1. 00
Better Health, Better Care, Lower Cost :
How telehealth and real-time analytics can help critical care
achieve this triple aim.
Emory Critical Care Center
Tim Buchman
22 March 2014
Disclosures: None Relevant to the Presentation

2. 11
Why monitor? :“Situation Awareness”

3. 22
Mica Endsley’s Original Conception
Human Factors 37: 32-64 (1995)

4. 33
SA involves more than “more data”

5. 44
SA “on the road”

6. 55
• Present all information in readily interpretable form, much as a
GPS receiver takes data from satellites and creates situational
awareness to provide a map back to health
Desiderata

7. 66
Situation Awareness: Why does this “feel right”?
1. The perception of the data
2. The comprehension of its meaning
3. The projection of that understanding into the future in
order to anticipate what might happen

8. 77
This is NOT SA…

9. 88
Because excess, uncorrelated data constitute
distractions…

10. 99
ICUs, Present Day
Loss of situational awareness
is easy and common

11. 1010
Staff cannot absorb more data. Really.
In today’s ICU,
there is too much opportunity for error

12. Do distractions matter in critical care?
An experimental study
Task: Alarm and vent checks Distraction:”I’m ready for handover!”
Miss rate, 25%

13. 1212
The Four V’s of Data
Challenge National Security Medicine Need
Volume
“We are... swimming in
sensors... and drowning
in data"
• Medical literature
doubling every 19 years
• Torrent of patient data
•Management of
large data
•Transform data to
information
Velocity
Decision timelines range
from days to seconds
Decision timelines range
from days to seconds
Rapid extraction and
presentation
Variety
Range of data types:
imagery, video, signals,
seismic data, field
reports, informants,
news reports
Physiology, lab tests,
physician notes,
interventions, patient
history
• Data association
• Information
representation
• Data provenance
Veracity
Military operations,
targeting, collateral
damage, rules of
engagement
Diagnosis & treatment of
patients, life & death
decisions, side effects,
complications, malpractice
concerns
High-confidence
decisions: Costs of
mistakes are high

14. 1313
“In the moment”—what is the current physiologic status of
my patient?
“Flowing data”—What is the trajectory of my patient?
Data (4Vs)-> Monitoring-> Situation Awareness

15. 1414
Challenge Medicine Need
Patterned Biology, and especially
pathobiology , is not random.
The state space is “lumpy”.
Treatments are aimed at lumps.
Not all patterns are evident to
clinicians. Management of
large data requires
meaningful pattern detection.
Personalized There are three time scales that
influence personalization:
•Inherited aspects (“forever”);
•chronic aspects (acquired,
“allostasis”);
•acute aspects (immediate
threats, “homeostasis”)
Data often convolve all three
time scales. Knowing the
patient‟s set-points and
dynamics around the set
points matters.
Predictive Prediction horizons related to
the time scales, e.g.
•Lifetime risk for cancer
•Obesity risk related to
environmental stress
•Arrhythmia risk due to
electrolyte disturbance
All three horizons require not
only situation awareness but
also a mechanism of alerting
when the risks change. By
extension, risk-management
implies ongoing “what-if”
scenarios.
The 3 P’s that Matter to Health Care

16. 1977
(single dimension)

17. 1977
(multidimension)

18. 1717
Does this matter?

19. Yes, it does. An example…
Duration of hypotension before initiation of effective
antimicrobial therapy = critical determinant of survival, so
knowing a single parameter contributing to the state affects
decision-making
Kumar A, et al. Crit Care Med 2006;34:1589
State= “sepsis”

20. 1986

21. 1986 1969

23. 2
Simulation Evaluation of an Enhanced Bedside
Monitor Display for Patients With Sepsis.
Giuliano, Karen; RN, PhD; Johannessen, Ann;
RN, MSN; Hernandez, Cheri; RN, MSN; EdD, CCRN
AACN Advanced Critical Care. 21(1):24-
33, January/March 2010.
DOI: 10.1097/NCI.0b013e3181bc8683

24. Simulation Evaluation of an Enhanced Bedside
Monitor Display for Patients With Sepsis.
Giuliano, Karen; RN, PhD; Johannessen, Ann; RN,
MSN; Hernandez, Cheri; RN, MSN; EdD, CCRN
AACN Advanced Critical Care. 21(1):24-33,
January/March 2010.
DOI: 10.1097/NCI.0b013e3181bc8683

25. 2424
Does this matter?

26. One of our ICUs, 3 years ago

27. 2626
“Patterned, Personalized, Predictive”

28. 2727
●Physiologic time series
– Heart (EKG)
– Vasculature (Blood Pressure)
– Lungs (CO2)
– Brain (EEG)
– …
Detecting patterns at the bedside
Beat-to-beat heart rate
heart
n
t heart
n
t
1
time, sec
ECG II,
mV

29. Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466
Which is the healthy pattern?

30. Heart Failure
Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466
Which is the healthy pattern?

31. Heart Failure Heart Failure
Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466
Which is the healthy pattern?

32. Heart Failure Heart Failure
Atrial Fibrillation
Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466
Which is the healthy pattern?

