Interprofessional healthcare informatics is all about information. How we get information and what we can do with it. In this presentation we'll consider the essential key points related to healthcare information, data, information, and knowledge. The reference for this presentation unless otherwise noted Is chapter two of the optional, Hoyt text. The key points that we'll cover are data information and knowledge. Semantic tools for informaticists and health IT informatics roles. A core function of informatics is making information and knoweldge from data. Have you wanted information from your clinical information system and couldn't find it? Or if you've found it you couldn't make sense of it? Understanding the transformation of data to knowledge is key to ensuring you have the information and knowledge you need from the data that are in your system. We'll use these working definitions. Data means observations. Information means data with meaning. Knowledge means justifiable beliefs based on data and information. Numbers and symbols and signals and terms. Data abound in the electronic environment. For example, what do 98.6, 89, and female mean to you? Here are some possible ways of attributing meaning to these data. And thus, making data into information. The 98.6 is a normal temperature reading. Although it really could signify anything. Some possible meaningful combinations of the other data might be an 89 year old female, or an 89 pound female. Synthesis of these pieces of information suggests several possible patient descriptions. For example, the patient is an elderly afebrile woman. Another scenario is that the patient is a small afebrile woman. How can we know if one of these interpretations is correct? We need the data definitions, so that we can be sure that we are interpreting the data correctly. Did your interpretation of the data match mine? For our colleagues, who use the metric system, this example would take on an entirely different meaning, because a normal temperature in Celsius is 37, not 98.6. And 89 kilos would weigh 196 pounds. Once again, we are reminded of the importance of definitions for transforming data to information. Let's take a moment, so that you can check out your understanding so far. The matching exercise we just completed provides us with an example to demonstrate how data in the real world can align to provide useful information. We do this, whether we realize it or not, by using theory. That best guess or mental construct or worldview. That helps us makes best sense of the real world and the data. Useful information becomes available, when data, theory, and reality match. In the real world, a public health nurse visited a high risk pregnant teen who had substance use problems and needed health care. The implicit theory for this observation is that a high risk teen with pregnancy and substance use problems who uses health care will have better outcomes. Several data sets are needed to align with theory and connect to the real world. Demographic data to know the age of the mother, problem terms to know about the pregnancy and substance use problems. Health care intervention terms to know what type of care was delivered and outcome measures, to know what happened. Useful information becomes available when data, theory and reality match. And specific to this situation, we have used clinical data to show the outcomes of publis health nursing care for high-risk mothers. Key Point 2, Semantic tools. Informaticists rely on semantic tools to help transform data to useful information. Semantic is a term that relates meaning and language. So we could call these meaning tools. As we go through this list I will provide a general definition, examples and some websites for you to check out. I say, general definitions because these tools have overlapping attributes, and many of the examples we'll discuss fit several definitions. Let's pause here so that you can imagine a clinical scenario and some related data that you might want to collect. Imagine a clinical case, imagine one or more data points that would be helpful for that case. And as you review the websites, keep track of some of the terms or codes that seem useful for your case. Here we go, controlled vocabularies are words that have definitions and are represented by a code. Two examples are ICD-9-CM and RxNorm. Here are websites. Taxonomies and ontologies are forms of formal hierarchical classifications. They consist of defined terms and depict interrelationships between the terms. The Omaha System is in ontology for population health. Here are websites. Interface terminologies are terms that facilitate direct entry of information into a computer. Several nursing terminologies fit here. Here are websites. Reference terminologies are a set of concepts and relationships that provide a common reference point for comparisons and aggregation of data. Two examples of LOINC and SNOMED CT. Here are websites. Finally, interoperability standards are technical standards for the exchange, integration, sharing, and retrieval of electronic health information. Two examples are HL7 and XML. Here are websites. Semantic tools are exciting and there is so much to know. From this brief introduction, the take home message is, all semantic tools help convert data to information by lending meaning from a particular perspective. There is another semantic tool that each of us uses every day, and that is our own cognitive process. Our reasoning constantly transforms data to knowledge as we interpret the meaning of every present data that surround us. The informaticist tool, is particularly critical in two situations, semantic equivalence and semantic gaps. Semantic equivalence means that more than one word or expression means the same thing. Healthcare professionals and patients have diverse vocabularies and perspectives. For example, the terms, elevated blood glucose, and high sugars, probably mean much the same thing to the people who use them. In health care, many terms and abbreviations mean the same thing. Even the use of semantic tools, such as interface terminologies can result in semantic equivalents. Similarly a semantic gap means that the word or expressions fails to capture the full meaning of a situation. Semantic gaps occur because healthcare deals with the rich and diverse world of people, health and society, not just numbers. For example, the term elevated blood glucose fails to convey critical information about the patient's typical blood glucose. The patient's calorie intake, or medications. Informaticists are constantly dealing with equivalent and missing data. But these threats can often be resolved by the ultimate semantic tool, human reasoning. Our final key point is differentiating roles in health informatics, relative to data, information, and knowledge. Perhaps it is as simple as this, IT professionals manage data using hardware, software, databases, and algorithms. Informaticists deal with information and knowledge. Let's reflect on that notion, can we really classify our roles this way? In my mind, the best case scenario in interprofessional healthcare informatics is a multidisciplinary team of informaticists who understand data. How and where to store and retrieve data, and can manage data. And IT professionals who understand the information and knowledge needs of informaticists. There are lots of job opportunities for both informaticists and IT professionals, who can cross this boundary. In this presentation we explored three key points, Data, Information and Knowledge, Semantic tools for Informaticist, health IT/Informatics Roles. These points underlie the science and practice of interprofessional healthcare informatics. Test your knowledge.
