Data, Information and Knowledge

1. Data, Information and Knowledge Yaseen Hayajneh, RN, MPH, PhD

2. Quotes… Data is not information, Information is not knowledge, ….. Cliff Stoll & Gary Schubert Business isn’t complicated. The complications arise when people are cut off from information they need. John F. Welch, CEO of GE

3. Objectives • Understand the meaning of data, information and knowledge (DI&K); • Be able to distinguish between DI&K. • Be able to give examples of DI&K; • Understand the Value of DI&K in health informatics.

4. DATUM • Value of specific parameter for a particular object at a given point in time. • Examples: • Blood sugar level of patient DM (object) this morning (point in time) was 190 • Judging this this reading as HIGH, requires more data & knowledge. • Datum is singular of data. Data is plural. But you will see it frequently dealt with as single.

5. DATA Definitions 1 • Facts represented in a readable language (such as numbers, characters, images, or other methods of recording). • Empirical data are facts originating in or based on observations or experiences. • Raw facts representing events occurring in organizations before they have been organized and arranged into a form that people can understand and use.

6. DATA Definitions 2 • Factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation; • Data = Representations of reality • Data are Raw Facts & Figures [source Webster's Dictionary]

7. DATA • Data on its own carries no meaning. • Data must be processed before becoming meaningful. • Data are not Information until they have been organized for analysis or display. • Processed Data are Information

8. Data Types • Qualitative (Categorical) vs. Quantitative (Numerical) Data • Discrete vs. Continuous Data

9. Qualitative (Categorical) Data • Raw data that are labels or categories • Examples: • Class Standing (Fr, So, Ju, Sr); • Section # (1,2,3,4,5,6); • Auto Make (Ford, Nissan); • Questionnaire response (disagree, neutral, agree) • Qualitative data can be only discrete

10. Quantitative (Numerical) Data • The raw data that are numerical • Examples: • Value of Age , Height, Weight , • SAT Score, • Number of students arriving late for class, • Time to complete a task. • Quantitative data can be discrete or continuous.

11. Discrete Data • Data that can be divided into categories • Only certain values are possible (there are gaps between the possible values. • Generally, discrete data are counts. • Examples: • Number of students late for class (Three students and half is a not a possible value) • Number of crimes reported to SC police • Number of times the word number is used.

12. Continuous Data • Data with a potentially infinite number of possible values along a continuum. • Examples: • Age ((Patient age is 3 years, 4 months, 2 days, 6 hours, 32 minutes, 26 seconds, 14 nanosecond ...) • Height, Weight; • Time to complete a homework assignment

13. Input Process Output Feedback Processing of Data • Input – Raw Data • Processing of data can be done by a computer or human mind. • Processing – Convert raw data to information

14. Data to Information

15. Information • A collection of facts organized in such a way that have additional value beyond the value of the facts themselves. • Data that has been interpreted, translated, or transformed to reveal the underlying meaning • Intelligence resulting from the assembly, analysis or summary of data into a meaningful form. • Information is data in context. • It can be understood – it has a meaning.

16. Information • Information = Data that has been processed into a form that is useful. • Info. = Data which provides relevant clues or news • A collection of facts from which conclusions may be drawn; "statistical data"

17. Information

18. Characteristics of Valuable Information • Accurate: Accurate information is error free. In some case, inaccurate information is generated because inaccurate data is fed into transformation process. • Complete: Complete information contains all of the important facts. Example, a patient report that does not include all important diagnostic results is not complete.

19. Characteristics of Valuable Information • Economical: Information should also be relatively economical to produce. Decision makers must always balance the value of information with the cost of producing it. • Relevant: Relevant information is important to the decision maker. Information that infusion pump prices might drop is not relevant to the medical records archiving clerk.

20. Characteristics of Valuable Information • Flexible: Flexible information can be used for a variety of purposes . For example, information on the number of planned open heart surgeries can be used by purchasing officer to plan buying supplies, by a nurse manager to determine staffing levels, by marketing manager to use in marketing efforts and by the CEO to “brag” about.

21. Characteristics of Valuable Information • Reliable: Information that can be depended on. Reliability of information depends on the reliability of data collection methods and source of the information. • Simple: Sophisticated and detailed information may not be needed. Information overload happens when a decision maker has too much information and is unable to determine what is really important.

22. Characteristics of Valuable Information • Accessible: Information should be easily accessible by authorized users to be obtained in right format and at the right time to meet their needs. • Timely: Timely information is delivered when it is needed. Knowing patient’s morning blood sugar result the next day will not help when trying to determine insulin dosage today.

23. Characteristics of Valuable Information • Verifiable: You can check it to make sure it is correct, perhaps by checking many sources for the same information. • Secure: Information should be secure from access by unauthorized users.

24. Knowledge • Knowledge is information which has been intellectually processed, by man or by machine. • It has an immediate value without any further processing. • Knowledge is used to interpret information • Medical diagnosis based on patient diagnostic information – requires knowledge. • Modern health care needs structured knowledge for reference and decision support. • Knowledge enables HCOs to anticipate events

25. Knowledge • is a mix of experiences, concepts, beliefs, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information and that can be shared & communicated. • It originates and is applied in the mind of knower. In organizations, it often becomes imbedded not only in the documents or repositories but also in organizational routines, processes, practices, and norms.

26. Where is Knowledge? • You can find knowledge in: • Documents • Processes • Policies & Procedures • Rules • Guidelines • IT Applications • Management systems • Individuals’ Minds & behaviors

27. Data Management • The planning, development, implementation, and administration of systems for the acquisition, storage, and retrieval of data. • It involves strategic data planning, data element standardization, information management control, data synchronization, data sharing, and database development. Active data management increases system effectiveness and improves the accuracy and timeliness of data to derive maximum benefit.

28. Information Management • The administration, use, and transmission of information and the application of theories and techniques of information science to create, modify, or improve information handling systems. Filing systems, cognitive maps, manuals, and electronic databases are examples of devices that can prove useful in information management. A network of consultants is an additional way to ensure that necessary information will be readily available.

29. Knowledge Management • A discipline used to systematically leverage expertise and information to improve organizational efficiency, responsiveness, competency, and innovation. • It involves gathering, organizing, sharing, and analyzing knowledge in terms of resources, documents, experts, lessons learned documents, best practices, and people skills.

30. Database • An organized collection of logically related data: patient names, gender, insurance converge, etc. • It usually refers to data organized and stored on a computer that can be searched and retrieved by a computer program. • Turns raw data into structured data • Webster Definition: a usually large collection of data organized especially for rapid search and retrieval (as by a computer) .

31. Database Management • Tools and techniques to manage sets of alphanumeric data. Typically this involves the design of database systems and the programming to perform the desired functions. • Future systems will clearly also be required to handle images, audio, and video data.

32. Information base • Database containing information (e.g. reports, documents, interpreted data); see also information repository. • Information Base Example

33. HIV Knowledge Base Indiana University Knowledge Base Knowledge base • A knowledge base embodies knowledge about how to solve a problem in some problem domain. • Expert system (ES): A computer system that facilitates solving problems in a given field or application by drawing inference from a knowledge base developed from human expertise.

34. Value of Data & Information • Record events • To improve quality of care • Fast access to urgently needed information. • To improve operations • To save money • Just In Time ordering • Aid management decisions

35. How Valuable are Data? • To help you know the value of data, Imagine that you are the CEO of a hospital that just lost permanently the following Data: • Patient lists • Payroll information • Accounting details • Transaction details • Records of who owes you money • it would be catastrophic