Journal of Medical Systems, Vol. 9, Nos. 1/2, 1985

Challenges Facing the Distribution of an Artificial-Intelligence-Based System for Nursing*

Steven Evans

The marketing and successful distribution of artificial-intelligence-based decision-support sys- tems for nursing face special barriers and challenges. Issues that must be confronted arise particularly from the present culture of the nursing profession as welt as the typical organiza- tional structures in which nurses predominantly work. Generalizations in the literature based on the limited experience of physician-oriented artificial intelligence applications (predominantly in diagnosis and pharmacologic treatment) must be modified for applicability to other health professions.

B A C K G R O U N D

Basis of Experience

The basis for this report rests on our long history of direct involvement in the area of expert system development. For example, for over 10 years, there have been a variety of efforts in Creighton University's Health Sciences Center developing computer-based consultant systems for health professions applications.l-6 During the past 7 years, an artificial intelligence (AI) expert system for nursing has been under development, testing, and distribution. The system, called COMMES, after being refined and tested in nu- merous sites under diverse situations for a number of years, was readied for official external distribution in the middle of September 1983.

Numerous marketing efforts were undertaken, directed toward the largest for-profit hospital chains, individual hospitals and nursing homes, universities (both public and private), and foreign institutions (hospitals and educational institutions). As a result of this experience, we acquired extensive marketing experiences (together with system uses

From Instructional Science Research for the Health Sciences, Creighton University, Omaha, Nebraska 68178. * This paper was supported in part by a grant from the W.K. Kellogg Foundation of Battle Creek, Michigan.

79

0148-5598/85/0400-0079504.50/0 © 1985 Plenum Publishing Corporation

80 Evans

and user reactions) in the area of artificial-intelligence decision-support systems directed specifically to the nursing professional. We have meticulously monitored customer sites that have utilized the system. Concomitantly, we have developed a medical (physician- oriented) risk assessment expert system within the area of oncology, which we have been testing and will soon market nationally. We have also followed in the literature the experiences of colleagues who have attempted to introduce AI systems addressing phy- sician needs.

As all of these experiences have been evaluated, we have identified significant differences between nurses' reactions and responses and the experiences of ourselves and others who have attempted system distribution in physician-oriented arenas. The barriers and challenges we have identified for nursing have analogies to those in other domains, to be sure, but there are barriers peculiar to nursing, tied to the nursing professional role and nursing definition of their profession. These differences in professional reactions, barriers, and special challenges will be summarized in sections below.

Methodology Approach

Although we have had an extremely wide exposure to a variety of settings, insti- tutions, and individuals, the methodology of this report, in the end, is essentially reflec- tive; it synthesizes our experiences and represents an analysis of innumerable events and episodes. It is also based on user survey instruments we have used, but again, the numbers have been small, and we must rely on subjective interpretation.

Thus, although there is no double-blind study methodology that we have applied to this type of information, nonetheless the synthesis is over a wide area, a number of years of experience, and quite diversified settings. However, the analysis below will not be universal; these are, surely, hospitals that are headed by nurse CEOs that have total integration of the medical record, nursing system support, etc. Nonetheless, our presen- tation will identify predominant circumstances that generally hold far and wide.

Uses of This Report

The value of this kind of information is threefold: First, it is often this type of information that is learned and relearned the hard way when systems actually come to the marketplace. Hence, there is a practical value to disseminate information from applied settings, especially when an impact on professional practice is really desired. In addition, the problems we identify have real implications for system design and development. The needs that must be met are as real as any user interface issue that might be germane, be it a nonglare screen or a swivel CRT. Lastly, our synthesis shows that the "common wisdom" for physician-oriented systems must be substantialiy altered for nursing appli- cations.

Indeed, the assessment we shall provide specifically reinforces a larger view of the "interface" issue: In our view, interface questions must necessarily also involve (1) professional (e.g., nursing) interface characteristics that impinge on use and (2) orga- nizational interface issues that bear on acceptance. Thus, the limited idea of a "user- friendly" interface must expand to encompass cultural issues, including the culture of the profession as well as the culture of the organization in which the professional acts.

Artificial-Intelligence-Based System 81

It is more apparent, therefore, that there should be major differences between nurs- ing's needs and reactions to these systems and those of virtually all others in response to AI system introduction. This follows directly from the nursing role in the organization that differs significantly from the role and organizational relationship of other members of the health care team. This paper addresses in a practical sense how these professional and organizational differences impinge on system introduction, acceptance, and design, especially with respect to Al-based expert systems.

