the duke integrated subject cohort and enrollment research

H D W A 2 0 1 1 S A N D I E G O

Duke University Health System

1

H D W A 2 0 1 1 S A N D I E G O

Business Perspective

Use Cases

Technical Overview

Tips & Lessons Learned

Future Goals


2

H D W A 2 0 1 1 S A N D I E G O

2008 Enterprise Data Warehouse


3

H D W A 2 0 1 1 S A N D I E G O

Data goes back as far as 1996

4.09 Million Patients Represented

30 Million Encounters

17.55 Million CPT coded Procedures

39.1 Million CPOE Orders

271 Million Lab Records

250 Million Technical Charges

109 Million Measurements evaluated on 74,412 patients in the CDM disease registry

DSR Facts and Figures

H D W A 2 0 1 1 S A N D I E G O


2009 DISCERN Poster


5

H D W A 2 0 1 1 S A N D I E G O

An Open Source Integrated Subject Recruitment System

Bill Gilbert, Howard Shang, Jeffrey Ferranti, M.D., M.S.,

Duke Health Technology Solutions - Duke University

Health System, Durham, North Carolina

INTRODUCTION HL7 MESSAGE PROCESSING

• Failures or delays in meeting patient enrollment goals are widely

considered the most prevalent bottleneck to efficient clinical trials.

The secondary use of healthcare data coupled with open source

technologies promises to greatly improve the cost and efficacy of

research recruitment and enrollment. In recent years, the wide

spread implementation of Hospital Information Systems (HIS) have

led to the accretion of staggering amounts of healthcare data, but

few institutions are leveraging these data in support of research

enrollment. While several commercial and home grown systems

have attempted to ameliorate this problem, an open source

methodology for data-driven clinical research recruitment has yet to

be devised .

• The Duke University Health System has designed a

comprehensive open source solution that leverages both

retrospective data warehouse information and real-time Health

Level Seven (HL7) streams to aid in research recruitment efforts.

The Duke Integrated Subject Cohort and Enrollment Research

Network (DISCERN) uses these data sources to identify a cohort of

potential study recruits, cross reference this group with real-time

scheduling and lab data, and alert study personnel of potential

research candidates . The open source Mirth integration engine

(Mirth Project, supported by WebReach, Inc.) is being used to

ensure generalizability to other research institutions at minimal

cost. DISCERN can relay potential subject information to study

staff through clinic lists, email, and real time text pages. It can also

empower patients with context specific study information via the

Duke University Health System patient portal.

DESIGN METHODS

Improving the efficiency of subject enrollment will decrease the cost of clinical trials and ultimately accelerate lifesaving treatments into clinical

practice. The clinical trial timeline is amenable to technological solutions designed to shorten the recruitment process. Such solutions fall into

four broad categories: information, communication, workflow, and data management. (Marks 2002).

1) Information: Secondary Use of Retrospective and Prospective Data in Support of Research.

The design of the Duke Integrated Subject Cohort Enrollment Research Network (DISCERN) addresses the information category by utilizing both

retrospective and prospective data. The retrospective data is available in the data warehouse, and is used to build cohorts of patients that are potential

subjects for a clinical trial. In the case of rare conditions, retrospective data may provide what is needed to begin recruitment. More often, the inclusion or

exclusion criteria for subjects require current information about the patient, such as the last result for a particular laboratory test. Access to real-time Health

Level 7 (HL7) (HL7, Inc., Ann Arbor, MI) messages allows us to reason over real time data while identifying potential subjects. This comprehensive

approach leverages both retrospective and prospective data in an effort to define the best candidate list possible.

2) Communication: Leveraging Technology to Efficiently Communicate with Study Staff

Automatic communication with research staff is a large part of DISCERN. The design includes the ability to page study personnel, send secure email, post

advertisements on the Duke Health View Patient Portal, and provide "on demand" reports. Each clinical trial may choose one or more communication style

to receive information. For time critical studies, investigators may choose to be paged immediately once DISCERN identifies a potential subject.

The identification and recruitment of eligible patients during the course of busy clinical practice is difficult. This problem is compounded when physicians not

directly involved in a trial must maintain a working knowledge of trial timelines, inclusion and exclusion criteria, and consent details. (Tollman 2001)

DISCERN removes some of this burden on clinicians by e-mail alerting them of potential trial candidates on their clinic panel.

