The use of decision analysis to examine ethical decision making by critical care nurses

The use of decision analysis to examine ethical decision making by critical care nurses K a t h a r i n e K o s t b a d e H u g h e s , P h D , RN, a n d E i l e e n M c Q u a i d D v o r a k / P h D , a n d C h i c a g o , Ill.

RN, S i o u x City, I o w a ,

OBJECTIVE: To examine the extent to which critical care staff nurses make ethical decisions that coincide with those r e c o m m e n d e d by a decision analytic model. D E S I G N : Nonexperimental, ex post facto. SETTING: Midwestern university-affiliated 500 bed tertiary care medical center. SUBJECTS: One h u n d r e d critical care staff nurses randomly selected from seven critical care units.

Complete responses were obtained from 82 nurses (for a final response rate of 82%). M E A S U R E S : Tire dependent variable--consistent decision m a k i n g - - w a s measured as staff nurses' abilities to make ethical decisions that coincided with those prescribed by the decision model. Subjects completed two instruments, the Ethical Decision Analytic Model, a computer-administered instrument designed to measure staff nurses' abilities to make consistent decisions about a chemically-impaired colleague; and a Background Inventory. RESULTS: The results indicate marked consensus among nurses w h e n informal methods were used. However, there was little consistency between the nurses' informal decisions and those r e c o m m e n d e d by the decision analytic model. Although 50% (n = 41) of all nurses chose a course of action that coincided with the model's least optimal alternative, few nurses agreed with the model as to the most optimal course of action. The findings also suggest that consistency was unrelated (p > 0.05) to the nurses' educational background or years of clinical experience; that most subjects reported receiving little or no education in decision making during their basic nursing education programs; but that exposure to decision-making strategies was related to years of nursing experience (p < 0.05). C O N C L U S I O N S : The findings differ from related studies that have found a moderate degree of consistency b e t w e e n nurses and decision analytic models for strictly clinical decision tasks, especially w h e n those tasks were less complex. However, the findings partially coincide with other findings that decision analysis m a y not be particularly well-suited to the critical care environment. Additional research is needed to determine w h e t h e r critical care nurses use the same decision-making methods as do other nurses; and to clarify the effects of decision task (clinical versus ethical) on nurses' decision making. It should not be assumed that methods used to study nurses' clinical decision making are applicable for all nurses or all types of decisions, including ethical decisions. (Heart Lung ® 1997;26:238-48)

From the Marian Health Center, Sioux City, and the Marcella Neihoff School of Nursing, Loyola University, Chicago. Supported in part by a grant from the Clinical Research Program of the University of Illinois Hospital and Clinics. Reprint requests: Katharine Kostbade Hughes, PhD, RN, Coordinator for Clinical Integration, Marian Health Center, 801 Fifth St., Sioux City, IA 51101. Copyright © 1997 by Mosby-Year Book, Inc. 0147-9563/97/$5.00 + 0 2/1/80826 tDeceased

238

C

ritical c a r e n u r s e s

routinely take actions

b a s e d on d e c i s i o n s c h a r a c t e r i z e d b y v a l u e t r a d e - o f f s , t i m e c o n s t r a i n t s , a n d extraordi~

nary l e v e l s of u n c e r t a i n t y . In m a n y i n s t a n c e s , t h e s e decisions pose ethical dilemmas brought about by t e c h n o l o g y , l i m i t e d r e s o u r c e s , a n d o t h e r social, political, a n d e c o n o m i c factors. B e c a u s e of t h e farr e a c h i n g c o n s e q u e n c e s of t h e s e d e c i s i o n s , t h e r e is

MAY/JUNE 1997 HEART & L U N G

a pressing need to better understand the factors that influence these decisions. Yet, relatively little is known about the quality of critical care nurses' ethical decision making, or how these nurses respond to specific ethical dilemmas. Our study was undertaken to address these issues.

