured to output instructions and training materials to healthcare professionals. In some embodiments, the instructions and training materials comprise instructions on how to scan the QR code and use the system effectively. In some embodiments, the instructions and training materials comprise onboarding support. In some embodiments, the instructions and training materials comprise gathering user feedback and user questions.

[0108] In some embodiments, the scanning software system can be configured to detect unavailable network connectivity. In some embodiments, the scanning software system can be configured to store scan data locally on the scanning device. In some embodiments, the scanning software system can be configured to store scan data locally on the scanning device upon detection of temporary network outages or limited connectivity. In some embodiments, the scanning software system can be configured to output notifications, for example reminders, without network connectivity using the locally stored data. In some embodiments, the scanning software system can be configured to detect and connect to existing Wi-Fi networks. In some embodiments, the scanning software system can be configured to detect and connect to existing Wi-Fi networks to perform location tracking. In some embodiments, the scanning software system can be configured to detect and connect to existing Wi-Fi networks to output one or more of SMS alerts, email alerts, or other alert notifications. InAty Dkt No.: 52243-705601 some embodiments, the scanning software system can be configured to categorize data that can be transmitted over an unsecured network connectivity into risk level groups. In some embodiments, data categorized in a low risk level group can be stored and utilized to output reminders when the scanning device and software are connected to an unsecured network. In some embodiments, data categorized in a low risk level group can be prevented by the scanning software from being used in clinical decision support when the when the scanning device and software are connected to an unsecured network.

[0109] In some embodiments, the scanning software system and scanning software device can be integrated into the Internet of Things (loT), for example with Smart Stethoscopes. In some embodiments, the scanning software system can be configured to connect with other loT devices such as Smart Stethoscopes. In some embodiments, this loT connection can allow the scanning software system to perform enable real-time monitoring of usage and hygiene compliance involving the loT device, for example automatic detection of when the barrier is attached and removed, recording usage duration, or outputting reminders for replacement or cleaning of the loT device.

[0110] In some embodiments, the database can store one or more data fields received from the scanning software. The one or more data fields can comprise one or more of box QR Code, location of a scan, timestamp of a scan, number of single-use medical products in a packaging such as in a box or on a roll, for example the number of ViruShield Stethoscope Barrier™ in a box or on a roll, a healthcare professional ID, and single-use medical product type. In some embodiments, the QR Code data can comprise a primary key. In some embodiments, the primary key can be required for the scanning software to access the centralized database. In some embodiments, the scanning software is configured to perform operations comprising outputting the primary key to a centralize database for data retrieval, data management, and linking of usage records to individual single-use medical products. In some embodiments, the location of scan data comprises one or more of a room, ward, and department location where each scan occurred. In some embodiments, the location of scan data can be used to determine usage patterns, potential infection hotspots, and areas that might require additional attention or resources. In some embodiments, the scanning software system is configured to capture the exact time of each scan, accurate to the second. In some embodiments, the temporal data can be used for detailed tracking of usage, duration, and potential gaps in compliance involving single-use medical products, such as ViruShield Stethoscope Solution. In some embodiments, the scanning software system can be communicatively coupled with a medical facility or medical system ID system. In some embodiments, the scanning software can retrieve a medical professional ID from the medicalAty Dkt No.: 52243-705601 facility or medical system ID system. In some embodiments, the scanning software can output the medical professional ID data to the centralized database. In some embodiments, the medical professional ID is the ID number of the healthcare professional performing the scan. In some embodiments, the medical professional ID can be used for user-specific tracking and analysis, facilitating targeted training, performance evaluations, and identification of potential areas for improvement.[oni] In some embodiments, the scanning software system can be communicatively coupled with one or more Electronic Health Records Databases (EHRs). In some embodiments, the scanning software system can match usage of the single-use medical products, for example ViruShield Stethoscope Barrier usage, to specific patient encounters contained in the EHRs. In some embodiments, the matched EHRs and usage data can be utilized for understanding infection risk, tracking potential transmission pathways, and contributing to a more comprehensive picture of patient care. In some embodiments, additional data types can comprise single-use medical product type, for example ViruShield Stethoscope Barrier TM type, cleaning status, or other relevant parameters of usage patterns or infection control practices.

[0112] In some embodiments, a centralized database can be integrated with internet of things (loT) devices. In some embodiments, centralized database integration with loT can enable real-time monitoring of usage of loT devices or interaction with loT devices, or both. In some embodiments, patient data can be analyzed to predict using AI / ML models risk factors for HAIs or SAIs, or both. In some cases, patient data can include age, health history, current health conditions, past health events, hospital stay length, disease severity, patient location, patient healthcare providers, patient demographics, patient behavior, or any combination thereof. In some embodiments, infection control protocols can be developed using patient data. In some embodiments, hygiene, sterilization, cleaning, or other protocols can be developed using patient data. In some embodiments, customized infection control protocols can be generated for each patient or a group of patients with similar characteristics based on AI / ML optimized protocol prediction from patient data input.

[0113] In some embodiments, AI / ML can be used to identify high-risk patients. In some embodiments, high-risk patients can comprise patients at high risk for a HAI or SAI, or both. In some embodiments, AI / ML systems can receive input data comprising one or more factors associated with risk of HAI or SAI, or both. In some embodiments, the input data can comprise age, underlying health conditions, length of hospital stay, or any combination thereof. In some embodiments, AI / ML systems and models can be utilized to develop infection control protocols. In some embodiments, infection control protocols can compriseAty Dkt No.: 52243-705601 hygiene protocols. In some embodiments, hygiene protocols can be directed towards one or more medical instruments, machines, devices, or products. In some embodiments, hygiene protocols can be stethoscope hygiene protocols. In some embodiments, hygiene protocols can comprise cleaning or sanitization protocols. In some embodiments, input including patient risk factors can be analyzed to identify using AI / ML models or systems, previously unknown risk factors.

[0114] In some embodiments, AI / ML models and systems can be used to generate a risk score. In some embodiments, the risk score can be specific to a patient. IN some embodiments, the risk score can be based on patient data. In some cases, the patient data can comprise age, health history, current health conditions, past health events, hospital stay length, disease severity, patient location, patient healthcare providers, patient demographics, patient behavior, or any combination thereof. In some embodiments, the AI / ML models or systems can receive input data comprising data related to pathogens in the environment. In some embodiments, AI / ML models or systems can generate predictive risk scores or optimized protocols based on risk factors and pathogens in the environment for a patient location or a healthcare facility, or both.

[0115] In some embodiments, the scanning software can initiate regular backup procedures and disaster recovery plans.Artificial Intelligence and Machine Learning Models for Infection Control

[0116] In some embodiments, a system can comprise one or more artificial intelligence / machine learning (AI / ML) modules configured to predicts and manage potential disease outbreaks. In some embodiments, usage data of a single-use healthcare product such as a stethoscope barrier can be tracked using QR code scanning and linked to specific patient encounters using data within the EMR. In some embodiments, the AI / ML modules can be configured to identify and address potential infection risks through detailed usage tracking.

[0117] In some embodiments, AI / ML models or systems can comprise one or more of supervised clustering algorithms, unsupervised clustering algorithms, artificial neural networks (ANN), deep learning algorithms, decision trees, random forests, support vector machines (SVM), reinforcement learning, k-nearest neighbors (KNN), Bayesian networks, genetic algorithms, natural language processing (NLP), fuzzy logic systems, association rule learning, regression algorithms, classification algorithms, dimensionality reduction algorithms, ensemble algorithms, gradient boosting and AdaBoost, convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), Markov decision process, Q-learning, temporal difference (TD), Monte Carlo treeAty Dkt No.: 52243-705601 search, autoencoders, GANs (generative adversarial networks), linear discriminant analysis, perceptron, elastic net, XGBoost, LightGBM, CatBoost, optimal decision trees, support vector regression (SVR), logistic regression, ridge regression, lasso regression, Naive Bayes, latent Dirichlet allocation (LDA), or hierarchical clustering, or any combination thereof. In some embodiments, the AI / ML models or systems can be operatively coupled to natural language processing (NLP) software. In some embodiments, NLP software can comprise one or more large language models.