3) Workflow: Enhancing Workflow through the Thoughtful Application of Technology

Providing specific data on when and where subjects can be contacted may also achieve workflow enhancement. Rather than employing recruitment

methods such as cold calling or direct mailings, the study staff can meet with the patient in person, which is the most effective method of enrollment

(Verheggen 1998).

4) Data Management: Utilizing Economies of Scale for Data Storage and Security

Data management is also addressed by DISCERN. The retrospective data collected as a by-product of care will be stored in our secure data warehouse.

Strategies to integrate this data and provide the most accurate information possible are handled by our centralized data warehouse program. Prospective

data is managed by the rigorous standards of HL7 messaging. The data unique to the utilization of DISCERN is also stored in the Data Warehouse and

managed by the Information Management team. This centralized management of recruitment and enrollment data allows for economy of scale, and

removes the burden of data management from the individual investigator.

DISCERN is a comprehensive solution to the challenge of improving recruitment through the secondary use of health care data. Because DISCERN is

based upon open-source standards it is generalizable and easily adopted by other institutions for a relatively low cost.

OPEN SOURCE

DISCERN IMPLEMENTATION

A new patient is admitted to a clinic and a notification event message is

transmitted over TCP/IP.

MSH|^~\&|ClinicXYZ|W|PatientRegistry|UHN|2001052319

27||ADTÂ01|22139243|P|2.4

EVN|A01|200105231927|

PID||123^^|2216506^||John^Doe^^^MR.^MR.||19720227|M

|||123 Any Street^^TORONTOÔN^M6G 3E6^CA^H^~123 Any

Street^^TORONTOÔN^M6G 3E6^CA^M^|1811|(123) 456-

7890||E^ ENGLISH|S| PATIENT DID NOT

INDICATE|211004554^||||||||||||

The message is received by the Mirth gateway listening over TCP/IP.

Mirth Administrator Dashboard

Mirth HL7 Message Flow Start Process

Mirth Engine

Receive HL7

message From IB

Successful

ACK

Process Message

For DISCERN

Incomming

Message

Process

Outgoing

Message

ACK

Store Message

For

Processing

(DISCERN.MESSAGE_QUEUE)

Outgoing

Process

Process Message

For Storage

Mirth HL7 Channel

Process

Mirth HL7 Message Transformation

Mirth Server Manager

Mirth Channel Writer

Mirth Alert Error Handler

Web Based Future Appointment Report On

Demand

HL7 FeedReal Time Data



ADT

Orders

Labs

DEDUCECohort Generation

STABILITY

COOLCAP

ROCKET

STABILITY


STABILITY

COOLCAP

ROCKET



STABILITY

COOLCAP

ROCKET

STABILITY

DISCERN EngineDISCERN Engine


STABILITY

COOLCAP

ROCKET

Age ≥ 60HDL < 40GFR 29 > 60ICD-9 = “Diabetes”(AND) “Periph Art Dis“(AND) “CerebroVascular Dis”



STABILITY

COOLCAP

ROCKET

Age ≥ 60HDL < 40GFR 29 > 60ICD-9 = “Diabetes”(AND) “Periph Art Dis“(AND) “CerebroVascular Dis”

Eligible Patient Arrives at ClinicEligible Patient Arrives at Clinic

01: Patient : Doe, John | AB1234Has arrived : Duke South Clinic 2JMeets criteria for: STABILITY Trial



Eligible PatientsEligible Patients

1. Study cohort defined

by researcher.

2. Data warehouse

queried for eligible

subjects.

3. Future appointment

schedule report

available on demand

for eligible subjects.

4. Eligible subject

arrives in outpatient

clinic.

5. Notification of

principle investigator

via email and/or

page.

6. Subject is contacted

by investigator for

recruitment into

study.

ACKNOWLEDGEMENTS

The project described was supported by Grant Number UL1RR024128 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for

Medical Research, and its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Information on NCRR is available at

http://www.ncrr.nih.gov/. Information on Re-engineering the Clinical Research Enterprise can be obtained from http://nihroadmap.nih.gov/clinicalresearch/overview-translational.asp. The authors thank

Dr. Jeff Ferranti MD and the DUHS data warehouse team for their support and contribution.

