DigitalReasoning.com

Artificial Intelligence-Powered Oncology Software Value Creation & ROI Highlights

C A S E S T U DY

In 2016, HCA partnered with Digital Reasoning to

conduct a wide variety of data science experiments.

The most promising experiment was tested and

piloted. In 2017, a staged roll-out scaled our software

across the entire enterprise to provide 162 hospitals

with AI-powered cancer care. In one year, results

were impressive.

The overall system-wide volume growth was very

strong. The oncology volume growth rate tripled

from the prior year (2016), adding more than 10,000

cancer patients nationwide from Jan. 2017 to March

2018 (Figure 1).

Figure 1. Oncology Volume Growth

Validated net new oncology patients directly attributed to software (system-wide; Jan 2017 to March 2018)

Across all HCA’s markets, the results were positive.

For ease of comparison, Figure 2 shows an example

of a single market/MSA that compares to a health

system with one major hospital, cancer center and a

mix employed and affiliated physician groups. This

site saw 788 net new patients directly attributed to

the software from Jan. 2017 to March 2018 (Figure 2).

Figure 2. Oncology Volume Growth - Single MSA

Validated net new oncology patients directly attributed to software (Single market / MSA; 1 hospital & cancer center; Jan

2017 to March 2018)

Breast

# Retained

10,000

8,000

6,000

4,000

2,000

0Lung Complex

GIColon Total

10,227

1,2741,613

2,223

5,117

Breast

# Retained

1,000

800

600

400

200

0Lung Complex

GIColon Total

788

145

219282

142

DigitalReasoning.com

Figure 3. 12 Month Volume Growth – Single MSA

Validated net new oncology patients directly attributed to software (One major hospital with mix of owned and affiliated referring physician groups; Jan 2017 to Dec 2017)

Figure 3 provides an even easier comparison for

a single market oncology program by limiting

the same aforementioned market analysis to one

calendar year. 2017 saw our software discover, help

navigate and retain 540 net new oncology patients

for that hospital and cancer center (Figure 3).

Improved productivity and speed-to-treatment

show how AI helps Nurse Navigators via dynamic

triage, prioritization, and care complexity models.

Prior to implementation, navigators and care

coordinators would comb through pathology reports

to find positive results, triage, match to pathways

and document follow-up – all now automated. After

implementing our solution, the amount of time

spent directly interacting with patients doubled

(Figure 4). Navigator caseload increased by 50 to

250%, equivalent to hiring 114 new navigators. By

spending more time navigating and coordinating

care, more patients were able to receive the benefits

of nurse navigation and average speed to treatment

decreased by 5 days.

Figure 4. Navigator Time Analysis

% Time Spent (Before) % Time Spent (After)

Navigators’ Time Spent:Pre/Post Study

PRE - DIGITAL REASONING POST - DIGITAL REASONING

Direct Patient Interaction Physician Relations

Patient ID & Triage Data Entry

70% of time report reading, prioritizing, classifying into

pathways and documenting- non-value add activities.

65% of time now spent on outreach, patient navigation

and coordination with physician offices - value

accretive activities.

10%

10%

30%

35%

25%

40%30%

20%

C A S E S T U DY

JAN

# Retained in 2017

60

50

40

30

20

10

0FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

5155

59

43

373939

53

333236

63

ROI Highlights

DigitalReasoning.com

Oncology volume growth rate 5X

2X the amount of time spent directly

interacting with patients

Average speed to treatment decreased

by 7 days

Improved productivity and speed-to-treatment