LITERATURE REVIEW Clinical Decision Making. To date, much of the research on clinical nursing judgment has focused on the processes by which critical care nurses make clinical decisions. 1"7 In most instances, this research has relied on qualitative methods to study nurses' clinical decision-making processes. For example, Pyles and Stern a studied the methods critical care nurses use in the early detection and prevention of cardiogenic shock, and subsequently proposed the existence of a nursing "gestalt" to explain the decisiommaking processes used by experienced nurses. Smith 7 examined critical care nurses' descriptions of deteriorating patient conditions and concluded that it is this gestalt that facilitates the early identification of subtle clinical cues, thereby facilitating the decision-making process. Ethical Decision Making. When faced with an ethical decision, the nurse must ask, "What, all things considered,ought to be done in a given situation? ''8 While most decisions made by clinicians are concerned with what ought to be done to address the patient's needs, those addressing ethical issues tend to be less straightforward and more difficult in that the consideration of "all things" requires a "complex interplay of a variety of human faculties, ranging from empathy and moral imagination on the one hand to analytic precision and careful reasoning on the other. ''8 This interplay, in turn, suggests that ethical decision making requires that many individual ethical decisions be made and then integrated into an overall judgement about what ought to be done. 9 The situation becomes even more complex for ethical dilemmas that arise when there are conflicting moral claims, or when there is a choice between equally unsatisfactory alternatives. ~° Nurses who work in hospitals encounter ethical dilemmas when faced with multiple, conflicting obligations to which they must simultaneously respond. Z° Obligations to patients, families, profession, physicians, and fellow nurses are oftentimes at odds, as is the case when a nurse is confronted with an incompetent, unethical, or impaired colleague. The situation is further compounded in that the "right" response may hold negative consequences for the decision maker. These consequences include liti-

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gation for libel, slander, discrimination, or whistle blowing effects? 1~ Decision Analysis. Although clinicians' decisionmaking processes have been examined using pri~ marily qualitative methods, quantitative techniques have also been used, but to a much lesser extent. Decision analysis, the most frequently applied quantitative method, has been frequently used to study the quality of physicians' clinical decisions and to model specific medical problems. 12~5 The use of decision analysis to study the quality of nurses' clinical decisions has been limited to a few studies. ~6"2° Its use in critical care is even more limited, despite the fact that critical care environments are highly uncertain and valuecharged--two important requisites for decision analysis. In one of the few studies to address the use of decision analysis in critical care settings, Baumann and Deber 21 found that the assumptions of decision analysis were not met because critical care nurses did not perceive a finite set of alternatives, and experts could not agree on which alternatives were most appropriate, i.e., there appeared to be no "gold standard." Thus they concluded that decision analysis was an inappropriate tool for rapid decision making. Others 22 disagreed and argued that such limitations most likely reflect the developmental nature of nursing knowledge, rather than any methodologic flaw. Despite these arguments, decision analysis provides a systematic and quantitative approach to decision making under conditions of uncertainty and has been shown to be helpful in many different clinical situations. 12,15,23,24 Decision analysis recommends or "prescribes" the optimal decision or the alternative that should be chosen, assuming the clinician wishes to be logical and rational, yet acknowledge personal values and biases. Derived from expected utility theory, decision analysis posits that decision quality is a function of the probability of events and their corresponding value or utility. Once alternatives have been identified, and clinical probabilities and individual preferences for specific outcomes have been quantified, the clinician, vis-a-vis the model, calculates the "expected utility" or value for each decision alternative. Expected utility, then, is an average of all future values, weighted by the probabilities of different alternatives leading to different outcomes. As such, it is equal to the summed products of probability and utility for each alternative and its consequences. According to expected utility theory, the normatively optimal alternative is that which maximizes the clinician's future utility, i.e., the alterna-

