Big Data - Outcomes Performance Measured

Big Data Outcomes / Performance

Adele Allison, National Director of Gov’t Affairs SuccessEHS, Inc.

www.SuccessEHS.com • 888.879.7302

Big Data and Health Care

• Perspective on Data • Federal Policy and Data • Health IT Today • Data and Performance • Data Capture for Success • Questions


• Bit = 1 or 0 (on / off) → Binary Digit • Nibble = 4 Bits of Data • Byte = 8 Bits of Data

Bits, Nibbles and Bytes

Source: doi:10.1093/bioinformatics/btn582


• Bit = 1 or 0 (on / off) → Binary Digit • Nibble = 4 Bits of Data • Byte = 8 Bits of Data • Kilobyte (KB) = 1,024 Bytes • Megabyte (MB) = 1,048,576 Bytes

or 1,024 KB • 1 MB = 873 Pages of Plain Text (1,200

characters)

• 800 MB = Human Genome

Bits, Nibbles and Bytes

Source: doi:10.1093/bioinformatics/btn582


Gigabyte (GB)

Source: www.mozy.com

• 1 GB = 1,024 Megabytes

• 1 GB =7 Minutes HD-TV Video

• 2 GB = 20 Yards of Books on a Shelf

• 4.7 GB = Standard DVD-R


Terabyte (TB)

• 1 TB = 1,024 GBs • 1 TB = All X-rays in large hospital • 2 TB = Academic Research Library • 7 TB = Amount of Tweets/Day • 10 TB = All Printed Materials of U.S.

Library of Congress • 45 TB = Data Amassed by Hubble

Telescope first 20 years



• 1 PB = 1,024 TB • 1 PB = 20 Million, 4-drawer filing cabinets of text • 1 PB = 13.3 Years of HD-TV Video • 1.5 PB = Size of Facebook photos → 10 Billion • 20 PB = Data processed by Google EVERY DAY! • 50 PB = ALL Mankind’s written works from

Beginning of Recorded History (All Languages)

• 100 PB = Facebook data storage before IPO (2.1.2012)

• 300 PB = Facebook data today!

Petabyte (PB)



Zettabyte (ZB)

• 1 ZB = 1 Million Petabytes! • 1 ZB = 1,000,000,000,000,000,000,000

Bytes o That is 21 Zeros, or o 1 Sextillion Bytes

• If 1 GB = 60 Watt Bulb, then … • 1 ZB = 15.7 years of energy from the

Hoover Dam to power a 1 ZB Light Bulb for 1 hour



Who’s Using Big Data? • Snowden → NSA receives data from

Google, Facebook, Yahoo, YouTube, Skype, AOL and Apple

• Average Am. Stats o Avg. 150 Facebook friends/users (teens avg. 300)

o Avg. 150 add’l email/phone contacts/person o Total Avg. Electronic “Contacts” = 300

• PRISM → NSA’s surveillance program 300 Contacts x Their 300 Contacts

x Their 300 Contacts = 27 Million People


Who’s Using Big Data?

• Facebook → Presto • 1,000 Employees

o Run 30,000 interactive Queries per Day

o Over 1 PB of processing

• Open Source • Types of Queries → Trends,

Marketing, Business Intelligence


Perspective

• 1-40 MB = Average Size of a Patient’s EHR record o Excluding Images o 80 MB at Large Hospitals o Top Average Size, including

imaging, 3-5 GB

• 3,281 = Average Number of Active Patients for FP*

*Source: AAFP

Estimate: 12 MB x 3,200 = 38,400 MB / FP or 37.5 GB / FP


Real Ambulatory Metrics • 653 DBs totaling 59.2 TB • Wide variance in DB size → Average 90 GB • Smallest 3.5 GB – Largest 1.7 TB

o Average is > 50 users is 226 GB o Pictures, Word, Scanning, etc. ↑ Size Directly o Transactional data creates marginal increases

• Examples: o NJ CHC 44 Providers → 2.6 TB o CA Ped. Practice 3 Providers → 10 GB o LA School Based 1 Provider → 3.6 GB


Health Care “Score”

• Financial Data creates Individual Credit Score o Payment History Data o Amounts Owed o Length of Credit History o New Credit o Types of Credit


Health Care “Score”