[0118] In some embodiments, the AI / ML modules can be configured to predictively perform pattern recognition to perform one or more of: determine usage patterns corresponding to inventory levels, to monitor compliance with infection control protocols, to determine risk areas for noncompliance, to forecast supply needs and potential outbreaks, to perform contact tracing, to optimize infection control protocols, to allocate resources efficiently, and to tailor staff training programs.

[0119] In some embodiments, the system can receive as input healthcare professional and patient IDs, and the AI / ML modules of the system can analyze usage data to create predicted contact chains in case of an infection. In some embodiments, the system can output predictive recommended targeted testing and quarantine measures.

[0120] In some embodiments, the AI / ML modules of the system can predict the most effective delivery method and time for reminders, can generate gamification elements, and can generate personalized feedback to motivate healthcare professionals to adhere to infection control protocols.Training and Algorithms of AI / ML Systems for Infection Control

[0121] In some embodiments, the system further comprises one or more feedback mechanisms. In some embodiments, the one or more feedback mechanisms are configured to receive user feedback and generate output in response to the user feedback.

[0122] In some embodiments, AI / ML models and systems can be utilized to monitor usage of one or more medical devices, such as stethoscopes. In some embodiments, usage tracking data can be input to AI / ML systems for performance of pattern recognition in usage that may be associated with HAIs, SAIs, or both. In some embodiments, outbreaks of disease can be predicted by AI / ML models or systems from usage input data. In some embodiments, computer systems can be configured to use blockchain technology to secure usage data, for example a blockchain ledger for records of usage data.

[0123] In some embodiments, the system is configured to receive location and timestamp data using a scanning software from each QR code scan of the single-use medical product orAty Dkt No.: 52243-705601 packaging thereof, for example ViruShield Stethoscope Barrier™ dispenser rolls. In some embodiments, the system can be configured to receive data comprising WR code scan data from dispensers such as dispenser boxes or individually packaged medical products such as the ViruShield Stethoscope Barrier™ . In some embodiments, the single-use medical product can comprise a barrier or shield. In some embodiments, the barrier or shield can be disposable. In some embodiments, the barrier or shield can be contained within packaging. In some embodiments, the packaging can comprise sterile packaging. In some embodiments, the packaging can comprise packaging that facilitates contact with the exterior of the packaging only.

[0124] In some embodiments, gamification software can be used to encourage compliance with infection control protocols. For example, users such as healthcare providers can receive rewards through a software when practicing compliance with infection protocols, for example when practicing cleaning, sanitization, utilizing a barrier or shield, or other actions in compliance with infection control protocols.

[0125] In some embodiments, AI / ML models and systems can be used to generate one or more alerts for predicted lapses in compliance with infection control protocols. In some embodiments, software systems can be used to monitor healthcare worker behavior in realtime. In some embodiments, systems can use one or more hardware elements, such as one or more imaging devices or sensors, to detect healthcare worker activity. In some embodiments, the software system can detect hygiene activities. In some embodiments, hygiene activities can comprise hand washing, PPE usage, cleaning activity, usage of a barrier or shield, proper disposal of medical supplies or other medical materials, compliance with patient-specific protocols, or other healthcare provider actions. In some embodiments, the software can comprise AI / ML models or systems. In some embodiments, the AI / ML models or systems can predict one or more infection control lapses from received hygiene activity detection data. In some embodiments, the software can generate an alert based on the predicted one or more infection control lapses.

[0126] In some embodiments, AI / ML models and systems can be used to generate optimized infection control education, or customized infection control education, or both. In some embodiments, AI / ML models and systems can receive input comprising healthcare worker data. In some embodiments, the healthcare worker data can comprise healthcare worker hygiene practices, healthcare worker compliance with infection control protocols, healthcare worker movements, healthcare worker preferred learning style, healthcare worker daily tasks, or other healthcare worker related data. In some embodiments, AI / ML models andAty Dkt No.: 52243-705601 systems can be configured to generate educational programs or protocols based on the healthcare worker data.

[0127] In some embodiments, AI / ML models or systems can be configured to generate one or more simulated experiences related to infection control protocols. In some embodiments, the simulated experience can comprise interaction with a simulated patient. In some embodiments, the simulated experience can comprise simulation of infection protocol steps. In some embodiments, software systems, which can comprise AI / ML models or systems, can be operatively connected to one or more display devices. In some embodiments, the one or more display devices can comprise a computer. In some embodiments, the one or more display devices can comprise a virtual reality device, a smartphone, a tablet, or another display device. In some embodiments, the software systems can generate the simulation using the display device, for example to a healthcare worker.

[0128] In some embodiments, federated learning techniques can be implemented to preserve privacy in data analysis. In some embodiments, data received by AI / ML models or systems can comprise de-identified data, secure data, pooled data, or any combination thereof.

[0129] In some embodiments, the system comprises one or more cleaning software modules configured to clean data received as input. In some embodiments, cleaning the data comprises predictively detecting and removing any errors, inconsistencies, or missing values from a data set. In some embodiments, the system further comprises a data conversion module. In some embodiments, the data conversion module is configured to convert location data into a suitable format for clustering algorithms. In some embodiments, the data is converted by the data conversion module into latitude and longitude coordinates, or discrete location identifiers. In some embodiments, additional data can be input such as time of day, day of the week, or shift information, to enhance the clustering analysis.

[0130] In some embodiments, the system further comprises a clustering analysis module configured to select and perform an unsupervised clustering algorithm based on the characteristics of the data and desired outcomes. The clustering algorithms can comprise for example K-means clustering, or DBSCAN (Density-Based Spatial Clustering of Applications with Noise). In some embodiments, the AI / ML model can be trained using the selected clustering algorithms. In some embodiments, hyperparameter tuning can be used by the AI / ML model to optimize hyperparameters of the clustering algorithms.

[0131] In some embodiments, the system further comprises a hotspot identification module. In some embodiments, the hotspot identification module is configured to perform cluster analysis on the input data. In some embodiments, the hotspot identification module can be further configured to perform hotspot labeling of clusters for infection transmission. InAty Dkt No.: 52243-705601 some embodiments, hotspot labeling can comprise applying a clustering algorithm to location and timestamp data to dynamically predict clusters or "hotspots" of high usage of single-use medical products, for example high ViruShield Stethoscope Barrier™ usage without the need for predefined thresholds. In some embodiments, the hotspot labeling module can dynamically modify predicted clusters in response to changing usage patterns. In some embodiments, hotspot pattern changes can be tracked over time. In some embodiments, the system further comprises an intervention assessment module. In some embodiments, the intervention assessment module can predictively assess the effectiveness of one or more interventions using monitoring data comprising changes in hotspot patterns over time.

[0132] In some embodiments, the AI / ML models of the system can receive feedback training data to update the clustering model for changing usage patterns to maintain accuracy. In some embodiments, the feedback can be received from infection control teams and healthcare professionals. In some embodiments, the received feedback can be used to further train the AI / ML modules and update the predictive hotspot identification.Infection Control Monitoring Using Scanning Data Records

[0133] In some embodiments, the system can receive input data comprising check-in data or check-out data, or both. In some embodiments, check-in and check-out data can be received form scanning the QR code before and after each use of the single-use medical product, for example the ViruShield Stethoscope Barrier™. In some embodiments, check-in data comprises recorded QR code scan data at the start time of use of the single-use medical product. In some embodiments, the QR code scan data comprises one or more of the unique QR code, location of the scan, timestamp, the healthcare professional's ID, or the patient's ID.

[0134] In some embodiments, the system can be configured to securely transmit the QR code scan data to the centralized database. In some embodiments, the database can register the single-use medical product such as the ViruShield Stethoscope Barrier™ as "in use". In some embodiments, the database can match the “in use” QR code scan data to the appropriate location, user, and patient.

[0135] In some embodiments, the system can output check-out data to the database. In some embodiments, after finishing use of the single-use medical product, the healthcare professional can scan the QR code a second time. In some embodiments, once the QR code has been scanned a second time the system can update the local database, the central database, or both, marking the single-use medical product as "out of use" and recording the precise end time of its usage.Aty Dkt No.: 52243-705601

[0136] In some embodiments, the system can automatically calculate the duration of use, providing valuable data for analysis.

[0137] In some embodiments the system can be configured to flag any unusually long or short usage periods that might indicate potential issues or deviations from standard protocols, or instances when a patient encounter occurred with no accompanying check-in scan, prompting further investigation.

