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

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.Infusion pump, intervention, and lab data not in system 2.Tools not readily available to quantify the effectiveness of treatments 3.No tools to perform waveform analysis 1.Informatics infrastructure not robust. 2.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