Mirth

MSH|^~\&|PCM|A01|PHR|A01|20080903142352-0400||OMPÔ09|1154941|D|2.3|||AL|NE PID|1||AR6280^^^NONE^MRÂ||PLECZKOWSKI^PAUL^PED||20040714000000-0400|M||||||||||

Channel

Channel

Transforms Message to XML from a template

Transforms XML to Database calls Insert /Updates

Stores entire

message with

received ACK field

Insert into Message Queue

Where Message =

„MSH|^~\&|PCM|A01|PHR|A01|2009952-0400|

|OMPÔ09|1000000|D|2.3|||AL|NE

PID|1||^^^NONE^MRÂ|

|FJJEJ^PddL^PED||2004000000-0400|M|

|||||||||‟

Insert into Message Queue

Where Message =

„MSH|^~\&|PCM|A01|PHR|A01|2009952-0400|

|OMPÔ09|1000000|D|2.3|||AL|NE

PID|1||^^^NONE^MRÂ|

|FJJEJ^PddL^PED||2004000000-0400|M|

|||||||||‟

MSH|...

EVN|...

PID|John^Doe

MSH|...

EVN|...

PID|John^Doe

<MSH>...

<EVN>...

<PID>

<PID.1>John</PID.1>

<PID.2>Doe</PID.2>

</PID>

<MSH>...

<EVN>...

<PID>

<PID.1>John</PID.1>

<PID.2>Doe</PID.2>

</PID>

INSERT INTO Patients

VALUES(„John‟, „Doe‟);

Patient patient = new Patient();

patient.setFirstName(“John”);

patient.setLastName(“Doe”);

SQL

Java

<patient>

<name>

<first>John</first>

<last>Doe</last>

</name>

</patient>

XMLHL7 v2.X XML

Encoding

Transformation

(mapping, script, HL7

generator, XSLT) TO: [email protected]

SUBJECT: Visit

Dear John Doe,

Your last visit …

Email

Message transformation

http://www.ncrr.nih.gov/

http://nihroadmap.nih.gov/clinicalresearch/overview-translational.asp



H D W A 2 0 1 1 S A N D I E G O


2009 DISCERN Poster

2010 DISCERN in production

2011 DISCERN how-to


7

H D W A 2 0 1 1 S A N D I E G O

WHY? There is a great need for

effective adoption of

integrated technology and

information solutions that

enable high quality clinical

care, research, and education.


8

HOW IT WORKS? By leveraging and integrating

the existing capabilities of:

• EDW Retrospective Data

• HL7 Information Broker

• Open Source MIRTH Engine

H D W A 2 0 1 1 S A N D I E G O

QUALITY OF CARE: Improve real-time notification

of patient-related events to interested parties

RESEARCH Improve capacity to identify,

recruit and enroll subjects to clinical trials within a highly constrained timeframe


9

DISCERN ALLOWS ADVANCES IN TWO

SIGNIFICANT AREAS OF HEALTH CARE:

15%

85%

QI RESEARCH

H D W A 2 0 1 1 S A N D I E G O


10

• Adolescent HPV Study Recruitment

USE CASE 1

• Cord Blood Study

USE CASE 2

• General Medicine Readmission within the last 30 days

USE CASE 3

Research

Research

Clinical QI

Report

Text Page

Email

H D W A 2 0 1 1 S A N D I E G O


12

WHY?

Aid in the recruitment effort for a clinical study called “Alternate Dosing Study for HPV Vaccine “ (in collaboration with the CDC Control & Prevention) to determine the efficacy of longer dosing intervals.

HOW?

PROCEDURE: HAVE REC’D 2

DOSES OF HPV VACCINE

FUTURE APPT DATE

Generate a Cohort & Send Alert

GIRLS

AGED 9-18

PRIMARY CARE

PHYSICIAN

USE CASE 1: Adolescent HPV Study

H D W A 2 0 1 1 S A N D I E G O


13

USE CASE 1: Adolescent HPV Study

H D W A 2 0 1 1 S A N D I E G O

USE CASE 2: CORD BLOOD TRIAL


14

The early administration of umbilical cord blood cells may act as an important adjunct to total body cooling in cases of neonatal hypoxic ischemic encephalopathy (HIE)

The protocol mandates that cord blood be procured and prepared immediately after birth.

Cells must be administered within 24 hours of birth

Multiple missed opportunities at recruitment because of this aggressive timeline

H D W A 2 0 1 1 S A N D I E G O

USE CASE 2: CORD BLOOD TRIAL


15

INITIAL DATA Pre DISCERN (7/1/2009 – 1/1/2010)

7 infants underwent cooling at cord blood collection sites

The study team was not notified of study candidates for 6-24 hours

Patients With Cord Blood Successfully Procured : 0

Patients Successfully Enrolled in Trial : 0

Post DISCERN (1/1/2010 – 11/1/2010)

9 infants underwent cooling at cord blood collection sites

Study team was paged immediately for all cooling patients (Day or Night)

Patients With Cord Blood Successfully Procured: 9

Patients Successfully Enrolled in Trial : 8

H D W A 2 0 1 1 S A N D I E G O

USE CASE 3: PATIENT READMISSION ALERT IF DISCHARGED WITHIN 30 DAYS


16

WHY? To alert the discharging

physician if a General Medicine patient is admitted to a hospital or seen in the ED within 30 days of discharge.

