For IT Leaders in the healthcare and pharmaceutical industries looking to understand the impact of AI on their industries and how to overcome the ethical and efficiency challenges that come with its use.
23. Translational Medicine
Clinical Outcomes Prediction - Insight into Disease Progression
High throughput platforms like
sequencing provide millions of
features on molecular states
• Small number have clinical significance
Accurate predictions of clinical
outcomes from high-dimensional
molecular data
• Important and major challenge
ML / AI approaches current focus
Alberto Pascual
AI and Analytics Innovations,
Perkin Elmer
Cox
survival
model
… …
… …
… …
… … … …
… …
… …
… …
Risk backpropagation: sensitivity of predicted risk for each input feature and subject
• Partial derivatives of risk with respect to each input
Selecting features with highest rated features, we can identify common biomarkers.
24. Clinical Outcomes Prediction
Insight into Disease Progression
• Risk scores
calculated
across large
populations
• Click and see
survival
differences
• Discover
promising
treatments,
factors
25. High Content Screening
Automated microscopes capture
millions of images of cells
• Labelled with fluorescent stains, probes
Experiments profile effects of many
conditions
• Disturb cells – compounds, concentrations
• Quantify the visual response of cells
Screens are run with clear positive
and negative controls to ID ‘hits’
• Characterize a biological system and/or assay
Alberto Pascual
AI and Analytics Innovations,
Perkin Elmer
26. Today’s data analysis workflow
Images
Preprocessing
Features
• thousands
Hits
• compounds
close to positive
control
• (effective drug)
32. • Model trained on whole image, not individual
objects, no segmentation needed
• Label already defined in plate map, no
annotation required
• Returns prediction over whole image, e.g.
here this image contains the number 5
• Provides visualization of which cells /
subcellular regions differ between controls
AI-Automated High Content Screening
33. AI-Automated High Content Screening
AI-Automation Benefits
Addresses bottleneck in high-
content screenings
• Avoids custom segmentation and
feature extraction for each experiment
• Avoids complex assays, such as
organoids and co-cultures
Avoids feature-engineering focus
on subset of phenotypes
• May only measure nuclear translocation
of a probe; but in one condition it might
translocate to the membrane.
Hit Predictions
• Well, image
Image display
• Overlay
• Transparency
Filters
• Wells, plates
35. Cost of hospital acquired infections in
US is in excess of $45 billion
Readmissions lead to $41.3 billion in
costs
SSI #1 cause of HAI - 21.8%
SSI #1 cause of readmission in
surgical patients - 38.8%
1. Surgical Site Infection
36. 58% reduction in SSI in 2 years
74% reduction in SSI in 3 years
Estimated cost reduction during hospitalization:
$2.2 million for every 300 patients undergoing surgery
Gbegnon, A., et al. (2014). Predicting Surgical Site Infections in Real-Time. KDD Proceedings
Cromwell, J. (2018). Machine Learning and High Definition Care: More Powerful than a Pill. TIBCO NOW Proceedings, community.tibco.com
Outcomes — Better than a Pill
37. Data Flow
Data Entry / OR
Merge
Variables
R-
Predictive
model
TIBCO
Data
Scienc
e
GI
Data
Mart
EMR
Report
Results
Web / Intranet
Real Time Variables
Results + Variables
EMR Variables
Daily ETL
38. Important Variables, Model Summary
Variable K- NMI P-value
SSIs 1.000 < 0.001
*Duration 0.035 < 0.001
*EBL 0.043 < 0.001
*Ostomy 0.055 < 0.001
Procedure category 0.029 < 0.001
Surgeon 0.021 < 0.001
Total procedures 0.028 < 0.001
*Transfusion 0.028 < 0.001
*Wound class 0.014 < 0.001
Zip code 0.032 0.13
Location 0.013 0.11
OUTCOME
No SSI SSI
No SSI TN=88 FN=5
SSI FP=27 TP=6
* = variable collected in OR in real-time
COST
SSI $28,000
Intervention $500
Break-even 56 FP = 1 FN
41. The Operating Room is the most
expensive minute in the hospital
Every minute that an Operating Room
stands idle costs the hospital $625. And
one of the best ways to reduce idle time is
to identify, communicate, and mitigate
reasons for delays.
Spotfire analytics enables the optimization
of Surgery operations including on-time
starts, scheduling accuracy, and block
utilization.
2. Optimize Surgical Workflow
43. Surgery Analytics
Total
Cases Average Case Duration vs Scheduled
% On Time
StartsSurgeon
Kai
Gordon
Jonas
Nick
250
130
102
98
29%
15%
23%
34%
Surgeon Statistics
100
75
50
0
Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
# cases per month
44. Surgery Analytics
Total
Cases Average Case Duration vs Scheduled
% On Time
StartsSurgeon
Kai
Gordon
Kent
Irons
678
339
504
146
29%
15%
23%
34%
Surgeon Statistics
300
200
100
0
Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
# cases per month
45. Customer Success: Surgery Insights
32
operating
rooms
20,000
OR
patients
per year
ACS NSQIP
since 2007
Measurable results:
25% improvement in first case on-time
starts
20% increase in surgery block utilization
$450K estimated value of surgery time
“We use the Block Utilization Scorecards to identify
opportunities to optimize surgical capacity. The reports are
an invaluable management tool to quantify usage,
encourage timely block release, and to help us allocate
time to new surgeons.”
Surgery Business Manager
Saint Francis Hospital and Medical Center
617 bed
acute care
hospital
50. Toilet is fitted with technology that can detect a range of disease
markers in stool and urine, including those of some cancers, such
as colorectal or urologic cancers.
The device may appeal to individuals who are genetically
predisposed to conditions such as irritable bowel syndrome,
prostate cancer or kidney failure.
The toilet includes video cameras and motion sensing, combined
with deep learning CNNs; along with physical and molecular
analysis; to assess urine and stool physical characteristics.
The toilet automatically sends data to a cloud-based system for
safekeeping. In the future the system could be integrated into
health care provider’s system for assessment.
April 6, Nature Biomedical Engineering.
51.
52. Surgical site infection is the #1 cause of readmission in surgical patients. By
combining data from the electronic medical record, hospital operations
and real-time data from in the operating room, this AI/ML model
application predicts the risk of SSI in patients – before, during and after
surgical procedures.
One fascinating aspect of this problem is the very different cost of
intervention (~$500) compared with the cost of an SSI (~$28,000). As such,
the break-even cost of implementing an AI/ML model in this setting is
approximately 56 false positives to 1 false negative. In other words, ML
models can return a relatively large number of false positives, and still
create significant value.
This solution has emerged from the University of Iowa, and is currently in
scale-up testing across 6 hospital ORs. In this multi-center setting, OR data
is being shared via API-led services in cloud setting, With multi-OR data,
this AI/ML application will have ample opportunity to create significant
value, by borrowing strength across the facilities’ data.