33. Heart Failure Heart Failure
Normal Atrial Fibrillation
Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466
Which is the healthy pattern?

34. 3333
● Nonstationarity
– Statistics change with time
● Nonlinearity
– Components interact in unexpected ways ( “cross-talk” )
● Multiscale Organization
– Fluctuations/structures typically have fractal organization
Patterns of health->Inferences about “not” health
Healthy Dynamics

35. 3434
What “not health” looks like
Goldberger, Peng,
Costa. Nature 1999;
399:461;
Phys Rev Lett 2002; 89 :
068102
Healthy dynamics are poised between
too much order and total randomness
The breakdown
“data patterns”
are similar in
various organ systems

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“Not health” : infection (sepsis)
• Two similarly septic
patients
• First 24 hr of data
shown
• During the second 24
hr, the patient on the
right developed multiple
organ failure and died
on day 12.
Pontet J, et al, J. Critical Care (2003) 18:156

37. 22% reduction in mortality!
If data-driven prediction was a drug in this setting, that 22% reduction in mortality
would make it a BLOCKBUSTER

38. 3737
Reengineering Critical Care
Patients and
Conditions
Population Specification Populations
Care Path Development Fully Specified Care Processes
and Protocols
Current Care
Workflow
Modification
Delegation,
Algorithms
Situation Awareness,
Response
Caregiver and
Patient Activation
Low Efficiency and Reliability High

39. • Recognize physiologic decompensation as it occurs
• Classify decompensation by actionable mechanism
• Mitigate decompensation by reversal of cause and
supportive treatment
Situation Awareness,
Response

40. • Harvest data in motion
• Real-time analytics
• Intuitive display
• Reliable interventions
Situation Awareness,
Response

41. Center for Critical Care
Data in Motion and
Real-Time ICU Analytics

43. Testing Novel Analytics

44. Synchrography
πRadians

45. •Situation Awareness:
Current State
Philips eICU
ECCC
Coarse data
Fine data

46. Quasi-real-time display and analysis of physiologic
data: architecture that we are currently using

47. Center for Critical Care
Architecture Example
Filter ECG data
RR Beat Detector
SampEn COSEn LDS
Database
BedMasterEx
Filter ICU Beds

48. Center for Critical Care
ECG with beat detection

49. Analytics, etc.
MIT-BIH: 12 beats q30 min for 24 hours
400 600 800 1000 1200
0
100
200
300
AF
NSR
CHF III and IV
CHF I and II
meanofthestandarddeviation
mean RR interval

50. Center for Critical Care
Coefficient of sample entropy (COSEn)
• An entropy metric
optimized to detect
atrial fibrillation in
very short records.
• It has ROC area 0.98
for detecting AF in
12-beat records.
0 20 40 60 80 100
-4
-3
-2
-1
0
AF male
AF female
NSR male
NSR female
COSEn
Age (years)
Lake and Moorman, Am J Physiol, 2011
Demazumder et al, Circulation 2013

51. Center for Critical Care
Real-time COSEn/AF Example

52. 5151
Making the tools work: the eICU platform

53. Better
Health
(outcomes that matter to
patients and families)
Better
Care
(High-reliability and
evidence based)
Lower
Costs
(Optimal configuration of
people and materials)
Right Care, Right
Now,
Every Time
Execution Layer
Strategy
Workforce
Operations Plan
Ensuring that every
test, drug, and
procedure add value to
care
Event driven Intervention
1. Multiple event initiation triggers: such as requests from
site (eLert button); admission/transfer event; detection
of deterioration or collapse; advisory from another eICU
staffer
1. Consistent (normative) behaviors
2. Verification that outcomes are achieved
Processes Matter
1. Bundles are “DO-LISTS”
2. Standard list-driven responses to common care
challenges in critical care
3. Responses are also “DO-LISTS”
4. eRN and eMD are PARTNERS in verifying adherence to
standard bundles: DO-LISTS completed
5. eRN and eMD are PARTNERS supporting standard
responses to common situations. DO-LISTS completed
6. eICU collaborates with ICU staff to verify desired results
are driven by standard bundles and interventions
7. Scheduled e-rounding for initiation and adherence to
“bundles”
8. Two-person e Staff confirmation of DO-LISTS completion
9. Remote support by eICU for bundle/response order sets.
Value derives from what we do, making a difference
1. Debridement of drug lists
2. Elimination of unnecessary standing orders
3. Conversion to less expensive choice or route
4. Avoidance of complications (drug interactions)
ECCC-eICU
Driver Diagram
Key Drivers Interventions

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●A lot of technology, rivers of data, lots of
expense → opportunities to create and deliver
value
●„In the moment descriptions‟ of „where the
patient is‟ would be very helpful (“situation
awareness”)
●Predictive analytics to drive towards treatment
goals would be very helpful
●Predictive analytics that fail (patients off the
predicted trajectory) even more important
Takehomes

55. 5454
Questions?