System and method for timely notification of treatment

Abstract

A computer-assisted method to timely provide notifications of treatments, the method including receiving de-identified longitudinal medical records, receiving notification data, identifying anonymized patients that received the treatment, identifying notifications for the treatment that were received by the recipients, determining, for each of the identified notifications, whether the recipient is an anonymized patient identified as having received the treatment, determining, for each of the identified notifications for the treatment determined to be received by a recipient that is an anonymized patient identified as having received the treatment, a time relationship between the time when the treatment was received by the anonymized patient and the time that the notification was received by the recipient that is the anonymized patient, and determining, for each of the anonymized patients that received the treatment, associations between one or more time relationships for notifications received by the anonymized patient.

The invention claimed is: 
     
       1. A computer-implemented method comprising:
 identifying a set of notifications, wherein:
 a recipient received a notification, 
 an anonymized patient received a treatment, 
 the recipient patient based on (i) a set of first identifiers for a set of anonymized patients and (ii) a set of second identifiers for a set of recipients; 
 
 determining a set of time relationships between a set of treatments that were received by the set of anonymized patients and the set of notifications; 
 training a time decay model to identify, for each time relationship in the set of time relationships, a set of coefficients for a decay function representing an impact of the notification on the corresponding treatment relative to the time relationship; 
 computing, based on the set of coefficients and for each notification in the set of notifications, a score representing a likelihood that the notification being provided to the recipient impacted the corresponding treatment being received by the anonymized patient; 
 determining a notification plan for notifying potential patients of treatments based on the scores for each notification included in the set of notifications, wherein the notification plan is determined to increase a number of treatments to be subsequently received by the anonymized patients; and 
 scheduling the notifications based on the notification plan. 
 
     
     
       2. The method of  claim 1 , comprising:
 determining a forecast model based on scores computed for the set of notifications, where the forecast model stores, for each type of notification from among the set of notifications, a notification type label that is indicative of a relationship between a number of anonymized patients receiving the treatment and a number of recipients receiving notifications for the treatment. 
 
     
     
       3. The method of  claim 2 , comprising:
 scheduling notifications based on the forecast model. 
 
     
     
       4. The method of  claim 1 , wherein identifying the set of notifications comprises:
 determining that a second identifier for the recipient that received the notification and a first identifier for the anonymized patient that received the treatment both refer to the same person. 
 
     
     
       5. The method of  claim 1 , wherein determining time relationships between the treatments that were received by the set of anonymized patients and the notifications that were received by the set of recipients comprises:
 determining a first time when a treatment was received by a particular anonymized patient; 
 determining a second time when a notification for the treatment was received by a particular recipient corresponding to the particular anonymized patient; and 
 determining a time relationship that represents a time length between the first time and the second time. 
 
     
     
       6. The method of  claim 1 , comprising:
 determining for a particular patient that a first notification for the treatment was received by the particular patient and a second notification for the treatment was received by the particular patient. 
 
     
     
       7. The method of  claim 1 , wherein computing, based on the set of coefficients and for each notification included in the set of notifications, a score representing a likelihood that the notification being provided to the recipient impacted the corresponding treatment being received by the anonymized patient comprises:
 generating an impact model by applying a random survival forest analysis to the set of coefficients. 
 
     
     
       8. The method of  claim 7 , wherein the impact model represents an impact of a notification on a treatment being received exponentially decayed from time when the notification was initially provided to a patient to a time the treatment was received by the patient. 
     
     
       9. The method of  claim 1 , further comprising:
 determining an impact for a first notification to a recipient through a first type of notification based on a time relationship for the first notification; and 
 determining an impact for a second notification to the recipient through a second type of notification based on a time relationship for the second notification. 
 
     
     
       10. The method of  claim 1 , further comprising:
 aggregating impacts of a particular type of notification on a treatment being received over each anonymized patient included in the set of anonymized patients. 
 
     
     
       11. The method of  claim 2 , wherein determining a forecast model based on scores computed for the set of notifications, where the forecast model stores, for each type of notification from among the set of notifications, a notification type label that is indicative of a relationship between a number of anonymized patients receiving the treatment and a number of recipients receiving notifications for the treatment comprises:
 determining, for each type of notification from among the set of notifications, a curve fitting a number of anonymized patients that previously received the treatment with a number of notifications previously provided to the set of recipients. 
 
     
     
       12. The method of  claim 2 , wherein determining a forecast model based on scores computed for the set of notifications, where the forecast model stores, for each type of notification from among the set of notifications, a notification type label that is indicative of a relationship between a number of anonymized patients receiving the treatment and a number of recipients receiving notifications for the treatment comprises:
 determining the forecast model as a sigmoid function representing a diminishing effect of a number of notifications received by the set of recipients on a number of anonymized patients that receive the treatment. 
 