• Will Health Care data create Individual Health Score? o Lifestyle Choices (E.g., Smoking, BMI)

o Worksite Wellness (E.g., Environment)

o Activity Levels (E.g., Sedentary, Exercise)

o Nutrition (E.g., Chips v. Broccoli)

o Adherence / Compliance (E.g., Meds)

o Behavioral Health (E.g., Quality of Life Questionnaire, Sleep)

o Genomics (E.g., Genetic marker for Breast Cancer)


Measuring Knowledge


Affordable Care Act – By the Numbers

• 24 percent of federal budget goes to health care

• 36 times ACA mentions Patient Centeredness

• 15 times ACA references the Medical Home

• 73 times ACA mentions Accountable Care Organizations

• 93 times ACA references Quality Measures

• 29 times ACA links Quality to reporting Clinical Data

• 100 times ACA discusses Value-Based and Payment Modifiers as relates to Hospital/MD Reimbursement and Measures

• 12 SCOTUS Opinion mentions Broccoli


Patient Centeredness and Policy

• Behavioral Economics sought → About an Engaged Patient

• Federal Gov’t → Leadership role in Health Care Reform

• Transition → Episodic Care to Long-Term Healing and Wellness

• Patient Centered Care → Measured Quality Performance

• Federal Policymaking and Patient Centered Care o Regs CMS Meaningful Use Stage 2 – 7 Measures o Regs CMS Accountable Care Organizations (ACOs) – 7 Measures o Regs CMS Value-based Purchasing – Differential Payment o Regs CMS Public Measure Transparency – Physician Compare


Public Transparency

• IOM → $105 Billion Annually in Waste due to o Lack of Competition o Excessive Price Variation

• Obama Executive Order → CMS hospital pricing by Top 100 DRGs (May 8, 2013)

o Charge vs. Medicare Allowable http://go.cms.gov/124pbRh o E.g., Joint Replacement → $5,300 (Ada, OK) v. $233,000

(Monterey Park, CA)

• Medicare Data Access for Transparency and Accountability Act (H.R. 2843, S. 1180)


• Physician Compare Website – CY2014 o Provider Transparency o PQRS GPRO 2012 and 2013 Performance Data o ACO Performance Data o CG-CAHPS Patient Experience Survey Data (All groups by CY2015)

CMS Proposed Rule – CY2014


Measuring Knowledge


Data Analytics

• Prescriptive o How can we make it happen?

• Predictive o What will happen?

• Diagnostic o Why did it happen?

• Descriptive o What happened? Va

lue

and

Diff

icul

ty C

ontin

uum


Decision-Making and Health IT

• 4 Habits of High Performing Health Care Systems (NEJM, Dec. 2011)

1. Specification and Planning → Use data to trigger an “advanced plan”

2. Infrastructure Design → Workflows that: Deliver timely information at the right decision point

Simplify the process

Match the proper skills, resources to process

3. Measurement & Oversight → Realtime, Data-driven Operations

4. Self-Study → Apply measurements for ongoing learning and improvement


Data Impact Potential

• 5 Potential Areas of Impact → Quality, Patient Engagement, Efficiency / Revenue, Clinical Research, Risk / Liability

• Today, Market Remains … o Fragmented o Transaction-based o Acute Care / Reactive Care Delivery

• Reform Goal: Morph into agile, responsive system that is … o Proactive with a focus on prevention o Engaging the patient for wellness lifestyles o Managing complex patient populations

• Industry is Young → Health IT Hype Cycle


Gartner Hype Cycle

Leading Edge

Bleeding Edge


Big Data 5-10 Years


EHR Adoption Rates

• MU launched in 2011 → Physician Adoption around 20%

Source: Healthcare Technology Online, May, 2013


Measuring Knowledge


MU Cultural Shift

• 4 V’s of Health Care Data o Volume – Large data stores for research o Variety – Multiple approaches o Velocity

Info to Provider when with the Patient Info to Patient when they can still make a

behavior change

o Value – Data that drives a cultural shift and ongoing process improvement


Incremental Approach

• Incremental approach today positions for Incredible Future

• Call to Action … o What do market and regulatory changes mean? o Evaluate the Must Do vs. the Must Do o Collaborate with stakeholders

