IPQ engages with its clients to establish a collaborative approach to its model-building
IPQ typically accomplishes this using a two-phase model, adapted to specific client requirements.
Phase I: In collaboration with “the client”, IPQ will work to help identify and quantify both the critical “unmet clinical (or commercial) needs” and go beyond that to identify and quantify the even more critical “unstated, unmet needs”. We term this “root cause analysis” and work with the client to define, refine and prioritize requirements to address these gaps. IPQ then deconvolutes these “needs” to identify critical concepts and relationships necessary to address them and adds these to its proprietary model of the patient journey, as needed. This is instantiated within IPQ’s novel knowledge graph and identification of critical data sources, public and private, are undertaken.
Client data can be incorporated into this process. Use of IPQ’s knowledge graph identifies additional key concepts and relationships which are reviewed with the client to establish the “disease model” that incorporates the complexity of the real-world patient population, the need for refinement and stratification of existing disease/diagnosis categories and critical factors, e.g. conflicting guidelines, standards of care, reimbursement, that may exist. Phase I is typically done as a pilot study.
Phase II: IPQ will establish a unique federated data model whereby data sources, identified in Phase I, will be linked while maintaining individual data provenance. This enables further linking to national level public data sources in compliance with HIPAA and GDPR requirements. Critical evaluation of specific data fields within each data base, in support of the disease model, will be carried out to establish context and quality/confidence in existing data. This is critical to support quality analysis including AI/ML approaches as data fields with identical labels commonly do not contain data collected or computed in the same manner across multiple data sources. This can be a significant source of uncertainty affecting the analysis results and their potential interpretation because of limited content of contextual data maintained in EHR’s. These qualification factors are maintained in a parallel database so that later analyses can refer to these assessments prior to selection of data cohorts, etc. Missing data, potential biases in data and conflicting data are identified and incorporated into addressing the client’s original and expanded questions. In this manner, both the answers and their specific confidence levels or constraints can be established and reviewed with the client for further action, e.g. further data acquisition, etc.
Michael Liebman, * Stefania Pieroni, Michela Franchini, Loredana Fortunato, Marco Scalese, Sabrina
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Allen Glicksman, Misha Rodriguez, Lauren Ring, and Michael Liebman, Innovation in Aging, in press, Shifting the Paradigm, 2021
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Integrated Information for integrated Care in the General Practice Setting: Using Social Network Analysis to go
beyond the Diagnosis of Frailty in the Elderly,Franchini, M. Pieroni, S., Fortunato, L, Knezevic, T, Liebman, M.N., Molinaro, S, Clinical Translational Medicine (2016) 5:24
Franchini, M, Pieroni, S, Fortunato, L, Molinaro, S and Liebman, M.N, Current Pharmaceutical Design
(2015) 21(6): 791 – 805
The application of observational data in translational medicine: analyzing tobacco-use behaviors of
adolescents Journal of Translational Medicine 2012, 10:89 doi:10.1186/1479-5876-10-89
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