Goals: › Feedback to Provider

› Identify trends in readmissions

› Identify targets for QI

HOW?

DISCHARGE DATE < 30

DAYS

INPATIENT

LOCATION

DISCHARGE

PHYSICIAN

Join Cohort with real-time alert

H D W A 2 0 1 1 S A N D I E G O



17

IMPACT

Provide real time information & feedback to discharging physicians for assessment

Provide leadership with data for hospital-wide improvement of readmission statistics

H D W A 2 0 1 1 S A N D I E G O

We

ek o

f 3/2

1

We

ek o

f 3/2

8

We

ek o

f 4/4

We

ek o

f 4/1

1

We

ek o

f 4/1

8

We

ek o

f 4/2

5

We

ek o

f 5/2

We

ek o

f 5/9

We

ek o

f 5/1

6

We

ek o

f 5/2

3

We

ek o

f 5/3

0

We

ek o

f 6/6

We

ek o

f 6/1

3

We

ek o

f 6/2

0

We

ek o

f 6/2

7

We

ek o

f 7/4

We

ek o

f 7/1

1

We

ek o

f 7/1

8

We

ek o

f 7/2

5

We

ek o

f 8/1

We

ek o

f 8/8

We

ek o

f 8/1

5

We

ek o

f 8/2

2

No

. o

f N

otific

atio

ns

Week

General Medicine Notifications Weekly Totals

Total Notifications/Week

Total ER Returns/Week

Total Readmits/Week

Notifications Trends


H D W A 2 0 1 1 S A N D I E G O


19


Patients Survey

Physicians Survey

H D W A 2 0 1 1 S A N D I E G O



20

Appropriate

Yes

No

Not Answered

Preventable

Yes

No

Not Answered

Prescriptions Filled

Yes

No

Not Answered

Follow-up with Primary

Yes

No

Not Answered

H D W A 2 0 1 1 S A N D I E G O

“The DISCERN alerts have been very useful … and I believe will lead to a better understanding of the targets (and limits) for our readmission and care transitions work.”

Thomas A. Owens, MD

Chief Medical Officer

Duke Hospital Medicine Programs


22

“We have found the DISCERN readmission notifications a very powerful tool. It has helped us track with a great deal of granularity … surveys regarding the use of the notifications suggests that they have influenced provider practice related to transitions of care… [and] has helped generate project ideas for general medicine.”

Jonathan G. Bae, MD

Duke University Medical Center

“...it was extremely reassuring to study staff to have DISCERN on board… We expect creatinine stopping rules will be in place for this multicenter NICHD funded trial, and we believe the DISCERN safety monitoring is crucial for subject safety… C. Michael Cotten MD MHS Medical Director Neonatology Clinical Research Duke University Medical Center

H D W A 2 0 1 1 S A N D I E G O

Receive Real-Time

HL7 messages from Duke’s Institutional IB

Provide Retrospective Data

Run ETL Processes

Generate Cohorts

Use DEDUCE


23

INFORMATION BROKER

MIRTH INFORMATION MANAGEMENT

Listen

Filter

Transform

Send

HL7

HL7

HL7

H D W A 2 0 1 1 S A N D I E G O

Mirth is an HL7 gateway facilitates the routing, filtering, and transformation of messages between health information systems over a variety of protocols.

All configuration is done through a Java web client which can connect remotely


24

H D W A 2 0 1 1 S A N D I E G O


25

Cohort Surveillance

Real Time Business

Rules

Notification Engine

.

