A Practical Universal Consortium Blockchain Paradigm for Patient Data Portability on the Cloud Utilizing Delegated Identity Management

Author(s):  
Abdulbadi Sabir ◽  
Noora Fetais
2021 ◽  
Author(s):  
Shyam Visweswaran ◽  
Brian McLay ◽  
Nickie Capella ◽  
Michele Morris ◽  
John T Milnes ◽  
...  

Objective As a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research. Methods Because UPMC is one of the largest health care systems in the US with multiple vendors' electronic health record (EHR) systems, we designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data is stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including EHRs, health plans, and research studies; and includes knowledge for mapping to standard terminologies. Neptune enables efficient provisioning of data to large analytics-oriented data models and to individual investigators. Results Neptune contains data for more than 5 million patients longitudinally organized as HIPAA Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of IRB-approved research projects by local investigators and for national projects such as the Accrual to Clinical Trials (ACT) network, the All of Us Research Program, and the National Patient-Centered Clinical Research Network. Discussion The design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It features several desiderata of an RDW, including robust protected health information management, an extensible information storage model, and binding to standard terminologies at the time of data delivery. It also includes several unique aspects, including the physical warehouse straddling the University of Pittsburgh and UPMC networks and management under a HIPAA Business Associates Agreement.


1989 ◽  
Vol 28 (02) ◽  
pp. 69-77 ◽  
Author(s):  
R. Haux

Abstract:Expert systems in medicine are frequently restricted to assisting the physician to derive a patient-specific diagnosis and therapy proposal. In many cases, however, there is a clinical need to use these patient data for other purposes as well. The intention of this paper is to show how and to what extent patient data in expert systems can additionally be used to create clinical registries and for statistical data analysis. At first, the pitfalls of goal-oriented mechanisms for the multiple usability of data are shown by means of an example. Then a data acquisition and inference mechanism is proposed, which includes a procedure for controlling selection bias, the so-called knowledge-based attribute selection. The functional view and the architectural view of expert systems suitable for the multiple usability of patient data is outlined in general and then by means of an application example. Finally, the ideas presented are discussed and compared with related approaches.


1996 ◽  
Vol 35 (02) ◽  
pp. 88-92 ◽  
Author(s):  
E.-H. W. Kluge

AbstractKay and Purves' proposed narratological model of the medical record is based on the familiar phenomenological insight that the perception of data is conditioned by the conceptual framework of the perceiver. Unfortunately, unless handled very carefully, this approach will make the significance of a medical record unique to the person who constructed it and impermeable to outside scrutiny. However, when integrated into the analog-model of the medical record, the narratological model can be accommodated as the clinician-relative construction of a patient profile within the data that make up the medical record. Some implications for the construction of expert systems and competence analysis are indicated.


Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 393-P
Author(s):  
KHAWLA F. ALI ◽  
LIMA LAWRENCE ◽  
LAUREN A. BUEHLER ◽  
RONALD R. GAMBINO ◽  
MARWAN HAMATY

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