wherein a nurse can enter data about a client, ... answering the following questions: Can nursing's ..... may be suspect, Marcus says, they have been found.
Prolog: A Practical Language for Decision Support Systems in Nursing? Judy G. Ozbolt, Ph.D., R.N. Center for Nursing Research School of Nursing The University of Michigan obtain information that may be obvious to some easily retrieved by others from standard nursing textbooks or other readily available reference materials. (COMMES is marketed with an accompanying library of books and journals and is not clear that the computer is a necessary gateway to the paper library.) Third, because its knowledge base is highly specific to illness conditions and nursing procedures, keeping it up to date is expensive in both human and computer terms. Thus, although COMMES has been an important pioneer in the development of decision support systems for nursing and may evolve into a more practical system, it is not yet a fully satisfactory response to nursing's needs for decision support systems.
Abstract
nurses and that could be
Developing decision support systems for nursing has been limited by difficulties in defining and representing nursing's knowledge base and by a lack of knowledge of how nurses make decisions. Recent theoretical and empirical work offers solutions to those problems. The challenge now is to represent nursing knowledge in a way that is comprehensible to both nurse and computer and to design decision support modalities that are accurate, efficient, and appropriate for nurses with different levels of expertise. This paper reviews the issues and critically evaluates Prolog as a tool for meeting the
The other program of research and development in decision support systems for nursing has been at the University of Michigan. Unlike the group at Creighton, we have not yet produced a system ready for implementation. Instead, we have explored problems of decision support in nursing through a series of prototypes. Rather than defining the domain of nursing knowledge as "Whatever is included in the undergraduate curriculum plus whatever appears in the literature," we have looked for conceptual models and theories that would provide a logical structure for the elements and relationships in nursing's knowledge base. Using concepts from McCain (1965) and then from Orem (1971, 1980), we wrote programs in FORTRAN IV, BASIC, and PASCAL that used production rules to prompt the nurse for client data, analyze the data, and propose nursing diagnoses; the last program included aids to formulating objectives and interventions as well (Goodwin & Edwards, 1975; Ozbolt, 1982; Ozbolt, Schultz, Swain, Abraham, & Stein,
challenge.
DevelToping Decision S-port Systeems in Nurstng Atcotplismentst- ate. In contrast to medicine,
where a number of researchers have been working to develop decision support systems for the past two decades, nursing has given rise to only two programs of research and development aimed at supporting those decisions that are within its specific clinical domain. One of these has led to the development of the COMMES system at Creighton
University (Evans, 1984, 1985; Ryan, 1983). Originally designed to support education, the nursing component of COMMES has been extended and is currently marketed as an interactive system wherein a nurse can enter data about a client, pose questions, and receive information derived from the stored knowledge base of the undergraduate nursing curriculum at Creighton, with bibliographical references for further reading. Programmed as a semantic network with the ability to learn from new information, COMMES can provide consultation at the generalist level on a wide variety of topics, and may be extended into some specialty areas. Although it is innovative and ingenious in many ways, COMMES would appear to have three significant limitations. First, because it is not designed to be part of a hospital information system, COMMES cannot draw on stored, clientspecific data; the nurse posing a query must enter all the relevant data about the client. Second, a lengthy dialogue may be required to
1984). Although these programs demonstrated the feasibility of representing nursing knowledge and
decision structures in a decision support system, have chosen not to develop them further because of some inherent shortcomings in the conceptual models that we used to define the nursing knowedge base. Neither McCain's (1965) nor Orem's (1971, 1980, 1985) model adequately reflects the full range of issues with which nurses assist their clients, nor does either provide an adequate basis for selecting objectives and interventions appropriate to an individual client's situation. Furthermore, our programs shared a flaw with the COMMES system: the diagnoses, objectives, and interventions proposed were likely to be fairly obvious to we
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system for nursing, therefore, we must consider how to protect and promote what nurses already do well with clients and how to assist in those areas where they have difficulty. Overcoming the barriers. It is unlikely that nursing's conceptual models could be merged into one unified model, given their conceptual and
the nurse, if not from the start, at least by the time the nurse had entered client data via a lengthy dialogue with the computer. Our recent efforts, therefore, have been directed toward answering the following questions: Can nursing's
knowledge base be represented comprehensively, concisely, and logically? Can a decision support system be designed that will be of practical use to nurses in the way they actually make clinical
philosophical imcompatibility (Fawcett, 1984), and it will be some time, if ever, before one model of nursing emerges as empirically and theoretically superior. For the present, then, designers of decision support systems who seek a conceptual framework on which to structure nursing's knowledge base must simply choose one of the existing models.
decisions?