2. The receiving channel then feeds eleven customized channels

INFORMATION

BROKER

eGate

1. Mirth receives & sends > 500,000 HL7 msgs each day

3. Which further filters, joins, transforms and sends only relevant data.

H D W A 2 0 1 1 S A N D I E G O

The MIRTH engine is easily configured:

1. Specify source connector

LLP, HTTP, FTP, POP3, files, web services, and more…

2. Create or use built-in filters and validation

profiles (i.e. Accept only administrative,

financial, or lab events)


26

H D W A 2 0 1 1 S A N D I E G O

3. Create or use built-in transformers

Mapping transformer: map data from incoming message to variables

Script transformer: execute custom script on message (Ex. JavaScript)

HL7 message generator: construct HL7 messages from data source

XSLT transformer: run XLS Transformations on incoming HL7 v3 or XML encoded messages


27

H D W A 2 0 1 1 S A N D I E G O

4.Specify destination connector(s)

LLP, ODBC/JDBC, J2EE, JMS, XML, SMTP, files, web services, and more…

› All messages and transactions are logged to an internal or external database (Ex. Oracle)

› Can be configured to auto-generate HL7 acknowledgement response (ACK)


28

H D W A 2 0 1 1 S A N D I E G O


29

Source Connector

Destination Connectors

Filters and Validation

Transformers (mapping)

HL7 IB

Application

Ch

an

nel

• Message received from Broker HL7 Message

• HL7 Encoded into XML Source

• Accepted or discarded based on filter rules or validation

Filter

• Message segments and fields can be transformed using JavaScript

Transformation

• The binding map is sent to the connector

Destination

• Sent to multiple destinations if needed

Data

H D W A 2 0 1 1 S A N D I E G O


30

HL7 Feed Real Time Data

ADT

Orders

Labs Cohort

Surveillance

Real Time Business

Rules

Notification Engine

DEDUCE Cohort Generation

STABILITY

Gen Med Patients

Adolescent HPV

Age ≥ 60 HDL < 40 GFR 29 > 60 ICD-9 = “Diabetes” (AND) “Periph Art Dis“ (AND) “CerebroVascular Dis” Eligible Patient Arrives at Clinic

01: Patient : Doe, John | AB1234 Has arrived : Duke South Clinic 2J Meets criteria for: STABILITY Trial

Eligible Patients

H D W A 2 0 1 1 S A N D I E G O

Main Objectives:

› LISTEN

› FILTER

› TRANSFORM

› SEND


31

H D W A 2 0 1 1 S A N D I E G O


32

H D W A 2 0 1 1 S A N D I E G O


33

MSH|...

EVN|...

PID|John^Doe

<MSH>...

<EVN>...

<PID>

<PID.1>John</PID.1>

<PID.2>Doe</PID.2>

</PID>

INSERT INTO Patients

VALUES(„John‟, „Doe‟);

Patient patient = new Patient();

patient.setFirstName(“John”);

patient.setLastName(“Doe”);

SQL

Java

<patient>

<name>

<first>John</first>

<last>Doe</last>

</name>

</patient>

XML HL7 v2.X XML

Encoding

Transformation

(mapping, script, HL7

generator, XSLT)

TO: [email protected]

SUBJECT: Visit

Dear John Doe,

Your last visit …

Email

Message Encoding and Transformation

Mirth Corporation 18831 Von Karman Ave

Suite 300 Irvine, CA 92612

mailto:[email protected]

H D W A 2 0 1 1 S A N D I E G O

MSH|^~\&|DMCADT|DUH|DWHIS1||20103343223||ADTÂ01|DMCADT10034410|P|2.2|||NE

EVN|A01|20101100200200|||SIMS

PID|||ABC123||DOE^JANE||19001117|F||W|WHITE LANE ^^DURHAM^NC^277705ÛS^M|091|(919)555-1212|(919)555-1212|EN|S|LUT|XXX|012034442|||O

PV1||I|N79^7907^01|2|||1111111^SMITH^JOHNÂ|1111103^CLARK^JANEÂ||CAR||||H||||A||027|||||||||||||||||||A01||10||| 20103343223

ZV1|P|I|N||||||||||NADM

DG1||||CHF||A

Z99||6||| 20103343223


34

H D W A 2 0 1 1 S A N D I E G O

1. LISTEN & ENCODE HL7MESSAGES

2. FILTER THE DEFINED DATA


35

H D W A 2 0 1 1 S A N D I E G O

MSH|^~\&|DMCADT|DUH|DWHIS1||20103343223||ADTÂ01|DMCADT10034410|P|2.2|||NE

EVN|A01|20101100200200|||SIMS

PID|||ABC123||DOE^JANE||19001117|F||W|WHITE LANE ^^DURHAM^NC^277705ÛS^M|091|(919)555-1212|(919)555-1212|EN|S|LUT|XXX|012034442|||O

PV1||I|N79^7907^01|2|||1111111^SMITH^JOHNÂ|||CAR||||H||||A||027|||||||||||||||||||A01||10||| 20103343223

ZV1|P|I|N||||||||||NADM

DG1||||CHF||A

Z99||6||| 20103343223


36

IT’S A MATCH!

