Theoretical Foundations of Health Informatics

Characteristics of Systems

Systems are structured to perform their functions. Two different structural models operating concurrently can be used to conceptualize healthcare technical infrastructures. These are hierarchical and web. The hierarchical model is an older architectural model, and the terms, such as mainframe, that are used to describe the model reflect that reality. The location and type of hardware used within a system often follow a hierarchical model; however, as computer systems are becoming more integrated, information flow increasingly follows a web model. The hierarchical model can be used to structure the distribution of the computer processing loads at the same time as the web model is used to structure communication of health-related data throughout the institution. The hierarchical model is demonstrated in Fig. 2.3. Each individual computer is part of a local area network (LAN). The LANs join together to form a wide area network (WAN) that is connected to the mainframe computers. In Fig. 2.3 the mainframe is the lead computer or lead part. This structure demonstrates a centralized approach to managing the computer structure.

When analyzing the hierarchical model, the term system may refer to any level of the structure. In Fig. 2.3, an individual computer may be referred to as a system or the whole diagram may be considered a system. Three terms are used to indicate the level of reference. These are subsystem, target system, and supersystem. A subsystem is any system within the target system. For example, if the target system is a LAN, each computer is a subsystem. The supersystem is the overall structure in which the target system exists. If the target system is a LAN, then Fig. 2.3 represents a supersystem.

The second model used to analyze the structure of a system is the web model. The interrelationships between the different LANs function like a web. Laboratory data may be shared with the pharmacy and the clinical units concurrently, just as the data collected by nursing, such as weight and height, may be shared with each department needing the data. The internet is an example of a complex system that demonstrates both hierarchical and web structures interacting as a cohesive unit. As these examples demonstrate, a system includes structural elements from both the web model and the hierarchical model. Complex and complicated systems discussed later in this chapter can include a number of supersystems organized using both hierarchical and web structures.

Boundary, attributes, and environment are three concepts used to characterize structure. The boundary of a system forms the demarcation between the target system and the environment of the system. Input flows into the system by moving across the boundary and output flows into the environment across this boundary. For example, with a web model, information flows across the systems. Thinking in terms of boundaries can help to distinguish information flowing into a system from information being processed within a system. Fig. 2.3 can be used to demonstrate how these concepts can establish the boundaries of a project. Each computer in the diagram represents a target system for a specific project. For example, a healthcare institution could be planning for a new pharmacy information system. The new pharmacy system becomes the target system. However, as the model demonstrates, the pharmacy system interacts with other systems within the total system. The task group selecting the new pharmacy system will need to identify the functional specifications needed to automate the pharmacy and the functional specifications needed for the pharmacy system to interact with the other systems in the environment. Clearly, specifying the target system and the other systems in the environment that must interact or interface with the target will assist in defining the scope of the project. By defining the scope of the project, it becomes possible to focus on the task while planning for the integration of the pharmacy system with other systems in the institution. A key example is planning for the impact of a new pharmacy system in terms of the activities of nurses who are administering medications.

In planning for healthcare information systems, attributes of the system are identified. Attributes are the properties of the parts or components of the system. When discussing computer hardware, these attributes are usually referred to as specifications. An example of a list of patient-related attributes can be seen on an intake or patient assessment form in a healthcare setting. Attributes and the expression of those attributes play a major role in the development of databases. Field names are a list of the attributes of interest for a specific system. The datum in each cell is the individual system’s expression of that attribute. A record lists the attributes for each individual system. The record can also be seen as a subsystem of the total database system.

Systems and the Change Process

Dynamic homeostasis refers to the processes used by a system to maintain a steady state or balance. This same goal of maintaining a steady state can affect how clinical settings respond when changes are made or a new system is implemented.

Equifinality is the tendency of open systems to reach a characteristic final state from different initial conditions and in different ways. For example, two different clinics may be scheduled for the implementation of a new EHR. One unit may be using paper records and the other unit may have an outdated computer system. A year or two later, both clinical units may be at the same point, comfortably using the new system. However, the process for reaching this point may have been very different.

Entropy is the tendency of all systems to break down into their simplest parts. As it breaks down, the system becomes increasingly disorganized or random. Entropy is demonstrated in the tendency of all systems to wear out. Even with maintenance, a healthcare information system will reach a point where it must be replaced. Healthcare information that is transferred across many different systems in many different formats can also demonstrate entropy, thereby causing confusion and conflict between different entities within the healthcare system.

