clinical looking glass - amazon...

Clinical Looking Glass

Vignettes

• Summary

• What is CLG – Core Concepts

• Use Case – Diabetes

• Use Case – Pharma Analytics – Research

• Use Case – Time in Range Cost Savings Epo

Use

• API and Exporting Capabilities

Use Case – Predictive Analytics

• Use Case – PCMH Certification

• Architecture

• Q & A

“Longitudinal Analytics

for any Market”

Summary - Definition

Clinical Looking Glass is a powerful

analytic tool providing:

• On-demand healthcare analytics with

• Longitudinal and statistical capabilities

• “Bolt-On” analytical tool is EMR

agnostic – can sit on any data

warehouse

Summary - About CLG

• Developed and used by clinicians, administrators and

researchers

• Able to guide point of care clinical decision making, inform and

evaluate effects of interventions and treatments and drive

population health management

• Promotes academic and clinical rigor and innovation

• Scaleable , leverageable and valuable tool for the entire

healthcare universe: pharma, providers, payers and research

• A key differentiator in era of outcomes based performance and

greater demand for improved healthcare quality and reduced

cost (value)

Summary - CLG overview

• Started in 2002 with 3 FTE’s as part of Emerging Health

• 2013 – 22 FTE’s plus Emerging Health support

• Approx. $5.1 million annual budget and $40 million spent over past 10 years

• October 2013 Commercialization rights granted to Streamline Health and the product is known as – Looking Glass

• This is a review of the Montefiore Experience

Summary - Montefiore

Large academic medical center (Bronx, New York), 11th Largest

provider in the U.S.

4 hospitals, 1491 beds

94,000 discharges

280,000 emergency department visits

21 clinics , 2 million clinic visits

2010 - In 2010, 499,148 unique patients / 1.3M Bronx

Population = 38%

225,000 covered lives through IPA

The only Pioneer Accountable Care Organization in New York

State 2012

University Hospital of the Albert Einstein College of Medicine

Summary - CLG Development History

• Version 1.x (2002)

– Group comparison for 1 subject

(LOS)

– Embedded statistics engine


– Cohort Builder

– Time to Event for analysis


– Event Canvas with data mapping

– List any data element

– On-demand OLAP data cubes

• Version 4.x (2011-2013)

– Study Designer with iPad Viewer

App

– Performance

• 700 users trained – 5,000 analyses run per month

• Quality Improvement – Utilized in diabetes, asthma, LOS, hospital workflow, QI studies

• Research – Over 40 peer reviewed journal articles

– $18 million of Montefiore/AECOM research grants enabled by CLG analytics

• Education – Mandatory part of resident training, Albert Einstein College of

Medicine

– Utilized for CME at Montefiore

• Supports transformation of Montefiore into an ACO for 2012 and beyond

Summary - Utilization at Montefiore

What is CLG?

Core Concepts “Longitudinal Analytics

for any Market”

Story time

The Geriatrician and the Surgeons

“Use local anesthesia instead of general for elderly patients needing gall

bladder procedures to reduce complications.”

Physician decision-making did not require IT or analyst team

Creative power unleashed in real-time

Analysis model was

patient-centric

Story themes

Information must be “Patient-Centric”

Business-Centric “Patient-Centric”

How many admissions in 2011? How many unique individuals admitted in 2011?

How many visits for hypertension? How long does it take us to control hypertension?

How much do we spend on drug X? How many improved when on drug X?

Patient-centric cohort is key concept

1/1/2010 1/1/2011 1/1/2012

0

0 = index date

(start therapy)

0

0

0

0

0

0

0

0

0

1

2

3

4

5

6

7

8

9

10

Patient #

What % of new diabetic patients were controlled in the year 2010? 4 / 10 = 40%

Diabetes Control

= outcome

(achieve lab value)

0 = patient experience

Cohort concept (cont)

Enrollment 1 Year 2 Years

0

0 = index date

= outcome

(start therapy)

(achieve lab value)

0 = patient experience

0

0

0

0

0

0

0

0

0

1

2

3

4

5

6

7

8

9

10

Patient #

What % of new diabetic patients were controlled within 1 year? 5 / 10 = 50%

Diabetes Control

(same data, re-sorted)

