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I don’t know how to handle this Management question and need guidance.Cl o sin g Ca se 2
by Quirk in State Law,” US News and World Report, September 16, 2015;
J. Frank, “Ahead of 2015 Vote, Campaign Pushes Marijuana Tax Question
in Colorado,” The Denver Post, September 13, 2015; C. Cooper, “Colorado
Profits, But Still Divided on Legal Weed,” The Center for Public Integrity,
August 16, 2015; R. Baca, “Colorado Pot Sales Spike in June, Top $50
Million for First Time,” The Cannabist, August 13, 2015; H. Borrud, “Oregon
Pot Tracking System Will Use RFID Technology,” East Oregonian, June 14,
2015; S. Lohmeyer, “States Turn to Tech for Tracking Marijuana,” GCN.com,
May 26, 2015; K. Mulvaney, “Advocate Says Colorado Received $60 Million
in Taxes and Fees from Marijuana in 2014,” Providence Journal, January
11, 2015; K. Weise, “Tracking Colorado’s Legal Pot, Plant by Plant,”
Bloomberg BusinessWeek, March 17-23, 2014; R. Hiscott, “RFID Tags Track
Marijuana from Seed to Sale in Colorado,” Mashable, February 11, 2014.
1. Describe how database technology plays an important role ena­
bling Colorado to closely monitor the marijuana production and
distribution process.
2. Describe potential disadvantages in using RFID tags to monitor
marijuana production and distribution.
Closing Case 2
POM Big Data and the Treatment of Cancer
The Problem
The global statistics on cancer are sobering. Every year, 8 million
people die from cancer, and 14 million people discover they have the
disease. Approximately $100 billion is spent on cancer drugs globally.
Unfortunately, the majority of cancer treatments are not successful.
Despite years of effort by the medical establishment to persuade
doctors and hospitals to embrace electronic medical records (EMRs),
oncology data have remained difficult to access and use. (Oncology
is the branch of medicine that deals with the study and treatment of
Data on a single cancer patient can come from multiple sources,
including internists, oncologists, radiologists, surgeons, and labora­
tory and pathology reports. Even when the data are digitized, they
are often in an unstructured format. Rather than being organized in
databases, the data are often in multiple, inconsistent formats across
different lab reports and records. Making matters worse, much data
remain hidden in reports that have been written by hand and scanned,
in audio recordings, and in low-resolution PDF files printed from fax
machines. Finally, incompatible systems and strict privacy regula­
tions—for example, the Health Insurance Portability and Accounta­
bility Act, or HIPAA—that govern personal health information make it
even more difficult for data to be shared across thousands of oncology
Only a small fraction of cancer patient treatment data are being
collected systematically. Those data are typically collected from ran­
domized clinical trials, which cover only 4 percent of adult cancer
A Proposed IT Solution
Flatiron Health (Flatiron; www.flatiron.com) wants to help doctors
develop better treatment options for cancer. Founded in 2012, Flatiron
essentially fights cancer with organized data. With its OncologyCloud,
the company is helping oncologists enhance patient care. The com­
pany collects, organizes, and standardizes much of the information for
the 96 percent of patients not included in clinical trials and then offers
those data back to physicians in a format that can be analyzed.
The two Flatiron founders began their startup by visiting 60 can­
cer centers, speaking directly with experts, and visiting patients with
physicians. Working with oncology experts, they decided that the
most pressing need in cancer treatment was to organize the massive
volumes of clinical data that are scattered in the filing systems of
oncology treatment centers throughout the country. They proposed to
collect the data—both digital and otherwise—and then organize them,
aggregate them, and provide them to physicians, who can use the data
to make better decisions about how to treat their patients.
In theory, electronic medical records (EMRs) were supposed to
make such data aggregation and integration easier. Unfortunately,
those benefits have not totally materialized. In fact, more than 25 per­
cent of U.S. medical records remain in hard-copy format.
The Flatiron founders spent more than two years building what
they call a data model, which is their strategy to organize clinical
information into categories. Working with a team of physicians, they
decided to focus initially on one type of cancer: colon cancer. Using
published clinical trials, they extracted more than 350 data categories,
including demographics, geographic location, cancer stages, biologi­
cal markers of disease, and responses to therapies. Then they repeated
the process for other forms of cancer.