Barriers to development. Before nursing's knowledge base can be represented in a computer language, it must first be identified and defined in English (or another natural language). That has been difficult for three reasons. First, nursing values a holistic approach to the client. The American Nurses' Association, for example, has said that nursing is "the diagnosis and treatment of human responses to actual or potential health problems" (ANA, 1980, p.9). What, then, in the universe of knowledge, can be ruled out as not nursing knowledge? Second, because nurses dO have holistic concerns and because they have traditionally provided twenty-four hour coverage, they have often done whatever needs to be done, reinforcing the notion that nursing knowledge includes everything about human beings in health and illness. Finally, attempts to articulate what is and what is not nursing have resulted in not one conceptual model but many competing and incompatible ones. Johnson (1974, p.376), referring to different kinds of conceptual models for nursing, noted that Clearly,...different classes of approaches to understanding the person who is a patient, not only call for differing forms of practice toward different objectives, but also point to different kinds of phenomena, suggest different kinds of questions, and lead eventually to dissimilar bodies of knowledge. Faced with disagreement about the nature of nursing and a broad and loose definition of the content of nursing, how can designers of a clinical decision support system delineate a nursing knowledge base? Even if an adequate knowledge base were available, there are no clear precedents as to how it should be used in a decision support system to support the way nurses actually practice. In medicine there is an expectation that the physician will see and examine the patient, order diagnostic tests, and reflect on the results before diagnosing and prescribing. In this linear model of care, there is a built-in opportunity for time away from the patient during which the physician may consult a computer as well as colleagues and other references in order to arrive at appropriate decisions. The central activities of nursing, however, are carried on in the presence of the client, with the nurse simultaneously observing, diagnosing, and intervening in holistic psychological, physiological, social, and spiritual phenomena that evolve from moment to moment. While engaged in an intense diagnostic and therapeutic interaction with a client the nurse cannot be turning away to consult a computer about how to interpret the client's condition and behavior and how to respond. In order to design an appropriate decision support
One model that stands up well to the criteria of internal and external criticism proposed by Stevens (1979) is called Modeling and Role-Modeling (Erickson, Tomlin, & Swain, 1983). This paradigm is clear, consistent, and logically developed, and it provides theoretical bases for explaining and predicting client conditions and behaviors, including responses to specific nursing interventions. In offering a holistic view of the interactions of biophysical, psychological, social, cognitive, and spiritual phenomena and showing how theoretical knowledge can be applied to increase the effectiveness of nursing care, it meets the criteria of adequacy, utility, significance, and discrimination of nursing from other health professions and from other helping actions. In terms of scope, it offers both a "grand theory" of nursing and more specific propositions giving rise to testable hypotheses. It is complex enough to address the full range of "human responses to actual or potential health problems" (ANA, 1980). The complexity that makes it effective, however, also makes it difficult for any but expert nurses to practice within the framework of Modeling and Role-Modeling. Because of this complexity and because of Modeling and Role-Modeling's strength on the other criteria, a decision support system that built its knowledge base on the Modeling and Role-Modeling framework and helped nurses to practice in that context would be both adequate and useful. Because it deals with complex interrelationships of multiple factors it could help to organize data and make suggestions that were not obvious to the nurse. It would also have a practical advantage with regard to programming and maintenance: its knowledge base would be sufficiently abstract and general to apply to clients throughout the lifespan and across all conditions of health and illness, making it relatively concise and less often in need of updating.
