“meaningful healthcare analytics: now that we have it ... fall himss...oct 10, 2014 · single...

Copyright 2014 HBI Solutions, Inc. 1

“Meaningful Healthcare Analytics: Now That We Have I t, What Do We Do With

BIG DATA?”

2nd Vermont & New Hampshire Annual Fall Event October 10, 2014

Frank Stearns Executive Vice President

HBI Solutions, Inc.


Overview

• Current Environment • Analytics and Big Data • Analytic Platform • Big Data Example - Predictive Analytics • Importance of Visualization • Natural Language Processing • Criteria for Success Using Big Data


About HBI Solutions

founded in 2011

Talent and experience : • Stanford researchers • Frontline physicians • Software executives • Computer scientists, statisticians • Performance improvement

practitioners

We address the value-based healthcare imperative with a series of proprietary population and variance management applications to drive

improved outcomes and cost efficiencies


The Current Environment

New Solutions

are Required


The Healthcare Market is Buzzing with Buzzwords

Single Source of Truth

Data Governance

Population Health

Big Data

Enterprise Data Warehouse

Performance Dashboards

Hadoop

Predictive Modeling

Business Intelligence

????

Basically, it all condenses into one primary category:

Analytics


Analytic Needs Determine the Need for “Big Data”

What are your business objectives and drivers? • Risk contracts/ACOs – population management • Pressure to reduce costs • Requirements for outcome reporting • Alignment of treatments with disease (EBM)

What data do you have? • EMR/EHR data • Claims Data • Operational/Financial Data • Patient reported data (PHR, personal monitoring devices, health survey, etc.) • External data – evidence databases, social media, census, etc.

What type of analytic resources do you have? • Technical infrastructure – data warehouse, business intelligence tools, statistical tools • Informatics talent and resources • Data management talent and resources • Financial resources


Is it Big Data, or Not? (Velocity, Variety, and Volume)

Analytic Need Big Data Moderate Basic

Analyzing operational data to manage staffing and clinical assets (e.g. OR use)

Analyzing supply chain data to manage purchasing and inventory costs

Analyzing claims data to determine insurance rates, payments and assess risk pools

Analyzing clinical, financial and population based data to understand clinical risk of patients and populations – including NLP

Analyzing genomic and proteomic data for patterns related to disease, disorders and response to therapy

Linking social media, purchasing, personal health information with clinical, financial, and population health information.


Analytic Platform:

Data Integrity Checks

Data Staging Data Warehousing

Modeling and Prediction

Analytic Platform: Data Acquisition and Cleansing

Inpatient Data

Ambulatory Data

Emergency Data

Medication Prescription Data

Interface

Engine and ETL Layer

Cleaning and standardizing the data is 90% of the work!

• Data mapping to standard codes and definitions

• Lab = LOINC • Radiology = LOINC • Medications = RXNORM • Procedures = ICD/CPT • Diagnoses = ICD

• Apply numerous data integrity rules to new data as it arrives

• Build episodes and encounters

Transactional Platform:

Master Person Index

Language Terminology Engine

Clinical Data Repository

Claims Data


Analytic Platforms: Tools and Technology

SQL Server Data

Warehouse Cube

SQL Server Integration

Services

SQL Server Analysis Services

SharePoint / SSRS /

PowerView

For many data analytic and data management needs, tools such as the Microsoft stack of business intelligence tools can be used to move, transform, store, and view the analyses, reports, and dashboards.

Analytic Modeling

Performed on outside analytic servers


A Practical Example of Using “near Big Data”

The ability to accurately predict the risk of patients having future healthcare events that may be preventable is one of the more promising application of analytics using multiple

data sources


Predictive Risk Modeling Approach

Core Data: EMR

Claims Demographic

• Patient clinical history • Patient demographics • ICD9 coding • Comorbidities and chronic

diseases • Radiology test orders • Laboratory test results • Outpatient prescriptions • NLP report abstractions

Statistical Modeling and

Prospective Testing

Risk Score (Probability)

• Scores calibrated to probability

• Example: 80 = 80% likely to have the event

40,000 data elements…. ….reduced to 100 – 300 risk drivers, into an algorithm… ….into a single score

• Standardize and link data • EMPI • LOINC, ICD, CPT, Snomed

Coding • Apply edits – clean data • Apply advanced machine

learning approaches • Open source R • Data modeling

• “Random Forest” exploration • Ensemble modeling approach


Applying Predictive Risk Models

Population Risk Score (ED example)

Probability of future event (ED visit)

Predicted resource cost (ED cost)

Predicted total cost

How would you care for a patient, if you knew what was likely to happen?


Visualization is as important as analysis

• Display information in a way that is easily understood by end users

• Prioritize the sequence of information display based on organizational objectives

• Allow flexibility to explore information, while managing content, performance and complexity of user interface

• Integrate into the workflow for multiple constituents including, providers, care managers, operational managers, data analysts, executives


Visualization of Population Risk

14

The map shows where these patients originate.

This chart shows that for the selected health system there are 81 patients who have >70% chance of an ED visit, inpatient admission, and being high cost within the next 6 months

The most common Diagnoses for these patients are Hypertension and Diabetes


Natural Language Processing (NLP) will drive Big Data management needs

NLP applied to unstructured data and documents can provide very useful information to improve care management processes, predictive modeling, and patient level analysis

From HealthInfoNet, the Maine HIE, we have applied NLP to: – History and Physical Reports – Discharge Summaries – Outpatient Visit Notes – ED Visit Reports – Operative Notes – Consult Notes

Specific areas that we are addressing: – Improve comprehensiveness of Diagnosis codes – Identify underlying patient conditions:

• Social factors – home support, transportation issues, communication issues • Health factors – drug abuse, smoking status, family history of disease

Processing millions of notes through NLP, extracting data, and populating models is a Big Data application


Challenges in Applying Analytics to Big Data

• Managing Protected Health Information (PHI) – defining what we need to protect – ability to access data needed to help improve patient outcomes – incorporation of mental health and substance abuse information – controlling access to PHI

• Managing changes/updates to underlying source data systems – Upgrades or replacement of underlying source data systems (EMR) – Changes in coding conventions (ICD9 to 10) – Managing new data fields and date sources that become available

• New regulatory reporting requirements that consume IT and analytic resources – MU2 and MU3 – Value Based Purchasing and Core Outcome measures – HEDIS, ACO outcome measures – JCAHO

• Too much detail and not enough useable information – Building a data warehouse without the end game defined – Trying to put everything in one place instead of what is needed – Trying to create every imaginable report without prioritization


Keys to Analytic and Big Data Success

• Informatics Talent – Knowledge of health care data, statistics, research methods and data reporting – Experience developing technical, analytic and end user specifications

• IT resources – Data extraction and standardization capability – Experience and knowledge of analytic software – Strong relational and other various database schema knowledge

• Multiple tools for data standardization – Standard nomenclatures for clinical data and mapping tools

• Snomed CT, LOINC, RxNorm, ICD 9 and 10

– EMPI (patients and providers) – Interface engine and ETL capability

• HL7 messaging

• Project/Program Management – Data governance – Data use and security – Resource and project planning and tracking


Thank You!


Questions

Frank Stearns, Executive Vice President, HBI Solutions

[email protected]

mailto:[email protected]

“meaningful healthcare analytics: now that we have it ... fall himss...oct 10, 2014 · single...

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