February 19, Overview
Medical Informatics
Ida Sim, MD, PhD
February 19, 2002
Division of General Internal Medicine, and
Gr...
February 19, Overview
Outline
• Introduction
• Course Goals and Overview
• Computing Infrastructure for Health Care
– data...
February 19, Overview
Introduction: Ida Sim, MD, PhD
• PhD in Medical Informatics, Stanford
• Assistant Professor
– Genera...
February 19, Overview
Informatics and Clinical Care
• Institute of Medicine (IOM) report on med errors
– calls for electro...
February 19, Overview
Informatics and Clinical Research
• Human genome findings will need to be
translated into population...
February 19, Overview
Yet...
• Only ~12% of outpatient clinics have an EMR;
only 30% of hospitals have a website
• Much cl...
February 19, Overview
Course Goals (1)
• Understand the clinical, economic, and social
context in which information techno...
February 19, Overview
Course Goals (2)
• Be familiar with
– trends in consumer health informatics
– organizational aspects...
February 19, Overview
Context
• Few students working directly in informatics
• Desired outcome
– that you be able to under...
February 19, Overview
Course Overview
• 5 Lectures
– class participation expected
• Field trip: Palo Alto Medical Foundati...
February 19, Overview
Glossary
• Available at course home page, under
continuous development
• Please give feedback on how...
February 19, Overview
Outline
• Introduction
• Course Goals and Overview
• Computing Infrastructure for Health Care
– data...
Computing Infrastructure
Modern U.
Front
Desk
Radiology
Claims
Medical
Information
Bureau
Archive
WalgreensPrescribing
Pha...
February 19, Overview
Understanding the Infrastructure
• Clients and servers (the components)
• Data storage (how data is ...
February 19, Overview
Client/Server Model
• Computers can be servers and/or clients
• Web server “serves” web pages to “cl...
February 19, Overview
Internet Clients and Servers
itsa
medicine
ucsf.edu
nci.nih.gov cochrane.uk myhome.com
Main Trunk Ca...
February 19, Overview
Data Storage
• Computers can help us
– store, retrieve, query, compute, and report data
• For this t...
February 19, Overview
“Describing” the Data
• The extent to which the computer can help you
manage your data depends on ho...
February 19, Overview
“Describing” Data: To Humans
• For understanding and communication
– via a system for codifying mean...
February 19, Overview
“Describing” Data: To Computers
• For understanding and communication
– via a data model for describ...
February 19, Overview
Data Model Choices
• Data model should best allow you to
– do what you want to do with the data
• qu...
February 19, Overview
Flat File Model
• For understanding and communication
– data are encoded as ASCII
– BUT computers ca...
February 19, Overview
What is ASCII?
• Standard system for coding characters (A-Z, a-
z, 1-0, etc.) in machine language
– ...
February 19, Overview
Flat File Model
• For understanding and communication
– data are encoded as ASCII
– BUT computers ca...
February 19, Overview
Word Text 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
STAT...
February 19, Overview
Database Schema
• A database’s schema is a compact summary
description your database’s contents
• Da...
February 19, Overview
Flat File Data Schema
Word File
Carson Jackson 1 3/2/05 J 5
Hannah Hillary 2 1/2/05 C 2
Jonas Oscar ...
February 19, Overview
Flat File Advantages
• Easy, just start entering data, doesn’t need any
preliminary database work or...
February 19, Overview
Flat File Disadvantages
• Description of the data isn’t clear, and may not
even be understandable
– ...
February 19, Overview
Repeating Data in Flat File Model (1)
Word Text File
Carson Jackson 1 3/2/05 J 5
Hannah Hillary 2 1/...
February 19, Overview
Word Text File
Carson Jackson 1 3/2/05 J 5 x 4
Hannah Hillary 2 1/2/05 C 2
Jonas Oscar 1 1/1/05 J 3 ...
February 19, Overview
Flat File Disadvantages (cont.)
• Inefficient at finding a particular baby
– must look at records on...
February 19, Overview
Summary of Flat File Data Model
Factor Flat File Relational Object
Human-
understandable
Frequently ...
February 19, Overview
When Are Flat Files Useful?
• For a small, simple, “quick and dirty” databases
– few data items, sma...
February 19, Overview
Flat Files in Clinical Care
• Really no reason nowadays to build a flat file
system for clinical car...
February 19, Overview
Relational Data Model
• Data are arranged in tables made up of
columns and rows
– the columns are th...