Negentropy is the opposite of entropy. This is the tendency of living systems to grow and become more complex. This is demonstrated in the growth and development of an infant, as well as in the increased size and complexity of today’s healthcare system. With the increased growth and complexity of the healthcare system, there has been an increase in the size and complexity of healthcare information systems. As systems grow and become more complex, they divide into subsystems and then sub-subsystems. This is the process of differentiation and specialization. Note how the human body begins as a single cell and then differentiates into different body systems, each with specialized purposes, structures, and functions. This same process occurs with healthcare. If the mainframe in Fig. 2.3 were to stop functioning, the impact would be much more significant than if an individual computer in one of the LANs were to stop functioning.

Change within any part of the system will be reflected across the total system. This is referred to as reverberation. Reverberation is reflected in the intended and unintended consequences of system change. When planning for a new healthcare system, the team will attempt to identify the intended consequences or expected benefits to be achieved. Although it is often impossible to identify a comprehensive list of unintended consequences, it is important for the team to consider the reality of unintended consequences. The potential for unintended consequences should be discussed during the planning stage; however, these will be more evident during the testing stage that precedes the implementation or “go-live.” Many times unintended consequences are not considered until after go-live, when they become obvious. For example, e-mail may be successfully introduced to improve communication in an organization. However, an unintended consequence can be the increased workload from irrelevant e-mail messages. Unintended consequences are not always negative. They can be either positive or negative.

Unintended consequences is just one example of how difficult it is to describe, explain, and predict events and maybe even control outcomes in complex systems such as a healthcare institution. Starting in the 1950s, chaos theory, followed by complexity theory, began to develop and was seen as an approach for understanding complex systems. Both chaos and complexity theory involve the study of dynamic nonlinear systems that change with time and demonstrate a variety of cause-and-effect relationships between inputs and outputs because of reiterative feedback loops. “The quantitative study of these systems is chaos theory. Complexity theory is the qualitative aspect drawing upon insights and metaphors that are derived from chaos theory.”¹⁹ Box 2.1 outlines the characteristics of chaotic systems. The characteristics of chaotic systems provide a foundation for understanding how complex systems adapt over time. Such systems are termed complex adaptive systems (CAS). There are now several examples of how the concept of CAS is being use to understand phenomenon of interest in the healthcare literature.^23-30

Complex Adaptive Systems

A Complex Adaptive System (CAS) is defined as an “entity consisting of many diverse and autonomous parts which are interrelated, interdependent, linked through many interconnections, and behave as a unified whole in learning from experience and in adjusting (not just reacting) to changes in the environment. Each individual agent of a CAS is itself a CAS.”³¹ This definition is best understood by analyzing the characteristics of a CAS.^32–35

Cynefin Framework for Managing Uncertainly in CAS

A CAS, such as a healthcare organization, will exist with a constantly fluctuating balance between order and disorder. This can be thought of as a continuum with any one system or part of the system moving between order and disorder. The Cynefin Framework was developed to guide organizational management on this continuum, and it provides a helpful perspective for managing informatics-related projects within a healthcare institution. The Framework consists of five domains, as depicted in Fig. 2.4.³⁶ The first four domains (simple, complicated, complex, and chaotic) require the leader or manager to determine the level of order or disorder and adjust their management style to that reality. The fifth domain exists when the manager/leader is unclear which of the other four domains are predominant in a specific situation. As a situation moves toward the ordered end on the continuum, one of two domains will predominate. Those are the simple and complicated domains.

Simple Domain

A simple domain is highly ordered. Cause and effect are evident to the people involved in the setting. Processes are carried out using known procedures that are effective and efficient. This process involves (1) sensing or collecting the pertinent data, (2) categorizing the data, and (3) applying the appropriate response. An example would be administering medications on a clinical unit. The procedures are carefully designed to ensure the five rights of the patient are effectively and efficiently met. There are rules about what should be done if a patient refuses a medication, is off the clinical unit, or some other usual issue occurs. With this type of situation, everyone is expected to follow the procedure carefully. The concept of best practices is most appropriate in these types of situations. Often these types of processes or at least parts of their procedures are excellent candidates for computerization. Because these situations are highly ordered, they have little flexibility. A change in the environment of the unit can lead to chaos because there is no set procedure to be followed. For example, the procedure for administering medication may require that only registered nurses (RNs) open the medication cart. However, what happens in a disaster if no RNs are available? While micromanagement is contraindicated, the leadership in these situations should stay in the information loop, watching for early signs of change and avoiding a move to a chaotic domain.