The question remains, however, how the system could nelp nurses to make decisions in practice. In a series of studies of how nurses practice (Benner, 1982; Benner & Wrubel, 1982; Benner, 1984; Benner & Tanner, 1987), Benner and her colleagues have differentiated between what Polanyi (1962) called knowing that, or theoretical knowledge, and knowing how, or experiential knowledge involving intuition and insight. Benner (1982) has also described five levels of nursing proficiency, from novice to expert, as a continuum progressing from
80
might interact extensively with the theoretical knowledge base to arrive at decisions about client needs and nursing care. More expert nurses might use the theoretical knowledge base primarily to consider alternatives and to develop care plans and documentation quickly and efficiently. The union of the nurse's knowledge, experience, and judgment with the computer's capacity to store and process theoretical knowledge and data would lead to better clinical decisions than either nurse or computer
minimum of experiential knowledge and a maximum reliance on theoretical knowledge to a high degree of experiential knowledge with a minimum need to rely on theoretical knowledge expressed as rules and maxims. "The beginner must rely on a deliberative analytical method to build the clinical picture from isolated bits and pieces of information. The expert has the skill and the option to grasp the situation rapidly, to see the whole, or the gestalt" (Benner & Wrubel, 1982, p. 13). Dreyfus (1972, 1979) proposed that intelligent activities involving perceptive guessing, insight, understanding in the context of use, and recognition of varied and distorted patterns--just the sort of activities included in experiential knowledge--represent an exclusively human capacity, one that computers are unlikely ever to mimic successfully. If we accept this premise, we cannot expect to create a computer-based system that will "think" like an expert nurse. What computers could be designed to do, however, is to "count out alternatives once [the human user] had zeroed in on an interesting area.... Likewise, in problem solving, once the problem is structured and an attack planned, a machine could take over to work out the details ... " (Dreyfus, 1979, p. 301).
a
could achieve alone.
Where and how would the nurse use this system? Because effective nursing depends on a therapeutic interaction between nurse and patient, it is important to avoid introducing the computer as an intrusive, competing presence. Therefore, while a bedside terminal might be used for brief entries of factual data such as vital signs, the more prolonged computer interaction necessary for diagnosing ongoing conditions (as opposed to transitory ones that are diagnosed and resolved during a single nurse-client interaction), planning, and documenting care should occur elsewhere, preferably in a quiet room where the nurse is subject to fewer distractions than in the typical nurses'
station.
Thus, a decision support system could be designed to work with (not instead of) expert nurses in generating alternative hypotheses, goals, interventions, and consequences to consider in making a human judgment of what is most appropriate for a particular client. Such a function could help nurses to avoid the "error of tunnel vision, of the 'closed mind' or of 'projecting' the wrong set of data or their own fears into a situation," an error to which Benner (1987, p. 28) says even expert clinicians are susceptible. Similarly, once the nurse had decided on a course of action, the computer could generate detailed, individualized care plans and aids to documentation based on the stored knowledge base. With its stored base of theoretical knowledge, the system could also help less expert nurses to think through a situation, posing questions to obtain information based on judgment, not rules (e.g., "Has Mr. Jones suffered a significant loss?"), and proposing inferences based on that information and on the theoretical knowledge base (e.g., "The information you provide suggests that Mr. Jones may be experiencing morbid grief."). The system could also provide the nurse with options to obtain more information about the hypothesized nursing diagnoses (e.g., common manifestations of morbid grief). Thus, less expert nurses could be assisted to make optimal use of a complex knowledge base, such as that represented by the Modeling and Role-Modeling framework (Erickson, Tomlin, & Swain, 1983). A useful decision support system for nursing, then, would be based upon a theoretical knowledge base (the specifiable or formalizable aspect of nursing knowledge) but would draw upon the nurse's capacity to make human judgments (the nonspecifiable aspect of nursing knowledge). Less expert nurses
The Challenge
Efforts to date to develop decision support systems for nursing have had only modest success because their knowledge bases were too limited or too unwieldy and because they provided little real assistance to nurses in making decisions about care. Now, however, we have the means to overcome these limitations. Within the context of Modeling and Role-Modeling (Erickson, Tomlin, & Swain, 1983), a knowledge base has been delineated for the nursing care of clients of all ages and conditions (although supplementary modules might be needed for
some specialty settings). Furthermore, we are beginning to understand how nurses use knowledge to make clinical decisions. The challenge now is to find the appropriate technological tools and strategies to develop a system that can efficiently and accurately store and draw inferences from a theoretical knowledge base and a client data base, and that can communicate and interact effectively
with nurses.
