Promoting early childhood medical interventions

Abstract

Systems, methods, and tools may be configured for managing infant health. An exemplary system may communicate with a user device to collect and evaluate infant health metrics and parent usage. The system may configure an infant health trajectory by analyzing parent compliance and aggregating health data to generate an intervention dataset. Interventions may be sent to the user device based on predicted benefits and encouraged parental behaviors. Health metrics may modify the health trajectory and interventions based on infant development and parent engagement. The system may monitor user compliance so that interventions are presented to maximize the likelihood of completion.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Patent Application No. 63 / 424,719, filed November 11, 2022, the contents of which are incorporated herein by reference. [Background technology]

[0002] Infant medical interventions and / or medical health can be difficult for parents / caregivers to navigate during the infant's early years. Medical interventions can be difficult and tedious for users to read when presented in a table / list. Users may be more engaged with medical interventions if they are more interactive. Summary of the Invention [Means for solving the problem]

[0003] Systems, methods, and means for facilitating early childhood medical interventions may be disclosed. A device (e.g., an infant health management device) may include a processor. The processor may be configured to perform a number of actions. The device may be configured to receive a first infant health metric from a user device. The device may be configured to generate a first infant health trajectory and a corresponding first intervention dataset based in part on the first infant health metric and the population health trajectory. The device may be configured to determine first supplemental data for the first intervention dataset based in part on the first infant health trajectory and a respective expected benefit of each intervention in the first intervention dataset. The device may be configured to transmit a first intervention from the first intervention dataset selected based on the first supplemental data to the user device. The device may be configured to receive a second infant health metric from the user device. The device may be configured to generate a second infant health trajectory and a corresponding second intervention dataset based in part on the first infant health trajectory, the second infant health metric, and the population health trajectory. The device may be configured to determine second supplemental data for the second intervention dataset based in part on the second infant health trajectory and the respective expected benefits of each intervention in the second intervention dataset. The device may be configured to transmit to other user devices the second intervention from the second intervention dataset selected based on the first supplemental data.

[0004] In one example, the device may determine a first parent compliance metric based in part on the first infant health metric and infant health management device usage. For example, the first infant health trajectory may be based in part on the first parent compliance metric.

[0005] In one example, the first infant health metric may be based in part on one or more of a skin health metric, a gut health metric, or an immune training metric.

[0006] In one example, the first supplemental data may include a ranking of one or more interventions in the intervention dataset.

[0007] In one example, the first supplemental data may include a plurality of corresponding point values ​​for one or more interventions of the intervention dataset.

[0008] In one example, one point value of the plurality of point values ​​may indicate an incentive level for applying an intervention of the intervention dataset to an infant corresponding to the infant health management device.

[0009] In one example, a first point value of the plurality of point values ​​may correspond to a first listed intervention of a first intervention data set, a second point value of the plurality of point values ​​may correspond to a second listed intervention of the first intervention data set, and the first point value being higher than the second point value may indicate that the first listed intervention is preferred over the second listed intervention.

[0010] In one example, the interventions in the intervention dataset may be stored with corresponding values, which may represent health benefits of applying the intervention to an infant corresponding to the infant health care device. [Brief explanation of the drawings]

[0011] [Figure 1] 1 shows an exemplary diagram of a system that may be used to determine an intervention dataset. [Figure 2] 1 illustrates an example architecture diagram of an example system for supporting determination of an intervention dataset. [Figure 3] 1 shows an illustrative diagram of an example that may include one or more modules (e.g., software modules) for providing personalized medical data, status, and / or recommendations. [Figure 4] 1 shows an example of a gamified points system chart for enabling users to perform medical interventions. [Figure 5]1 illustrates an exemplary time span that may be associated with use of the system. [Figure 6] 1 shows an exemplary block diagram including one or more steps for providing drug intervention. [Figure 7] 1 shows an example diagram that may include one or more individual health trajectories combined, where the combination may be compared to a population health trajectory. [Figure 8] 1 shows an exemplary flowchart for processing input data to provide a medical intervention. [Figure 9] 1 illustrates an exemplary neural network that can be used to process training data and provide medical interventions. [Figure 10] 1 shows an exemplary diagram for ranking interventions based in part on health trajectories. [Figure 11] 1 illustrates an exemplary method for facilitating early childhood medical intervention. DETAILED DESCRIPTION OF THE INVENTION

[0012] Systems, methods, and means for facilitating early childhood medical interventions may be disclosed. A device (e.g., an infant health management device) may include a processor. The processor may be configured to perform a number of actions. The device may be configured to receive a first infant health metric from a user device. The device may be configured to generate a first infant health trajectory and a corresponding first intervention dataset based in part on the first infant health metric and the population health trajectory. The device may be configured to determine first supplemental data for the first intervention dataset based in part on the first infant health trajectory and a respective expected benefit of each intervention in the first intervention dataset. The device may be configured to transmit a first intervention from the first intervention dataset selected based on the first supplemental data to the user device. The device may be configured to receive a second infant health metric from the user device. The device may be configured to generate a second infant health trajectory and a corresponding second intervention dataset based in part on the first infant health trajectory, the second infant health metric, and the population health trajectory. The device may be configured to determine second supplemental data for the second intervention dataset based in part on the second infant health trajectory and the respective expected benefits of each intervention in the second intervention dataset. The device may be configured to transmit to other user devices the second intervention from the second intervention dataset selected based on the first supplemental data.

[0013] In one example, the device may determine a first parent compliance metric based in part on the first infant health metric and infant health management device usage. For example, the first infant health trajectory may be based in part on the first parent compliance metric.

[0014] In one example, the first infant health metric may be based in part on one or more of a skin health metric, a gut health metric, or an immune training metric.

[0015] In one example, the first supplemental data may include a ranking of one or more interventions in the intervention dataset.

[0016] In one example, the first supplemental data may include a plurality of corresponding point values ​​for one or more interventions of the intervention dataset.

[0017] In one example, one point value of the plurality of point values ​​may indicate an incentive level for applying an intervention of the intervention dataset to an infant corresponding to the infant health management device.

[0018] In one example, a first point value of the plurality of point values ​​may correspond to a first listed intervention of a first intervention data set, a second point value of the plurality of point values ​​may correspond to a second listed intervention of the first intervention data set, and the first point value being higher than the second point value may indicate that the first listed intervention is preferred over the second listed intervention.

[0019] In one example, the interventions in the intervention dataset may be stored with corresponding values, which may represent health benefits of applying the intervention to an infant corresponding to the infant health care device.

[0020] Health metrics, as disclosed herein, may be used during infancy to reduce the likelihood that infants will develop allergies (e.g., to foods, outdoor elements, etc.), skin conditions (e.g., eczema), and negative symptoms associated with poor gut health. For example, health metrics may be collected in an application. For example, health metric information may be captured, measured, collected, received, and / or determined by the application. In one example, an application may determine and / or receive health metrics from a database, server, sensor, medical device, electronic medical record, wearable device, smartphone, smartwatch, etc. The application may help engage people using it and keep them interested in the details (e.g., scientific details) that may be provided. The application may be presented in an understandable way to the user, such as a game. The application may encourage continued use of the application by gamifying the experience of entering data and completing interventions that allow for the retrieval of health metrics.

[0021] The healthcare data tracking and intervention applications described herein can aid in the healthcare of infants and young children. For example, digital health solutions may present technical challenges related to caregiver adherence and compliance with appropriate healthcare activities for the healthcare of infants and young children. Digitally delivered healthcare information, such as recommendations and / or specific interventions, faces technical challenges not present in human delivery (e.g., healthcare professionals and / or knowing friends and relatives) with regard to understanding and / or verifying adherence to ongoing care. By utilizing sensing, tracking, and data capture technologies, applications can aggregate and collate healthcare data and provide a comprehensive personal dashboard for users to interface with such data, tracking, and sensing technologies. Such technical features can enable real-time monitoring and intervention recommendations aimed at preventing infant health problems, such as allergies and skin conditions. The incorporation of gamification features can modify (e.g., enhance) user engagement, establish a non-traditional approach to infant healthcare, and promote active and informed parental involvement.

[0022] Specifically, to address allergen exposure in infants and young children, immunological data can be used to inform recommendations that can serve as a technological bridge between observed health data and practical interventions. Systematically reducing the likelihood of an allergic reaction by monitoring immunoglobulin (IBG) production in response to allergen exposure, exposure dose (e.g., optimal exposure dose), and frequency can be considered an unconventional solution to allergen introduction. Real-time biological feedback can be relied upon to guide parental behavior and infant care.

[0023] Delivery of health promotion kits may represent at least part of a technological solution by providing tangible items (or, in instances where kits are not available, suggested tangible actions) and / or interventions at calculated times to maximize infant health outcomes. Gamified engagements that simplify health management tasks into accessible, user-friendly interactions may be used to advance such an approach. This approach may represent an unconventional solution to challenges related to infant health management, enabling timely and satisfying implementation of health interventions and where digital guidance creates a comprehensive infant care strategy.

[0024] Various technologies may be used to sense, track, and / or capture health care data. Specific tests may collect and collate health care data and then provide that information to the user (e.g., via a personal dashboard). The user may receive interventions that may be completed periodically (e.g., daily) with real-time notifications about specific interventions that may result in positive outcomes for the infant. The notifications may enable the user to better manage their infant's health and (e.g., ideally) prevent more serious health issues, such as allergies, skin conditions, etc. Applications may allow people to capture health-related information in a gamified mechanism as a way to keep track of their infant's health. Users may monitor their infant's condition (e.g., gut health, skin health, allergy conditions, etc.) in real time to the user.

[0025] In one example, an infant may be exposed to an early allergen (e.g., a food that may cause an anaphylactic / allergic reaction or the release of histamine). Each exposure to the allergen may result in the production of immunoglobulins (IBGs) (e.g., antibodies). With each subsequent introduction of the allergen, greater amounts of IBGs may be produced. Higher amounts of IBGs produced may be associated with a decreased allergic response. The application may use this information to determine how much to expose the infant to over a period of time.