Complicated Domain

In this domain, the relationship between cause and effect is known but it is not obvious to everyone in the domain. As a result, knowledge and expertise are very important for managing complicated problems. These types of problems are managed by collecting and analyzing data before responding. The expert will know what data are pertinent, as well as different approaches for analyzing these data and different options for responding. There is often more than one right answer. Different experts may solve the problem in different ways. In addition, experts can identify an effective solution but not be able to articulate every detail of the cause-and-effect relationship. For example, health professional or a health informatics specialist may be planning the educational program to accompany a major system implementation in a healthcare setting. In such a situation, there is not one perfect answer but rather several excellent options that can be used to prepare users for the new work environment. In these cases, good practice is a more appropriate approach than best practice because good practice is more flexible and able to tap into the expertise of the expert. Forcing experts to use the “one right approach” usually results in strong resistance.

Because experts are the dominant players, there are problems in the complicated domain that a manager must consider. Innovative approaches offered by nonexperts can be easily dismissed. This can be a significant problem in hierarchical organizations such as healthcare. A group of experts with different approaches can disagree and become bogged down in “analysis paralysis.” In addition, all experts are limited in scope of expertise, and experts are not always able to realize when they have drifted out of their scope of expertise. As a situation moves toward the disordered end on the continuum, one of two domains will predominate. These are the complex and the chaotic domains.

Complex Domain

In the complex domain, the cause-effect relationship is not known. If the cause-and-effect relationship is not known, what data are pertinent data is also unknown. Decisions must be based on incomplete data in an environment of unknown unknowns. The difference between a complicated situation and complex situation can be demonstrated in analyzing the difference between a computer and a person. A computer expert can identify all of the parts of a computer and how they interact. The expert can predict with high accuracy how a computer will react in different situations. For example, a bad hard drive can prevent a computer from booting, but the response of a person to different situations cannot be predicted with the same accuracy. Because healthcare organizations are CASs and always in flux, many important situations and decisions are complex. In this domain, problems are not solved by imposing order. Rather than collecting many data, better solutions can emerge if the manager probes by trying small, fail-safe solutions. For example, a large medical center recently purchased a small rural hospital with little to no computerization in the clinical units. Imposing the information systems installed in the medical center could produce significant resistance and probably fail. Success is more likely if small pilot tests are conducted and then used to pull key leaders and users in when analyzing the results. In the analysis, patterns and potential solutions will begin to emerge. Obviously, this process is time consuming and a bit messy but not nearly as messy as failure. However, there are leadership tools to utilize in a complex domain as illustrated in Box 2.2.

Box 2.2

Management Tools in Complex Situations³⁶

1. Encourage open discussion so people are comfortable both speaking and listening.

2. Set boundaries as clear simple rules: such as, meeting will always end on time.

3. Watch for and use attractors. Listen for ideas that resonate with people to use as potential probes.

4. Encourage and look for new and novel ideas. For example, one provider may notice that clicking on the patient’s address may tell that provider how far the patient lives from the healthcare setting. This piece of information may be useful in making decisions about when to discharge or meet with family.

5. Do not look for predetermined results. Let answers emerge.

Chaotic Domain

In the chaotic domain, there is no discernable relationship between cause and effect; therefore it is impossible to determine and manage the underlying problem. In healthcare, the term crash is often used for this situation. For example, a patient is crashing or a computer crashed. In this situation, the goal is to first stabilize the situation and then assess potential causes. CPR will be tried before there is any attempt to determine why the patient would have experienced a cardiac arrest. In a crisis, the manager takes charge and gives orders. In our previous example dealing with medication administration, the health professional or health informatician may decide to unlock the medication cart for all patients and determine if family or other staff may be able to give at least some of the medications. In a chaotic situation, there is no time to seek input or second-guess decisions. However, once the situation begins to stabilize, this can be a unique opportunity for innovation. Organizations and individuals are more open to change after a crisis: note, for example, the number of patients who will change their lifestyle after they have experienced a myocardial infarction. In Fig. 2.4, the fifth domain is the center domain, existing between disorder and order.

