MAV-clic: management, analysis, and visualization of clinical data

Abstract

Objectives: Develop a multifunctional analytics platform for efficient management and analysis of healthcare data.

Materials and methods: Management, Analysis, and Visualization of Clinical Data (MAV-clic) is a Health Insurance Portability and Accountability Act of 1996 (HIPAA)-compliant framework based on the Butterfly Model. MAV-clic extracts, cleanses, and encrypts data then restructures and aggregates data in a deidentified format. A graphical user interface allows query, analysis, and visualization of clinical data.

Results: MAV-clic manages healthcare data for over 800 000 subjects at UConn Health. Three analytic capabilities of MAV-clic include: creating cohorts based on specific criteria; performing measurement analysis of subjects with a specific diagnosis and medication; and calculating measure outcomes of subjects over time.

Discussion: MAV-clic supports clinicians and healthcare analysts by efficiently stratifying subjects to understand specific scenarios and optimize decision making.

Conclusion: MAV-clic is founded on the scientific premise that to improve the quality and transition of healthcare, integrative platforms are necessary to analyze heterogeneous clinical, epidemiological, metabolomics, proteomics, and genomics data for precision medicine.

Keywords: HIPAA; analysis; data mining; database; healthcare; management.

Figure 1.

MAV-clic HIPAA-compliant product line architecture for clinical data handling, extraction, cleansing, transfer, load, store, structure, standardization, management, processing, analysis, quality assessment, visualization, security, tracking, searching, and reporting.

Figure 2.

MAV-clic big data workflow, handling input from multiple data sources, dynamic transfer of data using high-performance computing, partitioning of extracted heterogeneous data into different databases, and sharing of restructured and aggregated data.

Figure 3.

(A) MAV-clic features for cohort building and ontology making for patient data analysis using CMS proposed and customized measures. (B) Cohort building example: patients who visited UConn Health within the last 7 days for any diagnosis, any medication, and with any provider. (C) Measurement analysis example: patients visited a specific doctor within the last 365 days for particular diagnoses and lab tests. (D) Calculated measure outcome example: customized measurement analysis of patients diagnosed for diabetes, sinusitis, and aged over 65 years.