Definition

Laboratory results, in/out patient admissions, pharmacy,

radiologic data and all other types of events collected in

the Electronic Medical Records must be:

• chronologically (temporally) related and spun into a

• defined patient group (cohort paradigm) whose

• time period to outcome (trajectory) defines the…

Bottom Line: Quality of their Care

Clopidogrel example

Heart

Attack (In-Patient)

Clopidogrel (Out-

Patient

Prescription)

Prilosec (PPI) Death Rate

No

Prilosec (PPI)

Death Rate

0 30 900 Time to Event Time to Event Time to Event 365

Outcome

Temporal events leading to outcome

0 0

CLG study components

Groups I Analysis

CLG study = groups + outcome analytics

= “Index Date” I

Male diabetics I Death in 1 year

Female diabetics I Readmission 1 year

Use Case:

Diabetes Management “Longitudinal Analytics

for any Market”

Diabetes example: temporal map

New

Diabetic

A1C > 9

Hospitalization

Rate

Hospitalization

Rate


Outcome

Blackout Period Good Control

180

A1c <= 7.0

0

1) Build Cohorts 2) Study Outcome

Cohort 1 Index: A1C < 7

Cohort 2 Index: A1C > 9

Diabetes demo

Done: go to next

Diabetes study

Diabetics HgbA1c >9.5

Age >=65 I Achieve HgbA1c <7

(180 – 365 days)

Diabetes example: temporal map

New

Diabetic

A1C > 9

Hospitalization

Rate

Hospitalization

Rate


Outcome

Blackout Period Good Control

180

A1c <= 7.0

0

1) Build Cohorts 2) Study Outcome

Cohort 1 Index: A1C < 7

Cohort 2 Index: A1C > 9

Diabetics achieving

HbA1c of <=7 I

Diabetics achieving

HbA1c of >=9.0 I

Hospital Admission

Achieved a HgbA1c < = 7

Significant

difference

Financial impact of “good” diabetes mgmt

Average Annual Increased Difference in Inpatient Cost:

(Patients Under Bad Control vs. Good Control)

$760 per patient

1,086 (Patients Under Bad Control) * $760 = $825,360/year

Impact of “good” diabetes mgmt

A reduction of almost 1/3 in hospitalizations

Only ½ the story

More than a study…

It’s a tool for targeted remediation.

Remediation tool

Remediation list of patients

Implications of cohort view

335 (31%) Missing

Unlimited temporal complexity (US Patent#: 7917376 )

• Logical subgroups

• AND/OR/NOT

• Temporal rules b/w events

• Anchor events in calendar time • Group definition

• Outcome definition

Use Case:

Pharma Analytics –

Outcomes Research “Longitudinal

Analytics for any Market”

Recapitulate research in the Bronx

JAMA, March 4, 2009—Vol 301, No. 9 937

CLG in action: research scenario

Clopidogrel (Plavix):

• reduces platelet stickiness and clotting

• reduces risk of repeat myocardial infarction

• increases risk of GI bleed

Proton Pump Inhibitor (PPI - Prilosec):

• reduces risk of GI bleed

• possibly reduces efficacy of Clopidogrel

Clopidogrel example: temporal map

Heart

Attack (In-Patient)

Clopidogrel (Out-

Patient

Prescription)

Prilosec (PPI) Death Rate

No

Prilosec (PPI)

Death Rate


Outcome

Temporal Events Leading to Outcome

0 0

Clopidogrel demo

Done: go to next

Pts w MI

on clopidogrel

no PPI I

Pts w MI

0n clopidogrel

on PPI

I

Death

Repeat MI

Mortality rate (PPI vs. No PPI)

Mortality (PPI vs. No PPI)

Readmit with MI (PPI vs. No PPI)

Use Case:

Time in Range Cost Savings in Epo Use


for any Market”

Manage RBC with Epo

• Care Goal

– Manage the red blood cell count (RBC) for

patients with kidney failure in a safe range

• Therapy

– Erythropoetin stimulates production of RBCs

– Keep hematocrit (Hct) value in 29 to 32 range

• Concerns

– Hct > 32 associated with greater mortality

– Epo is expensive, don’t over prescribe

Clinical management question:

Was patient X’s hematocrit values well managed

over one year of Epo therapy?