Disorder Domain

Showden uses the term disorder to name this domain. Each of the other domains is focused on the type of situation or state of the organization. In the fifth domain, it is not the organization that is in disorder but rather the leader. The leader does not know which domain is predominant. If the leaders are aware of their confusion, they can begin by using the Framework to assess the situation. However, managers do not always realize what they do not know. If they do not realize the need to diagnosis the situation, there is a tendency to continue using their preferred management style.

While CASs are a unique type of system, all systems, whether they are complex or simple, change, and in the process interact and/or co-evolve with the environment. The basic framework of this interaction is shown in Fig. 2.2. Input to the system consists of information, matter, and energy. These inputs are then processed, and the result is output. Understanding this process as it applies to informatics involves an understanding of information theory.

Shannon-Weaver Information-Communication Model

Information theory was formally established in 1948 with the publication of the landmark paper “The Mathematical Theory of Communication” by Claude Shannon.42 The concepts in this model are presented in Fig. 2.5. The sender is the originator of the message or the information source. The transmitter is the encoder that converts the content of the message to a code. The code can be letters, words, music, symbols, or a computer code. A cable is the channel, or the medium used to carry the message. Examples of channels include satellites, cell towers, glass optical fibers, coaxial cables, ultraviolet light, radio waves, telephone lines, and paper. Each channel has its own physical limitations in terms of the size of the message that can be carried. Noise is anything that is not part of the message but occupies space on the channel and is transmitted with the message. Examples of noise include static on a telephone line and background sounds in a room. The decoder converts the message to a format that can be understood by the receiver. When listening to a phone call, the telephone is a decoder. It converts the signal back into sound waves that are understood as words by the person listening. The person listening to the words is the destination.

Shannon, one of the authors of the Shannon-Weaver Information-Communication theory, was a telephone engineer. He used the concept of entropy to explain and measure the technical amount of information in a message. The amount of information in a message is measured by the extent to which the message decreases entropy. The unit of measurement is a bit. A bit is represented by a 0 (zero) or a 1 (one). Computer codes are built on this concept. For example, how many bits are needed to code the letters of the alphabet? What other symbols are used in communication and must be included when developing a code?

Warren Weaver, from the Sloan-Kettering Institute for Cancer Research, provided the interpretation for understanding the semantic meaning of a message.⁴² He used Shannon’s work to explain the interpersonal aspects of communication. For example, if the speaker is a physician who uses medical terms that are not known to the receiver (the patient), there is a communication problem caused by the method used to code the message. However, if the patient cannot hear well, he may not hear all of the words in the message. In this case the communication problem is caused by the patient’s ear.

The communication-information model provides an excellent framework for analyzing the effectiveness and efficiency of information transfer and communication. For example, a healthcare provider may use CPOE to enter orders. Is the order-entry screen designed to capture and code all of the key elements for each order? Are all aspects of the message coded in a way that can be transmitted and decoded by the receiving computer? Does the message that is received by the receiving department include all of the key elements in the message sent? Does the screen designed at the receiver’s end make it possible for the message to be decoded or understood by the receiver?

These questions demonstrate three levels of communication that can be used in analyzing communication problems.⁴³ The first level of communication is the technical level. Do the system hardware and software function effectively and efficiently? The second level of communication is the semantic level. Does the message convey meaning? Does the receiver understand the message that was sent by the sender? The third level of communication is the effectiveness level. Does the message produce the intended result at the receiver’s end? For example, did the provider order one medication but the patient received a different medication with a similar spelling? Some of these questions require a more in-depth look at how healthcare information is produced and used. Bruce Blum’s definition of information provides a framework for this more in-depth analysis.

