Transcript Critical Care Bioinformatics at Columbia University Medical Center

Critical Care Bioinformatics at
Columbia University Medical Center
J. Michael Schmidt, PhD
Neurological Institute of New York
Columbia University College of Physicians and Surgeons
Disclosures
• CMA Microdialysis: Speaker’s Bureau
Clinical Needs for ICU Informatics System
• Understand patient physiology
• Evaluate effectiveness of treatments
• Potential for automatic processing of data for
diagnosis and prognosis
• Support clinical decision making
Also noteworthy…
• Digital Note
• Quality Assurance
• Clinical Research support
Meeting the Need
3 problems + 1
• Data Collection
• Data Interrogation
• Data Analysis
• Best use of data for clinical decision
making
Columbia Neuro-ICU System
The
GOOD
• 18 bed ICU
• Useful for threshold
based alarms and nursing
workflow.
• Data only saved for 72
hours then gone.
• Not all device data can go
into system
• Difficult to visualize data
• Saves parameter data
every 5 seconds SQL
database
• Saves visible waveform
data
• Requires ~ 300 GB /
year data storage
• Data from many
bedside devices do not
interface with the
bedside monitor
• If they do interface,
often ones want greater
precision or all available
parameters
Data Visualization
• Visualize data to
track effects of
interventions
• Can visualize
physiologic
relationships
• Multi-parameter
event detection
• Collect parameter and
waveform data
• Collecting data from
devices that do not go
into monitor
• Able to visualize data
over extended periods
of time
• Can elucidate
relationships among
different parameters
• Data stored
permanently
The
BAD
ICP
Osmolal-Serium
3% Saline
Mannitol 20%
340
100
400
2000
75 ml/hr
mmHg
320
50
300
200
1000
280
0
260
0
00:00
1/9/2007
00:00
1/10/2007
1/11/2007
0
ICP
Osmolal-Serium
3% Saline
Mannitol 20%
340
100
400
2000
75 ml/hr
mmHg
320
50
300
200
1000
280
0
260
0
00:00
1/9/2007
00:00
1/10/2007
1/11/2007
0
• Labs available via copy and paste
• Infusion pump data must be
manually entered
• Can’t get it from EMR
• Can’t get it from the pump
• Evaluate effectiveness of
interventions
– Did the intervention have the
desired effect?
– How quickly?
– Does repeated administrations
work equally effectively?
• Tools to evaluate waveforms are
lacking in our system.
• Phase analysis between MAP and
ICP to determine autoregulation
status.
• Heart rate variability analysis can
be used to understand
dysautonomia.
The
UGLY
Network Performance
• Equinox boxes communicate with the
server using UDP
• Must be in contact with server at
least once every 60 seconds else
goes offline
• Must be rebooted to reestablish the
connection
• Some will not go back
Network Reliability
ICU Pilot computers can’t always
connect to servers.
• Server and computer operate on
different networks. Connection can fail
(can’t ping server).
• Network Drive mapping fails
– Map network drive
– Setup ICU Pilot to use mapped network
drive for use as the database
• Start SQL client, which fails to connect
sometimes, or gets corrupted and needs
reinstall
Standard Informatics infrastructure for hospital
level system
Maintenance: Server failure lead to corrupted database
• Backup was not properly done
• Used recovery tool to get data – currently running
scripts to recover the data will take 2 years to
complete (need to write special program to make that
faster). Who is going to do that?
Standard Informatics infrastructure for hospital
level system
Server switch to Virtual server plus problems (OLTP versus OLAP)
• I/O disk write time delays as databases got large
– Need separate system for data processing
• Didn’t work the same.
– SQL didn’t load before bedmaster causing problems.
• Processor didn’t necessarily always have enough resources to meet
demand
Summary of Columbia experience
1. Store data from the
patient monitor
forever in open SQL
format
2. Can get data from
devices that do not
plug into the patient
monitor or do so
inadequately
3. Able to visualize
relationships among
parameters
1.
2.
3.
Infusion pump,
intervention, and
lab data not in
system
Tools not readily
available to
quantify the
effectiveness of
treatments
No tools to
perform waveform
analysis
1.
2.
•
•
•
Informatics
infrastructure not
robust.
Lead to:
Connectivity
failures
Backup systems
failed
Virtual server not
up to task.
• Other data systems not connected
– Have to copy and paste in lab values
– Infusion pump data must be manually entered
• Can’t get it from EMR
• Can’t get it from the pump
• No good tools to evaluate effectiveness of interventions
– Did the intervention have the desired effect?
– How quickly?
– Does repeated administrations work equally effectively?
• No tools for waveform analysis