CANCER CARE ENGINEERING

The Health System - Systems Engineering - TRIP - Health Services Research - Policy - Medical Informatics - Economics

Biological Sciences - Blood Biomarkers - Tumor Variations - Tumor Biomarkers/Dynamics mutations - Clinical Trials sessile polyps

INDIVIDUAL PATIENTS- Quality Care - Detection - Susceptibility- Epidemiology - Symptoms / QoL - Environmental Factors- Decision Support - Access

Accelerated translation of science to practice

Screening Rates

Identified best practices to dramatically improve and personalize tx

Better decision tools that aid providers, managers

A knowledge management system incorporating latest research advances so that every piece of new knowledge does not have to be manually assimilated by every provider

Methods of implementation and systems engineering to address systems complexity and speed of response

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Matt Burton, MD

Knowledge of Best and Actual Practices Clinical Pathways, Guidelines, Order Sets, Quality

Indicators

Clinical Workflow Prediction, CDSS, Order Management, Communication,

Care Delivery, and Documentation Simulate, Monitor, and Analyze

Clinical Data Results Delivery, Quality Measurement and Monitoring,

Feedback/forward (CDSS, Simulation), Knowledge Discovery

Virtual Hospital and Cancer Care Engineering

Matt Burton, MD

Focus on Translation Distill research knowledge to useful products that can be easily used by

providers, consumers, and others to overcome the greatest complexities in cancer care

Goal-Oriented Direct researchers towards overcoming barriers likely to result in the

greatest care system improvements;

Knowledge Improvement Systematic collection, analysis, and dissemination of cancer system data to

all participants for purposes of more effective distributed actions by system participants;

Metric Oriented Drive improvements by using key metrics that summarize system behavior,

such as the NIH statistics cited above;

Global Awareness Understand and direct work by considering it within a system wide

perspective;

Externalize Knowledge Reduce knowledge to models that can be widely learned and whose

properties can be tested for improvement.

6/17/2008cceHUB S.

Cremaschi 7

System Level: Indiana Regional

Cancer Care System Simulate the Indiana

colorectal cancer (CRC) care system to resemble current performance

Using the model: Identify areas of

potential improvement in current system

Develop and test various strategies to arrive at an optimal strategy

Investigators: Selen Aydogan-Cremaschi, PhD,

Purdue Discovery Park Brad Doebbeling, MD, MSc; Seza

Orcun, PhD, Purdue Discovery Park, David Haggstrom, MD, MAS, Multiple others

6/18/2008 cceHUB 8

1. Interview providers to identify and rank questions of interest

2. Develop CRC care system models and implement them

3. Validate the model 4. Run what-if scenarios to answer

questions of interest

6/18/2008 cceHUB 9

9

ScreeningSymptomatic

GeneralPopulation

SurvivorPopulation

Diagnosis

Staging &

Evaluation

Treatment

CancerPopulation

Death

disease free

not treatable or do not wish to be treated

cancer orcare complication or other

false positive

negative

positive

cancer or care complication or other

cancer or care complication or othercancer orcare complication or other

6/17/2008cceHUB S.

Cremaschi 10

1. Identify higher impact components of the CRC care system in Indiana:

a) From a healthcare system perspective, what indices or metrics might be early warning signals for managers or clinical leaders of where to intervene quickly?

b) If we consider the entire spectrum of care, can we have the greatest impact on CRC care mortality and cost of care by optimizing one of the components to perform in a highly reliable fashion? : --Screening, Screening Follow-up, Diagnosis, Treatment - early stage, late stage diagnosis, survivorship, palliative care

2. Determine necessary system resource capacities:a) If every positive abnormal screening test is followed up with

a colonoscopy, does Indiana have the necessary resources? b) What should the capacity of the high-volume facilities be in

order to be able to perform the necessary surgical procedures for CRC?

6/18/2008 cceHUB 11

Natural Development and Progression of CRC in Population Agent

NormalInvisible

Polyp Polyp < 1cm Polyp > 1cm

In Situ CRC

Local CRC

Regional CRC

Distant CRC

Prob. ofDev. Polyp

LOP Dist. between States

Symptomatic CRC

LOP Dist. from asymp.

to symp.

6/18/2008 cceHUB 12

Screening & Follow-up

FOBT

Sigmoidoscopy

Colonoscopy

Never Compliant

Screening Choice Compliance Intervals

Every 1 year

Every 2 years

Every 5 years

One Time Compliant

6/18/2008 cceHUB 13

Treatment Type Number Combinatio

ns Adherence Date Result Lifestyle

6/18/2008 cceHUB 14

Attributes Years in training Provider Type, Specialty Location/County Volume of patients/procedures Adherence with “Evidence-Based

Medicine” Treatment maps for CRC stages

using the interviews of CRC providers/specialists.

