download - Clinical and Translational Science Awards

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2 Οκτ 2013 (πριν από 3 χρόνια και 11 μήνες)

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The CRIO at UW
-
Health

Umberto Tachinardi, MD, MSc

Chief Research Information Officer

UW Health

Associate Dean for Biomedical Informatics

School of Medicine & Public Health

Director, Biomedical Informatics Core

Institute for Clinical and Translational Research (ICTR)


CRIO

HIMC

Clarity

COOs

CIO

Applications

IT

Help
-
Desk

Dean
-

CEOs

BCG

UW
-
Health

Council of CIOs

UWHC, MC, UWMF

Advisory
Commitees

Co
-
Director

TBN (MCRF)

Director

Umberto Tachinardi
MD,
MSc

Bioinformatics

Mark Craven PhD

Imaging Informatics

Elisabeth Burnside
MD

Clinical/Health
Informatics

Eneida
Mendonca

MD, PhD

Innovative
Technologies

Miron Livny PhD

IT Infrastructure

David Towers MBA
and MCRF
equivalent

EDW

U Tachinardi MD,
MSc and P Peissig
MBA

Adm
-
Assistant

Sue Ann Hubanks

HIMC

CTSA BMI Core

User request

Packaged
Servs
/Prods
available?

Packaged
Services/Prods

Project
Analysis

No

Yes

Research/

Development
req’d?

Consulting

Collaborations

Yes

No

Grant Proposal

Research Project
Analysis

Project viable

Project
not
-
viable

Data Analysis

Data Storage


Data Security

Data Modeling

Interfacing

Regulatory/Compliance

ICTR IT Infrastructure

Ad
-
Hoc EDW Queries

CTMS customizations

Image Processing (PACS)

NLP

Genomic Analysis

BARD

CRIS

TTRC

(Imaging Facility)

Training

Triage/Routing

REC

Image Analysis

Partnerships

Advanced Technologies (R&D)

Information
documentation, storage
and retrieval

Representation of logical
and probabilistic knowledge
and reasoning

Machine learning, including
data mining

Image and Signal Analysis

Networking, security,
databases

NLP, semantic technologies

Simulation and modeling

Software Engineering

BMIC

Services



Biomedical Informatics


proposed changes for next renewal

-
Goal: Define a common “BMI Core” for both CTSA and UWCCC

-
Opportunities

-
Across the board Epic implementation (HC/MF/Unity)

-
UW Health DW project (HIMC)

-
OnCore integration

-
Integrated IT infra
-
structure (servers, networks, storage)

-
Competitive Advantage

-
HIMSS Level 7 (Epic)

-
Strong Bioinformatics

-
Improved Clinical Informatics (new Faculty)

-
Coordinated human subject recruitment policies and tools

-
Data Governance plan

-
Marshfield Clinic partnership

-
Drivers

-
Cost efficiency (non unnecessary redundancies, common standards for all
NIH funded projects)

-
Research empowerment (trhough more robust and sophisticated tools and
services)

-
Collaboration vs Competition (same teams working towards the same
objectives)




HealthLink

(Clarity)

HealthLink

(Other)

OnCore

(Cancer)

OnCore

(ICTR)

DW
-
Metadata

DW
-
Ontology

Molecular, images data

Core Services

NLP

Bioinformatics

Security

Ontology

Interfaces

Finance

DW
-
Hybrid


UW
-
Biomedical Informatics Services

Registries

External DBs

Literature

Molecular Data

Biosamples

Images

ICTR (BMIC)

Epic

OnCore

PACS


omics


UW
-
DW

HIMC (UW
-
Health)

Security

Honest Broker

NLP

Ontologies

Data Services (
caBIG
, i2B2, HL7, Portal, APIs, SQL, FTP)

Networking/Computing/Storage

UWCCC Administrative Systems & IT
Infrastructure

SMPH

UWCCC Bioinformatics

Clinical & Health, IT

UWCCC

Informatics

The Clinical/Research

Data Warehouse

The Hybrid Model

ONTOLOGY

METADATA

EAV

DIMENSIONAL

Semantic

Implementation

Integrated Design

Event

General

Event

Measurable

Event

Substance

Observation

General

Observation

Measurable

Observation

Substance

EAV

Ontology Map

(26 levels)

Class

Relationship

RED

HIMC DW architecture (high level view)

Security


Auditing **

Security


Honest Broker **

Security


Access Management **

Healthlink

(Clarity/

Chronicles)

Other

ETL*

Interfaces*

HIMC DW

Atomic layer *

Data mart *

BO Universe

Views *

Reports/Dashboards

Reports

Metadata *

Ontologies **

Query
Tool **

Users

Users

Natural Language
Processing **

Ontology management **

NLP editing **

Red = non
-
HIMC

Blue = HIMC


* Partially developed

** Planned

The ecosystem for IT and
Analytics

IT

Analytics

Technology (i.e. Data

Center, Networks)

Data warehouse (HIMC,

Clarity)

Applications (i.e. email,

web, Epic, PeopleSoft)


Analytical

t
ools (Natural

Language Processing,
Honest Broker,
Ontologies)

The differences between IT and
anaytics

IT

Analytics

Mostly tactical and operational levels

Systems

operated at this level tend to support operational
workflows (email, Epic,
Peoplesoft
), the strengths are processes
automation (i.e. CPOE, logistics, billing, HR). The technological
infrastructure (servers, networks, desktop computers) is part of this
area, it establishes the platform where the operational systems rely
upon. This area helps process optimization, efficiency and
productivity.