239

tive with the highest e x p e c t e d utility. Because it forces decision makers to b e explicit about their preferences, it is especially useful in situations that are heavily value-laden. However, although decision analysis has b e e n u s e d to d e t e r m i n e whether clinicians make normatively optimal clinical decisions, 12,~7,19 its use has not b e e n e x t e n d e d to those clinical decisions with predominantly ethical implications. As for why decision analysis has not b e e n used yet for this purpose, the most likely explanation is that e x p e c t e d utility theory is frequently confused with utilitarianism. Utilitarianism rests primarily on the principle of providing the greatest good to the greatest n u m b e r of people. This principle argues for an existence that is both e x e m p t from pain and maximally pleasurable. Critics claim that this principle is one of e x p e d i e n c y in the s e n s e that it benefits some but violates principles of right. Utility theory, then, is frequently misconstrued as being b a s e d on expediency. In actuality, decision analytic models simply encourage the decision maker to separate the decision problem into its logical c o m p o n e n t parts, which can then b e analyzed and resynthesized so as to suggest the most optimal alternative.15 Because t h e s e models force clinicians to systematically evaluate their preferences for specific clinical outcomes, they might also facilitate ethical decision making that is consistent with those preferences. Yet, it is unknown whether decision analytic models can b e used to examine ethical decisions, or whether their use is b e s t restricted to those having only clinical consequences. Our research, part of a larger study that examined ethical decision making by staff nurses, was undertaken to examine the extent to which critical care nurses make ethical decisions that are logical and rational, and the extent to which these decisions are consistent with those r e c o m m e n d e d by a decision analytic model. METHODS

Design, Sample, and Setting. A nonexperimental, ex p o s t facto research design was used. Subjects were asked to participate in a time-consuming, somewhat tedious process during their off-duty hours and c o n s e q u e n t l y were reimbursed for their time. This s e r v e d to diminish the effects of self-selection in that unpaid volunteers t e n d to o v e r r e p r e s e n t those nurses interested in research and other professional activities. The target population was all staff nurses currently assigned to in-patient critical care units. A random sample of 100 critical care staff nurses was 240

drawn from the critical care units of a 500-bed public teaching hospital located in a large midwestern city. The sole inclusion criterion was that subjects work as staff nurses in the study hospital's seven intensive care units: surgical, medical, coro* nary care, transplant, cardiothoracic, and medical/ surgical step-down. Subjects were excluded from the study if they had worked on their current units for a period of less than 6 months. Exempt status was granted by the University Institutional Review Board. Variables and Their Measurement. Consistent decision making, or the extent to which staff nurses m a d e ethical decisions that coincided with those r e c o m m e n d e d by a decision analytic model, was m e a s u r e d with the Ethical Decision Analytic Model (EDAM). This instrument is designed to measure nurses' abilities to make consistent ethical decisions under uncertain conditions. Decisions were d e e m e d consistent when the nurses' informal or "everyday" decision-making processes led to the same outcome as that r e c o m m e n d e d by the decision analytic process; that is, both nurse and model agreed with one another as to the optireality of a particular alternative or course of action. The EDAM consists of a clinical scenario having potential clinical, ethical, and other consequences, e.g., legal, social, or economic. Each subject was asked to read the scenario and then rank order five mutually exclusive actions before assigning subjective probabilities, importance weights, and utilities to the respective actions, clinical states, and outcomes. (When asked to rank order, subjects were told to assign "1" to the most optimal alternative or the one they would use first, a "2" to the second b e s t alternative or the one they would use next, and so on.) The scenario was designed to present an "everyday" ethical decision that can b e m a d e i n d e p e n d e n t l y by staff nurses. Whereas other studies of critical care nurses' ethical behavior have examined more dramatic ethical dilemmas (e.g., do not resuscitate, fluid/nourishment decisions), such decisions are ultimately m a d e by physicians, and may or may not involve nursing input. Thus the dilemma used in our study was selected after querying nurse managers and clini* cal nurse specialists about ethical decisions they b e l i e v e d could b e m a d e solely by staff nurses, without input from a physician or other health care professional. The scenario presents an ethical dilemma in which the subject must d e c i d e what action to take with respect to a chemically impaired colleague, a not uncommon ethical and legal dilemma. 25,26