To automate the process of extracting data from medical records,
which can be labor intensive, Flatiron used matching algorithms
targeted at pinpointing values in lab reports. They also utilized nat­
ural-language processing to enable computers to read documents
and extract data from them. Such systems must be trained. To accom­
plish this task, Flatiron hired a team of 60 nurses to enter data on 500
patients by hand, creating a “training set” that was used to detect
errors in data that had been collected automatically. Data collection
errors were then fed back into the system as inputs to help improve the
automated collection process.
The Results
Using Flatiron’s OncologyCloud, oncologists are able to see the most
effective therapies for the most patients in similar circumstances. Fur­
ther, these physicians are able to evaluate their own treatment out­
comes against those of other specialists across the nation and then
quickly correct any deficiencies. The OncologyCloud also highlights
cost-effective therapies and wasteful healthcare spending. Finally, the
system helps to match patients with suitable clinical trials, hopefully
speeding up the development and approval of new medicines.
In 2014, Flatiron acquired Altos Solutions, which developed the
first oncology-specific electronic medical record. This acquisition gave
Flatiron a larger installed base and closer contact with physicians. By
October 2015 Flatiron systems were being used in 210 cancer centers
that collectively see about 300,000 new patients every year. Further,
in 2014 Google invested more than $100 million in Flatiron via Google
Ventures, the company’s venture capital unit.
In 2015, Flatiron and Guardant Health (www.guardanthealth
.com) began to collaborate to enable more effective cancer treatment.
Flatiron will provide the structure and all of the clinical trial informa­
tion for the OncologyCloud. Guardant’s commercially available cancer
90 CHAPTER 3 Data and Knowledge Management
screening product, Guardant360, will be used to collect data from
patients’ blood samples in a much more efficient manner than was
previously possible.
And the bottom line? In 2014, nearly 1.7 million Americans were
newly diagnosed with cancer. If oncologists using the OncologyCloud
could improve the patient survival rate by 5 percent, they would save
tens of thousands of lives every year.
Sources: Compiled from L. Ramsey, “Cancer Treatment Is on the Brink
of a Data Revolution,” Business Insider, September 22, 2015; N. Versel,
“GuardantHealth, Flatiron Health to Link Genomics, Analytics for
Personalized Cancer Care,” MedCityNews, August 19, 2015; C. Magee,
“GuardantHealth and Flatiron Health Team Up to Cure Cancer with Big
Data,” TechCrunch, August 19, 2015; “Fighting Cancer with Big Data,”
The Rambus Blog, August 10, 2015; T. Stephens, “California Initiative
to Advance Precision Medicine Funds UC Santa Cruz Pediatric Cancer
Project,” University of California at Santa Cruz News Center, August 3, 2015;
B. Marr, “How Big Data Is Transforming the Fight Against Cancer,” Forbes,
June 28, 2015; “Varian Medical Systems and Flatiron Health to Develop
Next Generation of Cloud-Based Oncology Software,” Flatiron Health
Press Release, May 26, 2015; “Foundation Medicine and Flatiron Health
Collaborate to Develop First In-Class Data Platform to Accelerate Precision
Medicine for Cancer,” Foundation Medicine, December 2, 2014; M. Helft,
“Can Big Data Cure Cancer?” Fortune, August 11, 2014; S. Baum, “Flatiron
Health Finds Ideal Match with Duke Cancer Care Research Director,”
MedCityNews, July 2, 2014; K. Noyes, “Flatiron Health’s Bold Proposition
to Fight Cancer with Big Data,” Fortune, June 12, 2014; N. Taylor, “Buzz:
Google Ventures Leading $100M Round in Oncology Big Data Platform,”
FierceBioTechIT, May 5, 2014; www.flatiron.com, www.guardanthealth
.com, accessed August 26, 2015.
1. Describe the Big Data issues in this case.
2. How does Flatiron use Big Data in its attempt to improve cancer

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