6/18/2008 cceHUB 15

Attributes Location Resources

▪ Screening/Diagnosis/Staging▪ Surgery▪ Radiotherapy▪ Chemotherapy▪ Hospice▪ Palliative

Volume of patients/procedures

System Level: Indianapolis Clinic System

Investigators: PI – Brad Doebbeling, MD, MSc; Co-PI –Jamie Workman-Germann, Purdue

University School of Engineering and Technology at IUPUI

Anticipated Outcome: (1) Develop cancer prevention and care process

maps and quality reports for Indianapolis clinics, (2) understand barriers to best practice care, (3) utilize a Cancer Care – Technical Assistance

Program (TAP) to help implement best practices in one or more clinics.

CRC Systems Redesign Project Aims:

▪ To understand EMR implementation impact of clinical processes

▪ To optimize patient flow by identifying process barriers to screening

▪ To increase CRC screening rates by removing clinical barriers

Methods:▪ Facilitation of interdisciplinary teams of IUMG and VAMC

primary care clinic staff and area supervisors ▪ Evaluation of existing clinical workflow▪ Systems engineering, Lean, Positive deviance principles

Scope of Work: Assessment of Primary Care CRC processes Development of data infrastructure to support

sustainability of initiative Develop and administer cultural assessment to

determine organizational readiness for IT and systems redesign initiative implementation.

Facilitate IUMG and VAMC project teams in application of systems engineering tools to:--Design and perform a pilot test of the process redesign, ensuring new processes meet clinical and economic objectives, timeline requirements, and project deliverables--implement new processes and systems with a robust control strategy to ensure long term sustainability of improvements

System Level: Cross System Information Awareness and Clinic

Investigators: PI - Caroline Carney Doebbeling, MD, MSc, Associate Professor of Medicine

& Psychiatry, IUSM; Research Scientist, IU Center for Health Services & Outcomes Research, Regenstrief Institute, Inc.; Director of Quality and Outcomes, Indiana Medicaid

Co-PI – Katherine Schilling, MLS, EdD, AHIP, IU School of Library and Information Sciences and IU School of Informatics

Specific Aims: (1) Develop a best practice CRC treatment map including recognition and

treatment of cancer-related distress; (2) identify opportunities for streamlining processes; and (3) pilot implementation of more efficient delivery of psychosocial services

(mental health and social work) concurrent with cancer treatment.

Long-term objective is to implement innovative patient screening and navigation systems to consistently deliver optimal psychosocial care.

CCE-3 – Literature Matrix

Universal screening in all clinics Distress Thermometer Triage to appropriate CL providers

- Moderate to Severe Distress: Mental health, social work, pastoral services

- Mild Distress: Primary Oncology team Barriers

- Too few providers

- Waiting lists

Recommendation : Can we identify, describe, and better understand “positive deviant” systems within treatment centers nationally that are engaged in best practices? How have they been successful in implementation?

CCE-HSR C. Carney Doebbeling

System Level: System Biology-Oncologist-Patient

Investigators: PI – Seza Orcun, PhD, Purdue Discovery Park Co-PI – Doraiswami Ramkrishna, PhD, Harry Creighton Peffer

Distinguished Professor of Chemical Engineering, Purdue Co-I – Eric Sherer, PhD, Purdue Discovery Park; VA Center of

Excellence on Implementing Evidence-based Practice; Tom Imperiale, MD, Professor of Medicine, IU School of Medicine and IU Center for Health Services & Outcomes Research, Regenstrief Institute

Anticipated Outcome: (1) Develop a population balanced model to predict efficacy of

oncology treatment, (2) validate model with oncologist usage, and (3) engineering modeling researchers in clinical settings partnering

on joint projects with oncologists, GI specialists and services researchers.

1. CRC prevalence model that includes intermediate polyp states and tumor genetic heterogeneity

1. Already several similar models for incidence2. None (that we know of) that include polyps or branching

2. Methodology to extract a minimal set of discrete patient model parameter sets from CRC & polyp prevalence / incidence data

1. Parameter sets are independent of demographics

=> Bayesian model for predicting likely parameter sets for an individual patient

1. Certain demographics may be more likely for certain parameter sets

2. Likelihoods adjust to additional patient information3. Predict incidence, treatment outcome, outcome

05/28/08 CCE-HSR E. Sherer

System Level: Cross System Information Awareness – Assimilation and

Integration of Data From All Projects

Investigators: PI – David S. Ebert, PhD, Professor of Electrical and

Computer Engineering, Director of Purdue University Regional Visualization and Analytics Center, Director of Purdue University Rendering and Perceptualization Lab, Purdue

Primary Objective: Full-fledged, interactive, integrated visual and statistical

analysis capability in a vital analytic environment that brings together massive, disparate, incomplete and time-evolving -omic data sets.

