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Medical Informatics
Ida Sim, MD, PhD
February 17, 2004
Division of General Internal Medicine, and
Graduate Group in Biological and Medical Informatics
UCSF
Copyright Ida Sim, 2004. All federal and state rights reserved for all original material presented in this course
through any medium, including lecture or print.
February 17, 2004: I. Sim
Overview
Medical Informatics
Outline
• Introduction
• Course Goals and Overview
• Computing Infrastructure for Health Care
– data storage
– networking
February 17, 2004: I. Sim
Overview
Medical Informatics
Introduction: Ida Sim, MD, PhD
• PhD in Medical Informatics, Stanford
• Assistant Professor
– General Internal Medicine
• Associate Director for Medical Informatics
– Program in Biological and Medical Informatics
• Interests
–
–
–
–
computer-assisted clinical decision-making
electronic scientific publication
economics of health information technology
meta-analysis, and evidence-based decision making
February 17, 2004: I. Sim
Overview
Medical Informatics
Informatics and Clinical Care
• Institute of Medicine (IOM) report on med errors
– calls for electronic prescribing
– Leapfrog initiative: financial rewards for hospitals
that use e-prescribing
• IOM report on “quality chasm”
– “A nationwide effort is needed to build a technologybased information infrastructure that would lead to the
elimination of most handwritten clinical data within
the next 10 years…”; asks for $1 billion for health
informatics
• Rise of consumer health informatics
– consumer may be next “driver” for health care
February 17, 2004: I. Sim
Overview
Medical Informatics
Informatics and Clinical Research
• Human genome findings will need to be
translated into population and clinical medicine
• RCTs now a $3.6 billion business (C. Scott, 7/00)
– in 1988, 95% of RCTs conducted by academics
– now, over 80% conducted by industry
– industry is seeking increased efficiency in a very
fragmented and complex business
• Computers needed to help translate research
results to practice
– over 10,000 RCTs indexed in 1999 Medline
February 17, 2004: I. Sim
Overview
Medical Informatics
Yet...
• Only ~12% of outpatient clinics have an EMR;
only 30% of hospitals have a website
• Much clinical research is still done using chart
abstraction and paper forms
• Medicine and medical research is information
intensive, but
– health sector invests only 2.5% of gross
revenue on information technologies (Gartner
Group, 2003)
– vs. 6% in comparable information-intensive
sectors (e.g., banking)
February 17, 2004: I. Sim
Overview
Medical Informatics
Course Goals
• Be familiar with core concepts in medical
informatics: vocabularies, interchange
standards, decision support systems
• Understand key concepts about electronic
medical records (EMRs) and data warehouses,
and their uses for clinical research
• Understand the clinical, economic, and social
context in which information technologies are
being developed and deployed in health care
February 17, 2004: I. Sim
Overview
Medical Informatics
Context
• Few students working directly in informatics
• Desired outcome
– that you be able to understand and converse
with “tech” folks
– that you have a better chance of recognizing
and taking advantage of opportunities in
• using informatics for your research work
• participating in innovative informatics projects
February 17, 2004: I. Sim
Overview
Medical Informatics
Course Overview
• 5 Lectures
– PowerPoint file up few days before lecture
– class participation expected
• Guest lecture: Palo Alto Medical Foundation
