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8/1/2017 1 Big Data : New and Emerging Big Data Strategies in Oncology Bringing Value from Big Data Analytics into Clinical Practice Charles Mayo, Ph.D. University of Michigan AAPM Annual Meeting TU-B-605-0 8:50-9:10 Room:605 Work is supported in part by a grant from Varian Medical Systems Disclosure Randy Ten Haken, PhD Marc Kessler, PhD Dan McShan, PhD Issam El Naqa, PhD Jean Moran, PhD Martha Matuszak, PhD Scott Hadley,PhD James Balter, PhD Yue Cao, PhD Dale Litzenberg, PhD Avi Eisbruch, MD Jim Hayman, MD Shruti Jolly, MD Dawn Owen, MD Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping Chen John Yao, PhD Lynn Holevinski Sue Merkel Sherry Machnak Denise Goodman Dawn Johnson Latifa Bazzi Grace Sun Alex Hayes Parth Janni It takes a village to make a Big Data Analytics Resource System
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Page 1: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

1

Big Data : New and Emerging Big Data Strategies in Oncology

Bringing Value from Big Data Analytics into Clinical Practice

Charles Mayo, Ph.D.University of Michigan

AAPM Annual Meeting TU-B-605-0 8:50-9:10Room:605

Work is supported in part by a grant from Varian Medical Systems

Disclosure

Randy Ten Haken, PhDMarc Kessler, PhDDan McShan, PhDIssam El Naqa, PhDJean Moran, PhDMartha Matuszak, PhDScott Hadley,PhDJames Balter, PhDYue Cao, PhDDale Litzenberg, PhD

Avi Eisbruch, MDJim Hayman, MDShruti Jolly, MDDawn Owen, MDReshma Jagsi, MDMichelle Mierzwa, MDTed Lawrence, MD PhD

Grant WeyburnCarlos Anderson, PhDXioaping ChenJohn Yao, PhDLynn Holevinski

Sue MerkelSherry MachnakDenise GoodmanDawn JohnsonLatifa BazziGrace SunAlex HayesParth Janni

It takes a village to make a Big Data Analytics Resource System

Page 2: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

2

Enter the DataClinical

Processes

Get the DataDemonstrate

Value

Use the dataResearch, PQI,

Admin

Improve Data Availability &

Access

Data – Value Cycle

ManualExtract, Transform, Load

Minimal upstream coordination required

Limited to relatively to small numbers of patients (10s-100s)

Enter the DataClinical

Processes

Get the DataDemonstrate

Value

Use the dataResearch, PQI,

Admin

Improve Data Availability &

AccessAutomated Electronic

Extract, Transform, Load

Lots of upstream coordination required

• access• standardization• people• resources• technical skills

Large numbers of patients (>> 1000s)

Data – Value Cycle

Page 3: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

3

So you want to build a Big Data Analytics Resource System ?

Most of your effort is going to be in building and improving ETL processes

• It takes a multi-disciplinary community that wants to make it real

• Invest in people with diverse skill sets

• Need commitment from leadership

University of Michigan – Radiation Oncology Analytics Resource

Zook, Barocas, Pasquale, et al. Ten simple rules for responsible big data research. PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi.1005399 March 30, 2017

• Acknowledge that data are people and can do harm

• Recognize that privacy is more than a binary value

• Develop a code of conduct for your organization, research community, or industry

Be proactive on the Ethics of Access

Page 4: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

4

Culture Shift: Standardize entry of Diagnosis and Staging -> Volume, Value

Data in the Electronic Medical Record

Encounter Notes in EMR (EPIC)

M-ROAR

• Huge volume of text data available• M-ROAR access (ie velocity) is fast (seconds)• Potentially really valuable source of information• The problem is variability …

the solution is standardization

MiChart (EPIC) M-ROAR

EMR Access + standardization -> Volume, Value• Automate harvesting regular data entered into notes in EMR• Presentation standardizations improve harvest-ability

ETL

Page 5: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

5

We can do even better…

Over 25,000 now,should get to >10x more

Culture Shift: Build templates in EMR with standardized schema for key data elements

• Standardized schema designed to function with regular expressions• Physician selection from drop down lists of standard values - > Fast, Easy, Accurate

Type of query Typical value

Practice Quality Improvement (PQI) Evidenced based approach to improving clinical processes and patient care

Translational Research Provide data sets needed for publications and grants

Administrative Support Ease access to data needed by front office e.g. Certificate of Need, Regulatory Groups, Institutional evaluation

Requests for data tend to fall into three categories

Page 6: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

6

Look at practical examples of using this resource to provide value

…. and what we’ve learned along the way

Tableau Dashboards providingend user self-service

SRS and SBRT Utilization Analytics

Value Categories• Administrative Support• Practice Quality Improvement

How can I look at how our SRS/SBRT program is evolving ?