PAST EXPERIENCE: THE M.D. MODEL

One Model Among Many

Virtually all artificial intelligence decision-support systems have been medical (in the restricted sense of physician) applications. Thus, the medical model has seemed obvious to researchers; it is the norm and the bench mark to most, if not virtually to all, AI researchers since this is practically the only forum that has been available. Moreover, most of the AI researchers who are health professionals are themselves physicians. Eth- nocentric tendencies, however, need to be reviewed, since false generalities may follow when lessons are applied cross-culturally.

The M.D.'s Locus of Control

The physician's use of clinically related medical systems operates under special circumstances. To begin, physicians have substantial professional activity independent of institutional authorities. That is, the typical office practice is a rather self-contained, small enterprise over which one or a few physicians have substantial control. In the sense of (clinical) system adoption, the physician has substantial control, if not exclusively so, over purchases, policy, and procedures. Hence, the physician is a professional with a predominantly individual (or limited group) practice setting, separated in large measure from the day-to-day choices of administrators of institutions in which he or she may be affiliated.

The M.D.'s Task Domain

AI medical expert systems have nearly exclusively addressed the clinical problem- solving activity of the physician, focusing on some of the subtlest tasks such as diagnosis, development of treatment plans, etc. As a result, the challenge to the ego of the physician is of the highest order; the system may attempt to rival the most complex tasks he performs. Thus, ego inflamation is a real possibility in response to system use. As a result of this, some generalities about system design have been formulated by several authors. For example, there has been the speculated requirement that medical AI systems must "explain" their logical processes since without justification the physician may not heed the answer. As we move away from the M.D. 's task domains, this type of generality becomes in fact a parochial observation.

82 Evans

The M.D.'s System Experience

The physician is also exposed to a variety of other backup computer-based systems that attempt to support his decision making. There are data bases providing drug infor- mation, characteristics of diseases, full-text retrieval of critical textbooks, etc. Hence, other systems resources are widely available and mitigate some of the pressures the physician faces in keeping up with professional practice. Exposure to these other systems, unconnected with his own office or hospital computer, also gives him sobering and realistic experiences with externally designed support systems (although not typically AI- related ones, of course).

These are just some of the kinds of characteristics that distinguish the physician's environment from other health professionals, particularly nurses.

THE NURSING SETTING: ANOTHER MODEL

Although this section of the paper addresses the nursing setting in particular, the point of the comparison should not be viewed as limited in fact to this single contrast. If we had taken the medical technologist instead, we could have equally well discussed an artificial intelligence expert system designed for this area wherein the characteristics were markedly different from either of the two areas already mentioned. The point is that any other (nonphysician) setting will likely be quite different, with another set of needs that must be addressed. It is this underlying point we make in contrasting an alternate discipline (nursing) with the physician perspective.

Nurses' Locus of Control

In the case of nursing, obviously a significant amount of nursing care occurs in hospital settings. True, clinics provide nursing care, and there are nurses in individual physician offices. But by and large, a dominant setting is the hospital or nursing home. Within this setting, we find a number of important features. First and foremost is the institutional element involved. The hospital is governed by hospital administrators, most of whom most likely are not nurses. System purchasing and evaluation often are done or initiated by the hospital data-processing center or the purchasing department. System acquisition regarding needs and requirements historically has been frequently in the hands of nonnursing professionals who are the real gate-keepers. The personal involvement of the physician in decision making about system needs and use is certainly not in any way similar to the nursing arena.

Systems Acceptance Criteria

System adoption for nursing in hospitals often requires appeasing or impressing budget managers who do not fully appreciate the nursing role or necessarily see much merit (as might be most desirable) in support of nursing functions. Reducing nursing would be attractive, from their economic point of view, while support for quality nursing care may not pay from their narrow accountant's view. Hence, the systems adopted tend to favor labor-reducing systems rather than decision-support systems.

Artificial-lntelligence-Based System 83

The Nurses' Task Domains

Much in the way of systems support that exists for nursing has focused on admin- istrative duties for nurses or on the administration of the nursing staff. Scheduling of nursing staff is but one example. Systems to help nurses define the acuity of the patient (related to the degree of seriousness of the patient's condition and the nursing care concomitantly implied) are tied to scheduling needs and ultimately to labor cost controls. The professional decision making or problem solving that nurses provide has heretofore been hardly addressed by any systems at all (before the COMMES system). Some systems have attempted to provide a compendium of all possible nursing care plans as a guide to nursing care, but these have not been decision-support systems as much as they have been simply encyclopedia-like systems.