– Thurs. Mar. 4, 1:00 to 2:30 pm
• Assignments
– 5 homeworks, no final exam
• Office “hours”: [email protected]
– http://www.epibiostat.ucsf.edu/courses/schedule/med_informatics.html
February 17, 2004: I. Sim
Overview
Medical Informatics
Outline
• Introduction
• Course Goals and Overview
• Computing Infrastructure for Health Care
– data storage
– networking
February 17, 2004: I. Sim
Overview
Medical Informatics
Computing Infrastructure
B&T
Clinic 2004
Logician
EMR
Front
Desk
HealthNet
(HL-7)
Medical
Information
Bureau
Radiology
Lab
Pharm Benefit
Manager
Specialist
Internet
UniLab
Intranet
Walgreens
Phone/Paper/Fax
Understanding the Infrastructure
• Clients and servers (the components)
• Data storage (how data is stored)
– flat file versus relational model
• Networking (how data gets back and forth)
February 17, 2004: I. Sim
Overview
Medical Informatics
Client/Server Model
Clients
Web
Server
• Computers can be servers and/or clients
• Web server “serves” web pages to “clients,” who
view these pages using a browser
– MS Internet Explorer or Netscape Communicator
February 17, 2004: I. Sim
Overview
Medical Informatics
Internet Clients and Servers
nci.nih.gov
myhome.com
cochrane.uk
amazon.com
Main Trunk Cables
aol.com
pacbell.net
medicine
itsa
ucsf.edu
LAN
February 17, 2004: I. Sim
Overview
Medical Informatics
at home
Data Storage
• Computers can help us
– store, retrieve, query, compute, and report data
• For this to happen, we must describe the data
in such a way that the computer
– “understands” the data
– can manipulate the data
• e.g., sort them, graph them, add numbers, perform
analyses
– can retrieve the data for later use
February 17, 2004: I. Sim
Overview
Medical Informatics
“Describing” the Data
• The extent to which the computer can help you
manage your data depends on how well you
described your data to it
• In JIFE database example, did you describe your data
– correctly: did Baby Oscar have jaundice?
• accurate, clear, consistent, etc.
– cleanly: with as little redundancy as possible
• don’t want Baby Oscar’s birthdate in 3 separate places
– sufficiently: all that is needed for later analyses
• captured ethnicity for anticipated analysis by ethnicity?
• what later analyses do you have in mind?
– understandably: for humans and for computers
February 17, 2004: I. Sim
Overview
Medical Informatics
“Describing” Data: To Humans
• For understanding and communication
– via a system for codifying meaning
• English language, mathematical notation,
– making the “code” itself concrete
142 108 96
3.9 24
• skywriting, a graph drawn on a sandy beach
• text on paper, an oil painting, lecture on audiotape
• For later retrieval
– a permanent or semi-permanent physical
embodiment of the description
• papers in a file cabinet, museum of runes
February 17, 2004: I. Sim
Overview
Medical Informatics
“Describing” Data: To Computers
• For understanding and communication
– via a data model for describing data to computers
• akin to “German prose on paper” or “Olde English
epic poetry on audiotape”
– standard data models to choose from include
• flat file
• relational
• object-oriented
• For later retrieval
– storage as 1’s and 0’s in
• random access memory: short term, until power off
• permanent memory on a hard disk: longer term
February 17, 2004: I. Sim
Overview
Medical Informatics
Data Model Choices
• Data model should be the best that allows you to
– do what you want to do with the data
• query, manipulate, share, merge
– handle the amount of data that you have
– handle the type of data that you have
• prose, numbers, xray images, audio files, etc.