[0026] Kits (e.g., physical packages) can be delivered to promote the health of infants and young children. For example, the kits can include one or more items that, when used at the appropriate time, can benefit the health of an infant. For example, the kits can include an allergen introduction for an infant. For example, one or more kits can be delivered within the first year of infancy so that an infant can have the healthiest first year possible (e.g., the year of allergen introduction). The kits can be delivered according to the appropriate dosage at a time (e.g., a specific time when the infant can tolerate a specific allergen dose). When delivering the kits, an application (e.g., a smartphone application) can provide engagement, simplicity, and support to the application user (e.g., the infant's parent) through gamification of tasks (e.g., interventions) in the application. In one example, the application can promote proper infant feeding, proper infant sleep (e.g., sleeping for a period of time or infant sleep training), and the development of a strong immune system throughout the infant's life.

[0027] FIG. 1 shows an exemplary diagram of a system that may be used to determine an intervention dataset.

[0028] In one example, the user 102 may include a parent or guardian of an infant or toddler. The user 102 may interface with a smart device 104. The smart device 104 may include a smartphone, a smartwatch, a computer, a laptop, a tablet, and / or the like.

[0029] The smart device 104 may include applications for receiving health metrics. The smart device 104 may provide passive or active tracking and / or location services. The smart device 104 may collect data about the infant, process data about the infant, share data about the infant, and / or store data related to app usage. For example, the smart device 104 may use one of its sensors or processors to collect health metrics and share the health metrics with a smartwatch, a testing device, and / or computing resources.

[0030] The smart device 104 may provide a user interface. The smart device 104 may provide health metric feedback and data. For example, the smart device may display responses to the completion of a health intervention or a list of interventions that may be completed by the user 102. The smart device 104 may perform activity tracking (e.g., of the infant and / or the user) and provide activity information (e.g., of the infant and / or the user).

[0031] In one example, the first health metric 106 may include a skin health metric, a gut health metric, an immune training metric, and / or a parent compliance metric.

[0032] In one example, the skin health metrics may include metrics related to the current skin condition of the infant. The skin health metrics may be derived by sensors that derive information about the user and / or the skin, for example, a skin conductance sensing system.

[0033] The skin conductance sensing system may measure skin conductance data including conductivity. The skin conductance sensing system may include one or more electrodes. The skin conductance sensing system may measure electrical conductivity by applying a voltage between the electrodes. The electrodes may include silver or silver chloride. The skin conductance sensing system may be placed on one or more fingers. For example, the skin conductance sensing system may include a wearable device. The wearable device may include one or more sensors. The wearable device may be attached to one or more fingers. The skin conductance data may change based on sweat levels.

[0034] The skin conductance sensing system may process the skin conductance data locally or transmit the data to a computing system. Based on the skin conductance data, the skin conductance sensing system may calculate skin conductance-related biomarkers, including sympathetic nerve activity levels. For example, the skin conductance sensing system may detect high sympathetic nerve activity levels based on high skin conductance. The data retrieved by the skin conductance sensing system may contribute to the skin health metric and the first health metric 106.

[0035] In one example, the skin health metrics may be based on photographs of the infant's skin and / or skin samples of the infant. For example, a user may submit photographs of the infant (e.g., of the infant's skin, the infant's hands, the infant's face) to identify specific features of the infant's skin that may be correlated to a skin condition (e.g., eczema). The photographs may be uploaded to a server for processing and further transmission to a health professional, who may review the photographs and assign specific tasks for the user to complete as a health intervention. For example, a task may include visiting a doctor to further analyze a skin condition noted by the medical professional upon review of the image. The medical professional may identify a birthmark (e.g., using the photograph), such as an infantile hemangioma, a simple nevus, a Mongolian spot, a vascular malformation, and / or a melanocytic nevus. The medical professional may upload the birthmark's identification to the application. The medical professional may suggest that the user visit a doctor for further analysis of the birthmark.

[0036] In one example, the skin health metric may be based on a skin sample obtained by a user and received at a processing facility. The processing facility may analyze the skin sample for one or more skin conditions. The skin sample may be analyzed for skin conditions common in newborns, such as desquamation, neonatal scalp dermatitis, intertrigo, heat rash, neonatal acne, erythema toxicum, and transient pustular melanosis.

[0037] In one example, the health metrics may include gut health metrics. The gut health metrics may be based on the user's responses to questions related to the infant's stool (e.g., frequency of stool passage, stool appearance, stool color, stool consistency). For example, the user may describe (e.g., as prompted by the app) the typical consistency of the infant's stool (e.g., bowel movements). The user may indicate whether the stool is soft, hard, not hard, not soft, mushy, runny, runny with some undigested food, and / or watery.

[0038] In one example, the application may request the user to indicate the type of stool observed. The application may provide an intervention (e.g., an intervention suggesting the type of food to feed). For example, the application may request that the user send a stool sample. The stool sample may be received (e.g., at a processing facility), and the stool sample may be tested (e.g., for gut health, infection, microorganisms present in the infant's bloodstream or intestine, microbial sources of infection, signs of colon cancer, diet). Gut health metrics may be affected by the test results of the stool sample, and new interventions may be suggested (e.g., as a result of the stool sample test results).

[0039] In one example, the immune health metric may be based on infant allergy health data collected about the infant. The infant allergy health data may contribute in part to the immune health metric. The immune health metric may contribute in part to a first infant health metric. For example, the allergy health data may include symptoms, medications, and / or behavioral routines. For example, symptoms may relate to the nose (e.g., itchy nose, sneezing, congestion, decreased smell / taste, snoring, clear or discolored nasal discharge), eyes (e.g., itchy eyes, watery eyes, red eyes, dry / irritated eyes, swollen eyelids, discharge), throat (e.g., sore throat, itchy throat / palate, coughing, hoarseness, clear or discolored post-nasal drip), ears (e.g., itchy ears, stuffy ears, tinnitus, hearing loss), head (e.g., headache, facial pressure or pain), and / or lungs (e.g., itchy lungs, chest tightness, wheezing). For example, data obtained regarding medication may include medication dosage, medication taken (e.g., antihistamine (e.g., tablet, nasal)), aspirin, nonsteroidal anti-inflammatory drug (Advil, Motrin, Tylenol), and / or medication routine, etc. For example, data obtained regarding behavioral routines may include tasks for a parent and infant, such as keeping windows closed, using air conditioning, washing hands regularly, vacuuming / cleaning floors if possible, refraining from going outside when pollen / mold / weed counts are high, washing hands after playing outside, taking a shower, and / or changing clothes, etc.

[0040] In one example, an application may collect information about an outdoor environment to partially contribute to health metrics. For example, the collected data may include location-based pollen (e.g., grass, trees, weeds, mold, dust). For example, the collected data may include location-based weather (e.g., temperature, temperature change, barometric pressure, humidity, wind, precipitation). For example, the collected data may include location-based pollution (e.g., carbon monoxide, non-methane hydrocarbons, nitric oxide, nitrogen dioxide, ozone, PM10, PM25, and / or sulfur dioxide).

[0041] In one example, an application may collect information about indoor environments to partially contribute to health metrics. For example, the collected data may include indoor information (e.g., allergens, mold, dust, etc.). For example, the data may include indoor climate information (e.g., temperature, temperature change, humidity, humidity change, etc.). In one example, the data may include indoor particulate information. For example, air quality data may be determined by correlating the current location of the user device 102 with its respective carbon monoxide, non-methane hydrocarbon, nitric oxide, nitrogen dioxide, ozone, PM10, PM25, and / or sulfur dioxide levels.

[0042] In one example, user habits may be determined to determine particulates entering an infant's home. For example, parental occupation at the parent's grade level may be queried. For example, hobbies, length of current residence, type of location (e.g., urban, suburban, rural / country), and type of home (e.g., house, apartment / condo, houseboat, mobile home, etc.) may be queried. For example, questions may be asked regarding the actual location (e.g., city, town, urban neighborhood, or nearest city), heating system (e.g., radiant, forced air, heat pump, wood-burning stove, pellet stove, etc.), air conditioning system (none, central, window unit), and / or air filter (e.g., high-efficiency particulate air (HEPA), electrostatic). For example, questions may be asked regarding floor types in various rooms (e.g., bedroom: carpet, wood / laminate, tile, cement, etc.). For example, questions may be asked regarding mattress type (e.g., regular, foam, air, waterbed, futon, etc.), pillow (e.g., synthetic, foam, down, feather, cotton, etc.), and comforter (e.g., none, down, synthetic, feather, etc.). For example, questions may be asked regarding whether the user has a zippered dust mite allergy cover / enclosure (e.g., whether the pillow / mattress / comforter / box spring has a cover) and whether the user has pets (e.g., the user may select the type of pet the user has, the number of pets the user has). For example, questions may be asked regarding mold / mildew presence (e.g., whether the presence of existing mold / mildew is a minor problem or a major problem).

[0043] In one example, the first health metric 106 may include a parent compliance metric. For example, the parent compliance metric may be based on parent application usage. The parent compliance metric may be based on acquired data corresponding to the parent application usage.

[0044] For example, a user's activity may be monitored by the application. In one aspect, a data usage pattern may be generated for the user by the application. The user's current data usage activity may be monitored to detect deviations in data usage from the user's usage pattern. When a deviation is detected, the system may send an alert message to the user or another user indicating that an anomaly has occurred or to continue using the app, allowing the user to respond to the anomaly or enter the application for continued use. Deviations in application usage may be logged by the application.

