HLST 2040 * Lec 1

Report
HLST 2040 – Lec 2
Health Informatics 1
Agenda for today
• Finish Last week’s lecture
• Clinical Decision Support System
• Administrative Decision Support System
Last Week
• We talked about theories – Change theory
• We talked about systems – Open System
• We talked about hardware- for example,
CPU;RAM;ROM etc.
• Discussed of the course outline
• Ch.1, Ch. 2 and Ch.21
Responding to Change – Pg.23
• Innovators – 2.5% who test out new technology
• Early adapters – 13.5% who are role model for others
• Early majority- next 34% who are willing to adopt an
innovation but not lead
• Late majority –next 34% who change because of peer
pressure
• Laggards – Last 16% who will change only when there is no
alternative
Historical Development of Education
Programs in Health Care Informatics
• Traditionally, individuals interested in mathematics and
engineering were captivated by the idea of creating a
machine that would communicate and tabulate
information more quickly than a human.
• Hollerith (1800s)
– 1950 – Russians - information management-Informatika
– 1960s – Medical informatics integrated into physician education
• France, Holland, Belgium, USSR, USA (late 1960s to early
1970s)
Historical Development of Education
Programs in Health Care Informatics
• 1985 – Covvey, Craven, and McAlister; first
articulated the idea of a specialist prepared in health
care computing education
– Covvey – describes skills needed by a health care
computing specialist that include a background in
computer science, health care, and managerial experience
– We will look at more professions later in the lecture
Organizations Promoting Health
Informatics in Canada
• COACH www.coachorg.com
• CIHI www.cihi.ca
• Canada Health Infoway www.infowayinforoute.ca
• eHealth ontario
http://www.ehealthontario.on.ca/en/about
• HIMSS www.himss.org
Health Care Informatics Literacy – Pg.31
• Health Care Informatics Literacy includes:
– Application of professional knowledge
Professional
– Information literacy
Knowledge
– Computer literacy
Health
Informatics
Literacy
Computer
Literacy
Information
Literacy
What does Health Informatics Facilitate?
• Health informatics facilitates the delivery of:
– Efficient care
– Cost-effective care
– High-quality care
• How do we measure how HI literate we are?
– There are competencies that each professional
has
– For example, informatics nurse
Computer Literacy
• Computer literacy is defined as the ability to use
current computer technology as a problem-solving
tool in a health care setting.
• Requires a basic understanding of:
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–
–
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Computer hardware and how it functions
Ways computers can be connected
Common software applications
Emerging applications of computers in health care
Nursing Informatics
• Evolve as nurses participated in the early
initiatives in hospital information system
adoption in various health agencies across the
nation.
• Improved systems meant, specialized nursing
components and even free-standing nursing
information.
• Early systems were primarily imported from
other countries like USA.
• By the late 1980s, most hospitals had at least a
rudimentary information system
Computer Hardware: Physical Parts
of a Computer – Pg. 42
• We talked about CPU last week
– Memory
• Read-Only Memory (ROM) – Cannot be written to
• Random Access Memory (RAM) – This is the memory
that we talk about when we are buying a PC
• RAM is not permanent
Computer Hardware: Physical Parts
of a Computer
• Storage devices hold data and programs when not in
use
– Magnetic storage
• Internal hard disks
• Floppy disks
• Zip and Jaz disks
– Optical storage
• Compact Disc–Read-Only Memory (CD-ROM)
• Compact Disc Read Write (CD-RW)
Computer Hardware: Physical Parts
of a Computer
• Input devices capture data in digital format
– Alphanumeric and function entry
– Voice entry – Doctors dictating patient notes into the EMR
– Image entry - PACS
• Output devices create a display of computergenerated information
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–
–
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Monitors
Printers
Plotters
Speakers
Computer Connectivity
• Wired networks
– Local Area Network (LAN) - Library
– Wide Area Network (WAN) – Internet is a good example of
a WAN
• Wireless networks
• Modems
– Telephone modems
– Cable modems – Connect the computer to the internet
• Internet – Is it the same as the WWW?
Computer Software
• Operating system
– Controls the functioning of the computer by managing tasks,
data, and devices
– Very important software
– Common systems:
• Microsoft Windows
• Apple’s Macintosh MAC OS
• UNIX
• Linux
• Graphical user interface
– Allows use of a mouse to select icons and menu items
– Establishes consistent functionality to programs that work with it
Computer Software
• Software applications
– Perform specific tasks with a particular operating system
– Common applications
• Word processing
• Spreadsheets
• Database Management Systems (DBMS)
• Bibliographic management programs
• Presentations programs
• Graphic programs
• E-mail applications
• Web browser software
• Web authoring programs
Evaluating and Improving Literacy
• Diversity of skills
– People entering similar situations usually have widely
different levels of literacy
• Identify literacy objectives and measure existing
knowledge
– Clearly identify what skills are needed for success
– Use an evaluation tool
• Offer training to increase knowledge
– Design a curriculum based on competencies
– Develop a training program
• Evaluate the results of training
– Real-life and lifelong learning focus
Applications of Professional Knowledge
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•
•
•
•
Lifelong learner
Clinician
Educator/Communicator
Researcher
Manager
DECISION SUPPORT SYSTEMS
What is Decision-making?
• Classic view: focus on “analysis” between
alternatives.
• Comprehensive view: decision making is
knowledge-based and knowledge-intensive
activity.
