ADHERENCE MONITORING THROUGH MACHINE LEARNING AND COMPUTING MODEL APPLICATION
A computer-implemented method, computer program product, and system, include a processor(s) obtaining, records representing members of a sample population with identifying attributes associated with each member, where all members of the sample population possess a common trait. The processor(s) obtains intervention(s) to address the common trait; each intervention has configurable dynamic elements, The processor(s) query with parameters based on the attributes members of the sample population, data source(s), to extract environmental data relevant to the sample population. The processor(s) analyze the environmental data and the intervention(s) and select an intervention to deploy to the sample population. The processor(s) configures the selected intervention, to optimize performance of the selected intervention.
1 . A computer-implemented method comprising:
obtaining, by one or more processors, records representing members of a sample population, wherein each record for member of the sample population comprises one or more identifying attributes associated with each member, wherein all members of the sample population possess a common trait;
obtaining, by the one or more processors, from a repository, based on the common trait, one or more interventions utilized to address the common trait, wherein each intervention comprises configurable dynamic elements defining implementation attributes for each intervention;
querying, by the one or more processors, utilizing parameters based on the one or more identifying attributes associated with each member, for a portion of the members of the sample population, over an Internet connection, one or more data sources, to extract environmental data relevant to the sample population;
analyzing, by the one or more processors, the environmental data and the one or more interventions to select an intervention of the one or more interventions to deploy to the sample population, wherein deployment of the intervention is predicted to address the common trait by meeting a pre-defined efficacy threshold; and
configuring, by the one or more processors, the dynamic elements defining implementation attributes of the selected intervention, to optimize performance of the selected intervention, wherein the configured implementation of the intervention is predicted to meet or exceed the pre-defined efficacy threshold.
2 . The computer-implemented method of claim 1 , further comprising:
deploying, by the one or more processors, the configured selected intervention to clients utilized by members of the sample population.
3 . The computer-implemented method of claim 1 , wherein the environmental data is selected from data descriptive of items selected from the group consisting of: social aspects, physical aspects, socioeconomic aspects, and demographic aspects.
4 . The computer-implemented method of claim 1 , wherein one or more of the data sources comprises a social media platform.
5 . The computer-implemented method of claim 1 , wherein one or more of the data sources comprises a current events repository.
6 . The computer-implemented method of claim 1 , wherein the one or more interventions are selected from the group consisting of: a social intervention, a behavioral intervention, an informational intervention, a technological intervention, and a systemic intervention.
7 . The computer-implemented method of claim 1 , wherein the selected configured intervention is predicted within a given probability to address the common trait.
8 . The computer-implemented method of claim 1 , wherein the parameters based on the one or more identifying attributes associated with each member comprise a common parameter indicating a community characteristic of the sample population, and wherein types of data comprising the extracted environmental data relevant to the sample population is based on the community characteristic.
9 . The computer-implemented method of claim 8 , wherein the community characteristic is selected from the group consisting of: rural, urban, and suburban.
10 . The computer-implemented method of claim 1 , further comprising:
updating, by the one or more processors, in the repository, data associated with the selected intervention of the one or more interventions utilized to address the common trait, wherein the updating comprises retaining the configured dynamic elements defining the implementation attributes of the selected intervention as a predictive model of the optimized performance of the selected intervention.
11 . The computer-implemented method of claim 2 , further comprising:
monitoring, by the one or more processors, the sample population via the deployed configured selected intervention, for a given period of time;
determining, by the one or more processors, over the given period of time, if the configured implementation of the intervention has continuously met or exceeded the pre-defined efficacy threshold; and
updating, by the one or more processors, in the repository, data associated with the selected intervention of the one or more interventions utilized to address the common trait, wherein the updating comprises retaining the configured dynamic elements defining the implementation attributes of the selected intervention as a predictive model of the optimized performance of the selected intervention, wherein the predictive model reflects the determination.
12 . The computer-implemented method of claim 1 , further comprising:
obtaining, by the one or more processors, records representing members of the sample population;
obtaining, by one or more processors, from the repository, based on the common trait, the predictive model of the optimized performance of the selected intervention; and
deploying, by the one or more processors, the configured selected intervention to clients utilized by members of the sample population.
13 . The computer-implemented method of claim 12 , further comprising:
monitoring, by the one or more processors, the sample population via the deployed configured selected intervention, for a given period of time;
determining, by the one or more processors, over the given period of time, if the configured implementation of the intervention has continuously met or exceeded the pre-defined efficacy threshold of the predictive model; and
updating, by the one or more processors, the predictive model, based on the determining.
14 . A computer program product comprising:
a storage medium readable by one or more processors and storing instructions executed by the one or more processors to perform a method, performing the method comprising:
obtaining, by the one or more processors, records representing members of a sample population, wherein each record for member of the sample population comprises one or more identifying attributes associated with each member, wherein all members of the sample population possess a common trait;
obtaining, by the one or more processors, from a repository, based on the common trait, one or more interventions utilized to address the common trait, wherein each intervention comprises configurable dynamic elements defining implementation attributes for each intervention;
querying, by the one or more processors, utilizing parameters based on the one or more identifying attributes associated with each member, for a portion of the members of the sample population, over an Internet connection, one or more data sources, to extract environmental data relevant to the sample population;
analyzing, by the one or more processors, the environmental data and the one or more interventions to select an intervention of the one or more interventions to deploy to the sample population, wherein deployment of the intervention is predicted to address the common trait by meeting a pre-defined efficacy threshold; and
configuring, by the one or more processors, the dynamic elements defining implementation attributes of the selected intervention, to optimize performance of the selected intervention, wherein the configured implementation of the intervention is predicted to meet or exceed the pre-defined efficacy threshold.
15 . The computer program product of claim 14 , performing the method further comprising:
deploying, by the one or more processors, the configured selected intervention to clients utilized by members of the sample population.
16 . The computer program product of claim 14 , wherein the environmental data is selected from data descriptive of items selected from the group consisting of: social aspects, physical aspects, socioeconomic aspects, and demographic aspects.
17 . The computer program product of claim 14 , wherein one or more of the data sources comprises a social media platform.
18 . The computer program product of claim 14 , wherein one or more of the data sources comprises a current events repository.
19 . The computer program product of claim 14 , wherein the one or more interventions are selected from the group consisting of: a social intervention, a behavioral intervention, an informational intervention, a technological intervention, and a systemic intervention.
20 . A system comprising:
a memory;
one or more processors communicatively coupled to the memory; and
program instructions executed by the one or more processors, via the memory, to perform a method, performing the method comprising:
obtaining, by the one or more processors, records representing members of a sample population, wherein each record for member of the sample population comprises one or more identifying attributes associated with each member, wherein all members of the sample population possess a common trait;
obtaining, by the one or more processors, from a repository, based on the common trait, one or more interventions utilized to address the common trait, wherein each intervention comprises configurable dynamic elements defining implementation attributes for each intervention;
querying, by the one or more processors, utilizing parameters based on the one or more identifying attributes associated with each member, for a portion of the members of the sample population, over an Internet connection, one or more data sources, to extract environmental data relevant to the sample population;
analyzing, by the one or more processors, the environmental data and the one or more interventions to select an intervention of the one or more interventions to deploy to the sample population, wherein deployment of the intervention is predicted to address the common trait by meeting a pre-defined efficacy threshold; and
configuring, by the one or more processors, the dynamic elements defining implementation attributes of the selected intervention, to optimize performance of the selected intervention, wherein the configured implementation of the intervention is predicted to meet or exceed the pre-defined efficacy threshold.