February 19, Overview
Flat File Admissions Database
Robert Lee, 000-01-001, M, 09-Jul-70,B/T Healthnet
31-Dec-94 to 12-Jan...
February 19, Overview
Review of Problems with Flat Files
• Implicit structure, implicit data schema
• Schema may change fr...
February 19, Overview
InpatientMasterTable
ID Name Sex Birthdate Insurance
000-01-001 Lee M 09-Jul-70 B/T Healthnet
000-01...
Relational Admissions Database
InpatientMasterTable
ID Name Sex Birthdate Insurance
000-01-001 Lee M 09-Jul-70 B/T Healthn...
February 19, Overview
Relational Database Schema
• The schema is the names of the tables and their
column names
– Inpatien...
February 19, Overview
Pros of Relational Model
• Database is always consistent
– built-in prevention against insert, delet...
February 19, Overview
Cons of (Traditional) Relational Model
• Profusion of tables and keys can be confusing
– higher orga...
February 19, Overview
Summary of Relational Data Model
Factor Flat File Relational Object
Human-
understandable
Frequently...
February 19, Overview
Object Data Model
• Data arranged in conceptual groups, with
prototypes and their attributes
Patient...
February 19, Overview
Inheritance
• Special classes of data can be modeled
efficiently
Admission
-admit date
-discharge da...
February 19, Overview
Pros and Cons of Object Model
• Pros: Can represent very complex data types
and data relationships
–...
February 19, Overview
Summary of Object Data Model
Factor Flat File Relational Object
Human-
understandable
Frequently Not...
February 19, Overview
Summary of Data Model Choices
• For storing clinical and clinical research data,
use a relational mo...
February 19, Overview
The Model vs. The System
• Data model
– the generic abstract structure of the information
• domain i...
February 19, Overview
DBMS Features for System Selection
• Memory capacity
• Multi-user support and transaction
management...
February 19, Overview
Other DBMS Features
• Security
– can have logins and different levels of access
• database administr...
Computing Infrastructure
Modern U.
Front
Desk
Radiology
Claims
Medical
Information
Bureau
Archive
WalgreensPrescribing
Pha...
February 19, Overview
HealthSystem Minnesota
• 1.6 million patient visits per year, 270,000
capitated lives, 460 physician...
February 19, Overview
Summary on Data Storage
• How a computer stores information can have
serious implications for
– data...
February 19, Overview
Understanding the Infrastructure
• Clients and servers (the components)
• Data storage (how data is ...
February 19, Overview
Internet = Network of Networks
itsa
medicine
ucsf.edu
nci.nih.gov cochrane.uk myhome.com
Main Trunk ...
February 19, Overview
What Happens over Network Cables?
itsa
medicine
ucsf.edu
nci.nih.gov cochrane.uk myhome.com
Main Tru...
February 19, Overview
• Protocol = grammar for machines talking to
each other
• Protocol for the WWW = http
• WWW vs. Inte...
February 19, Overview
Networking BandwidthSim: Computer Infrastructure 1/26/00
Connection Type
Speed
(in kilo bits per sec...
February 19, Overview
Networking Media
• Copper wire (twisted pair)
– generally not well suited to high bandwith transmiss...
February 19, Overview
Significant Issue in HealthCare
• UCSF spent ~$100 million on networking in
the late 1990’s
• Health...
February 19, Overview
Conclusions
• Computing infrastructure for health care is very
complex, very fragmented, has lots of...
February 19, Overview
Teaching Points
• If you want computers to do “smart” things with
your data (e.g., retrieve, sort, g...
February 19, Overview
References
• L.T. Kohn, J.M. Corrigan, M.S. Donaldson, To Err is
Human: Building a Safer Health Syst...
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  • Rindfleisch an dBrutlag paper
  • Rindfleisch an dBrutlag paper
  • Be familiar with core concepts in medical informatics: vocabularies, interchange standards, decision support systems
    Understand how computers are used to manage information in health care (electronic medical records, data warehouses, etc.) and to support clinical research
    Be familiar with trends in consumer health informatics
    Be familiar with organizational aspects of successfully using health information technology
    Be familiar with intellectual property and electronic privacy issues
    Have an overview of the research, funding, and training opportunities in medical informatics
  • HOW -- is data stored, sent,
    Next class -- what is sent and meaning
  • Hook up 2 computers using a piece of string and nothing will happen, just like 2 cups and a string.