MAY/JUNE 1997 HEART & LUNG

Specifically, the subject must weigh several decision alternatives against their possible ethical, clinical, social, legal, and professional consequences. The alternatives include: (1) doing nothing and adopting a "wait-and-see" approach; (2) reporting the incident to the state board of nursing; (3) reporting the incident to the state nurses association's (SNA's) peer assistance network; (4) reporting the incident to the head nurse, a personal friend of the impaired nurse; and (5) individually confronting the impaired nurse in private. A Background Inventory (13I) was used to measure covariates thought to influence clinical and ethical decision making. The BI, a 29-item written questionnaire was designed to address the nature and extent of nurses' clinical nursing experience and formal education. In addition, the 13Iwas used to measure those covariates that might influence clinical and ethical decision making such as prior education in decision making, use of probabilistic decision-making strategies, and incorporation of personal and professional values into the decision~making process. Although the BI did not differentiate per se between education for ethical versus clinical decision making, subjects were asked how often their values (moral, religious, professional, family) influence their decisions. They were also queried as to source of those values (educational program, religious instruction, fellow nurses). The 13I, which has been evaluated by a psychometrician and pilot tested, was successfully used in a related study. 19 Development of the EDAM. The EDAM is similar to instruments used by Grief, 17 Elstein et al., ~2 and others ~9,2° in that it uses decision analytic techniques to study nurses' decision making. It differs in that it uses these methods to study decisions having ethical and legal, as well as economic and clinical consequences. After the development of the hypothetical scenario, the next step was to delineate the mutually exclusive independent nursing decision alternatives that could potentially resolve the ethical dilemma. The various outcomes that might be expected to result from these alternatives were then identified. The scenario (dilemma), the decision alternatives (potential solutions), and outcomes were developed after consultation with a nurse ethicist and several adult acute care nurse clinicians. At the time of instrument development, the ethicist was a doctorally prepared nurse, dean of a university nursing program, and initiator of a center for nursing ethics devoted to preparing nurses to identify and respond to ethical dilemmas. The clinicians

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were masters-prepared, acute care, clinical nurse specialists on the staff of a large urban universityaffiliated medical center. Once the decision alternatives and outcomes had been developed, the next step was to develop a multiattribute decision model that represented the various decision alternatives and their potential outcomes. The model was structured with use of standard decision analytic principles and procedures, and reflects the uncertainty inherent in each narrative. The model is graphically represented as a decision tree (Figure 1). A corresponding algorithm or algebraic representation of the decision model was used to calculate expected utilities for each alternative (Figure 1, bottom). The decision tree also was used to develop the probability and utility sections of the EDAM. Probability statements were written to correspond with each decision tree branch and used to elicit each nurse's subjective probability assessments (or estimation of likelihood that each action or event will lead to a specific outcome). The statements were then transformed into a 10-point Likert-type respons e format, in which the subjects were asked to indicate the likelihood that the specified decision alternative would lead to the specified outcome. For example, the probability that the third decision alternative--D3, "report the incident to the SNA's peer assistance network"--would prevent any clinical repercussions was assessed by asking subjects, "what is the likelihood that reporting the incident to the SNA's peer assistance network would resolve the issue and prevent any kind of impact on the patient." To further illustrate this example, the resulting probability, which ranged from 0 to 10, corresponds to the first tree branch for the third alternative. Each nurse's relative preferences for the various potential outcomes (terminal aspects of the decision tree) were assessed using the standard reference gamble, a method that has been used successfully in various patient and clinician populations. 27"29 The standard reference gamble is a multistep process based on the assumption that a choice between alternatives is a choice between gambles. It asks subjects to: (1) rank order the possible alternatives; (2) assign a rank of 1 to the best outcome and a rank of 0 to the worst; and (3) choose between an intermediate guaranteed outcome and a gamble, which involves a certain probability of yielding either the best or worst out~ come. The process continues until the subject is indifferent to the choice of guaranteed outcome versus gamble. 3° In this particular study, a laptop computer was used to generate the questions

241

D1 D2 cllnloall__

no clinical impact influence, but with insignificant effects --significant,

ethical[ ,,

negative effect on patients

no ethical conflict minor conflict, e.g. sense of discomfort

D3 major, deeply disturbing ethical conflict no negative social consequences inor consequences, e.g. friendships affected L--major "whistle-blowing" effects legal I

i

no litigation litigation~

profeosional [ n o

D4

monetary damages

no damages disciplinary action

L disciplinary

__rlicense affected actionL_licens e unaffected

D5 DI= D2: D3: D4: DS:

Do nothing and adopt a "walt-and-see" attitude Report the incident to the state board of nursing Report the incident to the state nurses' association's peer assistance network Report the incident to your head nurse Confront the impaired nurse and describe what you saw

Note= The above tree branch is shown only once because of limited space. actuality, it is repeated at

D 1, 2, 4, and 5.

