Value of future adherence

1. A system, comprising:

a processor; and

a memory configured to store computer readable instructions that, when executed by the processor, cause the system to:

generate a list of candidates using, at least, medical history data;

calculate one or more values associated with medication adherence for each candidate in the list of candidates, wherein calculating the one or more values includes:

determining a probability of non-adherence for each candidate in the list of candidates,

determining a probability of conversion from non-adherence to adherence for each candidate in the list of candidates, and

determining a cost reduction for each candidate, in the list of candidates, when the candidate is considered to be adherent;

calculate a score for each candidate by combining the probability of non-adherence, for each candidate, by the probability of conversion from non-adherence to adherence, for each candidate, and further combining the cost reduction for each candidate when the candidate is considered to be adherent;

apply the calculated score to each candidate and generate an ordered listing of each candidate;

based on the calculated score for each candidate, filter the ordered listing of each candidate;

generate a subset listing of candidates based on the filtered ordered listing; and

generate a user interface for display that includes, at least, the subset listing of each candidate, where each candidate in the subset listing is displayed in association with an indication of medication adherence and a value associated with the score and corresponding to possible future adherence to at least one medication for each candidate.

2. The system of claim 1 , wherein a threshold value associated with the calculated score is determined.

3. The system of claim 2 , wherein each candidate from the ordered listing is filtered from the ordered listing when the calculated score is below the threshold value.

4. The system of claim 1 , wherein each candidate from the ordered listing is filtered from the ordered listing based on a percentage of candidates with a high calculated score.

5. The system of claim 1 , wherein an intervention cost is associated with each candidate, and the user interface is configured to generate a display of the intervention cost in association with each candidate.

6. The system of claim 1 , wherein an intervention cost is associated with each candidate, and each candidate from the ordered listing is filtered from the ordered listing based on the intervention cost.

7. The system of claim 1 , wherein an intervention cost is associated with each candidate, and each candidate from the ordered listing is filtered from the ordered listing when the intervention cost exceeds the calculated score.

8. The system of claim 1 , wherein a plurality of interventions are associated with each candidate, each intervention includes an associated cost, and the calculated score is weighed against the associated cost for each intervention to select an intervention from the plurality of interventions.

9. The system of claim 1 , wherein the calculated score for each therapy corresponds to a monetary cost associated with future adherence to the therapy.

10. The system of claim 1 , wherein the system is further caused to:

obtain data packets including, at least, medical history data including the one or more therapies for each candidate; and

create a resultant data file using the medical history data for each candidate.

11. A method for determining a value of future adherence, comprising:

at an information processing system having at least a processor and a memory:

generating a list of candidates using, at least, medical history data;

calculating one or more values associated with medication adherence for each candidate in the list of candidates, wherein calculating the one or more values includes:

determining a probability of non-adherence for each candidate in the list of candidates,

determining a probability of conversion from non-adherence to adherence for each candidate in the list of candidates, and

determining a cost reduction for each candidate, in the list of candidates, when the candidate is considered to be adherent;

calculating a score for each candidate by combining the probability of non-adherence, for each candidate, by the probability of conversion from non-adherence to adherence, for each candidate, and further combining the cost reduction for each candidate when the candidate is considered to be adherent;

applying the calculated score to each candidate and generate an ordered listing of each candidate;

based on the calculated score for each candidate, filtering the ordered listing of each candidate;

generating a subset listing of candidates based on the filtered ordered listing; and

generating output data that includes the calculated score in association with the each candidate in the subset listing of candidates.

12. The method of claim 11 , wherein

a threshold value associated with the calculated score is determined, and

each candidate from the ordered listing is filtered from the ordered listing when the calculated score is below the threshold value.

13. The method of claim 11 , wherein each candidate from the ordered listing is filtered from the ordered listing based on a percentage of candidates with a high calculated score.

14. The method of claim 11 , wherein an intervention cost is associated with each candidate, and the user interface is configured to generate a display of the intervention cost in association with each candidate.

15. The method of claim 11 , wherein a plurality of interventions are associated with each candidate, each intervention includes an associated cost, and the calculated score is weighed against the associated cost for each intervention to select an intervention from the plurality of interventions.

16. A non-transitory computer readable storage medium configured to store computer readable instructions that, when executed by a processor of an information processing system, cause the information processing system to provide execution comprising:

generating a list of candidates using, at least, medical history data;

calculating one or more values associated with medication adherence for each candidate in the list of candidates, wherein calculating the one or more values includes:

determining a probability of non-adherence for each candidate in the list of candidates,

determining a probability of conversion from non-adherence to adherence for each candidate in the list of candidates, and

determining a cost reduction for each candidate, in the list of candidates, when the candidate is considered to be adherent;

calculating a score for each candidate by combining the probability of non-adherence, for each candidate, by the probability of conversion from non-adherence to adherence, for each candidate, and further combining the cost reduction for each candidate when the candidate is considered to be adherent;

applying the calculated score to each candidate and generate an ordered listing of each candidate;

based on the calculated score for each candidate, filtering the ordered listing of each candidate; and

generating output data that includes the calculated score in association with the each candidate.

17. The non-transitory computer readable storage medium of claim 16 , wherein

a threshold value associated with the calculated score is determined, and

each candidate from the ordered listing is filtered from the ordered listing when the calculated score is below the threshold value.

18. The non-transitory computer readable storage medium of claim 16 , wherein each candidate from the ordered listing is filtered from the ordered listing based on a percentage of candidates with a high calculated score.

19. The non-transitory computer readable storage medium of claim 16 , wherein an intervention cost is associated with each candidate, and each candidate from the ordered listing is filtered from the ordered listing based on the intervention cost.

20. The non-transitory computer readable storage medium of claim 16 , wherein an intervention cost is associated with each candidate, and each candidate from the ordered listing is filtered from the ordered listing when the intervention cost exceeds the calculated score.