  • focus today on representing and storing data in computers, you need to know some standard ways for doing this and what their strengths and weaknesses are. later lecture discuss a little about how computers manipulate or “reason” about data
    Key point is that Lots of things we take for granted that must be clearly specified for computers;
  • To answer this question, must first introduce the concept of a data schema
  • first need to introduce concept of data schema, which allows us to talk about the overall structure of a database
  • talk about database systems as opposed to models in just a littl ebit
  • HOW -- is data stored, sent,
    Next class -- what is sent and meaning
  • Hook up 2 computers using a piece of string and nothing will happen, just like 2 cups and a string.
  • <number>
    The grammar or transmission
    .
  • bandwidth is limited both by low-level protocol used and network cable medium
  • Lecture Notes

    1. 1. February 19, Overview Medical Informatics Ida Sim, MD, PhD February 19, 2002 Division of General Internal Medicine, and Graduate Group in Biological and Medical Informatics UCSF Copyright Ida Sim, 2002. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.
    2. 2. February 19, Overview Outline • Introduction • Course Goals and Overview • Computing Infrastructure for Health Care – data storage – networking
    3. 3. February 19, Overview 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 knowledge publication – economics of health information technology – meta-analysis, and evidence-based decision making
    4. 4. February 19, Overview 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 technology- based 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
    5. 5. February 19, Overview 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
    6. 6. February 19, Overview 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%of revenue in information technologies – vs. ~10% in comparable information-intensive sectors (e.g., banking)
    7. 7. February 19, Overview Course Goals (1) • Understand the clinical, economic, and social context in which information technologies are being developed and deployed in health care • 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
    8. 8. February 19, Overview Course Goals (2) • Be familiar with – trends in consumer health informatics – organizational aspects of successfully using health information technology – intellectual property and electronic privacy issues • Have an overview of the research, funding, and training opportunities in medical informatics
    9. 9. February 19, Overview 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 • doing research in the field of medical informatics
    10. 10. February 19, Overview Course Overview • 5 Lectures – class participation expected • Field trip: Palo Alto Medical Foundation – date and time TBA • Assignments – 4-5 homeworks, no final exam • Office “hours”: sim@medicine.ucsf.edu – http://www.epibiostat.ucsf.edu/courses/schedule/med_informatics.html
    11. 11. February 19, Overview Glossary • Available at course home page, under continuous development • Please give feedback on how to improve the glossary • No question is too stupid...
    12. 12. February 19, Overview Outline • Introduction • Course Goals and Overview • Computing Infrastructure for Health Care – data storage – networking
    13. 13. Computing Infrastructure Modern U. Front Desk Radiology Claims Medical Information Bureau Archive WalgreensPrescribing Pharm Benefit Manager Benefits Check HealthNet Formulary Check Lab UniLab B&T Eligibility Authorization Personal Health Record Logician EMR Outsourced Electronic Medical Record Specialist Referral Referral Authorization Internet Intranet Phone/Paper
    14. 14. February 19, Overview 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)
    15. 15. February 19, Overview Client/Server Model • 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 Clients Web Server
    16. 16. February 19, Overview Internet Clients and Servers itsa medicine ucsf.edu nci.nih.gov cochrane.uk myhome.com Main Trunk Cables amazon.com at home pacbell.net aol.com LAN
    17. 17. February 19, Overview 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
    18. 18. February 19, Overview “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
    19. 19. February 19, Overview “Describing” Data: To Humans • For understanding and communication – via a system for codifying meaning • English language, mathematical notation, – making the “code” itself concrete • 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 24 142 108 3.9 96
    20. 20. February 19, Overview “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
    21. 21. February 19, Overview Data Model Choices • Data model should best allow 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 ASCII entries – 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
    22. 22. February 19, Overview Flat File Model • For understanding and communication – data are encoded as ASCII – BUT computers cannot understand the meaning of the ASCII text and numbers
    23. 23. February 19, Overview What is ASCII? • Standard system for coding characters (A-Z, a- z, 1-0, etc.) in machine language – American Standard Code for Information Interchange. – used in almost all present-day computers • Since it encodes single characters one at a time, ASCII does not support any coding of word meaning
    24. 24. February 19, Overview Flat File Model • For understanding and communication – data are encoded as ASCII – BUT computers cannot understand the meaning of the ASCII text and numbers • 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
    25. 25. February 19, Overview Word Text 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 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 Flat File Examples
    26. 26. February 19, Overview 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)
    27. 27. February 19, Overview Flat File Data Schema Word 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 • 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
    28. 28. February 19, Overview 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 an ASCII file
    29. 29. February 19, Overview 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
    30. 30. February 19, Overview Repeating Data in Flat File Model (1) Word Text 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 Carson Jackson 2 3/3/05 J 4 Jonas Oscar 1 1/3/05 J 4 Jonas Oscar 1 1/1/05 J 3 • Jackson/Carson’s gender might change from one record to another, or...