[0138] In some embodiments, HAIs and SAIs can be predictively modeled in silico by AI / ML models or systems. In some embodiments, synthetic patient data can be generated as input for the AI / ML models or systems. In some embodiments, the AI / ML models or systems can predictively model one or more infection control strategies. In some embodiments, the AI / ML models or systems can predictively model one or more interactions between one or more factors from synthetic patient data and one or more infection control strategies. In some embodiments, the AI / ML models or systems can predict a generalized infection pattern. In some embodiments, the AI / ML models or systems can predict a contact tracing map. In some embodiments, the AI / ML models can predict the risk of infection for one or more simulated patients using the in silico data.

[0139] In some embodiments, AI / ML models or systems can generate one or more predictive simulated outbreak models. In some embodiments, the AI / ML models or systems can generate one or more modified simulated outbreak models based on one or more inputs. In some embodiments, the one or more inputs can comprise selected infection control actions or protocols. In some embodiments, the AI / ML models or systems can generate a predictive result output of the simulated outbreak model. In some embodiments, the predictive result output can comprise a predictive evaluation of the outcome of the simulated outbreak model. In some embodiments, the predictive evaluation can comprise a total, such as an infection rate total, for example an HAI or SAI infection rate total, a total number of patients infected, a map of area affected, a percentage of infections, total number of infections, a value representing deaths, a value representing survival, or another metric.Infection Control Monitoring Integrating Electronic Medical Records (EMR), Electronic Health Records (EHR), and / or Customer Relationship Management Data (CRM)

[0140] In some embodiments, the system can comprise an electronic medical record (EMR) integration module. In some embodiments, the system can comprise an electronic health record (EHR) integration module. In some embodiments, the system can comprise a customer relationship management data (CRM) integration module. In some embodiments, the EMR or EHR or CRM integration module can be communicatively coupled with an EMRAty Dkt No.: 52243-705601 database system, an EHR database or system, or a CRM database or system. In some embodiments, the EMR or EHR or CRM integration module can be configured to assess compatibility of the scanning software with one or more target EMR system(s), one or more target EHR systems, or one or more target CRM systems. In some embodiments, the one or more target EMR systems, target EHR systems, or target CRM systems can be used in a preacute, acute, or post-acute location. Identify the appropriate integration points and data exchange mechanisms.

[0141] In some embodiments, the system can perform Data Mapping. In some embodiments, Data Mapping comprises defining the specific data elements to be exchanged between the single-use medical product, for example the ViruShield Stethoscope Barrier™ and the EMRs. In some embodiments, the data mapped comprises the QR code mapped to a unique identifier in the EMR data, EHR data, or CRM data, such as device ID or serial number, Patient ID, Healthcare professional ID, timestamp of usage, or location of usage.

[0142] In some embodiments, the system receives an indication to transfer data from a local database to the EMR database, EHR database, or CRM database. In some embodiments, the indication can comprise a real-time QR code scan, a time-dependent automatic transfer, in response toa synchronization request from the EMR database, EHR database, or CRM database, or in response to a validation check.

[0143] In some embodiments, the system can be configured to request that the usage data be integrated into one or more specified sections of the patient's EMR, EHR, or CRM. In some embodiments, visualizations can be provided within the EMR, EHR, or CRM to represent usage patterns and trends over time.

[0144] In some embodiments, the system can filter and analyze the integrated data to gain insights into infection control practices, identify potential transmission pathways, and support clinical decision-making.

[0145] In some embodiments, EHR data, EMR data, or CRM data, or any combination thereof, can be received as input by the AI / ML models or systems. In some embodiments, EHR data, EMR data, or CRM data, or any combination thereof, can be input into AI / ML models or systems to track HAI trends, SAI trends, or both. In some embodiments, the AI / ML models or systems can generate predictive trend models of HAIs and SAIs, predict outbreaks, and predict intervention effectiveness from the trend or input data, or both.

[0146] In some embodiments, AI / ML models and systems can be used across various healthcare facilities to detect best practices or harmful practices. In some embodiments, AI / ML models and systems can be utilized to predict optimized practices for healthcare facilities. In some embodiments, AI / ML models or systems can receive input comprising HAIAty Dkt No.: 52243-705601 rates, SAI rates, or both for one or more healthcare facilities. In some embodiments, AI / ML models and systems can be configured to generate predictions of optimized practices or risk groupings based on the HAI and SAI input.AI / ML Systems for Monitoring Compliance Using Predicted Usage Patterns

[0147] In some embodiments, the system can be configured to analyze usage data to identify high-traffic areas or departments with frequent use, and AI / ML modules of the system can predictively indicate hotspots for infection transmission.

[0148] In some embodiments, the system can be configured to perform compliance monitoring. In some embodiments, compliance monitoring can comprise the system generating reports on scan rates. In some embodiments, infection control teams can monitor adherence to infection control protocols and identify individuals or departments with low compliance. In some embodiments, the system can output automated reminders or alerts. In some embodiments, automated reminders or alters can be triggered by missed QR code scans.

[0149] In some embodiments, the system can identify which single-use medical product, for example ViruShield Stethoscope Barrier™ was used on affected patients and trace their movement and usage history. In some embodiments, the system can predict potential transmission pathways and identify at-risk individuals, enabling targeted testing, quarantine measures, and other interventions to curtail the spread of infection.

[0150] In some embodiments, the system can be configured to perform predictive modeling for outbreak forecasting. In some embodiments, predictive modeling for outbreak forecasting can comprise receiving input comprising historical data to predictively identify potential risks. In some embodiments, the historical data can be mapped to data from Electronic Health Records (EHRs) such as patient outcome data.

[0151] In some embodiments, the system can comprise one or more AI / ML models utilizing predictive ML algorithms to detect unusual patterns or outliers in the usage data. In some embodiments, unusual patterns can comprise abnormally long or short usage times, scans in unexpected locations, or sudden drops in compliance.AI / ML Models or Systems Output to Users

[0152] In some embodiments, the system can connect to the hospital's Wi-Fi network or Bluetooth Low Energy (BLE) beacons to detect single-use healthcare products such as ViruShield Stethoscope Barrier™ that are in close proximity to scanning devices but were not scanned recently. In some embodiments, the system can output gentle reminder notificationsAty Dkt No.: 52243-705601 to nearby devices associated with healthcare professionals, prompting them to complete the necessary scans.

[0153] In some embodiments, the system can incorporate elements of gamification to create a positive culture around infection prevention and boost compliance rates. In some embodiments, gamification can comprise awarding points, badges, or recognition on leaderboards for consistent usage aligned with protocols.

[0154] In some embodiments, the system can output notifications comprising clear scan confirmations and educational text pop-ups within the app after each scan. These pop-ups can reinforce proper hygiene practices and increase awareness about infection prevention.

[0155] In some embodiments, the system can output customizable reports. In some embodiments, the customizable reports can comprise reports visualizing usage data, compliance rates, or potential hotspots, or any combination thereof.

[0156] In some embodiments, the system can comprise a GUI display for visualizing the identified hotspots on a map or floor plan of a healthcare facility, highlighting areas with higher usage intensity.Natural Language AI / ML Processing for User Feedback Interpretation and Model Feedback Training

[0157] In some embodiments, the system can receive input comprising feedback from healthcare professionals on their experiences with the system. In some embodiments, the feedback can comprise open-ended text fields for comments and suggestions, rating scales to gauge overall satisfaction, specific questions about usability, features, and perceived impact on infection control. In some embodiments, the system can store user feedback data securely, ensuring compliance with data privacy regulations.

[0158] In some embodiments, the system comprises one or more LLMs for predictively detecting speech patterns of the feedback data. In some embodiments, the system can clean the data prior to analyzing the data. In some embodiments, cleaning the data can comprise converting input user feedback text to lowercase, removing punctuation, standardizing abbreviations or acronyms, eliminating common words such as "the," "and," and "is”, reducing words to their root forms, and eliminating missing values, typos, and irrelevant information.