[0045] Data corresponding to application use may be logged in a dataset corresponding to a parent compliance metric, and the data corresponding to application use may partially contribute to the parent compliance metric. For example, a high parent compliance metric may result in the application notifying the user to continue logging daily activities and submit data associated with the infant. For example, a low parent compliance metric may result in the application notifying the parent to return to the application and continue using the application. In one example, a low parent compliance metric may result in the application notifying the parent with words of encouragement associated with continued use of the application.

[0046] One or more devices may be installed in an infant's environment to be used to monitor a user's data usage, e.g., to contribute to parental compliance metrics and / or detect deviations from a user's usage patterns. For example, a user's browsing activity on a user device 104. Similarly, browsing activity on a personal computer, laptop computer, and / or wireless device may be monitored. By utilizing devices (e.g., content service / display / access devices) that may already be installed in the user's home to identify inconsistencies and inconsistencies in user activity (e.g., including content consumption), physical health and safety (e.g., of an infant) may be monitored without installing additional monitoring equipment such as motion sensors, pressure sensors, and temperature sensors required for a standalone health monitoring system. In one example, monitoring may be performed by a gateway interface device, such as a cable modem or router, through which various other devices connect to one or more external networks.

[0047] The gateway interface device benefits from being in a relatively centralized location within the home data network, making it easier to monitor data traffic. In one example, the monitoring software may be loaded into the memory of the cable modem and executed by the cable modem processor, requiring minimal additional installation effort. Monitoring may be performed on one or more devices (e.g., push servers, content servers, and / or application servers) in the local office, within the network, e.g., a cloud network with distributed computing and / or data storage devices and / or capabilities, or any other device.

[0048] The application may provide a predictive assessment when looking at demographic and other information, when incorporating health metric data, etc. It may provide personalized recommendations, such as suggestions on what to do and what not to do. The recommendations may encourage the user and help the user understand how they provided health benefits to their infant. In one example, the user may be provided with information on how taking an intervention may help the infant later. For example, if an infant is given a small amount of an allergen, giving the allergen may reduce the infant's reaction to the allergen later in life.

[0049] The application may have access to the infant's medical records. The medical records may be pre-loaded. If the infant has a history of a particular health problem, the infant's medical history may be used by the application to analyze the infant's health metrics. Thus, the infant's medical history and measured health metrics may provide context as to what medical problems or potential medical problems may occur for the infant. Over time, the application may receive more data, allowing it to become smarter as the data set grows. This may allow for better integration of conditions.

[0050] The application may present healthcare data in a particular way that is more practical to the user. The healthcare data may be filtered to be relevant to the user based on the user's selection and an understanding of the context in which the user is viewing it. The application may use the user's selection to make sense of the data itself. For example, when the application collects information, the healthcare data may be interpreted differently if the user clicks on one intervention over another. For example, if the user selects feeding the baby peanut butter over learning materials, the application may interpret that the user is more likely to perform the action as opposed to learning the materials. The same may be true in reverse.

[0051] The application may explain the data to the user. The data may be actionable through color coding, lists, and / or simple approaches. For example, if a user determines that an infant has a fever, the application may explain the fever and the impact the fever may have on the infant. As an example, the explanation may provide symptoms associated with an infant's fever, and the application may provide interventions related to the fever, which may depend on the parent indicating that the infant has a fever. The application may explain managing the symptoms of an infant's fever. The application may output different interventions based on different content that may occur and whether the user is concerned about the fever or the severity of the fever. The application may output different interventions for fever depending on a parent compliance metric and whether the parent compliance metric indicates how the user is likely to behave (e.g., whether the user is more likely to take a specific action versus whether the user is more likely to view educational materials related to fever).

[0052] The application may conduct a type of screening or risk assessment, which may be quantitative and / or psychometric, such that the application makes specific recommendations to improve health or manage symptoms.

[0053] The application may (e.g., may also) function as a notification alert system (e.g., via push notifications). For example, if there is some kind of abnormal health metric, or if other data sources are abnormal, a notification may be sent to the user indicating an alert to remedy the abnormality. The notification may tell the user to pay attention to current (e.g., infant) abnormalities and provide self-generated surveys about the infant's health, infant's body parts, and infant's well-being.

[0054] The data center 108 may include any server resource suitable for remote processing and / or storage of information. For example, the data center 108 may include a server, a cloud server, a data center, a virtual machine server, etc. In one example, the user 102 may communicate with the data center 108 via a smartphone 104. In one example, the smart device 104 may communicate with the data center 108 via a proprietary wireless link. The hardware and wireless link capabilities of the data center may be equal to or greater than the hardware capabilities of the smart device 104. The wireless link used by the smart device 140 may be configured to include mobile wireless protocols, such as global system for mobile communication (GSM), 4G long-term evolution protocol (LTE), 5G, and 5G new radio (NR), and any of various mobile Internet of Things (IoT) protocols. Such protocols may enable the smart device 104 to communicate more easily without manual configuration, for example, when the user is mobile and traveling away from home or the office.

[0055] The first health trajectory 110 may be generated by the data center 108. The first health trajectory may be generated based in part on the first infant health metric 106 and the population health trajectory.

[0056] A first intervention dataset may be generated based in part on the first health trajectory 110. The first intervention dataset may be a list of suggestions for the user to complete with respect to the infant. The suggestions may include completing tasks, viewing educational materials, interfacing with materials delivered in a kit, etc. In one example, users completing tasks in the first intervention dataset may contribute in part to a parent compliance metric.

[0057] The second infant health metrics 114 may include the same metrics as those included in the first infant health metrics, e.g., a second gut health metric, a second parent compliance metric, a second skin health metric, and / or a second immune training metric. For example, the second gut health metric, the second skin health metric, and / or the second immune training metric may be received at one or more of a processing facility (e.g., similar to the first infant health metric) or a data center (e.g., similar to the first infant health metric). The second parent compliance metric may be based on user device compliance, and the user's device usage may affect the parent compliance metric (e.g., how / if the user interfaces with the device).

[0058] In one example, the second infant health metric 114 may be based in part on one or more of the first infant health metric 106, the first infant health trajectory 110, and / or the first intervention dataset 112. For example, if the user uses the application infrequently as indicated by the parent compliance metric, the second parent compliance metric may take into account the infrequent use as indicated by the parent compliance metric, or vice versa if the parent compliance metric indicates regular use of the application.

[0059] The second health trajectory 116 may be generated by the data center 108. The second health trajectory 110 may be generated based in part on the second infant health metric 114 and the population health trajectory.

[0060] A second intervention dataset 118 may be generated based in part on the second health trajectory 116. The first intervention dataset 118 may be a list of suggestions for the user 102 to complete with respect to the infant. The suggestions may include completing tasks, viewing educational materials, interfacing with materials delivered in a kit, etc. In one example, the user 102 completing the tasks in the first intervention dataset 118 may contribute in part to a second parent compliance metric.

[0061] FIG. 2 illustrates a diagram of an example architecture system 200 of an example system for supporting determination of an intervention dataset.

[0062] Architecture system 200 may include I / O devices 202, processors 204, and / or memory / storage 206. In one example, I / O devices 202 may include disk controllers with control registers, flash controllers, controllers for other high-performance non-volatile storage devices, control registers, PCIe controllers with control registers, network information controllers with control registers, and / or other I / O devices with control registers. In one example, I / O devices 202 may be integrated I / O devices or may be one or more external I / O devices. For example, each of the I / O devices may include a set of control registers. For example, each individual set of control registers may include configuration information specific to the I / O device of which the I / O device is a part, to enable the I / O device to function as programmed and as desired.

[0063] In one example, I / O device 102 may be a separate, self-contained component of architecture system 200. An I / O device may include functionality required to perform its particular function, including a set of functionality that may be common to each of the I / O devices. In architecture system 200, an I / O device may function as a self-contained component within the system. For example, architecture system may include a shared I / O configured with a set of shared functionality. For example, the set of shared functionality may not be included on an individual I / O device and may be removed from I / O device 202. In one example, I / O device 202 may interact with a shared I / O unit for use of one or more of the set of shared functionality. For example, the set of shared functionality may reside in a single location on shared I / O device 202 for use by components of the architecture system. In one example, the set of shared functionality may be distributed across multiple locations.

[0064] The I / O device 202 may include a transmitter and a receiver that enable wireless communication using any suitable communication protocol, e.g., a protocol suitable for the embedded application. For example, the transmitter and receiver may be configured to enable a wireless personal area network (PAN) communication protocol, a wireless LAN communication protocol, a wide area network (WAN) communication protocol, etc. The transmitter and receiver may be configured to communicate via Bluetooth using any supported or custom Bluetooth version and / or any supported or custom protocol, including, e.g., A / V Control Transport Protocol (AVCTP), A / V Distribution Transport (AVDTP), Bluetooth Network Encapsulation Protocol (BNEP), IrDA Interoperability (IrDA), Multi-Channel Adaptation Protocol (MCAP), and RF Communications Protocol (RFCOMM). In one example, the transmitter and receiver may be configured to communicate via Bluetooth Low Energy (LE) and / or Bluetooth Internet of Things (IoT) protocols. The transmitter and receiver may be configured to communicate via a local mesh network protocol, such as ZigBee, Z-Wave, Thread, etc. For example, such protocols may enable the transmitter and receiver to communicate with nearby devices, such as a user's cell phone and / or a user's smartwatch.Communication with local network devices such as mobile phones may further enable communication with other devices over a wide area network (WAN), to remote devices, over the Internet, over a corporate network, etc.

[0065] The transmitter and receiver may be configured to communicate via a LAN protocol, such as an 802.11 wireless protocol like Wi-Fi, including, but not limited to, communications in the 2.4 GHz, 5 GHz, 6 GHz, and 60 GHz frequency bands. Such protocols may enable the transmitter and receiver to communicate with a local network access point, such as a wireless router in a user's home or office. Communication with the local network access point may further enable communication with other devices present on the local network, or with remote devices over a WAN, over the Internet, over a corporate network, etc.