Align goals with public health, hospitals, payers, patients, clinicians, vendors

Identify potential barriers and formulate solutions o Primary Care Health IT Application

New approaches to managing patient populations for prevention

Use of data for purpose-driven performance improvement


CDS Workflow

• Great place to start! • MU1 → Implement 1 CDS Intervention

and Track Adherence • MU2 → Implement 5 CDS

Interventions, align with 4+ CQMs • MU3 → Implement 15 CDS

Interventions


Text to Code Translation • Guidelines are Narrative → Human Readable • AHRQ eRecommendation for Technical Specification →

Machine Readable • Consistency in coded logic statements aligns:

o Development Costs o Implementation Timelines o Uniformity of data for comparative effectiveness

• AHRQ 5-Rights of CDS o Get the Right Information o To the Right Person o In the Right CDS Format o In the Right Technology Channel o At the Right time in the Patient Workflow


CDS Workflow eMeasure NQF 0059; CMS CQM 122 → Hemoglobin A1c screenings of Poorly Controlled Diabetic Patients

Report the percentage of patients age 18-75 with diabetes who had hemoglobin A1c > 9.0% during the measurement period

The “Right” The Answer Redesign

• Initial assessment of DM; target A1c value of ≤ 7%

• A1c at least 2x/year for stable patients.

• More frequently for poorly controlled.

Get the Right Information

1.Rule-logic pre-built? 2.Available in EHR? 3.If pt. presents for

unrelated issue, will system alert?

4.Can EHR generate list of non-compliant pts. for outreach based on pt. communication preference?


CDS Workflow


• Monitor and treat hyperglycemia with a target A1c of 7%

To the Right Person

1.Who needs this information during clinic workflows?

2.Who needs this information for non-compliance tracking and outreach?


CDS Workflow


In the Right CDS Format

1.What is the proper CDS Format(s) to manage DM A1c?

2.What can my EHR provide?

3.Can alerts, order sets and documentation templates be customized?

• Alerts / Reminders • Reference Guidelines • Condition-focused

Order Sets • Pt. Reports • Flowsheets • Documentation

Templates • Other


CDS Workflow


1.Will the alert be a pop-up note or will the user have to prompt?

2.Can communication for outreach be done via secure email?

3.Should an alert be sent to the Patient Portal?

In the Right Technology Channel

Alert!

• Mobile Device • Internet Patient Portal • EHR • PHR • Other


CDS Workflow


1.Can and should CDS information be provided at more than one time of the patient workflow?

2.Can alerting be configured (E.g., based upon severity)?

3.Can patient education / information be customized?

At the Right Time in the Patient Workflow

• Pt. Registration • Assessment / Triage • Exam Room / PE • Treatment / Plan Dev. • Performing Orders • Check-out • Remote / After Hours


Let Technology Work for You!

CDS Type

Point-of-Care

Workflow

Pop.

Mgmt. Workflow

EHR Adoption Maturity

Level

Types of CDS Technology Solutions

Improves Quality of Care

Improves Patient Safety

Cautionary Notes

Patient Alerts X X Beginner-Moderate

Rx Interactions, Formulary, Delinquent Orders and Deferred Orders

X X Alert Fatigue can result in clinicians ignoring alerts.

Patient Reminders

X X Beginner

Patient Portal, Secured Patient emails, Text Messages, Form Letters/Postcards, Phone Call List, Auto-Phoning

X X

System should automatically identify the patient’s preferred method of communication under HIPAA.

Evidence-Based Clinical Guidelines

X X Beginner-Moderate

Pre-designed CDS rules-engine, Point-of-Care alerts, Intelligence Prompting, Patient Education

X X

System needs to allow for customizable guidelines; variance in recognized standards of care


Let Technology Work for You!

CDS Type

Point-of-Care

Workflow

Pop.

Mgmt. Workflow


Level


Improve Quality of Care

Improve Patient Safety

Cautionary Notes

Order Sets X X Moderate

Wellness age 65+ and pediatric, Chronic Disease Management

X X System needs to allow for customization.

Flow-Sheets X Moderate

-Advanced

Vitals, Lab results, Ante-partum, growth charts

X X System needs to allow for customization and graphing.

