‘Precision Health’: Balancing Reactive Care and Proactive Care Through the Evidence Based Knowledge Graph Constructed from Real-World Electronic Health Records, Disease Trajectories, Diseasome, and Patholome

2020 ◽  
pp. 113-133
Author(s):  
Asoke K Talukder ◽  
Julio Bonis Sanz ◽  
Jahnavi Samajpati
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Knowledge Graph ◽  
Evidence Based ◽  
Health Records ◽  
Electronic Health ◽  
Proactive Care ◽  
Precision Health

Assessing function of electronic health records for real-world data generation

2018 ◽  
Vol 24 (3) ◽  
pp. 95-98 ◽  
Author(s):  
Daphne Guinn ◽  
Erin E Wilhelm ◽  
Grazyna Lieberman ◽  
Sean Khozin
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Data Generation ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Electronic Health

scholarly journals Secondary Use of Electronic Health Records for Building Large, Real-World ILD Cohorts

2021 ◽  
Author(s):  
E.D. Farrand ◽  
O. Gologorskaya ◽  
H. Mills ◽  
L. Radhakrishnan ◽  
H.R. Collard ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Health Records ◽  
Secondary Use ◽  
Electronic Health

scholarly journals A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

2014 ◽  
Vol 05 (02) ◽  
pp. 463-479 ◽  
Author(s):  
P. Ryan ◽  
Y. Zhang ◽  
F. Liu ◽  
J. Gao ◽  
J.T. Bigger ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
Real World ◽  
World Population ◽  
Health Records ◽  
Target Populations ◽  
Electronic Health

SummaryObjective: To improve the transparency of clinical trial generalizability and to illustrate the method using Type 2 diabetes as an example.Methods: Our data included 1,761 diabetes clinical trials and the electronic health records (EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical Center of New-York Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A1c (HbA1c) values.Results: Greater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c<7 and fewer than 45% of studies allow HbA1c>10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c<7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions excluding patients >80 or <18 years. Most of the real-world population fell within this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80. The GIST for age was 0.75. Conclusions: We contribute a scalable method to profile and compare aggregated clinical trial target populations with EHR patient populations. We demonstrate that Type 2 diabetes studies are more generalizable with regard to age than they are with regard to HbA1c. We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions and other continuous or binary variables. We envision the potential use of EHR data for examining the generaliz-ability of clinical trials and for defining population-representative clinical trial eligibility criteria.Citation: Weng C, Li Y, Ryan P, Zhang Y, Liu F, Gao J, Bigger JT, Hripcsak G. A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records. Appl Clin Inf 2014; 5: 463–479 http://dx.doi.org/10.4338/ACI-2013-12-RA-0105

scholarly journals Pathways, Outcomes, and Costs in Colon Cancer: Retrospective Evaluations in Two Distinct Databases

2011 ◽  
Vol 7 (3S) ◽  
pp. 52s-59s ◽  
Author(s):  
J. Russell Hoverman ◽  
Thomas H. Cartwright ◽  
Debra A. Patt ◽  
Janet L. Espirito ◽  
Matthew P. Clayton ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Electronic Health Records ◽  
Clinical Outcomes ◽  
Evidence Based ◽  
Health Records ◽  
Electronic Health

Retrospective evaluations of electronic health records and claims databases to assess clinical outcomes and costs associated with evidence-based pathways in colon cancer.

scholarly journals Author Correction: Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Daniel M. Bean ◽  
Honghan Wu ◽  
Ehtesham Iqbal ◽  
Olubanke Dzahini ◽  
Zina M. Ibrahim ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Adverse Drug Reactions ◽  
Knowledge Graph ◽  
Drug Reactions ◽  
Health Records ◽  
Electronic Health

scholarly journals Convergence of Blockchain, Autonomous Agents, and Knowledge Graph to Share Electronic Health Records

2021 ◽  
Vol 4 ◽  
Author(s):  
Yao Yao ◽  
Meghana Kshirsagar ◽  
Gauri Vaidya ◽  
Jens Ducrée ◽  
Conor Ryan
Keyword(s):  
Electronic Health Records ◽  
Autonomous Agents ◽  
Knowledge Integration ◽  
Clinical Decision Making ◽  
User Authentication ◽  
Clinical Decision ◽  
Knowledge Graph ◽  
Autonomous Agent ◽  
Health Records ◽  
Electronic Health

In this article, we discuss a data sharing and knowledge integration framework through autonomous agents with blockchain for implementing Electronic Health Records (EHR). This will enable us to augment existing blockchain-based EHR Systems. We discuss how major concerns in the health industry, i.e., trust, security and scalability, can be addressed by transitioning from existing models to convergence of the three technologies – blockchain, agent-based modeling, and knowledge graph in a decentralized ecosystem. Each autonomous agent is responsible for instantiating key processes, such as user authentication and authorization, smart contracts, and knowledge graph generation through data integration among the participating stakeholders in the network. We discuss a layered approach for the design of the proposed system leading to an enhanced, safer clinical decision-making system. This can pave the way toward more informed and engaged patients and citizens by delivering personalized healthcare.

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