Nursing systems to date have usually not really addressed the difficult problem of nursing decision making and thus have not presented the same class of threats that AI systems in medicine have. Hence, the nursing ego has not been as threatened as has been the case in medicine.

NURSING AND SYSTEM USE

The Hospital MIS: A Special Case

Characteristic of nursing is prior high system use but usually in the very narrow context of a hospital's MIS system. Most hospitals have some sort of record keeping that is accessible to nursing. As a result, the patient's chart, or his lab work, or his admission dates, etc., can be viewed via computer by the nurse as needed. This system is supported by the hospital and usually is likely to be quite fast, is frame- or screen- driven, and may have numerous bells and whistles (often that were purchased or devel- oped by the data-processing center staff).

The result of this experience is an often biased view by the nurse of what to expect of systems. The expectations derived from this narrow and peculiar setting does not easily carry over to stand-alone or delivered systems. The hospital may also have inte- grated some of the systems it supplies. However, an externally developed n e w product may or may not be compatible or easily integrated. Access to the hospital's data base by externally created systems is a complicated question and very difficult for an external system to address. Even more critical, few have any idea of what they would "integrate" even if everything were possible. Overall, expectations by nurses for hospital-wide com- patibility can be and often are skewed quite unrealistically due to past experiences focused on their hospital-supplied systems.

Looking for Packages

Another tendency within nursing is the expectation of, or the wish for, the "packaged cassette." This partially stems from the fact that historically there had been less research and development on the part of nursing professionals than there has been for physicians. Even now, for example, of the numerous for-profit, large hospital chains we have en- countered, the Hospital Corporation of America is one of the few that has anything

84 Evans

worthy of the name of real research and development directed toward nursing needs. Thus, many of the major chains with whom we have had interactions (accounting for over 1,000 hospitals or one-seventh of the total in the nation) started with a cassette mentality for nurses that only looks for specific systems packages for specific concrete needs (e.g., a system package to "teach patient teaching for the rehabilitation of em- physema patients"). There have been few, if any, major, long-term systems R&D de- velopment efforts focusing on nursing decision making; COMMES in this respect is quite alone among the AI decision-support systems.

Negative Economies of Scale

In a hospital setting, any system for nursing may affect 20, 40, or many more nurses, over three shifts a day, 7 days a week. Necessarily any system that operates in this context is likely to be large, more extensively used, etc. The economies of scale do not operate in favor of nursing in this respect: The total cost is likely to be higher if for no other reason than it must run on a good-sized "mini" computer to support such usage, or it is a time-sharing service and hence may have long (and expensive) connect-times, or it must have multiple stations to accommodate the entire hospital. All of these scenarios imply extensive costs. Large aggregate costs due to the nature of the nursing constituency represent a challenging situation that systems implementors must address for nursing.

The Crowded Nursing Station

Nursing pods or stations already have hospital-supplied stations and terminals for a variety of purposes; there is a growing crowding condition already developing that im- pinges on new nursing applications. Yet questions of space (and the need for more space) are complex hospital administration questions that the nurse cannot address alone.

Recognizing Differences

From these comparisons, it becomes apparent that the nursing working environment in regard to systems applications is quite different from the physician, and the nurse faces substantially different conditions in addressing system acceptance and evaluation than does a physician counterpart. If these differences are not recognized, the introduction of systems in nursing will be stymied. Thus, when system introduction can beneficially address nursing decision making, as can AI expert systems, system acceptance may nonetheless be hindered by organization challenges described above.

SYSTEM BARRIERS TO ADDRESS

From the prior discussion, it becomes apparent that a new set of expectations arises on the part of nursing professionals for system performance. In conjunction, there is a set of barriers unique to the nursing role and environment that also must be addressed. These are elaborated below.

Artificial-Intelligence-Based System 85

Unreasonable User-Friendliness

From the experience of using hospital-based MIS systems that are tuned to specific needs, there is an often overwhelming expectation by nurses that every system will be unrealistically "fr iendly." Indeed, system performance may be expected to surpass any reasonable expectations of even a human. The nursing experience with the hospital's MIS as well as the constant trumpeting of user-friendliness, coupled with nursing's lack of wide exposure to the limitations of systems in general, has created widespread un- realistic expectations. Often there is also the added expectation that all systems will be so advanced that learning will be immediate, and system mastery will be instantaneous.

All in all, user-friendly expectations are reasonable criteria in their own right and are commentaries on how well the popular press and professional journals have instilled high expectations. Nonetheless most large-scale decision-support systems that address very subtle problein'solving very likely will not meet expectations held in nursing quarters far and wide. Hence, all complex system use will be sobering and even disappointing until experience begins to modify expectations. In short, the present hype of popularists has exceeded reasonable limits, and typical required systems manipulations in general will continue to be incongruent with nursing expectations in many quarters.

