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HLST 2040 – Lec 2

HLST 2040 – Lec 2

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HLST 2040 – Lec 2

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  1. HLST 2040 – Lec 2 Health Informatics 1

  2. Agenda for today • Finish Last week’s lecture • Clinical Decision Support System • Administrative Decision Support System

  3. 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

  4. 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

  5. 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

  6. 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 Historical Development of Education Programs in Health Care Informatics

  7. Organizations Promoting Health Informatics in Canada • COACH www.coachorg.com • CIHI www.cihi.ca • Canada Health Infoway www.infoway-inforoute.ca • eHealth ontariohttp://www.ehealthontario.on.ca/en/about • HIMSS www.himss.org

  8. Health Care Informatics Literacy – Pg.31 • Health Care Informatics Literacy includes: • Application of professional knowledge • Information literacy • Computer literacy Health Informatics Literacy

  9. 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

  10. 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: • Computer hardware and how it functions • Ways computers can be connected • Common software applications • Emerging applications of computers in health care

  11. 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

  12. 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

  13. 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)

  14. 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 computer-generated information • Monitors • Printers • Plotters • Speakers

  15. 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?

  16. 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

  17. 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

  18. 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

  19. Applications of Professional Knowledge • Lifelong learner • Clinician • Educator/Communicator • Researcher • Manager

  20. Decision Support Systems

  21. 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

  22. 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

  23. What Tool Can Function as a DSS • Spreadsheet • RDBMS • DSS with “What-if Capability” • Expert Systems • Read pg. 95

  24. 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

  25. CDSS Helps in • Making Diagnosis • Facilitating the process of care • Preventing errors • Enhancing patient safety • Controlling costs • See pg. 121

  26. 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

  27. Mycin Expert System • http://neamh.cns.uni.edu/MedInfo/mycin.html

  28. Popularity Problems • Book explains why CDSS have not been reached widespread acceptance – Pg. 117

  29. Types of knowledge involved in DM • Experiential – related to recognition or induction • Scientific – deals with cognition or deduction

  30. 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

  31. Clinician’sNew Decision Making Model

  32. Knowledge • Knowledge-based activities – Defining “knowledge-based”making a decision is like creating a new piece of knowledge • DSS use non-knowledge-based and knowledge-based 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

  33. 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

  34. How DSS supports Decision Making? • DSS can support DM only if the knowledge is in a usable format • Understanding usable formats- symbols that the DSS knows

  35. 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

  36. 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

  37. 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

  38. Knowledge Discovery in Large Datasets for Clinical Decision Support Development • Definitions of KDD-Pg. 124 • Definition of data-mining • See Fig 5-2

  39. 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

  40. 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

  41. Administrative DSS • Help to deliver healthcare as a business or service (Do it in a better way)

  42. Core features of ADSS

  43. ADSS Criteria: • 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

  44. 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

  45. Quantitative Techniques • Forecasting • Inventory models • Simulation • Linear Programming

  46. 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

  47. 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 inventory-free workplace facilitated by just-in-time deliveries gained popularity

  48. 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