Longer term goal---linkage with systems level data—cross projects with EMR, claims, structure, process, outcomes

“The Dashboard Project” Initial pilot, creation of an interactive,

integrated dashboard of facility-level colorectal cancer performance measures to inform the process of cancer care and systems management in the VAMC.

An example of functionality would be the ability to view CRC-related, facility-wide data output by clinics, treatment providers or risk-level of patient populations.

Brad Doebbeling, MD, MSc; Selen Aydogan-Cremaschi, PhD,; Matt Burton, MD; Timothy Carney, MPH, MB,;Jason Saleem, PhD; Darrell Baker, RN; David Haggstrom, MD; Tom Imperiale, MD; Charles Kahi, MD, and Chris Suelzer, MD

Dashboard Report – Corporate View

Critical, Clinically-Relevant Questions of Interest:

What percentage of patients who have received physician-ordered FOBT cards, are not returning them? What factors are contributing to non-compliance?

Regarding follow up after a positive FOBT screen, what percentage of patients is notified within the required 14 days of the results?

What percentage of patients with colonoscopy orders to follow-up for positive screens isn’t getting colonoscopy completed? What factors are contributing to this gap?

What percentage of patients with a positive screen get needed colonoscopy within the required 30 days?

06/08 CCE-HSR B. Doebbeling

Aug2008

-Project kick-off

Feb 2008

-Questions of Interest-Interview VAMC CRC care providers & administrators-Identify critical questions of interest-Identify CRC performance measures for visualization

-Detailed Definitions of CRC Performance Measures

Apr 2008

May2008

June2008

-Review -Literature for dashboards for healthcare system-Implemented healthcare dashboards

-Conceptual Design of Dashboard

Sep2008

-Database & Software Selection

-Mock Implementation

Oct2008

Dec2008

-Usability Testing & Dashboard Refinement

INITIATION PHASE PHASE I PHASE II

-Prototype Implementation and Testing

-Dissemination-Proposal Development

June2009

System Level: Repository of Data for All CCE Projects, Preparation of

Data From All Projects, and Strategic Statistical Analysis

Investigators: PI – Marietta L. Harrison, PhD, Purdue University; Co-I,

Laura Jones Myers, PhD, George Allen, IU School of Medicine & VA COE

Anticipated Outcome: (1) Utilitarian project to provide an electronic repository

of all CCE project data, (2) Develop a procedure and tools for cleaning and

validating CCE project data, and (3) Determine a strategy for combining and analyzing

the disparate data from all projects

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Aim 1: Identify key approaches to CDS development for CRC screening at two VAMC sites and two nationally recognized non-VA sites, for effective CDS integration into clinical workflow.

Aim 2: Develop and test CDS design alternatives for improved integration into clinical workflow through a controlled simulation study and subsequent implementation.

Research Team: Brad Doebbeling, MD, MSc (PI); David Haggstrom, MD, MAS; Jason Saleem, PhD ; Laura Militello, MA; Heather Hagg, MS; Shawn Hoke and Lori Losee, and West Haven VA, Columbia, South Carolina VA, Partners Healthcare (Harvard).

Social Subsystem

Technological Subsystem

External Environment

Organizational Structure / Work System Design

Joint Optimization

Social Subsystem

Technological Subsystem

External Environment

Organizational Structure / Work System Design

Joint Optimization

Training and research components: Aim 1: To identify patient-level characteristics

associated with underuse & overuse of surveillance care among CRC survivors colonoscopy, CT scans, CEA tests, history & physical

Aim 2: To determine whether organizational or physician characteristics are associated with the quality of CRC surveillance care

Aim 3: To develop and test a CRC survivor’s personal health record that promotes high-quality follow-up care

Develop methods and tools for effective use of unstructured data such as narrative text in VA EHR

Improve text processing, text mining and de-identification capabilities

Applied projects Seven VAMCs with informatics research

capabilities participating--Salt Lake City, Nashville TVHS, Indianapolis, Palo Alto, Portland, Tampa, West Haven. Also Boston – MAVERIC, Pittsburgh-Philadelphia, Mayo Clinic, Carnegie Mellon.

Ability to extract free (tumor stage, etc) through a web services model.