Unlikely Compatibility

Even over the intermediate term, externally developed stand-alone decision-support systems will not take into account to any great extent local hospital idiosyncracies or extensively interface with records and materials their nurses already utilize. Integration of systems has not been achieved in areas far more stable than clinical decision-making areas. Much more time will likely be needed before this will be accomplished.

Tools versus Packages

More difficult to achieve is a transition from a "packaged cassette" mentality to a research and development mentality. Yet this shift is necessary if powerful decision- support systems are to be effectively used. There is R&D occurring in the schools of nursing in academic health centers and in special, more progressive arenas (such as the Hospital Corporation of America already mentioned as a notable example); this, however, is not the rule in the nurses' working environment in most hospitals. It is also not the case for physicians in general, but their notion of R&D has been instilled in the mythology of medicine in the image of physician-as-scientist, so that R&D is at least natural to the M.D. perspective. Since the decision to accept a system for nursing rests as much in the hands of administrators who give even less credence to R&D for nursing (the cost-benefits in their minds being even more intangible), their propensity to accept more valuable yet more general systems is still in its formative steps.

E X P E C T E D S Y S T E M D I F F E R E N C E S

Incorporating Support

Arising from its different task domains, nursing has different system design require- ments than other areas. Being more outcome-oriented, nursing would not as likely de-

86 Evans

mand to know " h o w " the system arrived at its conclusions. It is not that they are uninterested; rather it is often an acceptable notion in nursing that decision-support sys- tems could produce very intelligible cogent responses, recognized as valid on its face by a nurse, without extensive logical verification.

Application of Rules

Another feature that may distinguish nursing systems from physician-related ones relates to the recursive or even reiterative nature of rule application as used in expert systems. The working situation is extremely complex in the nursing environment, and decisions have to be reconsidered and reviewed. But there is not the same kind of diagnosis undertaken as is done in medicine. Nursing diagnosis, a valid and now growing area of research, has in fact a different character than does medical diagnoses. Thus, a nursing expert system's applications of rules takes on different kinds of characteristics than physician-directed ones. Moreover the nursing role has many disparate facets, in- cluding care, support, and teaching. Although in many respects identical to physician interests in the abstract, in practice the activities are obviously markedly different. In short, AI decision-support systems that address a variety of nursing activities can be expected to have widely different rule-application characteristics than the primarily diag- nostic decision-support systems of medical interests. Often rule applications may be applied in one (or just a few) passes, depending on the application. The process of abduction, characterizing the medical diagnosis system CADUCEUS for example, is far less applicable (if at all) either for nursing diagnosis or for nursing care plan develop- ment. 7 In particular, the COMMES nursing decision-support system does not use the typical if-then rule-based approach found in most other expert systems.

C H A L L E N G E S A H E A D

System Adoption By Nurses

In approaching system implementation for nursing, our discussion above helps iden- tify several major challenges. The first is to address the problem of the separation of decision making between those who undertake system consideration (nurses) and those responsible for system adoption (nonnursing administrators). Nurses must have greater control over decisions affecting their professional activity if decision-support systems that address quality (no less cost-efficient care) can be introduced. In some areas, this shift is advancing, although in many other areas nursing is being reduced in influence as they are seen as a high-cost element in the hospital cost equation, an element more easily reduced, politically, than perhaps other factors.

Quality Care as a Criterion

In addition, system evaluation must genuinely incorporate the issue of impact on quality care. The current extent of the lip service to quality care is ubiquitous; the practice is often fixation on the lowest labor cost per shift. Systems that address decision making must be addressed in terms of their total impact on the economic state of the hospital or

Artificiai-Inteillgence-Based System 87

care facility. This is already being achieved somewhat since studies are showing that maintaining quality nursing care (e.g., R.N. staffing) represents one of the most signif- icant ways to reduce cost per patient day, for example.8 The blatant fact of risk man- agement also argues for the need for quality, again on clean economic grounds. In short, such total systems analysis and concomitant evaluation must be forthcoming to lay the basis for the consideration of at least some nursing AI decision-making systems within a global economic perspective.

Stand-Alone Nursing Systems

The widespread practice to provide nursing systems through the hospital main-frame computers will also have to be reconsidered. As additional systems are introduced, the hospital main-frame will grow more overextended; new patient record features and cost- accounting already strain many systems and data-processing budgets. The concept of the value of the nursing role will have to permeate top-level decision making in the hospital in order that consideration be given to the nursing professionals and their own system needs. Systems development for nursing by the hospital data-processing staff will have to give way to the incorporation of external systems not able to be duplicated by hospital data-processing staff.

