implementing biomedical informatics approaches to ... · 5/30/2014 · implementing biomedical...

Implementing Biomedical Informatics

Approaches to Facilitate Translational and Clinical

Research

Institute of Medicine May 30, 2014

David A. Fenstermacher, Ph.D.

Chief Research Information Officer

Professor, Dept. of Biostatistics

Virginia Commonwealth University

Quickly and accurately identifying patients for clinical trials that are based on molecular biomarkers

Integrating heterogeneous data from multiple sources to continuously screen patients for clinical trial eligibility and automated reporting

Developing processes that prompt clinicians about potential patient eligibility

Creating frameworks for evaluating patients at multiple sites for rare biomarker clinical trials

Copyright ©2014, VCU Massey Cancer Center. All rights reserved.

Use of clinical data,

molecular data and tissue

to qualify and validate

biomarker as a robust

subject of clinical research.

Evaluation and In Silico

design of clinical trial using

patient clinical and

molecular data as support.

Use of patient database

and network to rapidly

identify patients at “trial-

ready” sites.

Detailed clinical and

molecular characterization

of non/responders to enrich

value of clinical trial results.

Pre-clinical validation of biomarker as determinant

of response

Pre-trial go/no-go

decision and enhanced

trial design capability

Pre-existing, trial-ready

populations at

established high-profile

sites

Superior data mining and

analytical capabilities

In Silico Trial

Design

Precision

Patient ID

Post Trial

Analytics Preclinical

Assessment


• Patients in MCC HRI 357,295

• Female 217,169

• Date Dx Jan 06 – Dec 11 16,871

• Vital Status: Alive 11,592

• Age at Dx (≥ 50) 8,232

• Primary Site BC 2,883

• BC Histology 1,881

• Stage III and IV 267

• ER+ 197

• Patients with TES 36


Automatically Identify patients (or subjects) Integrate data and provide trial eligibility

communication to clinicians prior to patient visits

Provide sample size estimates - feasibility Create clinical datasets that may be linked

with biospecimen databases or provide annotation for other genomic data


System integrates three key areas focusing on the clinical trials recruitment process- automated matching, screening, notification of providers and tracking of eligibility status over time

Software system developed to assist in quickly and efficiently screen and identify cohorts /sets of patients meeting specific clinical or nonclinical criteria

3 components of CTED are: Automated Matching Tool

MD Alert

Tracking system


Figure 2 Massey Cancer Center Cancer Research Informatics and Services (CRIS) Core

Components, Relationships & Data Infrastructure

VCUHS

Cancer

Registry

Automated

Cancer Extraction

Application (ACE)

Links, Screens, Parses

and stores data for

surveillance and research

Analytics

Services• Datasets

• Data linkages

• Analysis Support

Clinical Trials Data

Management System

(ONCORE)CT administration

and compliance

Clinical Trials Eligibility

Database (CTED)• Tracking Tool

• Manual Query Tool

• Automated Matching Tool

for assessing CT eligibility

CRIS

Components

Primary Data

Entry by

Clinical Research

Staff

External

Data

Feeds

for

CRIS

CRIS

Internal

Data

Store

BMT DBDecision

SupportIDX Scheduling

System

EMR Clinical

Notes

IP/Professional

Billing

Pathology

Shadow DB

• Surgical Pathology Reports

• Clinical Pathology Reports

• IP/OP & Professional Billing

• IDX Scheduled Visits

Universal Data Store

• Inpatient discharges

• Radiology Rpts

• Surgery Rpts

• Clinical notes all specialties

eGate HL7

Dictations• radiology,

• operative notes

• DC summaries

Tissue & Data

Acquisition &

Analysis Core

Clinical and

Cancer Control

Research

Information

System

(MCCIS)Claims/linked data

Repository


• Provides an automated electronic solution to efficiently identify patients meeting specific inclusion/exclusion criteria (clinical, demographic etc.)

• Scheduled, automation screens all patients repeatedly to determine eligibility or match status (based on each patient’s current clinical status)

• Screens data for ALL patients across the health care system ( 700,000 patients)

• Presents that list with linked documentation to the research staff and/or PI

• Customizable


Offers the opportunity to select patients based on combinations of discrete variables as well as easily customizable searching of free text:

• Demographics • Diagnoses

• Treatments

• Clinical Lab test results

▪ Tumor markers (PSA, CEA, CA19 etc.)