[0066] The transmitter and receiver may be configured to communicate via a mobile wireless protocol, such as the global system for mobile communication (GSM), 4G long-term evolution protocol (LTE), 5G, and 5G new radio (NR), as well as any of various mobile Internet of Things (IoT) protocols. Such protocols may allow the transmitter and receiver to communicate more easily without manual configuration, for example, when a user is mobile and traveling away from home or the office.

[0067] The processor 204 may include electronic hardware components such as multiple processors. In one example, the processor 204 may include a digital processing unit. For example, the processor 204 may include a microprocessor (e.g., single-core and multi-core), a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an analog and / or digital application-specific integrated circuit (ASIC), or the like, or a combination thereof. For example, the processor 204 may execute, process, or run instructions, code, code segments, software, firmware, programs, applications, apps, processes, services, daemons, etc. For example, the processor 204 may execute software applications / programs (e.g., infant health metrics 106, 114 and health trajectories 110, 116, respectively, which may be stored in the memory / storage 206). The processor 204 may include hardware components (e.g., finite state machines, sequential logic, and combinatorial logic) and other electronic circuitry that may perform functions necessary for the operation of the present invention. The processor 204 may communicate with electronic components via serial or parallel links, including universal buses, address buses, data buses, control lines, and the like.

[0068] The memory 206 may include electronic hardware data storage components such as read-only memory (ROM), programmable ROM, erasable programmable ROM, random access memory (RAM) such as static RAM (SRAM) or dynamic RAM (DRAM), cache memory, a hard disk, a floppy disk, an optical disk, flash memory, a thumb drive, a universal serial bus (USB) drive, etc., or combinations thereof. In one example, the memory 206 may be embedded in the processor 204 or packaged in the same package as the processor 204. The memory 206 may include a computer-readable medium. The memory 206 may store instructions, code, code segments, software, firmware, programs, applications, apps, services, daemons, etc., executed by the processor 204. In one example, the memory 206 may store software applications / programs / data (e.g., infant health metrics 106, 114 and health trajectories 110, 116). The memory 206 may also store settings, data, documents, audio files, photos, movies, images, databases, etc.

[0069] The network 212 may include long-distance data networks such as private corporate networks, virtual private networks (VPNs), public commercial networks, interconnections of networks such as the Internet, etc. The network 212 may provide connectivity to the smart devices 104 and the data center 108.

[0070] The computing resources 212 may include any server resources suitable for remote processing of information and / or storage of information. For example, the network 212 may include servers, cloud servers, data center 108, external data centers that enable functionality of the network, virtual machine servers, etc. In one example, a smartwatch may communicate with the network 212 via its own wireless link, and a smart device 104 may communicate with the network 212 via its own wireless link.

[0071] Through hardware, software, firmware, or various combinations thereof, the processor 204, alone or in combination with other processing elements, may be configured to perform the operations of embodiments of the present invention. Specific embodiments of the present technology are described in connection with the accompanying drawings. The embodiments are intended to describe aspects of the present invention in sufficient detail to enable those skilled in the art to practice the invention. Changes may be made to the manner in which the processor operates without departing from the scope of the present invention. The system may include additional, fewer, or alternative functions and / or devices, including those described elsewhere herein.

[0072] FIG. 3 illustrates an example illustrative timeline 300 that may include one or more modules (eg, software modules) for providing personalized medical data, status, and / or recommendations.

[0073] In one example, the timeline 300 may include a beginning 302 and an end 304. Between the beginning 302 and the end 304, kit 1 306, kit 2 308, kit 3 310, and kit 4 312 may be sent to the user. In one example, fewer than four kits (e.g., one kit, two kits, three kits) may be received by the user between the beginning 302 and the end 304. In one example, more than four kits (e.g., five kits, six kits, seven kits, etc.) may be received by the user between the beginning 302 and the end 304. In one example, the frequency with which the receiver receives kits may depend on the user's interaction with the application. For example, if the user does not interact with the application frequently (e.g., a subjective criterion determined by logic within the app), the application may determine that the user should not receive a kit. In one example, if the user does not interact with the application frequently, it may be determined (e.g., by the app) that the user will receive the same kit as they previously received. For example, if a user interacts heavily with an application before receiving a first kit 306, and then has little or no interaction with the application, it may be determined (e.g., by the app) that the user receives a second kit 308 that is similar (e.g., identical) to the first kit 306. The same logic may apply to the use of an application between the second kit 308 and a third kit 310, or between the third kit 310 and a fourth kit 312, etc.

[0074] In one example, the content of the first kit may be determined based on answers to registration questions. For example, a user may submit answers to registration questions, and the application may determine the content to be placed in the first kit 306. The answers may contribute in part to the first health metric 106. In examples where the first health metric contributes in part to a first health trajectory, the first health trajectory may partly determine the content of the first kit 306. In one example, the second health trajectory may partly determine the content of the second kit 308. In one example, subsequent health trajectories may partly determine the content of the third kit 310 and / or the fourth kit 312, etc.

[0075] Kits 306, 308, 310, 312 may include one or more of vitamins, prebiotics, probiotics, skin health products, sleep health products, solid foods, and / or allergens. For example, first kit 306 may include prebiotics and vitamins (e.g., infant bifidobacteria and vitamin D supplements). For example, second kit 308 may include prebiotics and probiotics. For example, third kit 310 may include one or more of skin products, sleep products, and / or allergens (e.g., introduced allergens).

[0076] In one example, a user's 102 use and reported use of content in kits 306, 308, 310, 312 may contribute to infant health metrics 106, 114 (e.g., and health trajectories 110, 116). For example, if a user consistently uses content in a first kit 306 as directed by an app, content usage may be reported (e.g., by the user 102) to an application. For example, if a user 102 reports content from a first kit 306 to an app, data correlating to application usage may be applied to a second infant health metric 114 and a second health trajectory 116 (e.g., may partially contribute to the second kit 308).

[0077] The kits 306, 308, 310, 312 may include educational materials (e.g., books, cards, pamphlets) for the user 102 to observe, read, and report to the application (e.g., that the user has completed observing the educational materials). For example, completing the educational materials may contribute in part to the infant's health metrics 106, 114 and health trajectories 110, 116.

[0078] In one example, the contents of the wellness kits 306, 308, 310, 312 may be made known by an application. The application may include the contents of the wellness kits 306, 308, 310, 312 (e.g., completing educational materials) as interventions in the intervention datasets 112, 118. For example, if the user 102 completes a particular intervention (e.g., reads a particular brochure included in one of the wellness kits 306, 308, 310, 312), the user 102 may mark (e.g., in a list of interventions) that the user 102 has completed that particular intervention. In one example, depending on the user's 102 use of the content included in one of the health kits 306, 308, 310, 312, the intervention datasets 112, 118 may be ranked such that the user 102 is more likely to complete the tasks in the health kit 306, 308, 310, 312 (e.g., giving an infant or toddler the content in the kit or reading the educational materials in the health kit 306, 308, 310, 312 may be worth more points than other interventions).

[0079] FIG. 4 shows an example of an exemplary gamified points system chart 400 for enabling a user to perform a medical intervention.

[0080] In one example, the gamified points system chart 400 represents a scheme in which points may be earned for completing certain tasks. For example, points may be earned by using materials in the health kits 306, 308, 310, 312 (e.g., feeding infant food / nutrients / vitamins included in the health kits 306, 308, 310, 312 or reading educational materials included in the health kits 306, 308, 310, 312).

[0081] In one example, points may be earned for reading or viewing educational materials (e.g., cards, booklets, notes, etc.) within the app, completing an intervention (e.g., a coach as shown in FIG. 4 ), etc. Interventions may include reading / viewing educational materials across the application or wellness kits 306, 308, 310, 312, or separate interventions included in the intervention datasets 112, 118.

[0082] The gamification points system chart 400 may be aligned with the example timeline 300 of the health kits 306, 308, 310, 312 for the indicated month. The example timeline 300 may be aligned with the column numbers of the gamification points system chart 400. For example, the user's 102 interactions with the health kits 306, 308, 310, 312 may affect the number of points assigned to the total in the gamification points system chart 400 in the top row.

[0083] In one example, points may be awarded in the kit column with completion of the educational materials included in the health kits 306, 308, 310, 312. In one example, points may be awarded in the card column with completion of the educational materials included in the application itself. For example, completing a card may correlate to points (e.g., more points) in the card row. Not completing a card may correlate to no points (e.g., fewer points) in the card row. The same logic may apply to points awarded in the card, coaching, and ePRO columns. The column titles in FIG. 4 are not exhaustive, and one skilled in the art will understand that other titles, groups, and / or point measurement techniques may be used without departing from the spirit of the present invention.

[0084] In one example, if a user completes an intervention, points may be awarded in the coaching queue. For example, the intervention may include providing the infant with specific ingredients, allergens, vitamins (e.g., specific vitamins), and / or gut health products (e.g., probiotics / prebiotics such as bifidobacteria / lactic acid), and immune training foods (e.g., nutritional supplements). The intervention may include tasks, such as sleep training the infant, getting the infant to sleep by a certain time, and / or introducing food diversity (e.g., solids / liquids, different ingredients).

[0085] The interventions may further include interventions for the user 102 to complete, such as addressing the user's own sleep habits, breastfeeding, taking gut health supplements (e.g., prebiotics / probiotics), having fun with the infant, or interacting with the infant.

[0086] In one example, ePRO may be a specialist (e.g., to provide specialized care / suggestions). In one example, ePRO may be a device or application that suggests tasks to the user to complete. In one example, the user 102 may earn points in the ePRO column when they engage with a specialist (e.g., a guide, a pediatric allergy specialist, and / or a sleep coach) as directed by the application. The application may award points based in part on the type of specialist met and consulted, how long the consultation with the specialist was, notes from the specialist, and / or completion of a task (e.g., an intervention) suggested by the specialist. In one example, the user may earn points in the ePRO column when they complete an intervention suggested by the application.