• Standard data model choices
– flat file: one long list of text characters
– relational: tables of columns and rows
– object: data arranged in conceptual groups
• Usual clinical research choice is flat file/relational
• Clinical databases are increasingly becoming
relational
February 17, 2004: I. Sim
Overview
Medical Informatics
Flat File Model
• For understanding and communication
– data are encoded as strings of characters
• one character at a time, no concept of a “word” or “sentence”
– so, computers cannot understand the meaning of data
• “male” is just a string of 4 characters
• For storage
– in a single file (e.g. a Word or STATA file)
– “flat” structure: start with one baby’s data, and
keep adding data baby by baby
• Like writing all your data from beginning to
end onto one piece of paper and putting that
paper into your file drawer
February 17, 2004: I. Sim
Overview
Medical Informatics
Flat File Examples
Word Text File
Carson Jackson
Hannah Hillary
Jonas Oscar
1
2
1
3/2/05
1/2/05
1/1/05
STATA File
Carson,Jackson,1,3/2/05,J,5
Hannah,Hillary,2,1/2/05,C,2
Jonas,Oscar,1,1/1/05,J,3
February 17, 2004: I. Sim
Overview
Medical Informatics
J
C
J
5
2
3
Database Schema
• A database’s schema is a compact summary
description your database’s contents
• Database schema = description of database
– what type of data
– how that data is conceptually arranged
• E.g., schema for research paper
– intro, methods, results, discussion (text)
– tables (table) and figures (graphic)
– pictures (image)
February 17, 2004: I. Sim
Overview
Medical Informatics
Flat File Data Schema
Word File
Carson Jackson
Hannah Hillary
Jonas Oscar
1
2
1
3/2/05
1/2/05
1/1/05
J
C
J
5
2
3
• Which fields are
– first name, DOB, case status, last name, exam score,
gender
• Flat file schemas are implicit
– is in the mind of whoever is entering the data
– can change from record to record
• maybe first baby’s name is Jackson Carson and the second
baby’s name is Hannah Hillary
February 17, 2004: I. Sim
Overview
Medical Informatics
Flat File Advantages
• Easy, just start entering data, doesn’t need any
preliminary database work or knowledge
• Can do with any word processor
– Word, WordPerfect, editor for STATA or SAS,
Excel, SimpleText
• Cheap
• Can be exported to analysis programs
• Portable
– almost all programs can read in a flat file
February 17, 2004: I. Sim
Overview
Medical Informatics
Flat File Disadvantages
• Description of the data isn’t clear, and may not
even be understandable
– meaning of the data items is not explicit
• unclear that the last column is the neuropsych exam
score
– structure is not explicit
• does last name always precede first name?
• Inefficient and prone to error for representing
repeating data fields
– e.g., if each baby has more than one neuropsych
exam score
February 17, 2004: I. Sim
Overview
Medical Informatics
Repeating Data in Flat File Model (1)
Word Text File
Carson Jackson
Hannah Hillary
Jonas Oscar
Carson Jackson
Jonas Oscar
Jonas Oscar
1
2
1
2
1
1
3/2/05
1/2/05
1/1/05
3/3/05
1/3/05
1/1/05
J
C
J
J
J
J
5
2
3
4
4
3
• Jackson/Carson’s gender might change from
one record to another, or...
February 17, 2004: I. Sim
Overview
Medical Informatics
Repeating Data in Flat File Model (2)
Word Text File
Carson Jackson
Hannah Hillary
Jonas Oscar
1
2
1
3/2/05
1/2/05
1/1/05
J
C
J
5
2
3
x
4
4
3
• Implicit structure to repeating data
– is the nth column always the nth neuropsych
exam score?
• can a missed exam be denoted by an X?
• Whatever data schema there is, may vary from
record to record
February 17, 2004: I. Sim
Overview
Medical Informatics
Flat File Disadvantages (cont.)
• Inefficient at finding a particular baby
– must look at records one by one from beginning
to end
– no guarantee that you have found all the
information for that baby unless you look all
the way to the end
• Inefficient at manipulating data
– to see list of male babies, must make a new file
• Difficult to share since the database itself gives
no clues about what data is in each field
February 17, 2004: I. Sim
Overview
Medical Informatics
Summary of Flat File Data Model
Factor
Flat File
Humanunderstandable
ComputererstandableΣ
Complexity of data
Querying
Manipulating
Amount of data
Type of data
Sharing and merging
Frequently Not
February 17, 2004: I. Sim
Relational
No
Simple
Inefficient
Inefficient
Small
Text, Numbers
Very Difficult
Overview
Medical Informatics
Object
When Are Flat Files Useful?