[0159] In some embodiments, the system can comprise a sentiment analysis model configured to execute a sentiment analysis algorithm, for example Lexicon-based approach such as VADER, or SentiWordNet, or a Machine learning-based approach such as Naive Bayes, Support Vector Machines, or deep learning models.Aty Dkt No.: 52243-705601

[0160] In some embodiments, the sentiment analysis model can be trained on a labeled dataset of healthcare-related text with sentiment annotations. In some embodiments, the system can apply the sentiment analysis model to categorize user feedback as positive, negative, or neutral. In some embodiments, the system can further comprise a topic modeling module trained on preprocessed user feedback data to discover latent topics or themes. In some embodiments, topic modeling can comprise Latent Dirichlet Allocation (LDA) or Nonnegative Matrix Factorization (NMF). In some embodiments, the system can be configured to perform sentiment analysis comprising using an LLM to track overall sentiment trends by user group, location, or other relevant factors over time to gauge user satisfaction and identify potential issues.

[0161] In some embodiments, the system can output tailored reports to different audiences based on sentiment analysis results, such as infection control teams, hospital administrators, or product development teams.Artificial Intelligence and Machine Learning Models for Inventory Control and Inventory Management

[0162] In some embodiments, the system can track inventory levels and optimize supply chain management. In some embodiments, the system can predict restocking needs by monitoring usage frequency.

[0163] In some embodiments, the system can comprise a product design AI / ML model configured to generate various color, shape, and texture options for non-reusable medical products such as the stethoscope barrier.

[0164] In some embodiments, AI / ML models and systems can be configured to predict and model reimbursement for non-reusable medical products. In some embodiments, AI / ML models and systems can receive data comprising federal reimbursement data such as Medicare reimbursement data, Medicaid reimbursement data, veteran’s affairs reimbursement data, state-level medical reimbursement data, or any combination thereof for one or more non-reusable medical products. In some embodiments, AI / ML models and systems can receive input data comprising cost of non-reusable medical products, usage data of non- reusable medical products, HAIs, SAIs, or any combination thereof. In some embodiments, AI / ML models and systems can be configured to predict outcomes related to reimbursement for non-reusable medical products based on input data comprising HAI modeling or SAI modeling in relation to usage data and reimbursement data. In some embodiments, AI / ML models and systems can be configured to generate one or more financial predictions, for example reimbursement amount prediction, or estimates of success of reimbursement for nonAty Dkt No.: 52243-705601 reusable medical products from input comprising predictive HAI models, predictive SAI models, usage data, or reimbursement data, or any combination thereof.EXAMPLES

[0165] The following examples are provided to further illustrate some embodiments of the present disclosure, but are not intended to limit the scope of the disclosure; it will be understood by their exemplary nature that other procedures, methodologies, or techniques known to those skilled in the art may alternatively be used.EXAMPLE 1: Hotspot Identification

[0166] Data Collection: The system collects data on every scan of a ViruShield Stethoscope Barrier TM dispenser roll, recording the QR code, location (e.g., room number, ward), and timestamp.

[0167] Preprocessing: The data is cleaned and transformed into a suitable format, and additional features like time of day are added.

[0168] Clustering: A DBSCAN algorithm is trained on the data, grouping scans that are close in space and time into clusters.

[0169] Hotspot Identification: Clusters with a high frequency of scans are identified as potential hotspots.

[0170] Visualization: A heatmap is generated, overlaying the hotspots onto the hospital floor plan, with warmer colors indicating areas of higher ViruShield Stethoscope BarrierTM usage.

[0171] Intervention: Infection control teams focus their efforts on these hotspot areas, implementing targeted cleaning and education programs.

[0172] Monitoring and Refinement: The system continues to collect data and retrain the clustering model periodically, adapting to changes in usage patterns and enabling ongoing optimization of infection control efforts.

[0173] By incorporating unsupervised clustering for hotspot identification, the system can dynamically adapt to evolving usage patterns and proactively identify areas of increased infection risk, further enhancing its value proposition and contributing to a safer healthcare environment.

[0174] In one non-limiting exemplary workflow 100 as shown in FIG. 1, QR code scan data 101 can be received by a predictive compliance system 102. The predictive compliance system 102 can comprise a provider ID, data relating to past provider QR code scans, HAI data, SAI data, or any combination thereof. As shown in FIG. 1, EHR data, EMR data, CRMAty Dkt No.: 52243-705601 data, or any combination thereof, from a database 103 can be received as input. The EHR data or EMR data can comprise one or more patient records. As illustrated in FIG. 1, the patient records of the database 103 can comprise a patient ID, patient medical procedure data, time of patient medical procedure log data, healthcare provider ID, or any combination thereof. As shown in FIG. 1, data from the predictive compliance system 102 and data from EHR, EMR, or CRM databases 103 can be compared to generate a healthcare provider ID match 104, a time event match 105, or both. As shown in FIG. 1, for example, AI / ML models or systems can generate a provider usage pattern 106 from healthcare provider ID match data 104 or time event match data 105, or both. As shown in FIG. 1, for example, AI / ML models or systems can generate one or more HAI prediction patterns or SAI prediction patterns 107, or both, from healthcare provider ID match data 104 or time event match data 105, or both. As shown in FIG. 1, for example, one or more of provider usage patterns 106, HAI prediction patterns, or SAI prediction patterns 107, or any combination thereof can be used to generate an output 108 comprising one or more of compliance prediction, infection pattern identification, cost prediction models, inventory management results, or any combination thereof.

[0175] As illustrated, for example, in FIG. 2, AI / ML models and systems can receive a variety of inputs, process using a variety of models and algorithms, and generate a variety of outputs. As illustrated in the workflow 200 of FIG. 2, an AI / ML model or system 202 can receive input data 201 comprising HAI data, SAI data, provider use data of, for example a barrier or shield, infection control protocol data, patient CRM data, patient EHR data, patient EMR data, or any combination thereof. As illustrated in, for example FIG. 2, the AI / ML model or system 202 can comprise one or more models performing one or more functions such as HAI transmission pattern modeling, SAI transmission pattern modeling, anomaly detection, infection control modeling, inventory management modeling, HAI cost savings modeling, SAI cost savings modeling, contact tracing modeling, or hotspot identification modeling, or any combination thereof. As illustrated in FIG. 2, the AI / ML model or system 202 can utilize the models to generate output 203 comprising one or more of HAI transmission pattern prediction such as stethoscope-related transmission pattern prediction, infection control systems transmission pattern prediction, predicted infection risk transmission pattern prediction, or comprising inventory alerts, inventory messages, predicted infection alerts, predicted infection messages, predictive hotspot identification, cost savings calculators for HAI and SAI such as hospital-related cost savings calculators, healthcare provider office related cost savings calculators, ambulance or first responders cost savings calculators, or comprising modification of CRM data, EHR data, or EMR data.Aty Dkt No.: 52243-705601

[0176] As illustrated in, for example, FIG. 3, AI / ML system module types can be numerous and associated with a wide range of functionalities. For example, as illustrated in FIG. 3, AI / ML models or systems can generate instructional videos, animations, tutorials, and quizzes for users as part of a user experience and education: instructional materials module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can develop Al chatbots or virtual assistance for user guidance and support as part of a user experience and education: personalized onboarding module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can analyze data on HAI transmission or SAI transmission, or both and stethoscope usage for product improvement as part of a beyond the product: research and development module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can improve QR code readability and scanning capability as part of a QR code design optimization module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can enable system function without network connectivity as part of an offline functionality module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can provide scan confirmations and educational pop-ups as part of a user feedback and education module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can present usage data and compliance rates clearly as part of a data visualization module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can provide clinical and non-clinical reminders using existing infrastructure as part of passive usage reminders infrastructure. As further illustrated, for example, in FIG. 3, AI / ML models or systems can integrate tracking of stethoscope usage data such as lot number, patient name, date, and time, with patient records in EMR databases, EHR databases, and CRM databases as part of an EMR, EHR, and CRM tie-in module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can identify individually potentially exposed to infection or at risk of infection as part of a contact tracing module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can flag potential issues in usage data, exposure data, or contact tracing data as part of an anomaly detection module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can predict future trends and inform clinical decisions as part of a predictive modeling module. As further illustrated, for example, in FIG. 3, AI / ML models or systems can integrate a checkbox and modify health records as part of a CRM, EHR, and EMR integration module.EXAMPLE 2: LLM Feedback Sentiment Analysis

[0177] Healthcare professionals provide feedback through the app, expressing concerns about QR code scannability in low-light conditions.Aty Dkt No.: 52243-705601

[0178] Natural Language Processing (NLP) sentiment analysis identifies this as a negative sentiment and topic modeling categorizes it under "Usability Issues."