[0087] FIG. 5 illustrates an exemplary time span 500 that may be associated with system usage. In one example, a user may access kit descriptions, content cards, and educational materials throughout the duration of application usage. In one example, a user may access ePRO at a specific touchpoint. The touchpoint may coincide with the time the user receives the health kit (e.g., the month the user receives the health kit). The user may access the pediatric allergy specialist and sleep specialist after entering enough data for the pediatric allergy specialist and sleep specialist to analyze and provide specialized care for the infant or toddler. The specialist may suggest that the user visit a licensed specialist within their respective care area based in part on what the data suggests.

[0088] FIG. 6 shows an example block diagram 600 that includes one or more acts for providing a drug intervention.

[0089] In one example, a user may be prompted to answer a series of input questions at 602 and registration questions at 604. In one example, the input questions and registration questions may be the same set of questions. The answers provided by the user may be used to generate individual and population trajectories.

[0090] For example, input questions 602 and sign-up questions 604 may include habit determination and demographic information. In one example, user habits may be determined to determine particulates entering a child's home. For example, parental occupation at the parent's grade level may be queried. For example, hobbies, length of current residence, type of location (e.g., urban, suburban, rural / country), and type of home (e.g., house, apartment / condo, houseboat, mobile home, etc.) may be queried. For example, questions may be asked regarding physical location (e.g., city, town, urban neighborhood, or nearest city), heating system (e.g., radiant, forced air, heat pump, wood-burning stove, pellet stove, etc.), air conditioning system (none, central, window unit), and air filter (e.g., high-efficiency particulate air (HEPA), electrostatic). For example, questions may be asked regarding floor types in various rooms (e.g., bedroom: carpet, wood / laminate, tile, cement, etc.). For example, questions may be asked regarding mattress type (e.g., regular, foam, air, waterbed, futon, etc.), pillow (e.g., synthetic, foam, down, feather, cotton, etc.), and comforter (e.g., none, down, synthetic, feather, etc.). For example, questions may be asked regarding whether the user has a zippered dust mite allergy cover / enclosure (e.g., whether the pillow / mattress / comforter / box spring has a cover) and whether the user has pets (e.g., the user may select the type of pet the user has, the number of pets the user has). For example, questions may be asked regarding mold / mildew presence (e.g., whether the presence of existing mold / mildew is a minor problem or a major problem).

[0091] In one example, the input questions and sign-up questions may include demographic and personal questions. Questions may ask to determine the mother's name, email, mother's age, ethnicity, birthplace, race, sex, and gender. Questions may ask for the mother's health insurance and location. Questions may ask for the mother's household income information. The user may have the option to not answer questions.

[0092] The questions may ask for one or more of the infant's name, date of birth, first full term date, birth weight, current weight, birth height, current height, delivery type (e.g., natural / vaginal or Caesarean section (C-section)), ethnicity, place of origin, race, sex, and gender.

[0093] Answers to the input questions at 602 and the enrollment questions at 604 may contribute in part to the initial target goal at 606 and the health trajectory at 608 .

[0094] At 606, the initial target goals may include a chart depicting where the user's goals are with respect to the infant and how the user will care for the infant. Goals may be related to parental compliance, gut / immune health, and / or skin health.

[0095] At 610, the user may interface with the daily / weekly touchpoints using data obtained from the user (e.g., gamification data associated with FIGS. 3-5). Based on one or more of the input questions at 602, the sign-up questions at 604, or the daily / weekly touchpoints at 610, health trajectories may be created in categories and combined for an infant health trajectory including health trajectories within categories (see, e.g., FIG. 7).

[0096] At 612, the health trajectory may be compared to a population trajectory (e.g., for each category and for every category). In one example, the health trajectory may be compared to a population trajectory to measure the difference between the health trajectory and the population trajectory.

[0097] At 614, risk modeling can be used to determine supplemental data, interventions, and / or outcomes that users can take / experience to bridge the gap between the health trajectory and the population trajectory.

[0098] At 616, the intervention dataset may be displayed to the user. The interventions for the intervention dataset may be ranked. The rank may be determined based on the supplemental data (e.g., points associated with completing a task). The intervention dataset may be supplemented by the supplemental data. The supplemental data may include information that enhances and / or enriches the intervention dataset. For example, the supplemental data may include a ranking of one or more interventions in the intervention dataset. For example, the supplemental data may include corresponding point values ​​for one or more interventions in the intervention dataset. For example, the supplemental data may be based on the infant's trajectory and the expected benefit of each intervention in the intervention dataset.

[0099] In one example, an intervention may be supplemented with supplemental data. The supplemental data may include corresponding point values ​​for one or more interventions in the intervention dataset. The point values ​​may represent incentive levels for administering the corresponding interventions to the infant. For example, a first intervention that is better for the infant relative to a second intervention may be assigned a higher point value than the second intervention. For example, a first intervention that is more difficult to administer than the second intervention may be assigned a lower point value than the second intervention.

[0100] In one example, the point value may be adjusted based on whether the intervention is provided to the user via mail (e.g., in the form of a kit). For example, if the user receives the intervention via mail, the intervention may be assigned a higher point value than if the intervention was assigned in-app or by a medical professional via a call.

[0101] In instances where a user exhibits below average or inactive usage, the point value may be adjusted accordingly. For example, for a user whose activity history indicates below average engagement with the application (e.g., as indicated by a compliance metric), the point value may be inflated to encourage more engagement.

[0102] In one example, interventions may be stored with a corresponding value that represents the relative health benefit generally associated with the intervention, e.g., interventions that generally have a small health benefit may have a low value, and interventions that generally have a large health benefit may have a high value.

[0103] Ranking the interventions may include ranking the interventions based on whether the interventions are better or worse for the user (e.g., mother) and / or infant. Ranking the interventions may include ranking the interventions based on which interventions are easier or more difficult for the user to complete. The ranking of the interventions may be determined based on which interventions are more appropriate for the user to complete, for example, based on likelihood of completion, difficulty, total time to complete, etc.

[0104] The ranking of the interventions may determine which interventions are displayed to the user. For example, the top interventions may be displayed, or the top n interventions may be displayed. For example, interventions above a threshold may be displayed to the user (e.g., when the user is the type of user who completes the intervention, when the user benefits from the intervention, and / or when the user completes certain types of interventions more than others).

[0105] In one example, the interventions may be ranked in order of the likelihood that a user will complete the intervention. For example, the point value associated with an intervention may be static for all users of the application.

[0106] In one example, interventions may be ranked in order of the likelihood that a parent / caregiver will complete the intervention. The point value associated with each intervention may be dynamic. For example, if a parent is unlikely to feed their infant peanut butter and the parent has not covered the associated educational material suggesting the benefits of feeding their infant peanut butter, the app may rank completing the associated educational material higher than the task of actually feeding their infant peanut butter (e.g., the educational material will have a higher point value).

[0107] In one example, interventions may be ranked in order of points regardless of the likelihood that the parent / guardian will complete the intervention, e.g., the points associated with completing the intervention may be static, and the interventions may be ranked in order of points from most points to least points.

[0108] Figure 7 shows a chart comparing population health trajectories and individual health trajectories. Intervention Path 1 and Intervention Path 2 can be distributed between the population health trajectory and the individual health trajectory. For example, Intervention Path 1 and Intervention Path 2 can be desirable paths to bridge the gap between the population health trajectory and the individual health trajectory.

[0109] The gut health trajectory 704, sleep health trajectory 706, food diversity trajectory 708, and skin health trajectory 710 may be generated based in part on one or more of input questions, sign-up questions, or daily / weekly touchpoints. For example, risk modeling may use population trajectories and user input of information associated with the individual trajectory to arrive at an individual trajectory. In one example, the individual trajectory may be used to arrive at an intervention.

[0110] The population health trajectory may be based on population health data related to the health of a population. The population health data may be used to determine normative behavior and a population health trajectory. The population health trajectory may take normative behavior into account when generating the population health trajectory. The normative behavior may be used to evaluate how the population health data may affect an individual. For example, normative behavior determined from the population health data may indicate that sedentary infants may be at risk for obesity. For example, normative behavior determined from the population health data may indicate that infants who avoid an allergen may be at risk for later developing a severe allergy to that allergen.

[0111] Population health data may include data that can identify segments of a population that may be at increased risk due to certain characteristics. In one example, population health data may be collected from demographic groups of infants and young children, such as age, race, sex, gender, geography, parental fitness level, infant mobility, combinations thereof, and / or others. Population health data may indicate factors that can provide normative feedback, identify who belongs to populations at risk for disease, and / or be integrated into population health trajectories.

[0112] Population data 708 may be determined from app usage by users of the app (e.g., all users). Population data 708 may normatively apply data received from users of the app to arrive at individual health trajectories.

[0113] Population data can be determined from a media platform (e.g., a social media platform). In one example, a user may view and / or click on a health-related video, advertisement, or post. There may be an analysis that tabulates the number of views and / or clicks. Later (e.g., over the next few days or hours), the media platform may present similar videos, similar products, similar recommendations, and similar posts to individuals with similar health-related issues based on the number of views and / or clicks. Data corresponding to this use can also determine the type of intervention a user is most likely to take. The intervention can be used to determine one or more of Intervention Pathway 1 or Intervention Pathway 2.

[0114] The media platform may make assumptions, predictions, and hypotheses about why individuals are paying attention to the health-related data they are viewing or clicking on. Data corresponding to these assumptions, predictions, and hypotheses may be used to generate population health trajectories and individual health trajectories (e.g., based on users' use of the media platform). In one example, if an infant has problems related to colic, the user may be looking at sites looking for videos suggesting that the infant stop crying for unidentifiable reasons. These site views and clicks may trigger similar recommendations or websites related to pain medication, physical therapy, doing specific exercises, or diet and fluid retention, etc. The triggers, clicks, views, and data corresponding to the triggers, clicks, and views may be integrated into the population health trajectories and individual health trajectories.