     
     
       13. The method of  claim 2 , wherein determining a forecast model based on scores computed for the set of notifications, where the forecast model stores, for each type of notification from among the set of notifications, a notification type label that is indicative of a relationship between a number of anonymized patients receiving the treatment and a number of recipients receiving notifications for the treatment comprises:
 determining an initial forecast model for model patients based on the scores indicated by the impact model for the set of notifications; and 
 projecting the initial forecast model to potential patients. 
 
     
     
       14. The method of  claim 1 , wherein determining the notification plan for notifying potential patients of treatments based on the scores for the notifications included in the set of notifications comprises:
 receiving notification constraints for the notification plan; 
 determining that increasing a number of notifications for a particular channel increases a number of forecasted treatments to be received by anonymized patients included in the set of anonymized patients and satisfies the notification constraints; and 
 determining a total number of notifications of each type that increases the number of forecasted treatments to be received by anonymized patients included in the set of anonymized patients. 
 
     
     
       15. The method of  claim 3 , wherein scheduling the notifications based on the notification plan comprises:
 providing notifications for the treatment to the set of recipients. 
 
     
     
       16. The method of  claim 1 , wherein each anonymized patient included in the set of anonymized patients comprises a patient for which a patient identity cannot be determined but is distinguishable from other anonymized patients. 
     
     
       17. A computer system comprising one or more processors, configured to perform operations comprising:
 identifying a set of notifications, wherein:
 a recipient received a notification, an anonymized patient received a treatment, 
 the recipient corresponds to the anonymized patient based on (i) a set of first identifiers for a set of anonymized patients and (ii) a set of second identifiers for a set of recipients; 
 
 determining a set of time relationships between a set of treatments that were received by the set of anonymized patients and the set of notifications; 
 training a time decay model to identify, for each time relationship in the set of time relationships, a set of coefficients for a decay function representing an impact of the notification on the corresponding treatment relative to the time relationship; 
 computing, based on the set of coefficients and for each notification in the set of notifications, a score representing a likelihood that the notification being provided to the recipient impacted the corresponding treatment being received by the anonymized patient; 
 determining a notification plan for notifying potential patients of treatments based on the scores for each notification included in the set of notifications, wherein the notification plan is determined to increase a number of treatments to be subsequently received by the anonymized patients; and 
 scheduling the notifications based on the notification plan. 
 
     
     
       18. A non-transitory computer-readable medium, comprising software instructions, which when executed by a processor of a computer, causes the computer to perform operations comprising:
 identifying a set of notifications, wherein:
 a recipient received a notification, 
 an anonymized patient received a treatment, 
 the recipient corresponds to the anonymized patient based on (i) a set of first identifiers for a set of anonymized patients and (ii) a set of second identifiers for a set of recipients; 
 
 determining a set of time relationships between a set of treatments that were received by the set of anonymized patients and the set of notifications; 
 training a time decay model to identify, for each time relationship in the set of time relationships, a set of coefficients for a decay function representing an impact of the notification on the corresponding treatment relative to the time relationship; 
 computing, based on the set of coefficients and for each notification in the set of notifications, a score representing a likelihood that the notification being provided to the recipient impacted the corresponding treatment being received by the anonymized patient; 
 determining a notification plan for notifying potential patients of treatments based on the scores for each notification included in the set of notifications, wherein the notification plan is determined to increase a number of treatments to be subsequently received by the anonymized patients; and 
 scheduling the notifications based on the notification plan. 
 
     
     
       19. A computer system-implemented method comprising:
 determining a time relationship between a treatment that was received by a patient and a notification received by the patient;   executing a trained model to identify, for the time relationship, a coefficient for a decay function representing an impact of the notification on the treatment relative to the time relationship;   computing, based on the coefficient, a score representing a likelihood that the notification provided to the patient impacted the corresponding treatment being received by the patient;   determining a notification plan for notifying potential patients of treatments based on the score for the notification, wherein the notification plan is determined to increase a number of treatments to be subsequently received by potential patients; and   scheduling future notifications based on the notification plan.   
     
     
       20. The computer system-implemented method of claim 19, comprising training the model. 
     
     
       21. The computer system-implemented method of claim 19, comprising:
 determining a forecast model based on a score computed for each of multiple notifications, where the forecast model stores, for each type of notification from among the multiple notifications, a notification type label that is indicative of a relationship between a number of patients receiving the treatment and a number of recipients receiving notifications for the treatment.   
     