Mostly strategic

and tactical levels

Systems developed in this area are mostly targeted at transforming data
into information and knowledge. This is mostly an strategic decision
support area. Reports, dashboards, simulations, depend a lot on the
business characteristics, the meaning and quality of data, the data
crunching performed on high volume datasets. This area tend to be less
technological, and much more business dependent, it will help
planning, benchmarking and discoveries.

Process

drivers

CPOE, billing, admitting, scheduling, discharging are examples of
processes supported by this area. This area guarantees that security
and privacy are in place. It is mostly based on mission critical
systems. Highly regulated, users need to be well trained in
operating the systems. This area keeps the lights on.

Business drivers

Budget planning,

root cause analysis, comparative effectiveness,
scenario simulation (a new hospital, a new ward, more nurses,
etc
),
personalized medicine, cohort finding, are examples of non
-
routine,
applications at the strategic and tactical levels supported by analytics.
This area helps plan for the immediate and distant future.

Engineering, customization

The IT

area is highly structured, depend on well defined and
implemented methods that are known by all (users and operators).
This is why most of the solutions are purchased from third
-
parties.
More than developing methods, this area apply them. Systems like
Epic, will be customized to be more adequate to the local
characteristics. The core of the system is untouched, only parts of
the system can be customized. This area requires more
management (big contracts, large teams, complex IT technologies)
and less development.

Architecture, development

Different from conventional IT (data is shaped to satisfy

computer
needs)
, analytics is closer to the human

reasoning. Rather than
processes and workflows, this area deals with meaningful information
and knowledge. There are no commercial systems ready to fulfill the
needs of this area, therefore they need to be developed. There are
computational tools, available to help those developments, but they
assist mostly on how to transform and present information, identify
patterns, extrapolate and forecast. Analytics, because of the factors
described, is closer to be an architectural work.

The differences between IT and analytics

IT

Analytics

Management

The measures of a successful CIO are all related to excellence in
management. Perfect budgets, on time deliveries, regulatory
discipline, project and contract management. This well structured
mission, requires a risk
-
averse personality and a
standardized/predictable behavior, since most products support
mission
-
critical tasks in a very sensitive environment.

Creation/Vision

The CIO

for Analytics, is a different type of professional. This person
needs to be highly motivated to work in a less structured environment,
he/she likes to take risks (and know how to select the reasonable ones),
be imaginative and innovative. The products of the analytics will be as
good as the capacity to predict future trends, motivate people based on
concepts and ideas, rather than repeat/improve what others are doing.

Evolutionary

Like

any other engineering process, this area produces tools and
solutions that will allow for incremental improvements (i.e. more
security, faster systems, better interfaces, more integration). Epic,
for instance, gets better in every upgrade. Networks are more
reliable, secure and faster every year.

Transformative

Analytics is a parallel business to IT. While it depends on the existence

of a well built and managed IT environment, the purpose of the
analytics is to recycle data products (data stored on databases), in the
pursue of innovating, finding problems and opportunities, feeding
decision makers to change the business model, be it health care
business or clinical thinking.

Low

Intellectual Property

Because most of the systems on IT are purchased from
vendors, there is very little potential for institutional IP
development.


High
Intellectual Property

The area

of analytics provide an enormous potential for IP accrual.
Those IP assets can come from two sources: the methods and tools
developed in house, and also the findings (i.e. discoveries, high yield
datasets, best practices, risk management).

ROI (Return of Investment)

Conventional IT depends

on positive ROI. Although, the real ROIs
are very difficult to calculate and prove, the industry is moved by
those metrics. It is a conservative approach, and CIOs are trained to
follow those predicaments (very conservative).

RONI

(Risk of NOT Investing)

Health

care is a business in transformation. The science is different, the
population profile is changing, the costs are not sustainable, the
patients demand different products, chronic disease management is a
new challenge. Not developing tools and solutions that will help
understand, plan and actually change the status quo becomes an
enormous risk!

CIO

CRIO

EHR, ERP,

Quality, PACS

CTMS, IRB,

Bio Banking, Grants
Mgt
,
etc

JCAHO, AHRQ, MU, ACO, HIPAA, OSHA, HIE

I2b2,
caBIG
,

VIVO, IRB/HIPAA, clinicaltrials.gov

Customers:

Physicians, nurses, CXO, managers

Customers: Researchers,

students, faculty

HIMSS

AMIA

1000s FTEs

10s
-
100s FTEs

EDW

(Clinical, Quality)

EDW (Research, Education)

CIO vs. CRIO…

The “new guy”

IT

Analytics

Operations and workflows

Decision support

Technologies

Knowledge

Security

Risk

Predictability, reproducibility

Unpredictability, innovation

Engineering, implementation

Architecture, research

… and, IT vs. Analytics

Research (CTMS)
X Patient
Care (EHR)

Regulatory

Storage

Data
Ownership

State

Finance

HIPAA

Medium Size /
Fault tolerant

Healthcare
provider

Online

Billing

IRB

High Capacity /
Non fault
-
tolerant

PI

Offline

Grants/Contracts

IT

Data Center

Networks

Support

Training

Security

Applications

Analytics (is this the CRIO?)

Data Warehouse

Reporting

Semantic Mgt

Registries/Panels/Datasets

NLP

Data Governance