Dashboards X Advanced

Timely follow-up, Results signed-off, Delinquent orders and deferred orders, appt. compliance, protocol compliance

X X

System should have metric drill-through for details; requires consist workflows for data capture.


Let Technology Work for You! CDS Type

Point-of-

Care Workflow

Pop.

Mgmt. Workflow


Level


Improve Quality of Care

Improve Patient Safety

Cautionary Notes

Structured Knowledge-base Documentation Templates

X X Advanced

Intelligent prompting for differential diagnoses, clinical element prompting for symptoms, PE considerations

X X

While significant time savers, pre-filled forms/lists and auto-negatives/positives can result in “cookie-cutter” documentation.

Diagnostic Support

X X Advanced

Intelligence prompting for differential diagnoses, auto-monitoring based upon results (e.g. lab), recommended therapies

X X

Requires providers to break out of intuitive decision-making and adopt analytically decision-making.

Workflow Tools

X X Moderate

-Advanced

Point-of-care alerting, wait-time analysis, mobile devices, Internet, compliance tracking

X X

Critical to CDS adoption, successful workflow integration requires the clinician’s time and involvement.


Do’s and Don’ts Don’t Do

Practice “Cookie-Cutter” Medicine → Same Tests to all Patients with Similar Symptoms

Efficient, but not necessarily Effective

Practice Evidence-Based Medicine • Use HIE to look beyond 4-walls • Use Standardized Vocabularies

E.g., Sys. 1→ High BP; Sys. 2 → Elev. BP; Sys. 3 → HTN; Instead use SNOMED CT

Seek all answers from a Data Warehouse • Big, powerful but … • Expensive and not suitable for many day-to-

day needs

Leverage MU2 CCDA/CCD to support patient-specific tasks • Use coded data to standardize terminology • Supports HIE • Helps with predictive modeling • Can fill-in record gaps

Approach Data as a Hunter, Gatherer • Time consuming, expensive • Data is often not compatible

Domesticate Data by “Normalizing,” if possible • Map/Document using structured vocabularies

(E.g., SNOMED, LOINC) • Meets MU and other regs • Strive for consistent Data Capture


Do’s and Don’ts Don’t Do

Wait to consider new ways to use your data • Don’t wait for Big Data to knock on your

door • You have a wealth of enterprise data

today

E.g., Financial, Operational and Clinical

Aggregate data wherever you can afford to do so • Does your vendor have a Service-

Oriented Architecture (SOA) strategy? • Where can data come from?

E.g., Medical devices, Labs, Questionnaires

Limit your vision to your Health Care Organization; you will only be able to react to the market for competitiveness

Use free Public Health Data for Strategic Planning

E.g., Univ. of FL merged health data with Google Maps to create “heat” sensitivity for chronic dz. Found 3 counties underserved for breast screening and sent mobile units.


Measuring Knowledge


Here’s Your Patient

• Miss West • Belligerent • Some kind of Liver

Function Problem • Paranoia • Non-Compliant ALL the

time


• Patient presents for wart removal • Key → Must Consistently Capture Your Data • 4 CQM / PQRS Measures:

o Influenza & Pneumonia Immunizations o Breast & Colorectal Cancer Screenings

• What do you accomplish?

• 4 → 1

Patient Scenario


Consistent Data Capture

• Consistent Data Capture = Strong Reporting • Data Reporting Drives Performance for VBP • Data Reporting Provides Credit for treat the

“Miss West” Patient • Three Ways to Capture Data

o Performed (Here or Elsewhere)

o Not Performed (Medical Reason)

o Not Performed (Patient Refusal)


Health plans use claims data to build patient

complexity profiles

The patient complexity profile

must be repopulated

annually using calendar-year

claims data (i.e., patient

complexity starts at baseline every year).

Diagnosis Codes (ICD-9 and ICD-10) are

used to calculate patient

complexity.

Patient Complexity Data


The Impact of Documentation & Coding

Source: BCBSAL, Complete Picture of Health Documentation and Coding Improvement Initiative, Aug., 2013


CEUs or Copies [email protected] White Papers www.SuccessEHS.com Follow me on Twitter: www.twitter.com/Adele_Allison Added to The BRIEF or Questions: [email protected]

Big Data - Outcomes Performance Measured

Health & Medicine

Transcript of Big Data - Outcomes Performance Measured