Abstract: A method and system for providing patient indications to an entity, the method comprising: for each of a set of patients: by way of an application executing at a mobile computing device, accessing a sensor system and a log of use of a communication application executing on the mobile computing device; receiving a survey response dataset; generating a behavioral dataset, derived from the sensor system and the log of use; generating a predictive model derived from at least one of the survey response dataset and the behavioral dataset; generating comparisons of outputs of blocks of the method to threshold conditions; producing a set of indications corresponding to the set of patients, derived from generation of, for each of the set of patients, an indication in response to the comparisons; and transmitting a portion of the set of indications to the entity according to a resource constraint of the entity.

Abstract: A method and system for digitally providing healthcare to a patient, the method including receiving a log of use dataset associated with patient digital communication behavior at a mobile computing device, wherein the first log of use dataset corresponds to a time period; receiving a supplementary dataset corresponding to the time period; receiving a survey response dataset from the patient, the survey response dataset corresponding to the time period; receiving a care provider dataset in association with the time period; selecting a therapeutic intervention from a set of therapeutic interventions, based on processing with at least one of the first log of use dataset, the supplementary dataset, the survey response dataset, and the care provider dataset; generating a dynamic care plan modifiable over a time period; promoting the therapeutic intervention according to the dynamic care plan.

Abstract: Embodiments of a method and system for improving care determination for care providers in relation to a condition of a user associated with a mobile device can include: collecting a log of use dataset associated with user digital communication behavior at the mobile device; collecting a mobility supplementary dataset corresponding to a mobility-related sensor of the mobile device; determining a medical status analysis for a condition of the user based on at least one of the log of use dataset and the mobility supplementary dataset, the medical status analysis including at least one of a diagnosis and a therapeutic intervention associated with the condition; and promoting the at least one of the diagnosis and the therapeutic intervention to a care provider.

Abstract: Embodiments of a method and system for facilitating improvement of a user condition through tailored communication with a user can include receiving a log of use dataset associated with a digital communication behavior at a mobile device, the log of use dataset further associated with a time period; receiving a mobility supplementary dataset corresponding to a mobility-related sensor of the mobile device, the mobility supplementary dataset associated with the time period; determining a tailored communication plan for the user based on at least one of the log of use dataset and the mobility supplementary dataset; transmitting, based on the tailored communication plan, a communication to the user at the mobile device; and promoting a therapeutic intervention to the user in association with transmitting the communication.

Abstract: One method for supporting a patient through a treatment regimen includes: accessing a log of use of a native communication application executing on a mobile computing device by a patient; selecting a subgroup of a patient population based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving a regimen adherence model associated with the subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and communication behavior for patients within the subgroup; predicting patient adherence to the treatment regimen based on the log of use of the native communication application and the regimen adherence model; and presenting a treatment-related notification based on the patient adherence through the mobile computing device.

Abstract: One method for supporting a patient through a treatment regimen includes: accessing a log of use of a native communication application executing on a mobile computing device by a patient; selecting a subgroup of a patient population based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving a regimen adherence model associated with the subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and communication behavior for patients within the subgroup; predicting patient adherence to the treatment regimen based on the log of use of the native communication application and the regimen adherence model; and presenting a treatment-related notification based on the patient adherence through the mobile computing device.

Abstract: One method for supporting a patient through a treatment regimen includes: accessing a log of use of a native communication application executing on a mobile computing device by a patient; selecting a subgroup of a patient population based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving a regimen adherence model associated with the subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and communication behavior for patients within the subgroup; predicting patient adherence to the treatment regimen based on the log of use of the native communication application and the regimen adherence model; and presenting a treatment-related notification based on the patient adherence through the mobile computing device.