H D W A 2 0 1 1 S A N D I E G O

1. LISTEN

2. FILTER


37

3. TRANSFORM IT!

• Join Filtered HL7 & EDW Data

• Create contents for ALERT, EMAIL

or REPORT

H D W A 2 0 1 1 S A N D I E G O


38

SELECT * FROM

EDW.PATIENT_DISCHARGE

WHERE

PATIENT_ID = „ABC123‟

SQL <MSH>...

<EVN>...

<PID>

<PID.1> DOE^JANE </PID.1>

<PID.2>ABC123</PID.2>

</PID>

H D W A 2 0 1 1 S A N D I E G O


39

FROM: Data Warehouse Group

Duke Health Technology Solutions This is an Automated Email from DISCERN. DISCERN is a patient cohort alerting service provided by the Data Warehouse Group (DWG) of Duke Health Technology Solutions (DHTS). Please direct questions to Jane Smith [email protected] The visit type is: Inpatient Admission. The event took place on: 07/01/2011 00:05. The patient was discharged on 06/23/2011 17:13 by Dr. JANE DOE, M.D. 7 days ago with a diagnosis of CHF. The attending physician is: DR. GREGORY HOUSE The diagnosis for Inpatient Admission is: HYPOTENSION. PATIENT: SMITH, JOSEPH MRN: XXX123 TIME MESSAGE RECEIVED: 07/01/2011 00:05


H D W A 2 0 1 1 S A N D I E G O


40

DISCERN ALERTS & EMAILS

RESULTS SUPPORTING 22

PROJECTS

H D W A 2 0 1 1 S A N D I E G O

DISCERN framework is not a stand-alone tool but a service provided by a technical team.

The hybrid model requires both an enterprise feed of HL7 messages and an enterprise data warehouse.

All relevant staff must be alerted to the implementation.

It is IMPERATIVE that you follow up with the customer.

Modularize your MIRTH code for reusability.


41

H D W A 2 0 1 1 S A N D I E G O

Competition for patient enrollment may be exacerbated

Disruptive Technology

Requires effective communication and roll-out to all users


42

H D W A 2 0 1 1 S A N D I E G O

Extensibility of DISCERN tool directly to the research community

Extensibility of DISCERN services to other projects

› Stoplight Project (STAT Alerting System)

› Infectious Diseases

› Real Time DW

Extensibility to other patient health portal accounts and sources

Advertising research to patients


43

H D W A 2 0 1 1 S A N D I E G O

Combining open-source MIRTH HL7 messaging services with the data warehouse proves to be a highly effective model in addressing recruitment needs.

The flexibility of the model has proven powerful in addressing Quality Improvement health management initiatives.

The DISCERN model may help others seeking to deploy similar systems to achieve success.


44

H D W A 2 0 1 1 S A N D I E G O

Journal Publication pending in the Journal of American Medical Informatics Association (JAMIA)

“The design and implementation of an open-source, data-driven cohort recruitment system: The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN)”, In Press, Acceptance date 08/06/2011

Includes: Detailed Technical discussion of the Cardiac Readmit Project

(DUH Heart Center)

Detailed ‘How to’ Appendix


45

H D W A 2 0 1 1 S A N D I E G O

Howard Shang [email protected]

ACKNOWLEDGEMENTS:

Dr. Jeffrey Ferranti, M.D., M.S.

Chief Medical Information Officer

Duke Medicine

Monica Horvath, Ph.D

Senior Research Analyst

Health Analytics & Information Management

Duke Health Technology Solutions

Bill Gilbert [email protected]

Ilona Stashko, Senior Data Analyst

Duke Health Technology Solutions

Mirth Corporation

18831 Von Karman Ave Suite 300 Irvine, CA 92612

And thanks to all the staff that has been involved in the many successes of this project!


46

The project described was supported by Grant Number UL1RR024128 from the National Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research, and its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Information on NCRR is available at http://www.ncrr.nih.gov / Information on Re-engineering the Clinical Research Enterprise can be obtained from http://nihroadmap.nih.giv/clinicalresearch/overview-translational.asp The authors thank Dr. Jeff Ferranti MD and the DUHS data warehouse team for their support and contribution.





http://www.ncrr.nih.gov/.Information

http://nihroadmap.nih.giv/clinicalresearch/overview-translational.asp



the duke integrated subject cohort and enrollment research

Documents