In

Expected Utility (D1-5) = V {[(A1)] + B[(C2) + (D3)I} W {[(E4)] + F[(G5) + (H6)]} + X {[(17)] + J[(K8) + (L9)]} + Y {[(M10)] + N [ ( O l l ) + (P12)]} + Z {[(Q13)] + R[(S14) + (T15)]} where V-Z represent importance weights at each of the 5 main branches, A-T represent probabilities at each of the secondary and teriary branches, and 1-15 represent utilities for each of the various outcomes, located at the terminal aspects of the tree branches, Fig, l Decision tree for EDAM.

needed to assess utilities. The program iteratively altered the probability of desired outcomes until equivalence was achieved between the risk of an uncertain outcome and the desirability of a certain outcome, i.e., indifference. The process is repeated for each of the various outcomes. The

242

standard reference gamble method is considered the most valid and reliable means of assessing outcome preferences and is described in greater detail elsewhere. 15,3° Importance weights were assessed using a method similar to that used by Elstein et al. ~2

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Table I S o c i o d e m o g r a p h i c profile of final s a m p l e (N = 82)

Variable

Frequency

%

4 17 11 50

4.9 20.8 13.4 60.9

3 79

3.7 96.3

2 16 22 42

2.4 19.6 26.8 51.2

5 4 3 70

6.0 4.9 3.7 85.4

54 21 7 -----

65.9 25.6 8.5 -----

Race Hispanic Asian Black White Sex Male Female Basic n u r s i n g e d u c a t i o n Practical Diploma Associate Bachelors % C a r e e r i n a d u l t a c u t e care 0-24 25-49 50-74 75-100 Annual continuing education hours <20 20-40 >40 Age (yr) Years as n u r s e Years i n p o s i t i o n Hours per wk

Validity, Reliability, and Pilot Testing.

Decision

analytic instruments have not been rigorously validated by traditional methods because there are no readily available instruments that assess the clinician's subjective sense of probability and utility under simulated conditions; and probability and utility assessments are independent of each other even within subjects. However, because probabilities and utilities are assessed in a straightforward manner before being inserted into the model, they are not transformed into potentially different constructs. Moreover, probabilities and utilities are operational definitions of probability theory that is generally accepted as valid because it is based on explicit and clearly demonstrable mathematic principles. Therefore, instruments based on decision analytic principles are considered to be construct valid when they operationalize generally accepted decision analytic principles. To do this, instructions must be clearly understood by all subjects so that they can valid-

HEART & LUNG 'COL.26, NO. 3

Range

Mean

SD

q

h

m

m

m

m

m

m

m

m

m

m

m

b

h

b

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m

m

m

m

m

m

m

m

q

m

_

_

_

m

_

_

m

m

21-53 0-28 0-18 16-70

m

32.42 7.92 3.50 37.23

_

m

7.30 5.95 3.84 7.I0

ly report their clinical knowledge base (in the form of probability estimates) and values. This means that decision analytic instruments must be pilot tested and revised, if necessary (A. S. Elstein, personal communication, 1990). The test-retest method is used to demonstrate that the subjective probabilities and utilities are reliable and stable over time (A. S. Elstein, personal communication, 1990). For these reasons, the EDAM was pilot tested on a convenience sample of 15 registered nurses with varying amounts of education and clinical experience. Minor wording changes in the instructions were suggested by the pilot test subjects. These were incorporated into the final version of the EDAM. The EDAM was administered again 2 weeks later to the same 15 nurses and found to be reliable and stable over time. PROCEDURE

Nurses were approached on their units and provided with a brief description of the study.