Abstract: Embodiments of a method and system for improving care determination for care providers in relation to a condition of a user associated with a mobile device can include: collecting a log of use dataset associated with user digital communication behavior at the mobile device; collecting a mobility supplementary dataset corresponding to a mobility-related sensor of the mobile device; determining a medical status analysis for a condition of the user based on at least one of the log of use dataset and the mobility supplementary dataset, the medical status analysis including at least one of a diagnosis and a therapeutic intervention associated with the condition; and promoting the at least one of the diagnosis and the therapeutic intervention to a care provider.

Abstract: Embodiments of a method and system for facilitating improvement of a user condition through tailored communication with a user can include receiving a log of use dataset associated with a digital communication behavior at a mobile device, the log of use dataset further associated with a time period; receiving a mobility supplementary dataset corresponding to a mobility-related sensor of the mobile device, the mobility supplementary dataset associated with the time period; determining a tailored communication plan for the user based on at least one of the log of use dataset and the mobility supplementary dataset; transmitting, based on the tailored communication plan, a communication to the user at the mobile device; and promoting a therapeutic intervention to the user in association with transmitting the communication.

Abstract: Embodiments of a method and system for improving sleep characterization and/or a sleeping-related disorder for a user associated with a sleep session can include receiving a log of use dataset corresponding to user digital communication behavior at a mobile device, the log of use dataset associated with the sleep session; receiving a supplementary dataset characterizing activity of the user and/or mobile device, the supplementary dataset associated with the sleep session; characterizing a sleep-related parameter for the user based on at least one of the log of use dataset and the supplementary dataset; determining a sleep care plan for the user based on the sleep-related parameter, the sleep care plan including a therapeutic intervention; and promoting a therapeutic intervention to the user according to the sleep care plan.

Abstract: A method and system for digitally providing healthcare to a patient, the method including receiving a log of use dataset associated with patient digital communication behavior at a mobile computing device, wherein the first log of use dataset corresponds to a time period; receiving a supplementary dataset corresponding to the time period; receiving a survey response dataset from the patient, the survey response dataset corresponding to the time period; receiving a care provider dataset in association with the time period; selecting a therapeutic intervention from a set of therapeutic interventions, based on processing with at least one of the first log of use dataset, the supplementary dataset, the survey response dataset, and the care provider dataset; generating a dynamic care plan modifiable over a time period; promoting the therapeutic intervention according to the dynamic care plan.

Abstract: A method for evaluating cardiovascular health of a patient includes receiving a log of use dataset associated with patient digital communication behavior at a mobile computing device, wherein the log of use dataset is associated with a time period, receiving a supplementary dataset associated with the time period, generating a survey dataset based on a patient response to a survey, generating a cardiovascular health predictive model based upon at least one of the log of use dataset, the supplementary dataset, and the survey dataset, extracting a cardiovascular health metric from at least one of an output of the cardiovascular health predictive model, the log of use dataset, the supplementary dataset, and the survey dataset, wherein the cardiovascular health metric is associated with the time period; providing a cardiovascular-related notification to the patient; and automatically providing a cardiovascular therapeutic intervention at a cardiovascular device for the patient.

Abstract: A method and system for providing patient indications to an entity, the method comprising: for each of a set of patients: by way of an application executing at a mobile computing device, accessing a sensor system and a log of use of a communication application executing on the mobile computing device; receiving a survey response dataset; generating a behavioral dataset, derived from the sensor system and the log of use; generating a predictive model derived from at least one of the survey response dataset and the behavioral dataset; generating comparisons of outputs of blocks of the method to threshold conditions; producing a set of indications corresponding to the set of patients, derived from generation of, for each of the set of patients, an indication in response to the comparisons; and transmitting a portion of the set of indications to the entity according to a resource constraint of the entity.