[0115] The user consumer data may be used to generate a personal health trajectory and may be data regarding purchases made by the user, the user's purchasing behavior, financial decisions made by the user, information about financial accounts, etc. In one example, there may be situations where the user consumer data is useful for identifying particular health risks for the user's infant. For example, there may be indicators within the personal health data that partially generate the personal health trajectory. The indicators may indicate that the infant may be at risk for a particular health problem. The user consumer data may be evaluated to confirm that a particular health problem exists (e.g., if the user regularly purchases dry powder, it may be identified that the infant has diaper rash).

[0116] The analytics engine may analyze, modify, use, and / or create data from the infant health data, population health data, user consumer data, and / or population consumer data. In one example, the analytics engine may integrate the infant health data with the population health data. By integrating the infant health data with the population health data, a user may assess the health of their infant and determine how their infant's health compares to the health of other infants. In one example, a user may compare their infant's health risks with the health risks of a population.

[0117] In one example, individual health data at 702 and population health data at 704 may be input and analyzed at 710. The analysis at 710 may use normative data and output results to a health dashboard at 712. The health dashboard 712 may present customized health recommendations at 714. In one example, individual customer data at 706 and population customer data at 708 may be input (e.g., in addition to or separate from the individual health data at 702 and population health data at 704) and analyzed at 710. The example of individual customer data at 706 and population consumer data at 708 may pull consumer data from social media platforms.

[0118] FIG. 8 shows an exemplary flowchart for processing input data to provide a medical intervention.

[0119] At 802, the user may enter answers to the registration questions and input questions.

[0120] At 804, the answers can be used to create a user profile and associated infant health metrics, and the infant health metrics can be plotted against a normal control population curve (e.g., a population health metric).

[0121] An infant problems dataset and an infant health composite score may be created at 806. The infant health composite score may be used to help a user gauge where their infant stands relative to a normal control population corresponding to a normal control population curve.

[0122] At 808, a specific solution list (e.g., intervention dataset) may be created based on one or more of the infant problem dataset, the normal control population curve, or daily / weekly touchpoints (e.g., gamified daily / weekly touchpoints).

[0123] At 810, an intervention (e.g., a digital ranked list of interventions and / or a wellness kit) can be sent to the user. In a digital intervention, the user can be notified (e.g., by an app) to complete a particular intervention. The user can be further notified that completing the intervention is worth a set point amount. The user can be further notified that completing the intervention is associated with a benefit (e.g., that infant sleep training can be consistent with healthy sleep patterns throughout infancy and later life).

[0124] At 812, the behavior-based points system may rank interventions for future retention (e.g., learning that a user prefers a particular intervention over others). The behavior-based points system may contribute in part to determining future interventions for the user (e.g., interventions that the user is more likely to complete).

[0125] At 814, the interventions completed by the user may be retrieved and stored for future use and implementation.

[0126] FIG. 9 illustrates an exemplary neural network (NN) that can be used to process training data and provide medical interventions.

[0127] The detecting, predicting, determining, and / or generating described herein may be performed by a smart device, a computing system, and / or a computing system described herein, such as a smart device, based on measurement data and / or associated health metrics generated by a health metric determination device / system and / or an I / O device / system.

[0128] As disclosed herein, health data and / or health metric data may be captured using a number of devices. The health data and / or health metrics may be analyzed and / or processed using artificial intelligence (AI) and / or machine learning (ML). In one example, AI and / or ML may be used to make tailored recommendations to users. In one example, AI and / or ML may be used to enhance software by learning about users and informing them of what may or may not be working for them, relative to their typology, group, and / or normative population.

[0129] Machine learning is a branch of artificial intelligence that attempts to build computer systems that can learn from data without human intervention. These techniques may rely on the creation of analytical models that can be trained to recognize patterns in datasets, such as data collections. These models can be deployed to apply these patterns to data, such as health metrics, to improve performance without further guidance.

[0130] Machine learning can be supervised (e.g., supervised learning). Supervised learning algorithms can create mathematical models from training data sets (e.g., training data). The training data can consist of a set of training examples. The training examples can include one or more inputs and one or more labeled outputs. The labeled outputs can serve as supervised feedback. In a mathematical model, the training examples can be represented by an array or vector, sometimes called a feature vector. The training data can be represented by rows of the feature vectors that make up a matrix. Through iterative optimization of an objective function (e.g., a cost function), supervised learning algorithms can learn a function (e.g., a prediction function) that can be used to predict outputs associated with one or more new inputs. A successfully trained prediction function can determine outputs for one or more inputs that may not have been part of the training data. Exemplary algorithms can include linear regression, logistic regression, and neural networks. Exemplary problems that can be solved by supervised learning algorithms can include classification problems, regression problems, etc.

[0131] Machine learning can be unsupervised (e.g., unsupervised learning). An unsupervised learning algorithm can be trained on a dataset that may include inputs and can find structure in the data. The structure in the data can resemble groupings or clusters of data points. Thus, the algorithm can learn from training data that may be unlabeled. Instead of responding to supervised feedback, an unsupervised learning algorithm can identify commonalities in the training data and take action based on the presence or absence of such commonalities in each training example. Exemplary algorithms can include the Apriori algorithm, K-means, K-Nearest Neighbor (KNN), K-medians, etc. Exemplary problems that can be solved by an unsupervised learning algorithm can include clustering problems, anomaly / outlier detection problems, etc.

[0132] Machine learning can include reinforcement learning, which can be an area of ​​machine learning that concerns how a software agent can take actions in an environment to maximize some notion of cumulative reward. Reinforcement learning algorithms may not assume knowledge of an exact mathematical model of the environment (e.g., represented by a Markov Decision Process (MDP)) and may be used when an exact mathematical model may not be feasible. Reinforcement learning algorithms may be used in autonomous vehicles or in learning to play games against human opponents.

[0133] Machine learning may be part of a technology platform called Cognitive Computing (CC), which may comprise various fields such as computer science and cognitive science. CC systems may be able to learn extensively, reason purposefully, and interact naturally with humans. Self-learning algorithms, which may use data mining, visual recognition, and / or natural language processing, may enable CC systems to solve problems and optimize human processes.

[0134] In one example, at 902, inputs may be provided to the NN. User goals, user engagement, and user motivation may be determined and measured by an app or the NN (e.g., alone or in combination with each other) and used as inputs to the NN. The input data may include (e.g., in addition to) personality data, sleep data, infant information, user (e.g., adult) information, infant and / or user demographic information, and other information. The input data may be determined and measured by an app of the NN (e.g., alone or in combination with each other).

[0135] At 904, infant health trajectories (e.g., skin, gut, food, immunity, etc.), population health trajectories, and / or health outcomes (e.g., eczema, food allergies, colic, sleep) may be used as training data (e.g., input into one or more matrices for processing). The infant health trajectories may be cross-referenced with the input data to at least infer conclusions, outcomes, and / or health interventions associated with the health trajectories and input data.

[0136] At 906, the health interventions (e.g., Interventions A, B, C, and D) and outputs (e.g., best goal, best intervention, best language, outcome data, etc.) may be, for example, results / conclusions / outputs of the NN. Intervention A may correspond to a first intervention / intervention 1, and the same may apply to subsequent interventions (e.g., intervention B / second intervention / intervention 2, etc.). The outputs may be transmitted to a user device, and further processing may occur (e.g., on the user device and / or the NN) to determine, for example, at 908, whether the user benefited from the output. The determination of whether the user benefited from the output may be included in the training data at 904 for further processing by the NN (e.g., as feedback that is integrated into the training data for future use).

[0137] In one example, a user may add an infant to an application and create a routine that can be constructed or selected (e.g., by the user or by the NN). An intervention may be sent to the user, and the user may accept or reject the intervention. For example, a goal may be selected. If the user accepts the goal, a base change may be received and stored (e.g., for further processing). The goal may be modified, and an intervention may be recommended (e.g., daily). Depending on the user's answer and / or a determination that the user is likely / unlikely to complete the intervention, the answer and / or decision may be used for further processing (e.g., selecting more goals or recommending different goals). Engagement (e.g., parental compliance) may be determined based at least on the selection. Engagement may be measured directly from passive behavior metrics on the app, feedback from a QR code on the box, or questions asked directly.

[0138] FIG. 10 shows an exemplary diagram for ranking interventions based in part on health trajectories.

[0139] At 1002, the health trajectory may be used (eg, by the NN or the user device) to determine, at 1004, a set of example health interventions.

[0140] At 1006, for example, further analysis may be performed (eg, using the qualified and relevant input / output data) to rank each intervention in the set of health interventions.

[0141] At 1008, a user compliance metric (e.g., a parent compliance metric) may be affected by a user's willingness to complete an intervention at a particular time point, or a user's decision to complete a higher-ranked intervention over a lower-ranked intervention, and / or vice versa. For example, a user may not want to complete a higher-ranked intervention (e.g., giving an allergen to a child) before completing a lower-ranked intervention (e.g., learning the benefits / risks of giving an allergen to a child). The user's decisions may be analyzed and partially contribute to the parent compliance metric. For example, a particular intervention may be more likely to be suggested to a user depending on the user's willingness to complete the particular intervention at a particular time point (e.g., t=0, t=1, ..., t=Xi).

[0142] 11 illustrates an exemplary technique for a device for facilitating early childhood medical intervention. The device may include a processor for doing one or more of the following:

[0143] At 1102, the device may be configured to receive a first infant health metric from a user device.

[0144] At 1104, the device may be configured to generate a first infant health trajectory and a corresponding first intervention data set based in part on the first infant health metric and the population health trajectory.