[0179] The development team receives this insight and prioritizes improvements to the QR code scanning functionality in the next app update.

[0180] By incorporating NLP for user feedback analysis, the system and users can gain valuable insights into user experiences, identify areas for improvement, and proactively address concerns, ultimately leading to a more effective and user-friendly infection control solution.

[0181] In some embodiments, the system can provide one or more of instructional videos, personalized onboarding support, or Al chatbots for user guidance. In some embodiments, the system can further comprise software directed to gamification. In some embodiments, gamification can be applied to compliance, training, watching instructional materials, or any combination thereof. In some embodiments, the system can generate one or more report outputs. In some embodiments, the report outputs can be customizable. In some embodiments, the report outputs can comprise usage data, compliance rates, training compliance, assessment of compliance data, assessment of training compliance data, or any combination thereof.EXAMPLE 3: Inventory Management Forecasting

[0182] Gather historical usage data for each ViruShield Stethoscope BarrierTM, including: Timestamps of check-ins and check-outs, Duration of each use, and Location of use.

[0183] Data Preprocessing: Clean and organize the data, addressing any missing values, inconsistencies, or outliers.

[0184] Feature Engineering: Create relevant features for time series forecasting, such as: Total usage time, Frequency of use, Time since last maintenance, Environmental factors (if relevant data is available).

[0185] Perform Time Series Forecasting Model Development comprising:

[0186] Model Selection: Choose an appropriate time series forecasting model based on the data characteristics and desired prediction horizon. Common options include: ARIMA (Autoregressive Integrated Moving Average), Exponential Smoothing, Prophet (Facebook's open-source forecasting tool), Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks for more complex patterns.

[0187] Train the selected model on the historical usage and maintenance data.Aty Dkt No.: 52243-705601

[0188] Hyperparameter Tuning: Optimize the model's hyperparameters to achieve the best predictive performance.

[0189] Evaluation: Evaluate the model's accuracy using appropriate metrics (e.g., Mean Absolute Error, Root Mean Squared Error) on a hold-out validation set.

[0190] Perform Predictive Maintenance Implementation:

[0191] Prediction Generation: Use the trained model to generate predictions for each non-reusable medical product such as a ViruShield Stethoscope Barrier, estimating its remaining useful life or time until next maintenance.

[0192] Threshold Setting: Define thresholds for triggering replacement recommendations based on acceptable risk levels and the cost of downtime or failure.EXAMPLE 4: EMR, EHR, CRM, SaaS, and API Portal Integration

[0193] A nurse scans the QR code on a non-reusable medical product such as a ViruShield Stethoscope Barrier™ before using it on a patient.

[0194] The system captures scan data (e.g., healthcare professional ID, patient ID, timestamp, and location).

[0195] The barrier usage data is securely transmitted to the EMR software, EHR software, CRM software, or SaaS software via the API.

[0196] The EMR software, EHR software, CRM software, or SaaS software integrates the stethoscope usage data into the patient's record.

[0197] The infection control team can later access the EMR software, EHR software, CRM software, or SaaS software to analyze stethoscope usage patterns and identify potential infection risks or areas for improvement in infection control practices.

[0198] As illustrated in FIG. 4A, a user can input data into a user interface comprising a clinical and financial model input, for example a number of physician and patient events per year involving a stethoscope, knowledge of number of patient HAIs, knowledge of attributable cost of HAIs, knowledge of attributable LOS of HAIs, which can include “no”, and infection data for various infection types. As illustrated in FIG. 4A, the infection types can include catheter-associated urinary tract infections (CAUTI), surgical site infections (SSI), catheter-related bloodstream infection (CRB SI), ventilator-associated pneumonia (VAP), methicillin-resistant staphylococcus aureus infection (MRSA), and Clostridioides difficile infection (C. DIFF). As illustrated in FIG. 4A, each infection type can include input fields to input one or more of total annual infection number, attributable cost per infection, and attributable LOS per infection. As illustrated in, for example, FIG. 4A, this data can be for a hospital facility or system.Aty Dkt No.: 52243-705601

[0199] As illustrated in FIG. 4B, results of the software such as AI / ML models or systems based on the input data can be displayed as a table predicting total infections for each infection type, predicting total excess costs for each infection type, and predicting total excess hospital days for each infection type, as well as a sum total of predicted infections, a sum total of predicted excess costs, and a sum total of predicted excess hospital days. As illustrated in FIG. 4B, this predictive costs and hospital days data due to HAIs can be for a hospital facility or system.

[0200] As illustrated in FIG. 5A, a user can input data into a user interface comprising a clinical and financial model input, for example a number of physician and patient events per year involving a stethoscope, knowledge of number of patient HAIs or SAIs, knowledge of attributable cost of HAIs or SAIs, knowledge of attributable LOS of HAIs or SAIs, which can include “no”, and infection data for various infection types. As illustrated in FIG. 5A, the infection types can include catheter-associated urinary tract infections (CAUTI), surgical site infections (SSI), catheter-related bloodstream infection (CRBSI), ventilator-associated pneumonia (VAP), methicillin-resistant staphylococcus aureus infection (MRSA), and Clostridioides difficile infection (C. DIFF). As illustrated in FIG. 5A, each infection type can include input fields to input one or more of total annual infection number, attributable cost per infection, and attributable LOS per infection. As illustrated in, for example, FIG. 5A, this data can be for a skilled nursing facility, or a subacute long-term care hospital, or a long-term acute care hospital, or any combination thereof.

[0201] As illustrated in FIG. 5B, results of the software such as AI / ML models or systems based on the input data can be displayed as a table predicting total infections for each infection type, predicting total excess costs for each infection type, and predicting total excess hospital days for each infection type, as well as a sum total of predicted infections, a sum total of predicted excess costs, and a sum total of predicted excess hospital days. As illustrated in FIG. 5B, this predictive costs and hospital days data due to HAIs and SAIs can be for a skilled nursing facility, or a subacute long-term care hospital, or a long-term acute care hospital, or any combination thereof.

[0202] As illustrated in FIG. 6A, a user can input data into a user interface comprising a clinical and financial model input, for example a number of physician and patient events per year involving a stethoscope, knowledge of number of patient HAIs or SAIs, knowledge of attributable cost of HAIs or SAIs, knowledge of attributable LOS of HAIs or SAIs, which can include “no”, and infection data for various infection types. As illustrated in FIG. 6A, the infection types can include catheter-associated urinary tract infections (CAUTI), surgical site infections (SSI), catheter-related bloodstream infection (CRBSI), ventilator-associatedAty Dkt No.: 52243-705601 pneumonia (VAP), methicillin-resistant staphylococcus aureus infection (MRSA), and Clostridioides difficile infection (C. DIFF). As illustrated in FIG. 6A, each infection type can include input fields to input one or more of total annual infection number, attributable cost per infection, and attributable LOS per infection. As illustrated in, for example, FIG. 6A, this data can be for a physician office.

[0203] As illustrated in FIG. 6B, results of the software such as AI / ML models or systems based on the input data can be displayed as a table predicting total infections for each infection type, predicting total excess costs for each infection type, and predicting total excess hospital days for each infection type, as well as a sum total of predicted infections, a sum total of predicted excess costs, and a sum total of predicted excess hospital days. As illustrated in FIG. 6B, this predictive costs and hospital days data due to HAIs and SAIs can be for a physician office.

[0204] As illustrated in FIG. 7A, a user can input data into a user interface comprising a clinical and financial model input, for example a number of physician and patient events per year involving a stethoscope, knowledge of number of patient HAIs or SAIs, knowledge of attributable cost of HAIs or SAIs, knowledge of attributable LOS of HAIs or SAIs, which can include “no”, and infection data for various infection types. As illustrated in FIG. 7A, the infection types can include catheter-associated urinary tract infections (CAUTI), surgical site infections (SSI), catheter-related bloodstream infection (CRBSI), ventilator-associated pneumonia (VAP), methicillin-resistant staphylococcus aureus infection (MRSA), and Clostridioides difficile infection (C. DIFF). As illustrated in FIG. 7A, each infection type can include input fields to input one or more of total annual infection number, attributable cost per infection, and attributable LOS per infection. As illustrated in, for example, FIG. 7A, this data can be for nurse interactions with patients, EMT interactions with patients, first responder interactions with patients, ambulances, or any combination thereof.