▪ Serum chemistries

▪ Hematologic parameters


• Free text documents searched using text

strings/terms unique to your study- enables access

to information not otherwise (readily) available

▪ clinical notes (stage, detailed CA RX, medications)

▪ pathology reports (histology, genetic markers stage)

▪ radiology dictations (disease progression, mets)

▪ discharge summaries

• Algorithm-derived complex clinical concepts

(e.g. Newly diagnosed metastatic disease

from radiologic dictations )


Links output data from Automatch (patients matching study specific eligibility criteria) with VCU scheduling system each night.

If patient on a list from the query is scheduled for next day visit then designated personnel (research nurse, treating physician etc.) receive an email alert directing them to a secure website 24hours prior to the scheduled visit

Can access the information at any time (even at point of care)

TCC

Database

INTEGRATION OF PATIENT’S CLINICAL, BILLING AND MOLECULAR DATA

PROVIDES:

Clinical Trial Matching for Biomarker and non-Biomarker Clinical Trials

Comparative Effectiveness Research

(Outcomes Research) Post Trial Analytics

Improved ability to track and understand patient treatment outcomes and costs

Clinical Data

Molecular Data

Billin

g D

ata


VCU is currently exploring expanding CTED usage at two other cancer centers

VCU’s Center for Clinical and Translational Research (CCTR) has signed a MOU with Penn State Hershey to begin creating joint infrastructure for cohort discovery

Using I2B2 for data integration

CTED algorithms and tools leveraged in collaboration


• Pre-built data cubes / reports provided by centralized Decision Support group did not have the data elements / granularity required for many of the types of analyses this unit conducted, including assessment of potential number of patients eligible for a given clinical trial.

• Typically, existing data products were built with broad, current financial and / or operational reports in mind – for example, metrics for the entire hospital. And these were entirely unsuitable for creating datasets for researchers to mine for health services research on cancer care, and the enterprise-wide analytics staff did not have cancer-specific knowledge and expertise.

• Thus, we needed to create a system that could be used by a dedicated team for more granular financial and operational reports and to support clinical and health services research at the cancer center.


• Research inquiry

• Operational reporting

• Financial reporting

• Clinical inquiry and reporting

• Outreach efforts

• Maps

• Business development


• Enhanced ability to link our data with external / alternate data sources

• Cleaner data overall – far less manipulation required while working within the data provided weekly. Some manipulation is still required when linking with external / alternate data sources, depending on the source.

• Using MDAS, Analytic Services has fulfilled more than 200 research requests and over 70 investigations within the past three years, as well as filling a similar number of non-research requests:

• Research Services:

• Sample size calculations/feasibility for clinical trials

• Custom datasets:

• integrated across different sources of patient data (e.g., billing, cancer registry, bone marrow transplant, palliative care, appointment data, clinical data from clinical EMR)

• identifiable (requires IRB approval) or fully de-identified

• Analytic findings and interpretations in collaboration with health services researchers

• Non-Research Services

• Scheduled and ad-hoc data reports on clinical operations and finances

• Data-driven maps of cancer incidence, research sites, clinical sites, population characteristics

• Quality assurance analyses for improving cancer care


CTED and MDAS provide data management infrastructures that integrate heterogeneous data, automate reporting, track patients longitudinally and can alert clinicians prior to patient visits

Overall, molecular and clinical data generated at the point of care is being used for patient stratification, cohort discovery and multiple research projects

Increase knowledge regarding the relationship between therapeutics and molecular biomarkers

Expand resources to more chronic and acute diseases beyond cancer


Nevena Skoro, MPH R. Michael Sarkissian Karman Tam, MPH Karen Shineer, MSHA CPC

Translational Research Informatics Core Charles Geyer, M.D. Lynne Penberthy, M.D. Jonathan DeShazo, Ph.D. J. Brian Cassel, Ph.D. Valentina Petkov Chris Gillam Thomas Neumann Jim McDermott Jon Lowman Dolan Smith Tim Aro


implementing biomedical informatics approaches to ... · 5/30/2014 · implementing biomedical...

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