[0145] At 1106, the device may be configured to determine first supplemental data for the first intervention dataset based in part on the first infant health trajectory and a respective expected benefit of each intervention in the first intervention dataset.

[0146] At 1108, the device may be configured to transmit to the user device a first intervention from the first intervention data set selected based on the first supplemental data.

[0147] At 1110, the device may be configured to receive a second infant health metric from the user device.

[0148] At 1112, the device may be configured to generate a second infant health trajectory and a corresponding second intervention data set based in part on the first infant health trajectory, the second infant health metric, and the population health trajectory.

[0149] At 1114, the device may be configured to determine second supplemental data for the second intervention dataset based in part on the second infant health trajectory and the respective expected benefits of each intervention in the second intervention dataset.

[0150] At 1116, the device may be configured to transmit the second intervention from the second intervention data set selected based on the first supplemental data to the other user device.

[0151] At 1118, the device may be configured to display on the user device a second intervention from the second intervention data set selected based on the second supplemental data. The second intervention may be displayed based on a condition that a user of the user device is likely to complete the second intervention.

[0152] At 1120, the device may monitor compliance of the user device with respect to a second parent compliance metric. The second parent compliance metric may be based on compliance of the user device.

[0153] Furthermore, the device may be configured to perform one or more of the following steps:

[0154] In one example, the device may determine a first parent compliance metric based in part on the first infant health metric and infant health management device usage. For example, the first infant health trajectory may be based in part on the first parent compliance metric.

[0155] In one example, the first infant health metric may be based in part on one or more of a skin health metric, a gut health metric, or an immune training metric.

[0156] In one example, the first supplemental data may include a ranking of one or more interventions in the intervention dataset.

[0157] In one example, the first supplemental data may include a plurality of corresponding point values ​​for one or more interventions of the intervention dataset.

[0158] In one example, one point value of the plurality of point values ​​may indicate an incentive level for applying an intervention of the intervention dataset to an infant corresponding to the infant health management device.

[0159] In one example, a first point value of the plurality of point values ​​may correspond to a first listed intervention of a first intervention data set, a second point value of the plurality of point values ​​may correspond to a second listed intervention of the first intervention data set, and the first point value being higher than the second point value may indicate that the first listed intervention is preferred over the second listed intervention.

[0160] In one example, the interventions in the intervention dataset may be stored with corresponding values, which may represent health benefits of applying the intervention to an infant corresponding to the infant health care device.

[0161] In one example, the expected benefit of each intervention in the first intervention dataset may be determined based on how close the first infant health trajectory is to the population health trajectory.

[0162] In one example, the second health trajectory may approximate a population health trajectory based on users completing interventions in the first intervention dataset.

[0163] In one example, the device may receive information indicative of a user record and information indicative of an age of an infant from a user device. The device may generate first shipping instructions for a first infant health care kit associated with the user record, the first shipping instructions may include a first delivery date to the user and a first inventory of the first infant health care kit. The first inventory may include information indicative of a first health care asset and a diagnostic tool. The first delivery date may be calibrated based on the information indicative of the age of the infant.

[0164] The device may receive compliance information from a user device. The device may receive information indicative of diagnostic tool results. The device may generate second shipping instructions for a second infant health care kit associated with the user record. The second shipping instructions may include a second delivery date to the user and a second inventory of the second infant health care kit. The second inventory may include information indicative of a second health care asset. The second delivery date and the second inventory may be calibrated based on the compliance information.

[0165] The information may indicate the results of the diagnostic tool, and the information may indicate the age of the infant. A second intervention from the second intervention dataset may be displayed on the user device. The second intervention from the second intervention dataset may be selected based on the second supplemental data. The second intervention may be displayed based on a condition that a user of the user device is likely to complete the second intervention.

[0166] The compliance of the user device may be monitored with respect to a second parent compliance metric. The second parent compliance metric may be based on the compliance of the user device.

[0167] In one example, the first healthcare asset may include one or more of a probiotic, a skin care product, or an allergy induction product.

[0168] In one example, the diagnostic tool may include a stool collector.

[0169] In one example, the information indicative of the infant's age may be determined based on one or more of the date the infant was born, the infant's fetal age, or the infant's gestational age.

[0170] In one example, the compliance information may include a gamification score stored on a user device associated with the user record, and the gamification score may be associated with the user's completion of the educational material on the user device.

[0171] In one example, the technique may include receiving, from a user device, information indicative of a user record, information indicative of an infant's age, and compliance information. The technique may include receiving information indicative of a diagnostic tool result associated with the user record. The technique may include generating shipping instructions for an infant health care kit associated with the user record. The shipping instructions may include a delivery date to the user and an inventory of the infant health care kit. The inventory may include information indicative of health care assets. The delivery date and inventory may be calibrated based on the compliance information, the information indicative of the diagnostic tool result, and / or the information indicative of the infant's age.

[0172] In one example, an infant health care kit may include infant health care assets. The infant health care kit may include educational materials. The infant health care kit may include a package including the infant health care assets and educational materials, and the package may have an expected delivery date to a user. The expected delivery date, infant health care assets, and educational materials may be calibrated based on compliance information from a user device corresponding to the user. The information may indicate the results of a diagnostic tool, and the information may indicate the age of the infant.

[0173] The application may refer to "determining" various information. Determining information may include, for example, one or more of estimating information, calculating information, predicting information, or retrieving information from memory.

[0174] Additionally, the application may refer to "receiving" various information. Receiving, like "accessing," is intended to be a broad term. Receiving information may include, for example, one or more of accessing information or retrieving information (e.g., from memory). Furthermore, "receiving" typically involves operating in some way, such as, for example, storing information, processing information, transmitting information, moving information, copying information, erasing information, calculating information, determining information, predicting information, or estimating information.

[0175] For example, in the case of "A / B," "A and / or B," and "at least one of A and B," it should be understood that the use of any of the following " / ," "and / or," and "at least one of" is intended to encompass the selection of only the first enumerated option (A), or the selection of only the second enumerated option (B), or the selection of both options (A and B). As a further example, in the case of "A, B, and / or C" and "at least one of A, B, and C," such language is intended to encompass the selection of only the first enumerated option (A), or the selection of only the second enumerated option (B), or the selection of only the third enumerated option (C), or the selection of only the first and second enumerated options (A and B), or the selection of only the first and third enumerated options (A and C), or the selection of only the second and third enumerated options (B and C), or the selection of all three options (A, B, and C). This can be expanded to many items such as those listed, as will be apparent to those skilled in this and related arts.

[0176] Several examples are described. Features of these examples may be provided alone or in any combination across various claim classes and types. Furthermore, embodiments may include one or more of the following features, devices, or aspects, alone or in any combination across various claim classes and types.

[0177] [Embodiment] (1) An infant health care device, the device comprising: receiving, from a user device, a first infant health metric including a first parent compliance metric, the first parent compliance metric being associated with usage of the infant health management device; generating a first infant health trajectory and a corresponding first intervention data set based in part on the first infant health metric and population health trajectory, the first infant health trajectory being based on the first parent compliance metric; determining first supplemental data for the first intervention dataset based in part on the first infant health trajectory and a respective expected benefit of each intervention in the first intervention dataset; transmitting to the user device a first intervention from the first intervention data set selected based on the first supplemental data; receiving a second infant health metric from the user device; generating a second infant health trajectory and a corresponding second intervention data set based in part on the first infant health trajectory, the second infant health metric, and the population health trajectory; determining second supplemental data for the second intervention dataset based in part on the second infant health trajectories and respective expected benefits of each intervention in the second intervention dataset; and displaying, on the user device, a second intervention from the second intervention dataset selected based on the second supplemental data, the second intervention being displayed based on a condition that a user of the user device is likely to complete the second intervention. (2) An infant health management device as described in embodiment 1, wherein the first infant health metric is based in part on one or more of a skin health metric, a gut health metric, or an immune training metric. (3) An infant health management device as described in embodiment 1, wherein the first supplemental data includes a ranking of one or more interventions in the intervention dataset. (4) An infant health management device as described in embodiment 1, wherein the first supplemental data includes a plurality of corresponding point values ​​for one or more interventions in the intervention dataset. (5) An infant health management device as described in embodiment 4, wherein one point value among the plurality of point values ​​indicates a level of encouragement for applying an intervention from the intervention dataset to the infant corresponding to the infant health management device.

[0178] (6) a first point value of the plurality of point values ​​corresponds to a first listed intervention of the first intervention data set; a second point value of the plurality of point values ​​corresponds to a second listed intervention of the first intervention data set; An infant health management device as described in embodiment 4, wherein the first point value being higher than the second point value indicates that the first listed intervention is prioritized over the second listed intervention. (7) An infant health management device as described in embodiment 1, wherein each intervention in the intervention dataset is stored with a corresponding value, the corresponding value representing a health benefit of applying the intervention to the infant corresponding to the infant health management device. (8) A method for an infant health care device, the method comprising: receiving, from a user device, a first infant health metric including a first parent compliance metric, the first parent compliance metric being associated with usage of the infant health management device; generating a first infant health trajectory and a corresponding first intervention data set based in part on the first infant health metric and population health trajectory, the first infant health trajectory being based on the first parent compliance metric; determining first supplemental data for the first intervention dataset based in part on the first infant health trajectory and a respective expected benefit of each intervention in the first intervention dataset; transmitting to the user device a first intervention from the first intervention data set selected based on the first supplemental data; receiving a second infant health metric from the user device; generating a second infant health trajectory and a corresponding second intervention data set based in part on the first infant health trajectory, the second infant health metric, and the population health trajectory; determining second supplemental data for the second intervention dataset based in part on the second infant health trajectories and respective expected benefits of each intervention in the second intervention dataset; and displaying, on the user device, a second intervention from the second intervention dataset selected based on the second supplemental data, the second intervention being displayed based on a condition that a user of the user device is likely to complete the second intervention. (9) An infant health management device as described in embodiment 8, wherein the first infant health metric is based in part on one or more of a skin health metric, a gut health metric, or an immune training metric. (10) The infant health management device of embodiment 8, wherein the first supplemental data includes a ranking of one or more interventions in the intervention dataset.