Average?

Latest?

% of days in target range

What are the relevant hematocrit

ranges?

High

Low

Target

Time

Hem

ato

cri

t

32 -

29 -

X

X

X

X

X

interpolation carry forward

missing missing

How do you estimate Hematocrit

between blood collections?

Time

X

X

X

X

X

interpolation carry forward

missing missing

Low

Target

High

Hem

ato

cri

t

32 -

29 -

Time in Range Interpolation

CLG Time in Range method

• Longitudinal perspective

• Evaluates quality by counting time spent in

appropriate range of Hematocrit values

• Saves money while protecting patient’s health

by focusing on the relevant longitudinal metric

API and Exporting Capabilities

“Longitudinal


Exporting Temporally Enriched Data for

External Analysis

• Allow for Import of Temporally Enriched Data in:

– SAS

– SPSS

– STATA

– R

– Excel

• Advantage of this Technology

– temporally enriched data can be exported without requiring post

processing which may compromise patient privacy

– export results (counts of hospitalization) not the dates of service to

create intervals. This assures privacy throughout the analysis.

Analysis in your Favorite

Statistical Package

Niche BI Solution • Registry • DM System • Scorecard • Dashboard

CLG

API

CLG SDK

cohort method

STUDY

Application Programming Interface (API)

Platform for all clinical analytics solutions

Application

Programming

Interface (API)

enables:

• Longitudinal

perspective

• Statistical

sophistication

• Clinician

engagement

• Rapid BI

deployment

ACO Partner

ACO Partner

ACO Partner

Clinical & Financial Applications

Eagle MOC/MOTCentricity

EnterpriseSAP

Centricity

EMR

TREKS

PICIS

RIS-ICHR

ManagementKRONOS

EPF Ultra Triple G

Enterprise Data Warehouse

Apollo Peri-NatalOTTR

(Transplant)Wellsoft

CLG

Cohorts:

· Diabetes

· Cancer

· CHF

· Frequent Flyers, etc...

Events:

· Admissions

· Visits

· Transfers

· Claims

Measures:

· Smoking

· Diagnoses

· Procedures

· BP, Labs, Findings

Outcomes:

· Mortality

· Readmission

· Relapse

· Wellness

Clinical Rules

Governance

Performance

Dashboard

Diabetes

Registry

LOS

DashboardPredictive

Solutions

Report

Cards

Enterprise Objects

“What If” Analysis &

Metric Refinement

CLG Vision: Platform for All Clinical Analytics

Use Case:

Predictive Analytics CLG in Operations


for any Market”

Predicting 30 day readmission

• Predict likelihood of readmission at discharge

• Local model discovery using local clinical

intuition

• Agility in model refinement

Readmission Study Cohort

Readmit Risk?

Predictive Model

Today

Model development

Historical Data, Clinical Variables, Social Factors

Future Past

MRN

Probability

Score Influencers INDEXAdmission INDEXDischarge

00001 0.97 ER6mCount;Inpat6mCount;FluidAndElectrolyteDisorders 8/27/2012 8/28/2012

00002 0.83 ER6mCount;CharlsonScore;FluidAndElectrolyteDisorders 8/25/2012 8/28/2012

00003 0.72 FluidAndElectrolyteDisorders;ER6mCount;Inpat6mCount 8/26/2012 8/28/2012

00004 0.68 ER6mCount;CharlsonScore;FluidAndElectrolyteDisorders 8/19/2012 8/28/2012

00005 0.59 CharlsonScore;Obesity;ER6mCount 8/17/2012 8/28/2012

00006 0.58 CharlsonScore;FluidAndElectrolyteDisorders;Coagulopathy 8/12/2012 8/28/2012

00007 0.45 CharlsonScore;WeightLoss;Coagulopathy 8/23/2012 8/28/2012

00008 0.45 FluidAndElectrolyteDisorders;DeficiencyAnemia;Inpat6mCount 8/19/2012 8/28/2012