Cost Centers versus Profit Centers

In this same sense, nursing will have to become a center that is viewed not as merely a cost, a loss, but a contributor to the profit picture through a total-system analysis. This will facilitate the expenditure of money in areas that previously simply did not enjoy such lattitude. The criterion that a system is valuable if it cuts nursing staff must be replaced by criteria that include questions such as: Does it improve the valuable nursing role?

Ultimately, the impact of such systems may overshadow, if not match at least, the impact of AI decision-support systems in other areas (such as medical diagnosis). The reason is that nursing, for example, accounts for a significant part of any total hospital budget. Also, nursing care accounts for the preponderance of patient care as well as a predictor of patient release within the time frames set in DRG calculations. Thus, the financial payoffs in supporting nursing, be it risk management or DRG reimbursement, are all the more significant, if not equal to the significance of medical diagnosis decision- support systems. This observation derives from the recognition that patient care is ulti- mately a team care issue; the nurses' role on the team is as crucial as any, and perhaps moreso than some. Systems that support, extend, or enhance the capability" of the nurse ultimately are extremely valuable not only to the patient but also to the care facility on economic, social, and ethical grounds.

SUMMARY

Interfacing the Culture

There have been mountains of commentary regarding user interface and user "friend- liness." However this has been often restricted to physical issues or limited to specific

88 Evans

applications points of view. In the application of AI systems to other domains, with nursing being an important and notable case, a wider "interface" interpretation is needed. It is critical to recognize major organizational and professional differences.

There are, on the one hand, nursing needs, working environments, and the admin- istrative organization. On the other hand, there are other professionals markedly different, such as the physicians' role and related medical diagnosis activity. In this comparison, one can recognize that nurses have different systems barriers and challenges related to the culture of their profession and the culture of the working environment. Nurses thus present other system requirements and expectations that naturally arise from differences in their professional roles, task environments, and past experiences.

The Health Care Team

Responding to this difference, design criteria should reflect these alternatives. Med- ical informatics literature has become fixated and stereotyped, particularly during the past 10 years, since the primary AI focus has been on medical diagnosis for physicians. Generalizations have been derived from this one task environment and one professional specialty. The broader perspective of multiple members of the health care team and their needs should help expand criteria as well as enrich the understanding of challenges ahead. By defining some of these differences, this paper alerts potential researchers to differences that must be accommodated.

Anticipating Differences

The observation above regarding differences among health care team members cuts both way~ when AI systems are introduced and intended for one group (e.g., physicians) but are also used by others. The testing of the system by multiple cultures (i.e., different health professions) may yield unexpectedly dissatisfied or confounded users.

Expanded Notion of Interface

The viewpoint advanced is intended to reinforce an expanded concept of "interface" to include the differences in the task environment of health professionals as well as organizational differences and even professional cultural differences that must be con- sidered to achieve an effective introduction of complex AI decision-support systems for individual members of the health care team.

On an optimistic note, the spread of the use of the nursing expert systems we have developed into wider and wider areas in a variety of settings supports our confidence that the barriers can indeed be overcome and successes expected.

REFERENCES

1. Evans, S., The structure of instructional knowledge: An operational model. Instruct. Sci. 2:421-50, 1974. 2. Evans, S., Automated curriculum construction: Toward computer constructed education. Simulation and

Games, December 1976.

Artificial-Intelligence-Based System 89

3. Evans, S., A multi-functional health sciences consultant system. Proceedings of the Fourteenth lnterna- tional Conference on Systems Science, Hawaii, January 1981.

4. Evans, S., Nursing applications of an expert system. Proceedings of the Fourth Worm Congress of Medical lnformatics, Amsterdam, August 1983.

5. Evans, S., A computer-based nursing practice protocol writer. Proceedings of the Seventeenth Hawaii International Conference on Systems Science, Hawaii, January 1984.

6. Evans, S., Implementation of a computer-based testwriting consultant. Proceedings of the Eighth Annual Symposium on Computer Applications in Medical Care, Washington, D.C., 1984.

7. Pople, H. E., The foundation of composite hypotheses in diagnostic problem solving: An exercise in synthetic reasoning. Proceedings of the Fifth International Joint Conference on Artificial Intelligence, 1977.

8. Halloran, E., RN staffing: More care--Less Cost. Nursing Management, September 1983.