• For a small, simple, “quick and dirty” databases
– few data items, small number of records
– one set of predictors and one set of outcomes per
participant/subject
• i.e., no repeating data fields
• i.e., only one-to-one relations, no one-to-many
– quick and dirty
• for very few users (i.e. just you)
• you’re not planning on reusing this database later
• you’re not planning on sharing this database now or
later
February 17, 2004: I. Sim
Overview
Medical Informatics
Flat Files in Clinical Care
• Really no reason nowadays to build a flat file
system for clinical care databases
– lots of one-to-many relationships
• Many flat file systems are leftover from early
days of computerization
– old VA system in Mumps (ANSI Standard M)
– STOR, a pioneering system in the 1970s
• “STOR does not store data in a relational database - it is a flat
file data structure. To obtain it's data, I run queries off it and
download them into FileMaker Pro or Microsoft Access or
Excel and then manipulate the data into a form more easy to
read for providers.” Tirzah Gonzalez, DGIM STOR analyst
February 17, 2004: I. Sim
Overview
Medical Informatics
Relational Data Model
• Data are arranged in tables made up of
columns and rows
– the columns are the types of data
• fixed number of columns
• each column has a unique name (e.g., FirstName)
• has a “domain” of values that may appear in that
column
– domain=text for FirstName, domain=positive integers for
age
– the rows are the records themselves
• there can be an arbitrary number of unique unnamed
rows (i.e., the table can be arbitrarily long)
February 17, 2004: I. Sim
Overview
Medical Informatics
Flat File Admissions Database
Robert Lee, 000-01-001, M, 09-Jul-70,B/T Healthnet
31-Dec-94 to 12-Jan-95, admitted to Medicine with Acute MI, discharged with
Acute MI, COPD, Diabetes, CHF
27-Mar-96 to 31-Mar-96, admitted to Medicine with COPD, discharged with
Pneumonia, COPD, CHF, Diabetes
June Smith, 000-01-002,F,22-Oct-25,Medicare
02-Feb-95 to 16-Feb-95, admitted to Surgery for Total Hip Replacement,
discharged with THR, Acute MI, Diabetes
27-Feb-95 to 20-Mar-95, admitted to Medicine with Acute MI, discharged with
Acute MI,VF Arrest, Diabetes
Marissa Perez,000-01-003,F,13-Jun-57,B/T Pacificare
19-Nov-97 to 23-Nov-97, admitted to Gyn for metrorrhagia, discharged with
uterine fibroids, Diabetes
February 17, 2004: I. Sim
Overview
Medical Informatics
Review of Problems with Flat Files
•
•
•
•
Implicit structure, implicit data schema
Schema may change from record to record
Inefficient for finding a particular admission
Inefficient for pulling out all Acute MI
admissions
• Difficult to share or to understand later
• etc.
February 17, 2004: I. Sim
Overview
Medical Informatics
Relational Admissions Database (#1)
InpatientMasterTable
ID
Name
000-01-001
Lee
000-01-002
Smi th
000-01-003
Perez
AdmissionsTable
ID
Admit
Service
000-01-001
000-01-001
000-01-002
000-01-002
000-01-003
Med
Med
Surg
Med
Gyn
Sex
M
F
F
Birthdate
09-Jul-70
22-Oct-25
13-Jun-57
Insurance
B/T Healthnet
Medicare
B/T Pacificare
Admit Date
Discharge
Date
Admit
Diagno sis
31-Dec-94
27-Mar-96
03-Feb-95
27-Feb-95
19-Nov-97
12-Jan-95
31-Mar-96
16-Feb-95
20-Mar-95
23-Nov-97
Acute MI
COPD
THR
Acute MI
Menorrhagia
Principal
Discharge
Diagno sis
Acute MI
Pneumonia
THR
Acute MI
von Will ebrand's
Seconda ry
Discharge
Diagno ses
COPD
COPD
Acute MI
VF Arrest
Dia betes
Seconda ry
Discharge
Diagno ses
Dia betes (CHF)
