FIELD OF INVENTION

The present disclosure relates to secure and/or unsecure patient messaging. In particular, the present disclosure relates to a pharmacy engaging patients regarding medication in a secure and/or unsecure conversation anytime and anywhere.

BACKGROUND

Many health care providers/health care institutions (e.g., pharmacies, health centers, etc.) interact with customers (e.g., patients, etc.) to provide products and services. In interacting with customers, most health institutions seek ways to engage their customers in order to improve the delivery of their products and services.

Evolving technology trends and patient preferences call for integrating new technologies into the existing communication infrastructure of health care providers or institutions to enhance their communication with customers. For instance, a number of health care institutions currently communicate with customers using conventional techniques such as email, short message service (SMS), or phone calls without assessing optimal ways to use these tools based on patient preferences, relevant available customer data, etc.

Furthermore, most health care institutions fail to anticipate customer needs and appropriately communicate in a manner that not only engages customers but also improves health and wellbeing and enhances customer retention. This is largely because these health care institutions do not have or leverage customer data (e.g., customer expense data, customer health data, other behavioral data of customer, etc.) to determine efficient ways to communicate with and engage customers.

SUMMARY

The present disclosure relates to secure and/or unsecure patient messaging. In particular, the present disclosure relates to a pharmacy engaging patients regarding medication in a secure and/or unsecure conversation anytime and anywhere. The messaging session may be initiated by a patient, a centralized computing system associated with a health care institution (e.g., pharmacy), or a third-party contributor.

One innovative aspect of the subject matter described in this disclosure may be embodied in a computer-implemented method comprising generating a model profile, by a centralized computing system, the model profile being associated with a message type to identify at least one discrete opportunity to initiate dialogue with a patient; analyzing, by the centralized computing system, a patient profile associated with the patient to determine whether the patient profile fits the model profile; in response to determining that the patient profile fits the model profile, queuing, by the centralized computing system a message corresponding to the message type; sending the message to the patient; and adapting the model profile based on one or more of a patient response and other patient data stored in association with the patient profile.

Additionally, the message sent to the patient may be one of at least a portion of a one-way unsecure communication with the patient; and at least a portion of a two-way unsecure communication with the patient. The message could also be one of at least a portion of a two-way secure communication with the patient including a question from the patient; and at least a portion of a two-way secure communication with the patient including a request for health sensitive data associated with the patient.

Moreover, the centralized computing system may authenticate the patient based on a response from the patient. The centralized computing system may further adapt the model profile based on a response from the patient by generating an evaluation of a content and context of the response from the patient; and incorporating the evaluation into adapting the model profile to identify another discrete opportunity to initiate dialogue with the patient. The model profile may be generated based on one or more patient factors comprising one or more of a demographic factor describing a particular class of patients with shared characteristics to which the patient belongs to; a medication intake factor derived from prescription information of the patient; a diagnosis factor describing a cause and nature of a medical condition associated with the patient; an insurance factor derived from health insurance information associated with the patient; a clinical interaction factor describing a nature of historical interactions between the patient and a care team associated with the centralized computing system; a digital interaction factor describing a digital footprint of interactions between the patient and the centralized computing system; a retail purchase history factor associated with the patient; a payment history factor describing one or more sources of payment associated with prescription purchases of the patient; and a health record factor describing a medical history of the patient.

The centralized computing system may further apply machine learning to the one or more patient factors in generating the model profile and may further apply branching and conditional data analysis to a patient response to send a subsequent message to the patient. Additionally, the centralized computing system may adapt the model profile based on third-party data in one embodiment, the third-party data including captured health data associated with the patient.

Other implementations of one or more of these aspects include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