[0205] As illustrated in FIG. 7B, results of the software such as AI / ML models or systems based on the input data can be displayed as a table predicting total infections for each infection type, predicting total excess costs for each infection type, and predicting total excess hospital days for each infection type, as well as a sum total of predicted infections, a sum total of predicted excess costs, and a sum total of predicted excess hospital days. As illustrated in FIG. 7B, this predictive costs and hospital days data due to HAIs and SAIs can be for nurse interactions with patients, EMT interactions with patients, first responder interactions with patients, ambulances, or any combination thereof.

[0206] As illustrated in, for example, FIG. 8, EHR, EMR, or CRM systems or software can be modified to integrate a section, such as a checkbox, asking the practitioner to checkAty Dkt No.: 52243-705601 yes or no on whether a barrier or shield such as the ViruShield Stethoscope Barrier™ or a dispenser such as a box dispenser was used during a patient encounter. In some embodiments, the EHR, EMR, or CRM systems or software can prevent the practitioner from advancing or interacting with the software until the desired box is checked. In some embodiments, if the undesired box is checked, an alert can be sent. In some embodiments, the alert can be to a central system or to the practitioner, or both.EXAMPLE 5: Infection Control Protocol Generation and Implementation

[0207] For example, prior to rendering care, subject to applicable Federal, State, and local laws, relevant parties such as legal counsel & Chief Medical Officer (CMO) of a medical system or provider location can research, draft, and deploy sterile procedures to prevent hospital-acquired infections suffered by patients and clinical staff. Clinical staff can comprise, for example, physicians, nurses, CNA’s, orderlies, housekeeping & janitorial staff, and all other appropriate staff.

[0208] In some embodiments, relevant parties can approve stethoscope-related HAI / SAI protocols. This approval can be followed by adopting software, implementing training, beginning stethoscope-related HAI / SAI data collection using approved methodologies, and data collection through CRM / EMR / EHR systems. In some embodiments, all written sterile policies, procedure, practices, and trainings can be followed to prevent hospital-acquired infection(s). In some embodiments, this may include creation of a uniform medical device regulatory monitoring program, which may include assistance from WHO Infection Prevention and Control in Health Care initiative, CDC, FDA, OSHA.

[0209] In some embodiments, an exemplary infection control protocol optimized or customized using the AI / ML models or systems can include the steps: (1) Prior to arrival of patient, thoroughly wash hands with disinfectant soap & warm water, air dry, and apply Personal Protective Equipment (PPE). (2) Remove all non-sterile hospital equipment, and sterilize same before and after each and every patient encounter. Repeat steps (1) and (2) following departure of patient.

[0210] In some embodiments, an exemplary infection control protocol optimized or customized using the AI / ML models or systems can further include the steps: (3) Following arrival of patient, thoroughly wash hands with disinfectant soap & warm water, air dry, and apply Personal Protective Equipment (PPE). Repeat following departure of patient. Repeat Steps (2) - (3).

[0211] In some embodiments, an exemplary infection control protocol optimized or customized using the AI / ML models or systems can include the steps: (4) Break seal fromAty Dkt No.: 52243-705601 prior evening’s disinfection of stethoscope’s diaphragm, tube, insert stethoscope into ViruShield Stethoscope Barrier TM. (5) Use stethoscope as normal with ViruShield Stethoscope Barrier TM applied for first patient interaction. Repeat following departure of patient. Repeat Steps (2) - (5).

[0212] In some embodiments, an exemplary infection control protocol optimized or customized using the AI / ML models or systems can include the steps: (6) Following normal stethoscope use, remove used ViruShield Stethoscope BarrierTM (discard in medical waste receptacle), thoroughly wash hands with disinfectant soap & warm water (optional, but recommended: disinfect stethoscope’s diaphragm and tube with 70% Isopropyl Alcohol (IP A), chlorhexidine, or triclosan). Repeat following departure of patient. Repeat Steps (2) -(6).

[0213] In some embodiments, an exemplary infection control protocol optimized or customized using the AI / ML models or systems can include the steps: (7) Following arrival of next patient, thoroughly wash hands with disinfectant soap & warm water, air dry, again apply new sterile PPE, sterilize stethoscope and other hospital equipment, apply sterile gloves, reapply stethoscope into ViruShield Stethoscope BarrierTM. Do not participate in non-sterile procedures or practices. Do not use a common stethoscope patient to patient without sterilization (before and after). If caring for “at risk” patients (including, but not limited to, elderly, pediatrics, anyone with a compromised immune system, burn victims, patients with large surface wounds (e.g., motorcycle accident victims) only use stethoscope that has been previously sterilized. Repeat following departure of patient. Repeat Steps (2) -(7).

[0214] (8) Set up stethoscope diaphragm and tube for autoclaving at end of every shift.(9) Periodically replace stethoscope’s diaphragm and tube, or any time either becomes damaged or worn. Repeat Steps (2) - (9).

[0215] (10) Follow all infectious disease protocol issued by applicable regulatory authorities, follow industry best practices, and follow organizational policies, procedures, practices, training. Repeat Steps (2) - (10).

[0216] (11) Enter data into CRM / EMR / EHR, undertake quarterly audit analysis of actual HAI / SAI & germ / bug count reduction versus goal(s) of HAI / SAI reduction & germ / bug count reduction. Repeat Steps (2) - (11), and continuously improve Steps (1) - (11) based on outcomes.

[0217] (12) Rigorously use dedicated equipment (e.g. stethoscopes) for each patient.However, if this is not possible, decontaminate the items between each patient contact. For instance, if the stethoscope has to be used on different patients, it is essential that the fullAty Dkt No.: 52243-705601 stethoscope (i.e. staff hand contact as well as patient contact surfaces) be thoroughly cleaned first with water and soap using appropriate PPE to remove organic matter and then wiped with alcohol.

[0218] (13) All waste generated during this decontamination process should be treated as infectious waste. Items and equipment should not be moved between isolation rooms / areas and other areas of the HCF, unless they are appropriately discarded and disposed. If possible, use either disposable equipment or dedicate equipment, such as stethoscopes, blood pressure cuffs, thermometers, or other equipment to patients under Contact Precautions. If equipment needs to be shared among patients, it must be cleaned and disinfected between each patient use. Infection prevention and control of epidemic- and pandemic-prone acute respiratory diseases in health care Non-critical patient-care equipment (e.g. stethoscope, thermometer, blood pressure cuff, sphygmomanometer) should be dedicated to the patient, if possible. Any patient-care equipment that is required for use by other patients should be thoroughly cleaned and disinfected before use.Computing Systems

[0219] Referring to FIG. 9, a block diagram is shown depicting an exemplary machine that includes a computer system 900 (e.g., a processing or computing system) within which a set of instructions can execute for causing a device to perform or execute any one or more of the aspects and / or methodologies for static code scheduling of the present disclosure. The components in FIG. 9 are examples only and do not limit the scope of use or functionality of any hardware, software, embedded logic component, or a combination of two or more such components implementing particular embodiments.

[0220] Computer system 900 may include one or more processors 901, a memory 903, and a storage 908 that communicate with each other, and with other components, via a bus 940. The bus 940 may also link a display 932, one or more input devices 933 (which may, for example, include a keypad, a keyboard, a mouse, a stylus, etc.), one or more output devices 134, one or more storage devices 935, and various tangible storage media 936. All of these elements may interface directly or via one or more interfaces or adaptors to the bus 940. For instance, the various tangible storage media 936 can interface with the bus 940 via storage medium interface 926. Computer system 900 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers.Aty Dkt No.: 52243-705601

[0221] Computer system 900 includes one or more processor(s) 901 (e.g., central processing units (CPUs) or general-purpose graphics processing units (GPGPUs)) that carry out functions. Processor(s) 901 optionally contains a cache memory unit 902 for temporary local storage of instructions, data, or computer addresses. Processor(s) 901 are configured to assist in execution of computer readable instructions. Computer system 900 may provide functionality for the components depicted in FIG. 9 as a result of the processor(s) 901 executing non-transitory, processor-executable instructions embodied in one or more tangible computer-readable storage media, such as memory 903, storage 908, storage devices 935, and / or storage medium 936. The computer-readable media may store software that implements particular embodiments, and processor(s) 901 may execute the software. Memory 903 may read the software from one or more other computer-readable media (such as mass storage device(s) 935, 936) or from one or more other sources through a suitable interface, such as network interface 920. The software may cause processor(s) 901 to carry out one or more processes or one or more steps of one or more processes described or illustrated herein. Carrying out such processes or steps may include defining data structures stored in memory 903 and modifying the data structures as directed by the software.