     
       22. The computer system-implemented method of claim 21, in which scheduling the future notifications comprising scheduling the future notifications based on the forecast model. 
     
     
       23. The computer system-implemented method of claim 21, wherein determining the forecast model comprises determining, for each type of notification, a curve fitting the number of patients receiving the treatment with a number of notifications previously provided to the recipients. 
     
     
       24. The computer system-implemented method of claim 21, wherein determining the forecast model comprises:
 determining an initial forecast model for model patients based on the scores computed for each of the multiple notifications; and   projecting the initial forecast model to potential patients.   
     
     
       25. The computer system-implemented method of claim 19, wherein determining the time relationship between the treatment that was received by the patient and the notification that was received by the patient comprises:
 determining a first time when the treatment was received by the patient;   determining a second time when the notification for the treatment was received by the patient; and   determining a time relationship that represents a time length between the first time and the second time.   
     
     
       26. The computer system-implemented method of claim 19, wherein computing the score comprises generating an impact model by applying a random survival forest analysis to the coefficients. 
     
     
       27. The computer system-implemented method of claim 19, in which the notification comprises a first notification, and in which the method comprises:
 determining an impact for the first notification to the patient based on the time relationship for the first notification; and   determining an impact for a second notification to the patient based on a determined time relationship between a second treatment that was received by the patient and the second notification,   in which the first notification and the second notification are different types of notifications.   
     
     
       28. A non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
 determining a time relationship between a treatment that was received by a patient and a notification received by the patient;   executing a trained model to identify, for the time relationship, a coefficient for a decay function representing an impact of the notification on the treatment relative to the time relationship;   computing, based on the coefficient, a score representing a likelihood that the notification provided to the patient impacted the corresponding treatment being received by the patient;   determining a notification plan for notifying potential patients of treatments based on the score for the notification, wherein the notification plan is determined to increase a number of treatments to be subsequently received by potential patients; and   scheduling future notifications based on the notification plan.   
     
     
       29. The non-transitory computer readable medium of claim 28, in which the operations comprise training the model. 
     
     
       30. The non-transitory computer readable medium of claim 28, in which the operations comprise:
 determining a forecast model based on a score computed for each of multiple notifications, where the forecast model stores, for each type of notification from among the multiple notifications, a notification type label that is indicative of a relationship between a number of patients receiving the treatment and a number of recipients receiving notifications for the treatment.   
     
     
       31. The non-transitory computer readable medium of claim 30, in which scheduling the future notifications comprising scheduling the future notifications based on the forecast model. 
     
     
       32. The non-transitory computer readable medium of claim 30, wherein determining the forecast model comprises determining, for each type of notification, a curve fitting the number of patients receiving the treatment with a number of notifications previously provided to the recipients. 
     
     
       33. The non-transitory computer readable medium of claim 30, wherein determining the forecast model comprises:
 determining an initial forecast model for model patients based on the scores computed for each of the multiple notifications; and   projecting the initial forecast model to potential patients.   
     
     
       34. The non-transitory computer readable medium of claim 28, wherein determining the time relationship between the treatment that was received by the patient and the notification that was received by the patient comprises:
 determining a first time when the treatment was received by the patient;   determining a second time when the notification for the treatment was received by the patient; and   determining a time relationship that represents a time length between the first time and the second time.   
     
     
       35. The non-transitory computer readable medium of claim 28, wherein computing the score comprises generating an impact model by applying a random survival forest analysis to the coefficients. 
     
     
       36. The non-transitory computer readable medium of claim 28, in which the notification comprises a first notification, and in which the method comprises:
 determining an impact for the first notification to the patient based on the time relationship for the first notification; and   determining an impact for a second notification to the patient based on a determined time relationship between a second treatment that was received by the patient and the second notification,   in which the first notification and the second notification are different types of notifications.   
     
     
       37. A computing system comprising:
 one or more processors and a memory, the one or more processors and memory configured to perform operations comprising:
 determining a time relationship between a treatment that was received by a patient and a notification received by the patient; 
 executing a trained model to identify, for the time relationship, a coefficient for a decay function representing an impact of the notification on the treatment relative to the time relationship; 
 computing, based on the coefficient, a score representing a likelihood that the notification provided to the patient impacted the corresponding treatment being received by the patient; 
 determining a notification plan for notifying potential patients of treatments based on the score for the notification, wherein the notification plan is determined to increase a number of treatments to be subsequently received by potential patients; and 
 scheduling future notifications based on the notification plan.