Prolog: A Tool to Meet the Challenge? Representing knowledge. At the heart of any decision support system is its knowledge base. How this base is codified is a primary determinant of what the system will be able to do, as Brule (1986, pp. 38-39) has pointed out: The task of designing a knowledge representation scheme for any system that hopes to make use of artificial intelligence is one that has a critical importance. Ultimately, the style, format, and assumptions inherent in any knowledge representation scheme will have a pervasive impact on what can and cannot be encoded, processed, and ultimately accomplished in the system.
81
Two types of languages are available for codifying knowledge bases for decision support systems, list processing languages such as LISP and Pop-II and logic programming languages such as Prolog. Of the two types, Brule (1986, p. 78) believes that "Prolog is better suited to situations in which the knowledge to be processed consists of facts and relationships between the facts." This is because logic programming is based on writing programs as sets of assertions. These assertions are viewed as
having declarative meaning
as
those frames as in a semantic network" (Marcus, 1986, p. 162).
Supporting decision making. By using the theoretical knowledge base and a client data base to make logical inferences about nursing diagnoses, objectives, and interventions, a Prolog based system could assist both expert and less expert nurses to make decisions about care. By organizing related information, generating alternatives, posing questions, and making suggestions, it could support both the formalized, theoretical, scientific aspects of nursing and the nonformalizable, intuitive, artful aspects of nursing. Would designing such a system, however, be as simple as Marcus would make it appear? Probably not. Linking a client data base to a Prolog knowledge base would require innovative approaches to system design and programming. Brodie and Jarke (1986) point out that in Prolog, data base operations and other input/output functions are accomplished as side effects to the logic programming paradigm, making Prolog less than ideal for data base management systems. Nor can the problem of integrating a knowledge base and a data base be readily resolved by linking a Prolog system to a sophisticated data base management system, because of Prolog's difficulty in communicating with other languages. After considering the alternatives, they recommend the "tight integration" of a logic programming system with a data base management system. To achieve this Brodie and Jarke say it would first be necessary to resolve certain problems by research and development, including deciding how to divide the inference functions between the data base management system and the knowledge base system. Sciore and Warren (1986) support the "tight integration" approach, giving examples to show how it is possible to design a single integrated system combining inferencing and data retrieval. They propose that this can be done by extending a Prolog system to include some components of a data base management system. Assuming that the integration of the knowledge base and the client data base can be achieved, it will still be necessary to develop a user interface that will allow nurses to communicate with the system in a language that approaches English. Ennals, Briggs, and Brough (1984, p. 378) have described efforts to develop a user-friendly frontend translation program to aid children and other naive users to write programs using "an infix binary notation that closely resembles English." In this and other approaches they describe, however, the emphasis is on helping users to write programs. Nurses seeking decison support in clinical settings are not interested in writing programs. They need to be able to communicate with the system in English to pose questions and to enter and receive information. Perhaps the "procedural attachments" Marcus (1986) describes will resolve this issue, but their effect on program efficiency will have to
descriptive
statements about entities and their relations. In addition, the assertions derive a procedural meaning by virtue of being executable
by
an
interpreter. Indeed, executing
a
logic
program is much like performing a deduction on a set of facts (Cohen & Feigenbaum, 1982, p. 120). Because of these characteristics, Prolog would appear to be well suited for representing a nursing knowledge base structured within the Modeling and Role-Modeling (Erickson, Tomlin & Swain, 1983) paradigm. The Modeling and RoleModeling knowledge base consists of statements of facts and relations. For example, the paradigm describes the relationships of basic need satisfaction to object attachment and loss, developmental growth, and adaptive potential (Erickson, Tomlin, and Swain, 1983, pp. 86-92). The paradigm also relates theoretical principles to nursing aims and specific interventions. It thus provides a logical framework for deducing nursing diagnoses, objectives, and interventions. A program that could capture this knowledge base and exploit it to answer ad hoc questions about complex interrelationships would be useful indeed. How might Prolog do this? Without going into the details of Prolog syntax, which can be found in such standard texts as that of Clocksin and Mellish (1984), it can be noted that Prolog provides a means of stating relationships concisely. For example, the relation, "Need satisfaction promotes development" could be stated as "promotes (need satisfaction, development)." It would be possible to state all the relationships defined by Modeling and Role-Modeling in this way, but a program consisting merely of a long list of such statements would probably be very inefficient to run. Cohen and Feigenbaum (1982) note that in searching for the answer to an inquiry Prolog proceeds sequentially through the list of assertions, using exhaustive backtracking when necessary to try to satisfy all the conditions of the inquiry. Marcus (1986), by contrast, describes how knowledge needed to solve particular kinds of problems can be grouped in frames, with "slots" to be filled in with specific data and "procedural attachments" to pose English-language questions to the user to obtain the data (or get data from a data base) and to interpret the data so obtained. Such an approach appears very promising to structure the knowledge base for a nursing decision support system, particularly if the frames are linked hierarchially into taxonomies, "allowing the system to use the semantic richness of frames while being able to describe the relationships between
be evaluated.