    31. 31. February 19, Overview Word Text File Carson Jackson 1 3/2/05 J 5 x 4 Hannah Hillary 2 1/2/05 C 2 Jonas Oscar 1 1/1/05 J 3 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 Repeating Data in Flat File Model (2)
    32. 32. February 19, Overview 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
    33. 33. February 19, Overview Summary of Flat File Data Model Factor Flat File Relational Object Human- understandable Frequently Not Computer- “understandable” No Complexity of data Simple Querying Inefficient Manipulating Inefficient Amount of data Small Type of data Text, Numbers Sharing and merging Very Difficult
    34. 34. February 19, Overview 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 – 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 • E.g., infant database used in the Excel lab
    35. 35. February 19, Overview Flat Files in Clinical Care • Really no reason nowadays to build a flat file system for clinical care databases • 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
    36. 36. February 19, Overview 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)
    37. 37. February 19, Overview 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
    38. 38. February 19, Overview 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.
    39. 39. February 19, Overview InpatientMasterTable ID Name Sex Birthdate Insurance 000-01-001 Lee M 09-Jul-70 B/T Healthnet 000-01-002 Smith F 22-Oct-25 Medicare 000-01-003 Perez F 13-Jun-57 B/T Pacificare AdmissionsTable ID Admit Service Admit Date Discharge Date Admit Diagnosis Principal Discharge Diagnosis Secondary Discharge Diagnoses Secondary Discharge Diagnoses 000-01-001 Med 31-Dec-94 12-Jan-95 Acute MI Acute MI COPD Diabetes (CHF) 000-01-001 Med 27-Mar-96 31-Mar-96 COPD Pneumonia COPD CHF (Diabetes) 000-01-002 Surg 03-Feb-95 16-Feb-95 THR THR Acute MI Diabetes 000-01-002 Med 27-Feb-95 20-Mar-95 Acute MI Acute MI VF Arrest Diabetes 000-01-003 Gyn 19-Nov-97 23-Nov-97 Menorrhagia von Willebrand's Diabetes • Doesn’t handle secondary diagnoses very well – for many admissions, there are either too few or too many columns Relational Admissions Database (#1)
    40. 40. Relational Admissions Database InpatientMasterTable ID Name Sex Birthdate Insurance 000-01-001 Lee M 09-Jul-70 B/T Healthnet 000-01-002 Smith F 22-Oct-25 Medicare 000-01-003 Perez F 13-Jun-57 B/T Pacificare AdmissionsTable ID Admit Service Admit Date Discharge Date Admit Diagnosis Principal Discharge Diagnosis 000-01-001 Med 31-Dec-94 12-Jan-95 Acute MI Acute MI 000-01-001 Med 27-Mar-96 31-Mar-96 COPD Pneumonia 000-01-002 Surg 03-Feb-95 16-Feb-95 THR THR 000-01-002 Med 27-Feb-95 20-Mar-95 Acute MI Acute MI 000-01-003 Gyn 19-Nov-97 23-Nov-97 Menorrhagia von Willebrand's SecondaryDischargeDiagnosisTable ID Admit Date Secondary Discharge Diagnoses 000-01-001 31-Dec-94 COPD 000-01-001 31-Dec-94 Diabetes 000-01-001 31-Dec-94 CHF 000-01-001 27-Mar-96 COPD 000-01-001 27-Mar-96 CHF 000-01-001 27-Mar-96 Diabetes 000-01-002 03-Feb-95 Acute MI 000-01-002 03-Feb-95 Diabetes 000-01-002 27-Feb-95 VF Arrest 000-01-002 27-Feb-95 Diabetes 000-01-003 19-Nov-97 Diabetes
    41. 41. February 19, Overview 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)
    42. 42. February 19, Overview 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...
