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

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Practicing Medicine in the ED

Multitasking

Communication challenges

Interruptions

Workflow disruptions

Hand-offs

Team work

Challenges: Information management

Workflow optimization

1

2

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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

7

Tracking in Other Industries

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Tracking Patients in Healthcare –

It is a Simple World

2001

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Electronic Whiteboard

Electronic Tracking Board

(Version 0.1a)

2001

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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

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ED Discharge

Application

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Discharge

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ED Whiteboard “Movie”

Original intent:

• Bridging downtime periods

Unintentional (positive) consequences

• Review: appropriateness of ED diversion episodes

• Malpractice claims

• State investigations

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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

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Pneumonia

Pneumonia ?

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Pneumonia Care Process

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Results

Min

ute

s

Month

23

Computerized Decision Support

ED Information System Infrastructure:

ED whiteboard

Applications / Research:

Pneumonia detection system

Asthma decision support system

Forecasting ED overcrowding

1

2

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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

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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

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Asthma Detection System

Method: Developed & implemented a Bayesian Network

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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%)

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Initiating Asthma Guideline

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Computerized Decision Support

ED Information System Infrastructure:

ED whiteboard

Applications / Research:

Pneumonia detection system

Pneumococcal vaccination system

Forecasting ED overcrowding

1

2

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death was “a result of gross deviations from the standard of

care that a reasonable person would have exercised in this situation.”

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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

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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

Forecasting ED Crowding

34 http://mac01xd.mc.vanderbilt.edu:8080/crowd-war/netica

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The “Divide” IT in Medicine Medical Informatics

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Computer scientist

IT manager

nurses

physicians

technicians

(Bio-) Medical

Informatics

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ED: Computerized Decision Support

fast

intuitive

rich in content

optimized for workflow

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Psycho-social, organizational, political aspects…

“Change Management”

Decision Support Systems

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Creating a Culture of Informatics

billing

informatics

physicians

hospital

registration

……

nursing

Ambulance

services

Lessons learnt

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- “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

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Acknowledgment

National Library of Medicine:

• R21 LM009747

• R21 LM009002

• T15 007450 (Biomedical

Informatics Training Program)

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Questions