A final issue in decision support concerns
uncertainty. Marcus (1986, p. 153) says, "In -82
Brodie, M.L., & Jarke, M. (1986). On integrating logic programming and databases. In L. Kerschberg (Ed)., Expert database systems: Proceedings from the First International Workshop, Menlo Park, CA: Benjamin/Cummings, 1 91 -207. Brule, J.F. (1986). Artifical intelligence: Theory, logic, and application. Blue Ridge Summit, PA: Tab Books. Clocksin, W.F., & Mellish, C.S. (1984). Programming in Prolog (2nd ed.). Berlin: Springer-Verlag. Cohen, P.R., & Feigenbaum, E.A. (1982). The handbook of artificial intelligence (vol.ill). Menlo Park, CA: Addison-Wesley. Dreyfus, H.L. (1972). What computers can't do. New York: Harper & Row. Dreyfus, H.L. (1979). What computers can't do (Rev. ed.). New York: Harper-Colophon. Ennals, R., Briggs, J., & Brough, D. (1984). What the naive user wants from Prolog. In J.A. Campbell (Ed.), Implementations of Prolog. New York: Wiley, 376-386. Erickson, H.C., Tomlin, E.M., & Swain, M.A.P. (1983). Modeling and role-modeling: A theory and paradigm for nursing. Englewood Cliffs,
order to be used responsibly, expert systems must be able to communicate a confidence factor so that the user can judge the system's findings." Although the numerical values of confidence factors may be suspect, Marcus says, they have been found useful for ranking conclusions from best to worst. She describes three basic approaches to confidence factors, a "standard" approach, a "fuzzy logic" approach, and a Bayesian approach and shows how each can be managed in Prolog. To have confidence factors associated with inferences would be an important enhancement to a nursing decision support system. It will be necessary, however, to find a way to incorporate the rules for calculating confidence factors without detracting excessively from the speed or efficiency of the program.
Conclusions Prolog appears to be a promising tool for programming decision support systems for nursing. Its advantages over other approaches include conciseness and efficiency. The most important difference, however, is the capacity of logic programming to respond to ad hoc questions by drawing inferences from the knowledge base. Given a complex nursing knowledge base, a Prologbased system could help nurses of varying expertise to consider relationships and alternatives and could facilitate care planning and documentation. In order to achieve these objectives, however, several problems must be resolved. The nursing knowledge base must be represented in Prolog, the effects on system efficiency of including confidence factors must be determined, the ability of the system to draw upon and manage the client data base must be developed, and a user-friendly interface must be designed. Each of these tasks is formidable. Prior accomplishments and current trends, however, suggest that all are achievable. The time has come to begin to develop practical, useful systems that will support the science and the art of nursing.
NJ: Prent ce-Ha l.
Evans, S.. (1984). A computer-based nursing diagnosis consultant. Proceedings of the Eighth Annual Symposium on Computer Appi cations in Medical Care, 658-661. Evans, S. (1985). Clinical and academic uses of COMMES: An implemented AI expert system. Proceedings of the Ninth Annual Symposium on Computer Applications in Medical Care, p. 337. Fawcett, J. (1984). Analysis and evaluation of conceptual models of nursing. Philadelphia -
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