Blum Model

Bruce L. Blum developed his definition of information from an analysis of the accomplishments in medical computing.44 In his analysis, he identified three types of healthcare computing applications. Blum grouped applications according to the objects they processed. The three types of objects he identified are data, information, and knowledge. Blum defined data as uninterpreted elements such as a person’s name, weight, or age. Information was defined as a collection of data that have been processed and then displayed as information, such as weight over time. Knowledge results when data and information are identified and the relationships between the data and information are formalized. A knowledge base is more than the sum of the data and information pieces in that knowledge base. A knowledge base includes the interrelationships between the data and information within the knowledge base. A textbook can be seen as containing knowledge.⁴⁴ These concepts are well accepted across information science and are not limited to healthcare and health informatics.⁴⁵

Graves Model

Judy Graves and Sheila Corcoran, in their classic article “The Study of Nursing Informatics,” used the Blum concepts of data, information, and knowledge to explain the study of nursing informatics.46 They incorporated Barbara A. Carper’s four types of knowledge: empirical, ethical, personal, and aesthetic. Each of these represents a way of knowing and a structure for organizing knowledge. This article is considered the foundation for most definitions of nursing informatics.

Nelson Model

In 1989 Nelson extended the Blum and Graves and Corcoran data-to-knowledge continuum by including wisdom.47 This initial publication provided only brief definitions of the concepts, but later publications included a model.⁴⁸ Fig. 2.6 demonstrates the most current version of this model. Within this model, wisdom is defined as the appropriate use of knowledge in managing or solving human problems. It is knowing when and how to use knowledge in managing patient needs or problems. Effectively using wisdom in managing a patient problem requires a combination of values, experience, and knowledge. The concepts of data, information, knowledge, and wisdom overlap and interrelate as demonstrated by the overlapping circles and arrows in the model. Of note, what is information in one context may be data in another. For example, a nursing student may view the liver function tests within the blood work results reported this morning and see only data or a group of numbers related to some strange-looking tests. However, the staff nurse looking at the same results will see the information and in turn see the implications for the patient’s plan of care. The greater the knowledge base used to interpret data, the more information disclosed from that data, and in turn the more data points that may be generated. Data processed to become information can create new data items. For example, if one collects the blood sugar levels for a diabetic patient over time, patterns begin to emerge. These patterns become new data items to be interpreted. One nurse may notice and describe the pattern, but a nurse with more knowledge related to diabetes may identify a Somogyi-type pattern, with important implications for the patient’s treatment protocols. The concept of constant flux is illustrated by the curved arrows moving between the concepts. As one moves up the continuum, there are increasing interactions and interrelationships within and between the circles, producing increased complexity of the elements within each circle. Therefore the concept of wisdom is much more complex than the concept of data.

The introduction of the concept of wisdom gained professional acceptance in 2008 when the American Nurses Association included this concept and the related model in Nursing Informatics: Scope and Standards of Practice.⁴⁹ In this document the model is used to frame the scope of practice for nursing informatics. This change meant that the scope of practice for nursing informatics was no longer fully defined by the functionality of a computer and the types of applications processed by a computer. Rather the scope of practice is now defined by the goals of nursing and nurse-computer interactions in achieving these goals.

Using the concepts of data, information, knowledge, and wisdom makes it possible to classify the different levels of computing. An information system such as a pharmacy information system takes in data and information, processes the data and information, and outputs information. A computerized decision support system uses knowledge and a set of rules for using that knowledge to interpret data and information and output suggested or actual recommendations. A healthcare application may recommend additional diagnostic tests based on a pattern of abnormal test results, such as increasing creatinine levels. With a decision support system, the user decides whether the suggestion or recommendations will be implemented. A decision support system relies on the knowledge and wisdom of the user.

An computerized expert system goes one step further. An expert system implements the decision that has been programmed into the computer system without the intervention of the user. For example, an automated system that monitors a patient’s overall status and then uses a set of predetermined parameters to trigger and implement the decision to call a code is an expert system. In this example, the data were converted to information, a knowledge base was used to interpret that information, and the decision to implement an action based on this process has been automated. The relationships among the concepts of data, information, knowledge, and wisdom, as well as information, decision support, and expert computer systems, are demonstrated in Fig. 2.7.

In the model, the three types of electronic systems overlap, reflecting how such systems are used in actual practice. For example, an electronic system recommending that a medication order be changed to decrease costs might be consistently implemented with no further thought by the provider entering the orders. In this example, an application designed as a decision support system is actually being used as an expert system by the provider. Because there are limits to the amount of data and information the human mind can remember and process, each practitioner walks a tightrope between depending on the computer to assist in the management of a situation with a high cognitive load and delegating the decision to the computer application. This reality presents interesting and important practical and research questions concerning the effective and appropriate use of computerized decision support systems in the provision of healthcare.