Abstract: A method and system for providing therapy to an individual, the method comprising: transmitting a log of use dataset associated with communication behavior of the individual during a time period; receiving a supplementary dataset characterizing mobility of the individual in association with the time period; generating a survey dataset upon retrieving responses provided by the individual to at least one of a set of surveys, associated with a set of time points of the time period; generating a predictive model from a passive dataset derived from the log of use dataset and the supplementary dataset and the survey dataset; generating a report summarizing a mental health state of the individual, associated with at least a portion of the time period, from the passive dataset, the survey dataset, and an output of the predictive model; and rendering information from the report to a coach associated with the individual.

Abstract: A method and system for modeling behavior and pain-related state of an individual, the method comprising: receiving a log of use dataset associated with communication behavior of the individual during a time period; receiving a supplementary dataset characterizing activity of the individual during the time period; receiving a survey dataset including responses, to at least one of a set of symptom-assessment surveys, associated with a set of time points of the time period; generating a predictive analysis of a pain-related state of the individual associated with at least a portion of the time period, from at least one of the log of use dataset, the supplementary dataset, and the survey dataset; and generating an alert upon detection that a set of parameters from the predictive analysis of the pain-related state satisfy a threshold condition.

Abstract: A method and system for modeling behavior and a psychotic disorder-related state of a patient, the method comprising: receiving a log of use dataset associated with communication behavior of the patient during a time period; receiving a supplementary dataset characterizing mobility-behavior of the patient during the time period; generating a predictive model based upon a passive dataset derived from the log of use dataset and the supplementary dataset; transforming at least one of the passive dataset and an output of the predictive model into an analysis of a psychotic episode-risk state of the individual associated with at least a portion of the time period; and upon detection that parameters of the psychotic episode-risk state satisfy at least one threshold condition, automatically initiating provision of a therapeutic intervention for the individual by way of at least one of the computing system and the mobile communication device.

Abstract: A method and system for modeling behavior and depression state of an individual, the method comprising: receiving a log of use dataset associated with communication behavior of the individual during a time period; receiving a supplementary dataset characterizing activity of the individual during the time period; receiving a survey dataset including responses, to at least one of a set of depression-assessment surveys, associated with a set of time points of the time period; generating a predictive analysis of a depression-risk state of the individual associated with at least a portion of the time period, from at least one of the log of use dataset, the supplementary dataset, and the survey dataset; and generating an alert upon detection that a set of parameters from the predictive analysis of the depression-risk state satisfy a threshold condition.

Abstract: One method for detecting a change in health status of a patient includes: accessing a log of use of a native communication application executing on a mobile computing device by the patient; selecting a subgroup of a patient population based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving a health risk model associated with the subgroup, the health risk model defining a correlation between risk of change in a medical symptom and communication behavior for patients within the subgroup; predicting a risk of change in a medical symptom for the patient based on the log of use of the native communication application and the health risk model; and transmitting a notification to a care provider associated with the patient in response to the risk of change in the medical symptom for the patient that exceeds a threshold risk.

Abstract: One method includes: selecting a subgroup of patients associated with a health condition from a population of patients, patients in the subgroup exhibiting similar behavioral characteristics; for patients within the subgroup, characterizing communication behavior of a patient based on use of a native communication application executing on a corresponding mobile computing device by the patient during the period of time; extracting characteristics of medical symptoms of patients within the subgroup from surveys submitted by patients within the subgroup; identifying a relationship between communication behaviors of patients within the subgroup, characteristics of medical symptoms of patients within the subgroup, and a treatment regimen administered to patients within the subgroup; generating a treatment efficacy model for the subgroup based on the relationship, the treatment efficacy model defining a correlation between a change in communication behavior and efficacy of the treatment regimen in alleviating medic

Abstract: One method for supporting a patient through a treatment regimen includes: accessing a log of use of a native communication application executing on a mobile computing device by a patient; selecting a subgroup of a patient population based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving a regimen adherence model associated with the subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and communication behavior for patients within the subgroup; predicting patient adherence to the treatment regimen based on the log of use of the native communication application and the regimen adherence model; and presenting a treatment-related notification based on the patient adherence through the mobile computing device.