– New knowledge is created when a decision is made
– Because old knowledge is often altered or
discarded after each new decision is made
Decision Support Systems in
Healthcare
• 2 kinds
– Administrative
– Clinical
• DSS are from the world of Artificial Intelligence 
expert systems
• 2 important parts of expert systems are
knowledge base and inference engine (database)
• Expert system also has user interface
• Use knowledge, not just data or information
• How Humans make decisions – pg. 117
What Tool Can Function as a DSS
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•
•
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•
Spreadsheet
RDBMS
DSS with “What-if Capability”
Expert Systems
Read pg. 95
Clinical Decision Support Systems
• Help the clinician reach a decision
• Clinician is presented with a variety of
information
• Clinician is under stress and pressure from
various angles
• Do they take the decision for the clinician?
• 2 things that must be done to integrate CDSS
into clinical environment – Pg. 116
CDSS Helps in
•
•
•
•
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Making Diagnosis
Facilitating the process of care
Preventing errors
Enhancing patient safety
Controlling costs
See pg. 121
Decision Making
• Defining a decision – Pg. 118
• 2 Definitions are specified by the book
• How are decisions classified? Let us
look at the next slide
Mycin Expert System
• http://neamh.cns.uni.edu/MedInfo/mycin.ht
ml
Popularity Problems
• Book explains why CDSS have not been
reached widespread acceptance – Pg. 117
Types of knowledge involved in DM
• Experiential – related to recognition or
induction
• Scientific – deals with cognition or deduction
Decision Making and Knowledge
Representation
• Understanding decision making in health care
settings and the factors affecting decision making
• The development of knowledge-based systems
within health care
– What the clinicians sees versus underlying models
• Defining pattern recognition, pattern generation,
and interpretation
Clinician’s New Decision Making Model
Knowledge
• Knowledge-based activities
– Defining “knowledge-based”making a decision is like
creating a new piece of knowledge
• DSS use non-knowledge-based and knowledgebased approaches in their design.
– Example of non-knowledge based approach is on pg.
119
• Reasons why it is difficult to make a computer think like a
user-Pg. 119
Defining Knowledge
• Structuring knowledge for interpretation by a
computer
• Identifying the three types of knowledge
– Descriptive knowledge – simply a description of
something
– Procedural knowledge – step by step procedure
– Reasoning – know why
• Recognizing patterns over time
• Defining “inferencing” in the text - Pg. 120
How DSS supports Decision Making?
• DSS can support DM only if the
knowledge is in a usable format
• Understanding usable formatssymbols that the DSS knows
Steps in Creating DSS
• Develop DSS architecture – See pg.120
• Identifying standards when moving from DSS
to domain-specific CDSS
– For example, consistent representation of clinical
logic using HL7
Decision Making in Clinical Care
• History of CDSS use in clinical arena
• Appropriate use of CDSS used in health care delivery
-Is the CDSS being used for every possible use?
• Understanding the “Oracle” model of CDSS – not
applicable – pg. 122
Defining Clinical Decision Support Systems
(CDSS)
• Various definitions by experts using CDSS
• Identifying 8 competing demands for CDSS
application – PG. 122
• Ability to mine data warehouses
• Sai will explain what a data warehouse is
Knowledge Discovery in
Large Datasets for Clinical Decision
Support Development
• Definitions of KDD-Pg. 124
• Definition of data-mining
• See Fig 5-2
Future Requirements for CDSS
Development
• Need for understanding human decisions, why CDSS
failed in the past, the challenges of knowledge
representation, and issues surrounding health care
delivery
• Five areas to address for CDSS solutions
Conclusion
• Overview of why CDSS have not been successful
in the past
• Possibilities of new discovery-based approaches
• Use of discovery-based techniques in large clinical
data warehouses
Administrative DSS
• Help to deliver healthcare as a business or
service (Do it in a better way)
Core features of ADSS
ADSS Criteria:
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•
•
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Timeliness =timeframe
Objectivity=explicit
Integration=whole firm or dept.
Scope= bound by the demands of the decision under
consideration
• Priority = Prioritize which decision is to be taken
• - Pg.84
Quantitative Approach
• Levin has said that managers can make better
decisions by using quantitative apparoach
• Subject called Operations Research
• Model the problem by identifying the
variables involved – pg.85
Quantitative Techniques
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Forecasting
Inventory models
Simulation
Linear Programming
Forecasting
– Forecasting takes advantage of past
experience
– Knowledge about what happens in the past should
improve estimates about what will happen in the
future
– A variety of mathematical techniques can be
used to forecast demand for health care
• Time series extrapolation
• Causal techniques
– Judgmental forecasting solicits patient
feedback or expert opinion
Inventory Models
• Inventory decisions represent a balancing act: how
to balance the costs of maintaining inventory
against the costs of running short
• Simple deterministic models such as economic
order quantity were developed to optimally
manage one item of inventory
• Inventory control evolved to effectively manage
multiple lines of inventory simultaneously
• By the early 1980s, the concept of an inventoryfree workplace facilitated by just-in-time deliveries
gained popularity
Simulation
– Simulation can be applied with fewer assumptions
than queuing formulas to model the flow of patients
through a health care system
– The computer generates random patient arrivals
and service times each in accordance with
mathematical distributions reflecting the
performance of the system being modeled
– The simulation software then computes statistics
related to system performance
– Once the model reflecting the status quo is built,
models of alternatives can be built
– The performance of each alternative is then
compared to the status quo
Linear Programming
• Linear programming is generally used to
determine the best consumption of resources
in order to meet some objective.
– In health care, these can be used to: Schedule staff
and use of facilities
– Design the optimal production of medical services
or goods
– Establish the most efficient routes for transporting
patients
– Determine the most efficient use of space
HR-ADSS
– Human resources management software typically
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supports the following functions:
Recruitment and retention
Personnel administration
Management of payroll against budget
Training
Performance evaluation
• Management of compensation and benefits has
become an increasingly complex task facilitated
by decision support
Summary
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Last week’s remainder of Lec.1
DSS
CDSS
ADSS

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