gpt buchman
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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
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Why monitor? :“Situation Awareness”
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Mica Endsley’s Original ConceptionHuman Factors 37: 32-64 (1995)
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SA involves more than “more data”
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SA “on the road”
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• 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
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Situation Awareness: Why does this “feel right”?
1. The perception of the data2. The comprehension of its meaning3. The projection of that understanding into the future in
order to anticipate what might happen
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This is NOT SA…
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Because excess, uncorrelated data constitute distractions…
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ICUs, Present Day
Loss of situational awareness is easy and common
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Staff cannot absorb more data. Really.
In today’s ICU, there is too much opportunity for error
Do distractions matter in critical care?An experimental study
Task: Alarm and vent checks Distraction:”I’m ready for handover!”
Miss rate, 25%
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The Four V’s of DataChallenge 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
VelocityDecision 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
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“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
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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
1977
(single dimension)
1977
(multidimension)
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Does this matter?
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 affectsdecision-making
Kumar A, et al. Crit Care Med 2006;34:1589
State= “sepsis”
1986
1986 1969
time→
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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
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
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Does this matter?
One of our ICUs, 3 years ago
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“Patterned, Personalized, Predictive”
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●Physiologic time series–Heart (EKG)
–Vasculature (Blood Pressure)
– Lungs (CO2)
–Brain (EEG)
–…
Detecting patterns at the bedside
Beat-to-beat heart rate
heartnt heart
nt
1
time, sec
ECG II,mV
Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466
Which is the healthy pattern?
Heart Failure
Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466
Which is the healthy pattern?
Heart Failure Heart Failure
Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466
Which is the healthy pattern?
Heart Failure Heart Failure
Atrial Fibrillation
Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466
Which is the healthy pattern?
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?
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●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
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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
22% reduction in mortality!
If data-driven prediction was a drug in this setting, that 22% reduction in mortality would make it a BLOCKBUSTER
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Reengineering Critical Care
Patients and Conditions
Population Specification Populations
Care Path Development Fully Specified Care Processes and Protocols
Current CareWorkflow
ModificationDelegation,Algorithms
Situation Awareness,Response
Caregiver and Patient Activation
Low Efficiency and Reliability High
• Recognize physiologic decompensation as it occurs
• Classify decompensation by actionable mechanism
• Mitigate decompensation by reversal of cause and supportive treatment
Situation Awareness,Response
• Harvest data in motion
• Real-time analytics
• Intuitive display
• Reliable interventions
Situation Awareness,Response
Center for Critical Care
Data in Motion and Real-Time ICU Analytics
Testing Novel Analytics
Synchrography
π R
adia
ns
•Situation Awareness:Current State
Philips eICU
ECCC
Coarse data
Fine data
Quasi-real-time display and analysis of physiologic data: architecture that we are currently using
Numerics and Waveforms (240 Hz)
~ 10 sec latency
Center for Critical Care
Architecture Example
Filter ECG data
RR Beat Detector
SampEn COSEn LDS
Database
BedMasterEx
Filter ICU Beds
Center for Critical Care
ECG with beat detection
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
mea
n of
the
stan
dard
dev
iatio
n
mean RR interval
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
CO
SE
n
Age (years)
Lake and Moorman, Am J Physiol, 2011Demazumder et al, Circulation 2013
Center for Critical Care
Real-time COSEn/AF Example
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Making the tools work: the eICU platform
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 LayerStrategy
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 staffer1. Consistent (normative) behaviors2. Verification that outcomes are achievedProcesses 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 lists2. Elimination of unnecessary standing orders3. Conversion to less expensive choice or route4. 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
5555Questions?