Effective computerized systems are dependent on the quality of data, information, and knowledge processed. Box 2.4 lists the attributes of data, information, and knowledge. These attributes provide a framework for developing evaluation forms that measure the quality of data, information, and knowledge. For example, healthcare data are presented as text, numbers, or a combination of text and numbers. Good-quality health data provide a complete description of the item being presented with accurate measurements. Using these attributes, an evaluation form can be developed for judging the quality (including completeness) of a completed patient assessment form or for judging the quality of a healthcare website. The same process can be used with the attributes of knowledge. Think about the books or online references one might access in developing a treatment plan for a patient or consider a knowledge base that is built into a decision support system. What would result if the knowledge was incomplete or inaccurate or did not apply to the patient’s specific problem or if suggested approaches were out of date and no longer considered effective in treating the patient’s problem? What if the knowledge was not presented in the appropriate format for use?

Cognitive and Constructionist Learning Theories

1. How the learner takes input into the system

Social constructivism focuses on how group interaction can be used to build new knowledge.50 Group discussion in which learners share their perceptions and understanding through peer learning encourages new insight, as well as retention of the newly constructed knowledge. There is limited research on how groups such as interprofessional health teams actually learn via the group construction process. Two studies at Massachusetts Institute of Technology and Carnegie Mellon University found converging evidence that groups participating in problem-solving activities demonstrate a general collective intelligence factor that explains a group’s performance on a wide variety of tasks. “This ‘c factor’ is not strongly correlated with the average or maximum individual intelligence of group members but instead with the average social sensitivity of group members, the equality in distribution of conversational turn-taking and the proportion of females in the group.”^{51, p. 686} Certain characteristics of the individuals within the group and the group’s ability to work together as a whole can influence the effectiveness of the group. “A group’s interactions drive its intelligence more than the brain power of individual members.”⁵² Approaches to assessing and measuring the c facture within a group are now being explored with hopes that this measure could be used to support the effective of groups or teams.^53,54

Information once taken into the system is retained in several different formats. The three most common formats are episodic order, hierarchical order, and linked. For example, life events are often retained in episodic order. A list of computer commands is also retained in episodic order. Psychomotor commands learned episodically can become automatic. An example of this can be seen in the simple behavior of typing or the more complex behavior of driving a car. Cognitive learning tends to be retained in hierarchical order. For example, penicillin is an antibiotic. An antibiotic is a medication. Finally, information is retained because it is linked or related to other information. For example, the concept “paper” is related to a printer. The process by which information is retained in long-term memory (LTM) can be reinforced by a variety of teaching techniques. Providing the student with an outline when presenting cognitive information helps to reinforce the learner retaining the information in hierarchical order. Telling stories or jokes can be used to reinforce links between concepts. Practice exercises that encourage repeated use of specific keystroke sequences or computer commands assist with long-term retention of psychomotor episodic learning including muscle memory.

While LTM can retain large amounts of information, two processes can interfere with the storage of information in LTM. First, new information or learning may replace old information. For example, healthcare providers may become very proficient with a computerized order-entry system. However, over time they may forget how to use the manual system to place orders. This can be a problem if the manual system is the backup plan for computer downtime. Second, previously learned information can interfere with the learning of new information. This can be seen when a new computer system is installed and new procedures are implemented. Experienced users of the old system must remember not to use the old procedures that were part of that system. This can be especially difficult if the previous learning has become automatic psychomotor commands. If the instructor for the new system includes clues to remind the experienced users of the change, the process of replacing old learning with new information can be reinforced.

When planning educational programs for healthcare users, the health professional and health informatics specialist must first plan for intake of the new information via short-term memory (STM) and then for transfer of the new information to LTM. Several factors assist in moving information from STM to LTM. A list of these factors and examples of each can be seen in Table 2.2. Information that is stored in LTM is used in critical thinking, problem solving, decision making, and a number of other mental processes.