CHF (Diabetes)
Dia betes
Dia betes
• Doesn’t handle secondary diagnoses very well
– for many admissions, there are either too few or
too many columns
February 17, 2004: I. Sim
Overview
Medical Informatics
Relational Admissions Database
InpatientMasterTable
ID
Name
000-01-001
Lee
000-01-002
Smi th
000-01-003
Perez
AdmissionsTable
ID
Admit
Service
000-01-001
000-01-001
000-01-002
000-01-002
000-01-003
Med
Med
Surg
Med
Gyn
Sex
M
F
F
Birthdate
09-Jul-70
22-Oct-25
13-Jun-57
Insurance
B/T Healthnet
Medicare
B/T Pacificare
Admit Date
Discharge
Date
Admit
Diagno sis
31-Dec-94
27-Mar-96
03-Feb-95
27-Feb-95
19-Nov-97
12-Jan-95
31-Mar-96
16-Feb-95
20-Mar-95
23-Nov-97
Acute MI
COPD
THR
Acute MI
Menorrhagia
Seconda ryDischargeDiagnos isTable
ID
Admit Date
000-01-001
31-Dec-94
000-01-001
31-Dec-94
000-01-001
31-Dec-94
000-01-001
27-Mar-96
000-01-001
27-Mar-96
000-01-001
27-Mar-96
000-01-002
03-Feb-95
000-01-002
03-Feb-95
000-01-002
27-Feb-95
000-01-002
27-Feb-95
000-01-003
19-Nov-97
Seconda ry Discharge Diagnos es
COPD
Dia betes
CHF
COPD
CHF
Dia betes
Acute MI
Dia betes
VF Arrest
Dia betes
Dia betes
Principal
Discharge
Diagno sis
Acute MI
Pneumonia
THR
Acute MI
von Will ebrand's
Relational Database Schema
• The schema is the names of the tables and their
column names
– InpatientMasterTable(ID,Name,Sex,Birthdate,Insura
nce)
– AdmissionsTable(ID,AdmitService,AdmitDate,Disc
hargeDate,AdmitDiagnosis,PrincipalDischargeDiag
nosis)
– SecondaryDiagnosisTable(ID,AdmitDate,Secondary
DischargeDiagnosis)
• The schema is explicitly stated
– in a language called Structured Query Language
(SQL)
February 17, 2004: I. Sim
Overview
Medical Informatics
Pros of Relational Model
• Database is always consistent
– built-in prevention against insert, delete, and
update errors
• Based on formal set theory
– normalization saves storage space
– normalization supports more efficient searching
through the data
– standard schema definition and query language
available
• SQL=Structured Query Language
• Available as reliable commercial software
systems...
February 17, 2004: I. Sim
Overview
Medical Informatics
Cons of (Traditional) Relational Model
• Profusion of tables and keys can be confusing
– higher organizing principles are implicit
• e.g., a patient has only one primary diagnosis but
may have several secondary diagnoses
• Inefficient at representing complex semantic
relationships
– e.g., ICU admission is a type of admission
• Unable to capture certain types of data
– nested data
• e.g., admit diagnosis = MITable(location,Qwave,CHFStatus)
– images and other multimedia
– metadata (e.g., “Exam score corrected May 2nd, 2000”)
February 17, 2004: I. Sim
Overview
Medical Informatics
Summary of Relational Data Model
Factor
Flat File
Relational
Humanunderstandable
ComputererstandableΣ
Complexity of data
Querying
Manipulating
Amount of data
Type of data
Sharing and merging
Frequently Not
Yes
No
Yes
Simple
Inefficient
Inefficient
Small
Text, Numbers
Very Difficult
Complex
Efficient
Efficient
Very Large
Text, Numbers
Least Difficult
• We don’t normally think in tables...
February 17, 2004: I. Sim
Overview
Medical Informatics
Object
Object Data Model
• Data arranged in conceptual groups, with
prototypes and their attributes
Patient
-name
-gender
-b-day
-address
-insurance
-primary MD
-etc
February 17, 2004: I. Sim
Admission
Diagnosis
-admit date
-code
-discharge date
-attending MD
-admit, primary,
secondary dx
-etc.
