P2HR, a Personalized Condition-Driven Person Health Record
Health IT has recently seen a significant progress with the nationwide migration of several hospitals from legacy patient records to standardized Electronic Health Record (EHR) and the establishment of various Health Information Exchanges that facilitate access to patient health data across multiple networks. While this progress is a major enabler of improved health care services, it is unable to deliver the continuum of the patient’s current and historical health data needed by emerging trends in medicine. Fields such as precision and preventive medicine require longitudinal health data in addition to complementary data such as social, demographic and family history. This thesis introduces a person health record (PHR) which overcomes the above gap through a personalized framework that organizes health data according to the patients disease condition. The proposed personalized person health record (P2HR) represents a departure from the standardized one-size-fits-all model of currently available PHRs. It also relies on a hybrid peer-to-peer model to facilitate patient provider communication. One of the core challenges of the proposed framework is the mapping between the event-based data model used by current EHRs and PHRs and the proposed condition-based data model. Effectively mapping symptoms and measurements to disease conditions is challenging given that each symptom or measurement may be associated with multiple disease conditions. To alleviate these problems the proposed framework allows users and their health care providers to establish the relationships between events and disease conditions on a case-by-case basis. This organization provides both the patient and the provider with a better view of each disease condition and its progression.
Ben-Miled, Purdue University.
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