00009 0.43 Obesity;CharlsonScore;DrugAbuse 8/24/2012 8/28/2012

00010 0.40 FluidAndElectrolyteDisorders;DeficiencyAnemia;OtherNeurologicalDisorders 8/19/2012 8/28/2012

00011 0.37 FluidAndElectrolyteDisorders;DeficiencyAnemia;DrugAbuse 8/24/2012 8/28/2012

00012 0.37 CharlsonScore;FluidAndElectrolyteDisorders;DeficiencyAnemia 8/14/2012 8/28/2012

00013 0.32 FluidAndElectrolyteDisorders;CharlsonScore;DeficiencyAnemia 8/25/2012 8/28/2012

00014 0.32 CharlsonScore;FluidAndElectrolyteDisorders;Coagulopathy 8/25/2012 8/28/2012

00015 0.30 CharlsonScore;Coagulopathy;DeficiencyAnemia 8/27/2012 8/28/2012

Probability score of readmission

Intervention Team Daily Workflow

CLG A P I

MRN Probability Score

00001 0.97

00002 0.83

00003 0.72

00004 0.68

• Med reconciliation

• Setup referral appts

• Needs assessments

• Log actions

Care Mgmt System

Predictive Model reclassified patients

• High risk discharges went from 3000 to 500

• Readmit rate for high risk went from 19% to 46%

Efficacy of intervention under evaluation

Care Transition Team

Use Case:

PCMH Certification “Longitudinal Analytics

for any Market”

First at Montefiore: PCMH Level 3

South Bronx Health

Center for Children

and Families

… and

CLG Power Users!

Shareable CLG Study to facilitate PCMH Level 3 certification

Additional CLG Uses at SBHC and NYCHP

CLG

Quality Indicators

Patient Worklists

Outcomes Research Grant Seeking and Reporting

Program Evaluation

Architecture “Longitudinal


CLG SOA Integrated Architecture

Technologies Powering CLG

• User Interface

– ASP.NET ( Including AJAX Framework)

– Silverlight 5.0

– Infragistics , Telerik and Component Art controls.

– Silverlight Toolkit.

– MVC 4.

– PRISM

– IPAD - IOS

• Authentication/Authorization

– Single Sign On.

– Membership Provider Model.

– Forms Authentication.

• Services Layer

– Windows Communication Foundation 4.0

– Windows Workflow Foundation 4.0

– MVC 4 API Controllers

• Database

– SQL Server 2008 R2/ 2012

Backend Modules/ORM – Entity Framework – MEF – TEMPORAL Engines.

Statistics – Revolution R

Server/Operating Systems – Windows Server 2008 r2

Languages – C# 4.0 – VB.NET 4.0 – Visual C++ 4.0 – R – JavaScript. – JQUERY. – T-SQL/ Ansi-SQL – IOS 5.1

Framework – Net 3.5SP1, .NET 4.0 – PRISM – MEF – MVC

Process and Tools

Process:

– Scrum agile workflow model

– 4 week cadence

– Definition of Done

– Continuous integration

– Nightly build and auto deploy

– Automated system testing

– Data mapper to facilitate implementation

Tools:

– Visual Studio 2010 , 2012

– Team Foundation Server 2010 SP1

– SQL Server 2008 R2/2012

– Expression Blend

– Visio for UML modeling

– IBM DOORS for requirement model (in promo video)

72

http://www.youtube.com/ibmrational

http://www.youtube.com/ibmrational

Questions “Longitudinal


http://exploreclg.montefiore.org

74

If interested in Commercial

Contact I provide the contact

Clinical Analytics

Screenshot: Login Page

Sales Contact

Daniel McDuffie Vice President, Sales

Streamline Health

1230 Peachtree Street NE | Ste. 600 | Atlanta, GA 30309

[email protected] | www.streamlinehealth.net (C) 404.771.1781 NASDAQ: STRM

mailto:[email protected]

http://www.streamlinehealth.net/

clinical looking glass - amazon...

Documents