-name
-modifiers
Overview
Medical Informatics
Inheritance
• Special classes of data can be modeled
efficiently
Admission
-admit date
-discharge date
-attending MD
-admit, primary,
secondary dx
-etc.
is-a
ICUAdmission
-APACHE score
-ICU attending MD
February 17, 2004: I. Sim
Overview
Medical Informatics
Pros and Cons of Object Model
• Pros: Can represent very complex data
types and data relationships
– images, audio, inheritance, procedural data
(e.g., how to draw a graph of given data)
• Cons: Very complex
– inefficient since no formal mathematical
basis for storage and querying
– more difficult to share since data is more
complex
– commercial systems are flaky
February 17, 2004: I. Sim
Overview
Medical Informatics
Summary of Data Models
Factor
Flat File
Relational
Object
Humanunderstandable
ComputererstandableΣ
Complexity of data
Querying
Manipulating
Amount of data
Type of data
Sharing and merging
Frequently Not
Yes
Partially
No
Yes
Yes
Simple
Inefficient
Inefficient
Small
Text, Numbers
Very Difficult
Complex
Efficient
Efficient
Very Large
Text, Numbers
Least Difficult
Very Complex
Inefficient
Inefficient
Large
All
Rather Difficult
February 17, 2004: I. Sim
Overview
Medical Informatics
Summary of Data Model Choices
• Generally, use the RELATIONAL MODEL for storing
clinical and clinical research data
• Exceptions
– you have only one-to-one relations in your database,
which you are not intending on sharing or reusing
• use a flat file (e.g., Excel, STATA)
– you need to store complex, multimedia data
• consider an extended-relational database (aka object-relational)
– database designed using the object model
– data is stored and queried as a relational database
• but could probably work around this using newer standard
relational databases
February 17, 2004: I. Sim
Overview
Medical Informatics
The Model vs. The System
• Data model
– the generic abstract structure of the information
• domain independent, not a “product” per se
• Database management system
– is a real-world program that you can buy
Data Model
Flat file
Relational
Object
Example Database Management Systems
Small Scale (PC’s)
Large Scale (Mainframes)
Filemaker Pro
VA system (enhanced)
Access, MySQL
Oracle, Sybase, MySQL,
SQL Server
Informix
Objectivity
– stores information using a data model
– provides additional functionality
February 17, 2004: I. Sim
Overview
Medical Informatics
DBMS Features for System Selection
• Memory capacity
• Multi-user support and transaction
management
• Data entry forms
• Triggers and rules
• Security
• Backup and archiving
February 17, 2004: I. Sim
Overview
Medical Informatics
Other DBMS Features
• Security
– can have logins and different levels of access
• only database administrator can change data schema
• data entry person can only enter data into certain fields
• Backup and archiving
– safer if this is automatically done on a regular schedule
– standard for health care data is at least 7 years of
archiving
February 17, 2004: I. Sim
Overview
Medical Informatics
Computing Infrastructure
B&T
Logician
EMR
Front
Desk
HealthNet
Radiology
Medical
Information
Bureau
Lab
Pharm Benefit
Manager
Modern U.
Specialist
Internet
UniLab
Intranet
Walgreens
Phone/Paper
HealthSystem Minnesota
• 1.6 million patient visits per year, 270,000
capitated lives, 460 physicians, 4700
employees, 31 clinics, and over $400 million
in revenues (1998)
– over 50 computer and 50 paper systems
• “Maintaining the consistency of these tables in
various systems is impossible and creates
enormous problems for understanding let alone
improving our performance.”