[0179] (11) An infant health management device as described in embodiment 8, wherein the first supplemental data includes a plurality of corresponding point values ​​for one or more interventions in the intervention dataset. (12) An infant health management device as described in embodiment 11, wherein one point value of the plurality of point values ​​indicates a level of encouragement for applying an intervention from the intervention dataset to the infant corresponding to the infant health management device. (13) a first point value of the plurality of point values ​​corresponds to a first listed intervention of the first intervention data set; a second point value of the plurality of point values ​​corresponds to a second listed intervention of the first intervention data set; An infant health management device as described in embodiment 11, wherein the first point value being higher than the second point value indicates that the first listed intervention is prioritized over the second listed intervention. (14) An infant health management device as described in embodiment 8, wherein each intervention in the intervention dataset is stored with a corresponding value, the corresponding value representing a health benefit of applying the intervention to the infant corresponding to the infant health management device. (15) A system comprising: a memory that stores computer-executable instructions that are executable by a processor; The processor, upon execution of the computer-executable instructions, receiving, from a user device, a first infant health metric including a first parent compliance metric, the first parent compliance metric being associated with usage of an infant health management device; generating a first infant health trajectory and a corresponding first intervention data set based in part on the first infant health metric and population health trajectory, the first infant health trajectory being based on the first parent compliance metric; determining first supplemental data for the first intervention dataset based in part on the first infant health trajectory and a respective expected benefit of each intervention in the first intervention dataset; transmitting to the user device a first intervention from the first intervention data set selected based on the first supplemental data; receiving a second infant health metric from the user device; generating a second infant health trajectory and a corresponding second intervention data set based in part on the first infant health trajectory, the second infant health metric, and the population health trajectory; determining second supplemental data for the second intervention dataset based in part on the second infant health trajectories and respective expected benefits of each intervention in the second intervention dataset; displaying, on the user device, a second intervention from the second intervention data set selected based on the second supplemental data, the second intervention being displayed based on a condition that a user of the user device is likely to complete the second intervention; and monitoring compliance of the user device with a second parent compliance metric, the second parent compliance metric being based on the compliance of the user device.

[0180] (16) The infant health management device of embodiment 15, wherein the first supplemental data includes a ranking of one or more interventions in the intervention dataset. (17) The infant health management device of embodiment 15, wherein the first supplemental data includes a plurality of corresponding point values ​​for one or more interventions in the intervention dataset. (18) An infant health management device as described in embodiment 17, wherein one point value of the plurality of point values ​​indicates a level of encouragement for applying an intervention from the intervention dataset to the infant corresponding to the infant health management device. (19) a first point value of the plurality of point values ​​corresponds to a first listed intervention of the first intervention data set; a second point value of the plurality of point values ​​corresponds to a second listed intervention of the first intervention data set; An infant health management device as described in embodiment 17, wherein the first point value being higher than the second point value indicates that the first listed intervention is prioritized over the second listed intervention. (20) An infant health management device as described in embodiment 15, wherein each intervention in the intervention dataset is stored with a corresponding value, the corresponding value representing a health benefit of applying the intervention to the infant corresponding to the infant health management device.

Claims

1. 1. An infant health care device, comprising: receiving, from a user device, first infant health metrics including a first parent compliance metric, the first parent compliance metric being associated with usage of the infant health management device; generating a first infant health trajectory and a corresponding first intervention data set based in part on the first infant health metric and population health trajectory, the first infant health trajectory being based on the first parent compliance metric; determining first supplemental data for the first intervention dataset based in part on the first infant health trajectory and a respective expected benefit of each intervention in the first intervention dataset; transmitting to the user device a first intervention from the first intervention data set selected based on the first supplemental data; receiving a second infant health metric from the user device; and generating a second infant health trajectory and a corresponding second intervention data set based in part on the first infant health trajectory, the second infant health metric, and the population health trajectory; determining second supplemental data for the second intervention dataset based in part on the second infant health trajectories and respective expected benefits of each intervention in the second intervention dataset; and displaying, on the user device, a second intervention from the second intervention data set selected based on the second supplemental data, the second intervention being displayed based on a condition that a user of the user device is likely to complete the second intervention.

2. The infant health management device of claim 1 , wherein the first infant health metric is based in part on one or more of a skin health metric, a gut health metric, or an immune training metric.

3. The infant health management device of claim 1 , wherein the first supplemental data includes a ranking of one or more interventions in the intervention dataset.

4. The infant health management device of claim 1 , wherein the first supplemental data includes a plurality of corresponding point values ​​for one or more interventions in the intervention dataset.

5. 5. The infant health care device of claim 4, wherein one point value of the plurality of point values ​​indicates a level of encouragement for applying an intervention of the intervention dataset to an infant corresponding to the infant health care device.

6. a first point value of the plurality of point values ​​corresponds to a first listed intervention of the first intervention data set; a second point value of the plurality of point values ​​corresponds to a second listed intervention of the first intervention data set; 5. The infant health management device of claim 4, wherein the first point value being higher than the second point value indicates that the first listed intervention is prioritized over the second listed intervention.

7. 10. The infant health care device of claim 1, wherein each intervention in the intervention dataset is stored with a corresponding value, the corresponding value representing a health benefit of applying the intervention to an infant corresponding to the infant health care device.

8. 1. A method for an infant health care device, the method comprising: receiving, from a user device, first infant health metrics including a first parent compliance metric, the first parent compliance metric being associated with usage of the infant health management device; generating a first infant health trajectory and a corresponding first intervention data set based in part on the first infant health metric and population health trajectory, the first infant health trajectory being based on the first parent compliance metric; determining first supplemental data for the first intervention dataset based in part on the first infant health trajectory and a respective expected benefit of each intervention in the first intervention dataset; transmitting to the user device a first intervention from the first intervention data set selected based on the first supplemental data; receiving a second infant health metric from the user device; and generating a second infant health trajectory and a corresponding second intervention data set based in part on the first infant health trajectory, the second infant health metric, and the population health trajectory; determining second supplemental data for the second intervention dataset based in part on the second infant health trajectories and respective expected benefits of each intervention in the second intervention dataset; and displaying, on the user device, a second intervention from the second intervention data set selected based on the second supplemental data, the second intervention being displayed based on a condition that a user of the user device is likely to complete the second intervention.

9. 10. The infant health management device of claim 8, wherein the first infant health metric is based in part on one or more of a skin health metric, a gut health metric, or an immune training metric.

10. The infant health management device of claim 8 , wherein the first supplemental data includes a ranking of one or more interventions in the intervention dataset.

11. The infant health management device of claim 8 , wherein the first supplemental data includes a plurality of corresponding point values ​​for one or more interventions in the intervention dataset.

12. 12. The infant health care device of claim 11, wherein one point value of the plurality of point values ​​indicates a level of encouragement for applying an intervention of the intervention dataset to an infant corresponding to the infant health care device.

13. a first point value of the plurality of point values ​​corresponds to a first listed intervention of the first intervention data set; a second point value of the plurality of point values ​​corresponds to a second listed intervention of the first intervention data set; 12. The infant health management device of claim 11, wherein the first point value being higher than the second point value indicates that the first listed intervention is prioritized over the second listed intervention.

14. 9. The infant health care device of claim 8, wherein each intervention in the intervention dataset is stored with a corresponding value, the corresponding value representing a health benefit of applying the intervention to an infant corresponding to the infant health care device.

15. 1. A system comprising: a memory that stores computer-executable instructions that are executable by a processor; The processor, upon execution of the computer-executable instructions, receiving, from a user device, first infant health metrics including a first parent compliance metric, the first parent compliance metric being associated with usage of an infant health management device; generating a first infant health trajectory and a corresponding first intervention data set based in part on the first infant health metric and population health trajectory, the first infant health trajectory being based on the first parent compliance metric; determining first supplemental data for the first intervention dataset based in part on the first infant health trajectory and a respective expected benefit of each intervention in the first intervention dataset; transmitting to the user device a first intervention from the first intervention data set selected based on the first supplemental data; receiving a second infant health metric from the user device; and generating a second infant health trajectory and a corresponding second intervention data set based in part on the first infant health trajectory, the second infant health metric, and the population health trajectory; determining second supplemental data for the second intervention dataset based in part on the second infant health trajectories and respective expected benefits of each intervention in the second intervention dataset; displaying, on the user device, a second intervention from the second intervention data set selected based on the second supplemental data, the second intervention being displayed based on a condition that a user of the user device is likely to complete the second intervention; monitoring compliance of the user device with a second parent compliance metric, the second parent compliance metric being based on the compliance of the user device.

16. The infant health management device of claim 15 , wherein the first supplemental data includes a ranking of one or more interventions in the intervention dataset.

17. The infant health management device of claim 15 , wherein the first supplemental data includes a plurality of corresponding point values ​​for one or more interventions in the intervention dataset.

18. 20. The infant health care device of claim 17, wherein one point value of the plurality of point values ​​indicates a level of encouragement for applying an intervention of the intervention dataset to an infant corresponding to the infant health care device.

19. a first point value of the plurality of point values ​​corresponds to a first listed intervention of the first intervention data set; a second point value of the plurality of point values ​​corresponds to a second listed intervention of the first intervention data set; 18. The infant health management device of claim 17, wherein the first point value being higher than the second point value indicates that the first listed intervention is prioritized over the second listed intervention.

20. 16. The infant health care device of claim 15, wherein each intervention in the intervention dataset is stored with a corresponding value, the corresponding value representing a health benefit of applying the intervention to an infant corresponding to the infant health care device.

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