SKIM LEAN aims at exploiting Electronic Health Records (EHRs) to integrate knowledge derived from routine laboratory tests with background analysis of clinical databases, for the identification and early referral to specialist care, where appropriate, of patients with hypercholesterolemia, who may be inadequately controlled according to their cardiovascular (CV) risk level. SKIM LEAN addresses gaps in care that may occur through the lack of coordination between primary and specialist care, incomplete adherence to clinical guidelines, or poor patient's compliance to the physician's prescriptions because of comorbidities or drug side effects. Key project objectives include: (1) improved health professionals' competence and patient empowerment through a two-tiered educational website for general practitioners (GPs) and patients, and (2) implementation of a hospital-community shared care pathway to increase the proportion of patients at high/very-high CV risk (Familial Hypercholesterolemia, previous CV events) who achieve target LDL cholesterol (LDL-C) levels. Thanks to a close collaboration between clinical and information technology partners, SKIM LEAN will fully exploit the value of big data deriving from EHRs, and filter such knowledge using clinically-derived algorithms to risk-stratify patients. Alerts for GPs will be generated with interpreted test results. GPs will be able to refer patients with uncontrolled LDL-C within the shared pathway to the lipid or secondary prevention outpatient clinics of NIG hospital. Metrics to verify the project achievements include web-site visits, the number of alerts generated, numbers of patients referred by GPs, the proportion of secondary prevention patients who achieve LDL-C <100 mg/dl or a >50% decrease from baseline.
|Titolo:||Systematic Lab Knowledge Integration for Management of Lipid Excess in High-Risk Patients : Rationale and Design of the SKIM LEAN Project|
CALABRESI, LAURA (Corresponding)
|Parole Chiave:||hypercholesterolemia; high cardiovascular risk; cardiovascular risk; LDL-cholesterol; electronic health records|
|Settore Scientifico Disciplinare:||Settore BIO/14 - Farmacologia|
|Data di pubblicazione:||2-ott-2018|
|Digital Object Identifier (DOI):||10.3389/fdata.2018.00004|
|Appare nelle tipologie:||01 - Articolo su periodico|