Adult Learning Theories

In 1970, Knowles coined and defined the term andragogy.55 Andragogy is the art and science of helping adults to learn. Knowles’s model proposed that adults share a number of similar learning characteristics and that these characteristics can be used in planning adult educational programs. Table 2.3 lists a number of these characteristics and provides examples of how they can be used to plan for teaching adult users.^50,56-58

Learning Styles

All learners are not alike. They learn in different ways. They vary in how they take in and process information. There are preferential differences in seeing and hearing new information. Some learners process information by reflecting, whereas others process it by acting. Some learners approach reasoning logically, whereas others are intuitive. Some learners learn by analyzing, whereas others learn by visualizing. Learning theories concerning learning styles attempt to explain these differences. Experiential learning theory is one example.59 The first stage of Kolb’s theory involves concrete experience. For example, the learner may view a demonstration of a new healthcare information system.

As the learner begins to understand how the system works, he begins to think about how the system would work in his healthcare setting. This is the second stage, or reflection. In this stage, the learner reflects or thinks about the concrete experience. As the learner continues to think, he begins to form abstract conceptualizations of how the system functions. This is the third stage. Finally, the learner is ready to try using the system: this is the fourth stage, when the learner uses his abstract conceptualization to guide action. In Kolb’s model, these four stages exist on two intersecting continuums. These are Concrete Experience–Abstract Conceptualization (CE-AC) and Reflective Observation–Active Experimentation (RO-AE). There are individual differences in how learners use each of these four stages in their individual learning approaches, but all learners ultimately learn by doing. Using this model, Kolb developed a learning-assessment tool to identify individual learning styles. The intersection of the two continuums forms four quadrants, Diverger, Assimilator, Converger, and Accommodator, representing four individual learning styles. The learner plots a score along the CE-AC scale and along the RO-AE scale to identify which quadrant reflects his or her learning style.

A second, more widely used measure of individual learning styles is the Myers–Briggs Type Indicator.⁶⁰ This theory uses four continuums: Thinking–Feeling, Sensing–Intuition, Extroverted–Introverted, and Judging–Perceptive. A series of questions is used to determine where the learner falls on each of the four continuums. For example, a learner may be Thinking, Sensing, Extroverted, and Judging. The combination of where the learner falls on each of the four continuums is then used to form a composite picture of the learner’s individual learning style.

A health professional and health informatics specialist plan and implement educational programs for a variety of groups within the healthcare delivery system. These may include physicians, nurses, unlicensed personnel, administrators, and others. These groups vary widely in learning ability, education, motivation, and experience. However, a great deal of variation exists among the learners within each group. Learning styles help to explain these differences and are helpful in planning instructional strategies that are effective for individual learners within a group. Each of the four types of learning theories discussed in this chapter provides insights into effective approaches to teaching. Box 2.6 lists examples of principles that can be derived from these theories.

Planned Change

Kurt Lewin is frequently recognized as the father of change theory.61 His theory of planned change divides change into three stages: unfreezing, moving, and refreezing.⁶² As demonstrated in the discussion of homeostasis, systems expend energy to stay in a steady state of stability. A system will remain stable when the restraining forces preventing change are stronger than the driving forces for change. Initiating change begins by increasing the driving forces and limiting the restraining forces, thereby increasing the instability of the system. This is the unfreezing stage. The first stage in the life cycle of an information system involves evaluating the current system and deciding what changes, if any, need to be made. The pros and cons for change reflect the driving and restraining forces for change. If changes are to be made, the restraining forces that maintain a stable system and resist change must be limited. At the same time, the driving forces that encourage change must be increased. For example, pointing out to users the limitations and weaknesses with the current information management system increases the driving force for change. Also pointing out the advantages offered by a new system can increase driving forces for change.

Asking for user input early in the process before decisions have been made can decrease the restraining forces. However, this is true only if the users believe they are heard and accept that their representative is really representing their interests. If, however, the users believe their representatives are only “going through the motions of asking,” resistance to change will be increased. In addition, at this point when questions are being asked, it can be very effective to anticipate who the objectors might be and include them in the process. Once a decision is made to initiate change, the second stage, moving, begins.

The moving stage involves the implementation of the planned change. By definition, this is an unstable period for the social system. Anxiety levels can be expected to increase. The social system attempts to minimize the impact or degree of change. This resistance to change may occur as missed meetings, failure to attend training classes, and failure to provide staff with information about the new system. If the resistance continues, it can cause the planned change to fail. Health professionals and health informatics specialists as change agents must anticipate and minimize these resistive efforts. This can be as simple as providing food at meetings or a planned program of recognition for early adopters. For example, an article in the institution’s newsletter describing and praising the pilot units for their leadership will encourage the driving forces for change. It is important at each stage of the change process to evaluate and make needed changes to the initial plans; however, it is especially important during the moving phase to identify changes in the system and procedures that need to be modified. The goal is to avoid in the next phase “refreezing” a bad procedure or system.

