Date post: | 24-May-2015 |
Category: |
Health & Medicine |
Upload: | thearkvalais |
View: | 500 times |
Download: | 1 times |
Computerized Decision Support:
From Data to Information
Dominik Aronsky, MD, PhD
Dept. of Biomedical Informatics & Emergency Medicine
Vanderbilt University Medical Center Nashville Tennessee
and
ii4sm, Basel, Switzerland
2
? Clinicians:
MD,
RN,
admin
….
L
hunter & gatherer
Computerized Decision Support
Clinical
information
system
J
information manager
Sir William Osler
3
“Medicine is
a science of uncertainty
and
an art of probability”
Fundamental impact
on how we deal
with data in medicine
4
Computerized Decision Support
collect report
store 3.18 elevated
IF…THEN
…
ELSE
….
ENDIF
Decision Support Systems
5
Practicing Medicine in the ED
Multitasking
Communication challenges
Interruptions
Workflow disruptions
Hand-offs
Team work
Challenges: Information management
Workflow optimization
1
2
6
Computerized Decision Support
ED Information System Infrastructure:
ED whiteboard: “patient tracking”
Applications / Research:
Pneumonia detection system
Asthma decision support system
Forecasting ED overcrowding
1
2
11
ADT System
Registration
information
Disposition
information
Hospital
Bed Board
Application
Computerized
Patient Record
Computerized
Provider Order
Entry System
Radiology
System
Enterprise
Data
Warehouse
Whiteboard
Information
Radiology
Exam
Status
Bed
Request
Status of
Bed
Request &
Diversion
Status Patient
information Patient
location
Orders
Hospital Information System
ED Triage ED Order
Tracker
Triage
Information Order
Status
Whiteboard
Screenshot
Viewer
Whiteboard
Screenshots
ED Information System
Subject
Recruitment
Waiting
Room
ED Bed
Board
Registration
log
Treatment
Area
Staff
Roster
Recent
Discharges
ED Patient Tracking Board
15
ED Whiteboard “Movie”
Original intent:
• Bridging downtime periods
Unintentional (positive) consequences
• Review: appropriateness of ED diversion episodes
• Malpractice claims
• State investigations
16
Whiteboard:
Return on Investment
Direct benefit:
Additional revenues:
> $ 1.4 million / year
……
Indirect benefit: more accurate documentation
> $ 1.5 increased MD billing
JCAHO visit 2009
……
17
Computerized Decision Support
ED Information System Infrastructure:
ED whiteboard
Applications / Research:
Pneumonia detection system
Asthma decision support system
Forecasting ED overcrowding
1
2
23
Computerized Decision Support
ED Information System Infrastructure:
ED whiteboard
Applications / Research:
Pneumonia detection system
Asthma decision support system
Forecasting ED overcrowding
1
2
24
Asthma Detection: Objectives
Screening:
• Identify eligible patients early
• Screen all ED patients automatically
• Screen all ED patients in real-time
Workflow Integration:
• No additional data entry
• Inform clinicians before initial evaluation
Generalizability:
• Use only electronically recorded data
• Use only common data elements
Goal Alert clinicians about asthma guideline eligible patients
Overcome behavioral barrier of initiating guideline
25
Asthma Detection System
Computerized
Nurse Triage
• Coded chief complaint
• Coded asthma history
• Vital signs
• Demographics
Billing Record
Database
• Prior visit codes
– In- or outpatient
– ICD-9 = 493.*
Electronic
Medical Record
• Problem list (text)
– History of asthma
• Medication List (text)
– Asthma medications
27
Prospective Evaluation
• Study period: 4 weeks (Jan 27 - Feb 24, 2006)
• 2,006 encounters; 153 asthma patients (7.6%)
Sensitivity (fixed) Specificity Positive PV Negative PV
90% 89.9% 42.5% 99.1%
AUC = 97.1%
(CI: 95.5% - 98.1%)
29
Computerized Decision Support
ED Information System Infrastructure:
ED whiteboard
Applications / Research:
Pneumonia detection system
Pneumococcal vaccination system
Forecasting ED overcrowding
1
2
30
death was “a result of gross deviations from the standard of
care that a reasonable person would have exercised in this situation.”
32
Forecasting ED Crowding
Problem
No tools available to measure objectively
and manage proactively
Research opportunity
Using ED whiteboard data:
Develop a real-time prediction instruments to alert about impending ED
diversion
33
Forecasting ED Crowding
http://mac01xd.mc.vanderbilt.edu:8080/crowd-war/netica
Bayesian network:
Data collection from ED, OR, hospital, access center, etc., over 2 years
Identified 11 variables predictive of ED diversion: prospective evaluation
The “Divide” IT in Medicine Medical Informatics
36
Computer scientist
IT manager
nurses
physicians
technicians
(Bio-) Medical
Informatics
39
Creating a Culture of Informatics
billing
informatics
physicians
hospital
registration
……
nursing
Ambulance
services
Lessons learnt
40
- “Is it an important problem?” (Don Lindberg)
- Who cares?
- A very long way from design, implementation,
to evaluation.
- “Get (institutional) support”
- “If it can happen - it will” (Murphy)
- People – Process – Technology: understand the
data, workflow and processes
- “So what?” (Reed Gardner)
- “Change management” (Nancy Lorenzi)
- “Medical Informatics is a behavioral science.”
(Homer Warner)
… if ONE of them does not apply: Have Fun J
41
Acknowledgment
National Library of Medicine:
• R21 LM009747
• R21 LM009002
• T15 007450 (Biomedical
Informatics Training Program)