[0222] The memory 903 may include various components (e.g., machine readable media) including, but not limited to, a random access memory component (e.g., RAM 904) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phase-change random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 905), and any combinations thereof. ROM 905 may act to communicate data and instructions unidirectionally to processor(s) 901, and RAM 904 may act to communicate data and instructions bidirectionally with processor(s) 901. ROM 905 and RAM 904 may include any suitable tangible computer-readable media described below. In one example, a basic input / output system 906 (BIOS), including basic routines that help to transfer information between elements within computer system 900, such as during start-up, may be stored in the memory 903.

[0223] Fixed storage 908 is connected bidirectionally to processor(s) 901, optionally through storage control unit 907. Fixed storage 908 provides additional data storage capacity and may also include any suitable tangible computer-readable media described herein.Storage 908 may be used to store operating system 909, executable(s) 910, data 911, applications 912 (application programs), and the like. Storage 908 can also include an optical disk drive, a solid-state memory device (e.g., flash-based systems), or a combination of any of the above. Information in storage 908 may, in appropriate cases, be incorporated as virtual memory in memory 903.Aty Dkt No.: 52243-705601

[0224] In one example, storage device(s) 935 may be removably interfaced with computer system 900 (e.g., via an external port connector (not shown)) via a storage device interface 925. Particularly, storage device(s) 935 and an associated machine-readable medium may provide non-volatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for the computer system 900. In one example, software may reside, completely or partially, within a machine-readable medium on storage device(s) 935. In another example, software may reside, completely or partially, within processor(s) 901.

[0225] Bus 940 connects a wide variety of subsystems. Herein, reference to a bus may encompass one or more digital signal lines serving a common function, where appropriate. Bus 940 may be any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. As an example and not by way of limitation, such architectures include an Industry Standard Architecture (ISA) bus, an Enhanced ISA (EISA) bus, a Micro Channel Architecture (MCA) bus, a Video Electronics Standards Association local bus (VLB), a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCLX) bus, an Accelerated Graphics Port (AGP) bus, HyperTransport (HTX) bus, serial advanced technology attachment (SATA) bus, AI / ML systems, natural language processors (NLPs), large dataset architectures, or any combinations thereof.

[0226] Computer system 900 may also include an input device 933. In one example, a user of computer system 910 may enter commands and / or other information into computer system 900 via input device(s) 933. Examples of an input device(s) 933 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device (e.g., a mouse or touchpad), a touchpad, a touch screen, a multi-touch screen, a joystick, a stylus, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), an optical scanner, a video or still image capture device (e.g., a camera), and any combinations thereof. In some embodiments, the input device is a Kinect, Leap Motion, or the like. Input device(s) 933 may be interfaced to bus 940 via any of a variety of input interfaces 923 (e.g., input interface 923) including, but not limited to, serial, parallel, game port, USB, FIREWIRE, THUNDERBOLT, or any combination of the above.

[0227] In particular embodiments, when computer system 900 is connected to network 930, computer system 900 may communicate with other devices, specifically mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, and the like, connected to network 930. Communications to and from computer system 900 may be sent through network interface 920. For example, networkAty Dkt No.: 52243-705601 interface 920 may receive incoming communications (such as requests or responses from other devices) in the form of one or more packets (such as Internet Protocol (IP) packets) from network 130, and computer system 900 may store the incoming communications in memory 903 for processing. Computer system 900 may similarly store outgoing communications (such as requests or responses to other devices) in the form of one or more packets in memory 903 and communicated to network 930 from network interface 920. Processor(s) 901 may access these communication packets stored in memory 903 for processing.

[0228] Examples of the network interface 920 include, but are not limited to, a network interface card, a modem, and any combination thereof. Examples of a network 930 or network segment 930 include, but are not limited to, a distributed computing system, a cloud computing system, a wide area network (WAN) (e.g., the Internet, an enterprise network), a local area network (LAN) (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a direct connection between two computing devices, a peer-to-peer network, and any combinations thereof. A network, such as network 930, may employ a wired and / or a wireless mode of communication. In general, any network topology may be used.

[0229] Information and data can be displayed through a display 932. Examples of a display 932 include, but are not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display, a plasma display, a smartphone display, a VR display, an image protector, and any combinations thereof. The display can utilize display software such as SaaS. The display 932 can interface to the processor(s) 901, memory 903, and fixed storage 908, as well as other devices, such as input device(s) 933, via the bus 940. The display 932 is linked to the bus 940 via a video interface 922, and transport of data between the display 932 and the bus 940 can be controlled via the graphics control 921. In some embodiments, the display is a video projector. In some embodiments, the display is a head-mounted display (HMD) such as a VR headset. In further embodiments, suitable VR headsets include, by way of non-limiting examples, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headset, and the like. In still further embodiments, the display is a combination of devices such as those disclosed herein.

[0230] In addition to a display 932, computer system 900 may include one or more other peripheral output devices 934 including, but not limited to, an audio speaker, a printer, aAty Dkt No.: 52243-705601 storage device, and any combinations thereof. Such peripheral output devices may be connected to the bus 940 via an output interface 924. Examples of an output interface 924 include, but are not limited to, a serial port, a parallel connection, a USB port, a FIREWIRE port, a THUNDERBOLT port, and any combinations thereof.

[0231] In addition or as an alternative, computer system 900 may provide functionality as a result of logic hardwired or otherwise embodied in a circuit, which may operate in place of or together with software to execute one or more processes or one or more steps of one or more processes described or illustrated herein. Reference to software in this disclosure may encompass logic, and reference to logic may encompass software. Moreover, reference to a computer-readable medium may encompass a circuit (such as an IC) storing software for execution, a circuit embodying logic for execution, or both, where appropriate. The present disclosure encompasses any suitable combination of hardware, software, or both.

[0232] Those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality.

[0233] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0234] The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by one or more processor(s), or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that theAty Dkt No.: 52243-705601 processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0235] In accordance with the description herein, suitable computing devices include, by way of non-limiting examples, cloud computing platforms, distributed computing platforms, server clusters, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, and netpad computers.

[0236] In some embodiments, the computing device includes an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device’s hardware and provides services for execution of applications. Those of skill in the art will recognize that suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU / Linux®. In some embodiments, the operating system is provided by cloud computing. Those of skill in the art will also recognize that suitable mobile smartphone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research in Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.Non-transitory Computer Readable Storage Medium

[0237] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked computing device. In further embodiments, a computer readable storage medium is a tangible component of a computing device. In still further embodiments, a computer readable storage medium is optionally removable from a computing device. In some embodiments, a computer readable storage medium includes, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, distributed computing systems including cloud computing systems and services,Aty Dkt No.: 52243-705601 and the like. In some cases, the program and instructions are permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media.Computer Programs

[0238] In some embodiments, the platforms, systems, media, and methods disclosed herein include at least one computer program, or use of the same. A computer program includes a sequence of instructions, executable by one or more processor(s) of the computing device’s CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), computing data structures, and the like, which perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, those of skill in the art will recognize that a computer program may be written in various versions of various languages.