[See Len D’Avolio, Mahesh Merchant, Matt Burton]

Innovation, Interdisciplinary Collaboration, Focused on transforming Healthcare

Positive impact on healthcare

Reduction in cost of healthcare delivery

Increase in the value of healthcare delivery

Translating the project results to the benefit of the healthcare system

Possibility to leverage the Foundation, DoD, VA and AHRQ funding

Knowledge Patient with given demographic needs a Colonoscopy every 10 yrs

Clinical Workflow Pt J. Doe scheduled for routine H&P plan for Colonoscopy in next 3 mos. w/

PCo Clinical Reminder: “Dr. Smith, J. Doe needs a Colonoscopy” CPOE: “Colonoscopy for J. Doe is ordered and signed” Order Mgmt: “Colonoscopy for J. Doe scheduled on 7/12/08” Resource/ Supply Mgmt: “Need Colonoscopy suite, resources, and supplies

on 07/12/08 at 9:00AM for Pt with give requirements” Registration: “J. Doe has arrived for his Colonoscopy” Document Preparation: “Populate fields in Procedure Note for Colonoscopy” Documentation: “Colonoscopy begun/ completed at 9:03 AM/ 9:37 AM

7/12/08” Order: “Path Specimen for Polyp” Document Preparation: “Populate fields in Path Report on Joe Doe’s Polyp”

Clinical Data Preliminary Procedure Note for Colonoscopy on 7/12/08 is resulted Pathology Report on Colonoscopy on 7/12/08 is signed and resulted

Matt Burton, MD

System Level: Indiana Regional Cancer Care System

Investigators: PI – Selen Aydogan-Cremaschi, PhD, Assistant

Research Scientist, Purdue Discovery Park Co-PIs – Bradley N. Doebbeling, MD, MSc; Seza

Orcun, PhD, Associate Research Scientist, Purdue Discovery Park

Anticipated Outcome: A model that can explain the existing CRC care

system data, answer “what-if” questions about potential changes to the care system, and suggest improvements based on analyzing various options.

Mechanistic modeling of colorectal cancer (CRC) Includes genetic mutations and growth /

death dynamics Hypothesize mechanisms

▪ Underlying knowledge▪ Level of detail determined by measurements▪ Can be extended to incorporate additional

information as it becomes available Prediction of likely individual patient CRC

Temporal likelihoods of CRC Likely properties of CRC

05/28/08 CCE-HSR E. Sherer

System Level: Clinical

Project Team: PI – Brad Doebbeling, MD, MSc Co-Is – Selen Aydogan-Cremaschi, PhD, Assistant Research

Scientist, Purdue Discovery Park, VA HSR&D COE; Matt Burton, MD, Medical Informatics Fellow, Regenstrief Institute, Inc.; Timothy Carney, MPH, MBA, IU School of Informatics; Jason Saleem, PhD, Assistant Professor, VA COE and IUPUI School Engineering & Tech; David Haggstrom, MD, MAS, Assistant Professor, IU School of Medicine, VA CIEBP and IU CHSOR

Consultants – Darrell Baker, RN, Clinical Applications Coordinator, VAMC;; Tom Imperiale, MD, Research Scientist, Regenstrief Institute, Inc. and IU School of Medicine; Charles Kahi, MD, Roudebush VA Medical Center and Chris Suelzer, MD, Associate Chief of Staff for Ambulatory Care, Roudebush VA Medical Center

More effective use of IT is recommended in integrating point of care access to (e.g., Committee on Quality Health Care in America): Health literature and evidence-based guidelines; Computerized clinical data; Computerized decision support (CDS) systems; Automation of decisions to reduce errors; Electronic communication among providers and patients into

practice. Computerized CDS can improve clinician decision making and

support adherence to evidence-based guidelines. Colorectal cancer screening focus: high disease burden, relatively

low screening rates, strong evidence for screening effectiveness Failure to optimally integrate CDS into workflow has resulted in

inconsistent and incomplete implementation strategies.

Disparities in hospital selection Cultural disparities Gender & Age

Diagnosis at younger age = higher risk for psychosocial problems related to illness burden

Racial disparities (??) Literacy and Health Literacy

Low health literacy associated with less knowledge about colorectal cancer

Low health literacy associated with less knowledge about screening

Practical, life-management issues (insurance, employment)

Lower income, Medicare Part D issues Ability of providers to recognize distress

05/28/08 CCE-HSR K. Schilling

System Level: Physical CRC Sample and Raw Data Collection

Investigators: PI – Stephen D. Williams, MD; Gabi Chiorean, MD,

IU Simon Cancer Center, IU School of Medicine

Anticipated Outcome: Augments a DoD-funded project to collect

additional samples and clinical data based on an analysis of initial results.

To assess physical samples and laboratory analysis data from the CPTAC project and other Indiana based cancer specimens for purposes of understanding Indiana’s capacity for a total cancer care engineering project.

System Level: Opportunistic Project Refinement and

Integration of Regional and National Assets into Indiana CRC CCE Effort

Investigators: PIs – Joe Pekny, PhD, Purdue University;

Brad Doebbeling, MD, MSc, Indiana University

Primary Objective: Management of the portfolio of all projects

to maximize impact and to leverage success by further incremental investment