Data & Analytics in HealthCare
Data & Analytics Adoption Model in HealthCare for Access, Cost, Quality (ACQ)
- Three stages of data management via EDW – 1) data collection 2) data sharing / access and 3) data analytics
- Bulk of Data Collection are done via electronic health records (EMR/EHR) and health information exchanges (HIE/HIX)
- Data & Analytics is playing a big role in Health System Quality improvement and cost reduction efforts
- Healthcare Analytics Adoption Model borrows & leverage recommendations from HIMSS EMR Model, Health & Human Services (HHS), Food & Drugs Administration (FDA rules for Clinical Trials) and Center for Medicare & Medicaid (CMS)
- The Adoption Model may include – evaluating the industry’s adoption of analytics, roadmap for measure progress toward analytics adoption and evaluating vendor products for improving the quality of care while lowering costs and enhancing clinician and patient satisfaction
8 Stages of the Data & Analytics Adoption Model
- Adoption Stage 1 – Enterprise Data Warehouse (EDW) using Physical or Virtual Servers
- Adoption Stage 2 – Registration & Billing System including Patient Registries via Personal Portal
- Adoption Stage 3 – Electronic Health Record &Electronic Prescription System (EMR & eRX) for clinical observations and lab results
- Adoption Stage 4 – Management & Internal Reporting Tool / Dashboard
- Adoption Stage 5 – Statutory& External Reporting Tool / Dashboard i.e. Meaningful Use Reporting
- Adoption Stage 6 – Fraud & Abuse reduction using Claims Data
- Adoption Stage 7 – Population Health Management (PHM) & Care Management Analytics
- Adoption Stage 8 – Predictive Analytics (PA) leveraging PHM, CM, UM Data for Preventive Care to Cost Curve and Patient Care / Patient Satisfaction
Top use cases for data and analytics for Health IT organization
- Adoption Analytics – Clinical Workflows & Applications
- Claims Analytics – Fraud & Abuse
- Cost Analytics – Planning & Control
- Customer insight Analytics – Customer Care
- Discovery/search Analytics – Clinical Trials
- Meaningful Use Analytics – Positive Outcome
- Operations Analytics – Customer Service
- Predictive Analytics – Bend the Cost Curve
- Risk and Compliance Analytics – PHI & HIPAA
How much data and analytics contribute to Health organization’s decision?
- See the future
- Drive revenue growth or Bend the Cost Curve
- Clinical Workflows & Software Applications
- Customer Care
- Fraud & Abuse
- Guide future strategy
- PHI & HIPAA Compliance
- Planning & Control
- Product research and development
- Product research and development – Clinical Trials for Drugs discovery & approval for medical equipment by FDA
Challenges in Health organization in adopting data and analytics strategy
- Budget & Priority
- Information Explosion
- Inoperability of Data
- Managing Data Vs Analyzing Data
- PHI / HIPAA Challenge
- Quality of Data – Data duplication or Data Integrity
- Varied Application System within Health System
Metrics or benefits /business outcomes for data and analytics in HealthCare
- Cost Curve
- Ethical intent
- Go-to-Market for Drugs
- Legal compliance for PHI (Privacy) & HIPAA
- Population Health Management (PHM)
Proof of Data & Analytics working for Health Organization
- A code of ethics around data and analytics
- C-level support
- Claims Dashboard
- HEDIS STAR Rating
- Meaningful Use Metrics
- Pain Management Dashboard
- Prescription Dashboard for Controlled Substances
- Tracking customer views
- Transparency on what customer data is being used
Posted on June 5, 2015 | Categories: Data Scientist, IT Services | Posted by: admin
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