Analysis of Lab Values

Value Categories• Practice Quality Improvement• Translational Research

EMR -> MROAR• Batch processing possible• Reduce work• Integrate with treatment data

How can I look at trends in labsfor a patient or look at labs for a set of patients?

Page 7: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

7

Patient Cohort Identification

Value Categories• Practice Quality Improvement• Translational Research• Administrative Support

How can I find a list of patients treated in a particular way?

Treatment Timeline and Imaging Analytics

Value Categories• Practice Quality Improvement• Translational Research• Administrative Support

How long does it take to treat our patients and what imaging do we use?

Can we use our historical DVH data when we are examining new treatment plans?

• Statistical DVH Dashboard

• Disease Site DVH Metric Summaries

• Practical Statistical Metrics▪ Generalized Evaluation Metric▪ Weighted Experience Score▪ Difficulty Ranking Score▪ Experienced based priorities

Page 8: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

8

Statistical DVH Dashboard – Plan Summary Panel

Application is run from treatment planning system.

Uses visual and statistical metrics to comparethis plan to historical plans

Treatment Planning System

FileOr Database

JSON filePre-calculate Statistics and Parameters

Treatment Planning System

API

C#, R

API

Harvest Historic DVH Data From Treatment Planning System

Use Pre-Processed Historic Data to Improve Plan Evaluation

• ANN - Artificial Neural Networks • SVM - Support Vector Machines• LR - Logistic Regression

Support for Machine Learning

• ML algorithms are data hungry: models and validation

• Need realistic representations of clinical distributionsmove from 10’s to 1000’s of patients

• Foundation for resolving differences between models

Page 9: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

9

Wh

at it

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ans

(clin

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The information we have

More Information (Big Data) More Complexity Community Science

• Variability in data and processes undermines reaching the real potential of Big Data

The information we have

Wh

at it

me

ans

(clin

ica

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ou

tco

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𝒚 = 𝒂𝟎 + 𝒂𝟏𝒙 + 𝒂𝟐𝒙𝟐 + 𝒂𝟑𝒙

𝟑

More Information (Big Data) More Complexity Community Science

• Variability in data and processes undermines reaching the real potential of Big Data

• No one institution can be the solution for these issues

The information we have

Wh

at it

me

ans

(clin

ica

l pra

ctic

e ch

an

ge,

ou

tco

mes

mo

del

s…)

More Information (Big Data) More Complexity

𝒚 = 𝒂𝟎 + 𝒂𝟏𝒙 + 𝒂𝟐𝒙𝟐 + 𝒂𝟑𝒙

𝟑

𝑻𝒐𝒈𝒆𝒕𝒉𝒆𝒓,𝒘𝒆 𝒂𝒓𝒆 𝒕𝒉𝒆 𝒆𝒒𝒖𝒂𝒕𝒊𝒐𝒏

Community Science

• Variability in data and processes undermines reaching the real potential of Big Data

• No one institution can be the solution for these issues

• Viable solutions from community of individuals solving issues in their institutions then collaborating on shared solutions

Page 10: PowerPoint Presentationamos3.aapm.org/abstracts/pdf/127-35415-418554-126636.pdf · Reshma Jagsi, MD Michelle Mierzwa, MD Ted Lawrence, MD PhD Grant Weyburn Carlos Anderson, PhD Xioaping

8/1/2017

10

https://www.flickr.com/photos/96369280@N00/albums/72157684077833246

Summary

Analytics from Big Data fit readily into Clinical practice supporting Translational ResearchPractice Quality ImprovementAdministrative Support

Effort needed to build a Data Culture Clinical practice changesSupport for access, extraction and curation

Community ScienceDevelopment and publication of common standardsMulti-institutional data sets


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