    43. 43. February 19, Overview 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”)
    44. 44. February 19, Overview Summary of Relational Data Model Factor Flat File Relational Object Human- understandable Frequently Not Yes Computer- “understandable” No Yes Complexity of data Simple Complex Querying Inefficient Efficient Manipulating Inefficient Efficient Amount of data Small Very Large Type of data Text, Numbers Text, Numbers Sharing and merging Very Difficult Least Difficult • We don’t normally think in tables...
    45. 45. February 19, Overview Object Data Model • Data arranged in conceptual groups, with prototypes and their attributes Patient -name -gender -b-day -address -insurance -primary MD -etc Admission -admit date -discharge date -attending MD -admit, primary, secondary dx -etc. Diagnosis -code -name -modifiers
    46. 46. February 19, Overview Inheritance • Special classes of data can be modeled efficiently Admission -admit date -discharge date -attending MD -admit, primary, secondary dx -etc. ICUAdmission -APACHE score -ICU attending MD is-a
    47. 47. February 19, Overview 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 • Object-(or extended)-relational model is promising – design database using the object model – store and query it as a relational database
    48. 48. February 19, Overview Summary of Object Data Model Factor Flat File Relational Object Human- understandable Frequently Not Yes Partially Computer- “understandable” No Yes Yes Complexity of data Simple Complex Very Complex Querying Inefficient Efficient Inefficient Manipulating Inefficient Efficient Inefficient Amount of data Small Very Large Large Type of data Text, Numbers Text, Numbers All Sharing and merging Very Difficult Least Difficult Rather Difficult
    49. 49. February 19, Overview Summary of Data Model Choices • For storing clinical and clinical research data, use a relational model unless – you have a small, simple, quick and dirty database that you are not intending on sharing or reusing • use a flat file – you need to store complex, multimedia data • consider an extended-relational database • but could work around this using a relational database • How do data models relate to real systems?
    50. 50. February 19, Overview 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 – stores information using a data model – provides additional functionality Example Database Management Systems Data Model Small Scale (PC’s) Large Scale (Mainframes) Flat file Filemaker Pro VA system (enhanced) Relational Access, MySQL Oracle, Sybase, MySQL, SQL Server Object Informix Objectivity
    51. 51. February 19, Overview DBMS Features for System Selection • Memory capacity • Multi-user support and transaction management • Data entry forms • Triggers and rules • Security • Backup and archiving
    52. 52. February 19, Overview Other DBMS Features • Security – can have logins and different levels of access • 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
    53. 53. Computing Infrastructure Modern U. Front Desk Radiology Claims Medical Information Bureau Archive WalgreensPrescribing Pharm Benefit Manager Benefits Check HealthNet Formulary Check Lab UniLab B&T Eligibility Authorization Personal Health Record Logician EMR Outsourced Electronic Medical Record Specialist Referral Referral Authorization Internet Intranet Phone/Paper
    54. 54. February 19, Overview 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.”
    55. 55. February 19, Overview 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
    56. 56. February 19, Overview 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)
    57. 57. February 19, Overview Internet = Network of Networks itsa medicine ucsf.edu nci.nih.gov cochrane.uk myhome.com Main Trunk Cables local trunk cable through Berkeley amazon.com at home dial-in to itsa.ucsf.edu via modem pacbell.net aol.com or use a commercial Internet Service Provider (ISP) via dial--up or DSL LAN
    58. 58. February 19, Overview What Happens over Network Cables? itsa medicine ucsf.edu nci.nih.gov cochrane.uk myhome.com Main Trunk Cables amazon.com at home pacbell.net aol.com LAN
    59. 59. February 19, Overview • Protocol = grammar for machines talking to each other • 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) Networking Protocols
    60. 60. February 19, Overview Networking BandwidthSim: Computer Infrastructure 1/26/00 Connection Type Speed (in kilo bits per second, Kbps) CXR (12 Mbits) CT Scan (5.2 Mbits) Phone modem 14.4, 28.8, or 56 ISDN 64 to 128 3 min 1.4 min T1 1,000 Spread-spectrum RF 2,000 ADSL 6,000 to 7,000 Cable modem to 10,000 Infrared 16,000 Ethernet 10,000 100,000 on some sytems T3 45,000 ATM 155,000 over copper wires 622,000 over fiberoptic 8 sec 3.3 sec SONET 52,000 to 9,953,000
    61. 61. February 19, Overview 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
    62. 62. February 19, Overview 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
    63. 63. February 19, Overview 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
    64. 64. February 19, Overview 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
    65. 65. February 19, Overview 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)
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