Once the system is in place or the change has been implemented, additional energy is needed to maintain the change. This is the refreezing stage, and it occurs during the maintenance phase of the information system life cycle. If managed effectively by the change agent, this phase is characterized by increased stability. In this stage, the new system is in place, and forces resistant to change are encouraged. Examples include training programs for new employees, a yearly review of all policies and procedures related to the new system, and continued recognition for those who become experts with the new system.

In the current healthcare environment, several new information applications may be implemented at the same time. Not all of these implementations will affect everyone to the same degree. Different individuals and clinical units can be at different stages of change with different implementations. Taken together, the overall scope of change will create a sense of anxiety or excitement throughout the organization. It is important for health professionals and health informatics specialists to monitor the amount of change and the resulting tension in placing and planning for ongoing implementations.

Health professionals and informaticians may find helpful newer change models that have been built on Lewin’s premises. For example, Conner’s book Managing at the Speed of Change outlines key concepts to facilitate change in complex organizations:

• Create a burning platform for change (the burning need for change).

• Assess organizational culture, capacity, resistance, and responsiveness to create an effective plan for change.63

Find out how change has taken place in the past. Determine what worked in specific areas and what did not. Build on what has worked in the past. Understanding the culture and communication patterns of various units is critical to successful change management.

Diffusion of Innovation

Just as individuals vary in their response to innovation, organizations also vary. Five internal organizational characteristics can be used to understand how an organization will respond to an innovation.67

• Centralization: Organizations that are highly centralized, with power concentrated in the hands of a few individuals, tend to be less accepting of new ideas and, therefore, less innovative.

• Relative advantage: Is the innovation seen as an improvement over the current approach? For example, has the need to standardize with one patient documentation system forced certain clinical units to give up certain functionality? Alternatively, is the new system seen as an upgrade with new functionality?

The decision of individuals and organizations to accept or reject an innovation is not an instantaneous event. The process involves five stages.68 These stages can be demonstrated when a healthcare institution considers using blogs and wikis to support internal communication for all professional staff. The first stage of the innovation decision process is knowledge. In the knowledge stage, the individual or organization becomes aware of the existence of the innovation. Managers become aware of other institutions that are using these tools and begin to learn about the possible advantages. Mass communication channels are usually most effective at this stage. For example, the institution’s newsletter may carry a story about blogs and wikis and how staff might use these tools to support patient care and institutional goals. If the change agent does not have access to formal mass communication channels to reach all professional staff, the knowledge stage can be significantly delayed. Although personal information moves quickly via informal communication channels, cognitive information involving the processing of information and knowledge does not move as quickly through these types of channels.

Once individuals become aware of an innovation, they begin to develop an opinion or attitude about it. This is the persuasion stage. During the persuasion stage, interpersonal channels of communication are more important, and early adopters begin to play a key role. In the persuasion stage, attitudes are not fixed but are in the process of being formed. The health professional and health informatics specialist should work closely with early adopters in developing and communicating positive attitudes to others in the organization.

Once these attitudes become more fixed, individuals make a decision to accept or reject the innovation. This is the decision stage. It is at this point that individuals will decide to try these tools for themselves. For each person this decision can occur at a different point. The early adopters will decide to try the system before the early majority. In testing out the system, most people begin to discover new features or functions of the system. They also begin to discover potential problems. As they gain a better understanding of how to use the new functions, they also discover challenges, modifications, and adjustments that need to be made. Readers and health professional and health informatics specialist needs to be sensitive to these modifications because they will take on an added significance when formal and informal policies and procedures are developed. For example, workarounds can begin to develop at this point.

Once the decision has been made to accept the innovation, the implementation stage begins. The development of formal policies and procedures related to the innovation is a clear indication that the implementation stage is in place. The final stage is confirmation. At this point, the innovation is no longer an innovation. It has either been rejected or become the standard procedure. For example, certain key interinstitutional communication will depend on staff using these tools.

Using Change Theory