February 17, 2004: I. Sim
Overview
Medical Informatics
Summary on Data Storage
• How a computer stores information can have
serious implications for
–
–
–
–
data integrity
speed
ability to share data
security (via enhancements available to
relational database management systems)
• Relational model is generally the best choice
for storing clinical data
– but making sense of multiple databases is still
non-trivial
February 17, 2004: I. Sim
Overview
Medical Informatics
Understanding the Infrastructure
• Clients and servers (the components)
• Data storage (how data is stored)
– flat file versus relational model
• Networking (how data gets back and forth)
February 17, 2004: I. Sim
Overview
Medical Informatics
Internet = Network of Networks
nci.nih.gov
myhome.com
cochrane.uk
amazon.com
Main Trunk Cables
aol.com
local trunk cable
through Berkeley
pacbell.net
medicine
or use a commercial
Internet Service Provider (ISP)
itsa
via dial--up
or DSL
ucsf.edu
LAN
dial-in to itsa.ucsf.edu via modem
February 17, 2004: I. Sim
Overview
Medical Informatics
at home
Networking Media
• Copper wire (twisted pair)
– generally not well suited to high bandwith transmission
• Coaxial cable
– can carry high frequencies without leak
– cable industry has “more bandwidth by accident than
the telephone people have on purpose”
• Fiber optic
– highest bandwidth, but expensive and de novo
• Curb-to-home problem
– only phone and coax cables now run from curb to home
– hybrid fiber/coax cables and approaches coming
February 17, 2004: I. Sim
Overview
Medical Informatics
Networking Bandwidth
Sim: Computer Infrastructure
Connection Type
Phone mod em
ISDN
1/26/00
Speed
(in kilo bits per second, Kbps)
14.4, 28.8 , or 56
CXR
(12 Mbits)
CT Scan
(5.2 Mbits)
64 to 128
3 min
1.4 mi n
8 sec
3.3 sec
T1
1,000
Spread-spectrum RF
2,000
ADSL
Cable modem
6,000 to 7,000
to 10,000
Infrared
16,000
Etherne t
10,000
100,000 on some sytems
45,000
155,000 ove r copper w ir es
622,000 ove r fiberoptic
52,000 to 9,953,000
T3
ATM
SONET
February 17, 2004: I. Sim
Overview
Medical Informatics
What Happens over Network Cables?
nci.nih.gov
myhome.com
cochrane.uk
amazon.com
Main Trunk Cables
aol.com
pacbell.net
medicine
itsa
ucsf.edu
LAN
February 17, 2004: I. Sim
Overview
Medical Informatics
at home
Networking Protocols
• Protocol = grammar for machines talking to
each other
– e..g, protocol for the WWW = http
• WWW vs. Internet vs. Intranet vs. VPN
– WWW = http-based communication on Internet
– Intranet = network of networks restricted to
within an organization (usually implies only
http-based communication)
– Virtual Private Network is an Intranet that
physically uses part of the Internet
• Health-specific protocols needed (e.g., HL-7)
February 17, 2004: I. Sim
Overview
Medical Informatics
Significant Issue in HealthCare
• UCSF spent ~$100 million on networking in
the late 1990’s
• Health-specific networking “grammars” add to
complexity of infrastructure
• Many interactive services (e.g., realtime
teleconsultation) would need more bandwidth
than is commonly available
February 17, 2004: I. Sim
Overview
Medical Informatics
Conclusions
• Computing infrastructure for health care is very
complex, very fragmented, has lots of gaps, and
is saddled with lots of old technology
• Clinical (and research) databases are generally
more reliable and efficient if they are relational
rather than flat file
• Networking involves both hardware (cable) and
software (protocols); bandwidth limits wide
deployment of interactive technologies
February 17, 2004: I. Sim
Overview
Medical Informatics
Teaching Points
• If you want computers to do “smart” things with
your data (e.g., retrieve, sort, graph), you must
describe that data very explicitly
– what you don’t say the computer does not know
• Data models are standard abstract ways of
describing data
• To send data back and forth, you also need very
explicit “grammars” for communication
• Today = how of infrastructure; next class = what
February 17, 2004: I. Sim
Overview
Medical Informatics
References
• L.T. Kohn, J.M. Corrigan, M.S. Donaldson, To Err is
Human: Building a Safer Health System
(Washington: National Academy Press, 1999.)
• Crossing the Quality Chasm: A New Health System for
the 21st Century (Washington: National Academy
Press, 2001)
February 17, 2004: I. Sim
Overview
Medical Informatics