[0239] The functionality of the computer readable instructions may be combined or distributed as desired in various environments. In some embodiments, a computer program comprises one sequence of instructions. In some embodiments, a computer program comprises a plurality of sequences of instructions. In some embodiments, a computer program is provided from one location. In other embodiments, a computer program is provided from a plurality of locations. In various embodiments, a computer program includes one or more software modules. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.Software Modules

[0240] In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and / or database modules, or use of the same. In view of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, a distributed computing resource, a cloud computing resource, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, a plurality of distributed computing resources, a plurality of cloud computingAty Dkt No.: 52243-705601 resources, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non-limiting examples, a web application, a mobile application, a standalone application, and a distributed or cloud computing application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on a distributed computing platform such as a cloud computing platform. In some embodiments, software modules are hosted on one or more machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location.Databases and Large Data Sets

[0241] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for storage and retrieval of information, for example customer incident data. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entity -relationship model databases, associative databases, XML databases, document oriented databases, and graph databases. Further non-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, Sybase, and MongoDB. In some embodiments, a database is Internet-based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing-based. In a particular embodiment, a database is a distributed database. In other embodiments, a database is based on one or more local computer storage devices.Barrier / Shield Structure and Dispenser

[0242] As illustrated in the non-limiting example of FIG. 10A, the barrier or shield can comprise a barrier or shield that can cover a stethoscope. In some embodiments, the barrier or shield can comprise a curved or fish-like shape as shown in FIG. 10A. In some embodiments, the barrier or shield can comprise a plastic material, a polymer material, a latex material, a nitrile material, or any combination thereof.

[0243] As illustrated in the non-limiting example of FIG. 10B, the barrier or shield can be contained in a dispenser which can comprise a paper material, cardboard material, plastic material, metal material, polymer material, or any combination thereof.Aty Dkt No.: 52243-705601

[0244] While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the present disclosure may be employed in practicing the present disclosure. It is intended that the following claims define the scope of the present disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

Aty Dkt No.: 52243-705601CLAIMSWhat is claimed is:

1. A method for evaluating compliance with a protocol, the method comprising:(a) receiving QR code scan information from one or more sensors;(b) retrieving health information from one or more EHR databases associated with a plurality of time points based at least in part on the QR code scan information, wherein the health information retrieved corresponds to the QR code scan information;(c) determining, for at least a subset of the plurality of time points, a compliant action or a noncompliant action by a medical provider associated with the protocol, wherein the medical provider is associated with the QR code scan information;(d) determining a compliance of the medical provider with the protocol based at least in part on the determination of (c).

2. The method of claim 1, wherein the QR code scan information comprises information relating to a recorded time point.

3. The method of claim 1, wherein the QR code scan information comprises information relating to an identification of the medical provider.

4. The method of claim 1, wherein the QR code scan information comprises information relating to an identification of a patient associated with the medical provider.

5. The method of claim 1, wherein the QR code scan information comprises temporal information.

6. The method of claim 5, wherein the temporal information is associated with the medical provider.

7. The method of claim 5, wherein the temporal information is associated with a patient.

8. The method of claim 7, wherein the patient is associated with the medical provider.

9. The method of claim 1, wherein the one or more sensors comprise one or more QR code scanning sensors.

10. The method of claim 1, wherein the one or more sensors comprise a camera.11 . The method of claim 1, wherein the one or more sensors are comprised within a smartphone.Aty Dkt No.: 52243-70560112. The method of claim 1, wherein the one or more sensors are affixed to a surface.

13. The method of claim 12, wherein the surface comprises a wall of a building.

14. The method of claim 1, further comprising parsing the one or more EHR databases to detect one or more health records associated with the medical provider.

15. The method of claim 1, further comprising parsing the one or more EHR databases to detect one or more health records associated with at least a subset of the plurality of time points.

16. The method of claim 1, further comprising retrieving health information associated with one or more health events.

17. The method of claim 16, wherein the one or more health events comprise an interaction between the medical provider and a patient.

18. The method of claim 16, wherein the one or more health events comprise a treatment of a patient, wherein the treatment is provided by the medical provider.

19. The method of claim 16, wherein the one or more health events comprise a diagnosis of a subject, wherein the subject had previously interacted with the medical provided before the diagnosis.

20. The method of claim 1, wherein the compliant action comprises an interaction of the medical provider with a digital check-in model.21 . The method of claim 20, wherein the check-in model is associated with one or more steps of the protocol.

22. The method of claim 21, wherein the one or more steps of the protocol comprises utilizing a device as directed by the protocol.

23. The method of claim 22, wherein the device is a cover or shield.

24. The method of claim 23, wherein the cover or shield comprises a cover or shield for a stethoscope.

25. The method of claim 23, wherein the one or more steps of the protocol comprises obtaining the cover or shield.

26. The method of claim 24, wherein the one or more steps of the protocol comprises placing the cover or shield to cover at least a portion of the stethoscope.

27. The method of claim 1, wherein the compliant action comprises receiving information relating to the medical provider accessing a code.Aty Dkt No.: 52243-70560128. The method of claim 27, wherein the accessing the code is associated with one or more steps of the protocol.

29. The method of claim 28, wherein the one or more steps of the protocol comprises utilizing a device as directed by the protocol.

30. The method of claim 29, wherein the device is a cover or shield.31 . The method of claim 30, wherein the cover or shield comprises a cover or shield for a stethoscope.

32. The method of claim 30, wherein the one or more steps of the protocol comprises obtaining the cover or shield.

33. The method of claim 31, wherein the one or more steps of the protocol comprises placing the cover or shield to cover at least a portion of the stethoscope.

34. The method of claim 1, wherein the noncompliant action comprises not completing a digital check-in protocol of a digital check-in model.

35. The method of claim 1, wherein the noncompliant action comprises not entering or confirming a code within a time period or at a predetermined time checkpoint.

36. The method of claim 35, wherein the time period comprises a predetermined time period.

37. The method of claim 36, wherein the predetermined time period comprises about between 2 minutes and 30 minutes after completion of a previous element of the protocol.

38. The method of claim 35, wherein the time period comprises a dynamic time period.

39. The method of claim 38, wherein the dynamic time period comprises a time period between which the medical provider begins an interaction with a first patient, and begins an interaction with a second patient.

40. The method of claim 39, wherein the interaction with the first patient comprises the medical provider generating information relating to the first patient.41 . The method of claim 39, wherein the interaction with the second patient comprises the medical provider generating information relating to the second patient.

42. The method of claim 40 or 41, wherein the information comprises medical notes concerning the first patient or the second patient.Aty Dkt No.: 52243-70560143. The method of claim 1, wherein the compliance of the medical provider with the protocol is determined based at least in part on determining a number of the compliant actions or a number of the noncompliant actions.

44. The method of claim 1, wherein the compliance of the medical provider with the protocol is determined based at least in part on determining a ratio of an amount of the compliant actions to an amount of the noncompliant actions.

45. The method of claim 1, wherein the compliance of the medical provider with the protocol is determined at one or more individual time points of the plurality of time points.

46. The method of claim 1, wherein at least a subset of the plurality of time points is associated with a subject having a disease.

47. The method of claim 46, wherein the disease comprises an infectious disease.

48. The method of claim 1, further comprising determining a likelihood of the medical provider transmitting a disease based at least in part on the compliance of the medical provider with the protocol for one or more patients.

49. A system for assessing compliance with a usage protocol for a non-reusable medical product, the system comprising:(a) one or more processors configured to receive a plurality of QR code scan data inputs, wherein the QR code scan data inputs are each associated with a recorded time point data and a medical provider ID data;(b) a matching module communicatively coupled with an EHR database, wherein the matching module is configured to match one or more data elements associated with each of the plurality of QR code scan data inputs with one or more data elements of a record of the EHR database;(c) a predictive compliance module configured to predict compliance of a medical provider associated with the medical provider ID data based on usage patterns of the non-reusable medical product by the medical provider associated with the medical provider ID data, wherein the usage patterns are based on the output of the matching module, wherein the output of the matching module comprises matching the recorded time point data with one or more data elements of the record of the EHR database; and(d) an output module configured to output the compliance prediction from the predictive compliance module, wherein the compliance prediction output comprises a predicted compliance report for the medical provider.Aty Dkt No.: 52243-70560150. A method for tracking usage of a non-reusable medical product, the method comprising:(a) receiving data associated with a QR code scan;(b) retrieving a provider ID from a database using the QR code scan data;(c) retrieving from a remote database over a network, patient health record data, patient medical record data, or patient monitoring data;(d) matching the provider ID to a provider ID of the patient health record data, the patient medical record data, or the patient monitoring data;(e) predictively matching a time of the QR code scan data with a time of a health event recorded in the patient health record data, the patient medical record data, or the patient monitoring data;(f) generating, using an artificial intelligence or machine learning model, one or more patterns comprising a provider usage pattern, a hospital-acquired infection pattern, or a stethoscope- acquired infection pattern;(g) generating infection-related output comprising one or more predictions associated with the one or more patterns.

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