Resource Library-Healthcare Data Analytics for the Non-Analyst

Video Transcription

Video Summary

Pradeep Padilla presents an introduction to health data analytics aimed at non-analysts, framed within a healthcare quality competency framework. He outlines learning goals: understanding health data analytics competencies, healthcare data sources and tools, common data types and basic statistics/visualizations, the role of mental models in using data strategically, and how to build organizational learning around analytics.<br /><br />He explains how data work moves from tables to graphs to executive summaries, requiring both technical skills and strong communication. Using the DIKW hierarchy (Data–Information–Knowledge–Wisdom), he shows how raw data become meaningful only through interpretation and application, while emphasizing the growing challenge of misinformation.<br /><br />Padilla reviews core data categories: quantitative vs. qualitative, and subtypes (continuous/discrete; nominal/ordinal). He describes the “data everywhere” reality in healthcare—EHR/EMR, claims, billing, genomics, clinical trials, surveys, and newer sources like social media—grouped into clinical, operational, financial, and behavioral domains. He contrasts patient-level data with aggregate and case-based data for reporting and clinical insight.<br /><br />Key measurement concepts covered include validity, reliability (inter- and intra-rater), sensitivity, and specificity. He introduces common visual tools such as run charts and control charts for performance improvement. Analytics is defined as a lifecycle from collection to value, and he summarizes descriptive, diagnostic, predictive, and prescriptive analytics, plus a nine-step analytics workflow.<br /><br />In the administrative/operational section, he stresses data governance, ownership, data use and maintenance, pipelines, quality, compliance, and stakeholder communication. Final takeaways: non-analysts need a 360-degree understanding of analytics and must be able to interpret and communicate results to diverse audiences.

Asset Caption

Speaker: Pradeep Podila, CPHQ

What does promoting quality in managed care organizations require? How do newer, value-based payment models alter traditional approaches to meeting quality goals? From within a population health framework, Dr. Kaminski and Ms. Bodycot share experiences and insights from their work with the Medicare Shared Savings program, Medicare Advantage, commercial payers, and employers in advancing quality across a large accountable care organization.

Meta Tag

Competency Tag HDA2

Competency Tag Description Design data collection and data analysis plans to support quality, safety, regulatory and patient experience work activities

Domain Tag Health Data Analytics

Keywords

health data analytics

healthcare quality competency framework

DIKW hierarchy

EHR EMR data sources

claims and billing data

quantitative and qualitative data types

validity reliability sensitivity specificity

run charts and control charts

descriptive diagnostic predictive prescriptive analytics

data governance and compliance