243

Table II Critical care nurses' informal ranking of ethical decision alternatives: Frequencies and (percentages) Ranking First

Decision alternative

Do nothing, Wait-and-see Board of nursing Peer assistance Report to head nurse Confront Nurse Doe

7 2 2 16 55

(8.54) (2.44) (2.44) (19.51) (67.07)

Second

2 4 8 52 16

(2.44) (4.88) (9.76) (63.41) (19.51)

Third

8 8 52 I1 3

(9.76) (9.76) (63.41) (13.41) (3.66)

Fourth

8 47 18 2 7

(9.76) (57.32) (21.95) (2.44) (8.54)

Last

57 2I 2 1 1

(69.51) (25.61) (2.44) (1.22) (1.22)

Table III Decision analytic model recommendations: Expected utilities and ranking of decision alternatives E x p e c t e d utilities Decision alternative

Do nothing, Wait-and-see Board of nursing Peer assistance Report to head nurse Confront Nurse Doe

Range

Mean

SD

Rank

1-2225 1-3512 16-3090 1-2784 1-3494

551.1 1143.1 II25.5 1023.8 939.2

479.7 696.4 714.3 698.9 711.4

5 1 2 3 4

Once consent had been obtained, subjects were asked to complete the EDAM and BI. The probability and importance weight sections of the EDAM were administered with a pencil-and-paper format. The util!ty portion was administered on a laptop computer with use of an interactive program, rather than by interviewer owing to the computer's superior iterative properties, comparatively higher reliability, and greater acceptance by subjects in those situations involving sensitive, valueqaden information. 31,32 Completion time for both instruments was approximately 60 minutes per subject. DATA ANALYSIS Utilities, probabilities, and importance weights were obtained from each subject and inserted into the decision analytic model. Expected utilities for each alternative for each nurse were then calculated and rank ordered by a custom-developed com~ puter program. The process was carried out for each subject so that a total of 82 unique rank orderings resulted. The program then compared 244

the rank order of the model-recommended decisions with the sample's informal rank ordering. Descriptive statistics were calculated for these aggregate data. In addition to examining nurses' aggregate decb sions, individual nurse's informal decisions were also examined, relative to each nurse's decision analytic model. That is, the data were also examined for the extent to which each nurse made decisions that were consistent with the model, based on his or her unique probability estimates and utilities. "Most optimal" consistency existed when the subjects' first-ranked or most preferable action alternative coincided with the model's most optimal decision alternative, i.e., the one having the highest expected utility. Stated differently, most optimal consistency existed when the individual subject and model "agreed" as to the best or most optimal decision alternative. "Least optimal" consistency existed when the subjects' last-ranked or least preferable action alternative coincided with the model's least optimal decision or that having the lowest expected utility. MAY/JUNE1997 HEART & LUNG

Table I V R a n k order differences b e t w e e n n u r s e a n d m o d e l r e c o m m e n d e d alternatives Rank Most o p t i m a l

Second

Third

Fourth

Least o p t i m a l

Nurse

Confront Nurse Doe

Head n u r s e

Peer assistance

Board of nursing

Do nothing, Wait-and-see

Model

Board of nursing

Peer assistance

Head n u r s e

Confront Nurse Doe

Do nothing, Wait-and-see

Table V Extent to w h i c h individual nurse's informal decisions coincided w i t h m o d e l - r e c o m m e n d e d decisions by type of education a n d years of experience Nurse-Model c o n s i s t e n c y by: Type of nursing e d u c a t i o n D i p l o m a (n = 18) Associate (n = 22) Baccalaureate (n = 42) Years of nursing experience <2 (n = 19) B e t w e e n 3-9 (n = 35) >10 (n = 28) No. nurses in a g r e e m e n t w i t h m o d e l as to m o s t or least optimal decision alternative (n = 56)*

Most optimal n(%)

Least optimal n(%)

4 (22.2) 5 (22.7) 6 (14.3)

9 (50.0) 10 (45.5) 22 (52.4)

4 6 5 15

(21.1) (17.1) (17.9) (18.3)

11 18 12 41

(57.9) (51.4) (42.9) (50.0)

*Number does not equal 82 because not all nurses agreed with the model as to the least and/or most optimal alternative

RESULTS Of the 100 nurses who were asked to participate, complete data were obtained from 82 (response rate = 82%). As Table I shows, the sample consisted of primarily white females whose basic nursing education took place in baccalaureate programs. The overwhelming majority (85%) had spent most of their careers caring for acutely ill adults. The average subject was 32 years old, had almost 8 years of nursing experience, and had held her or his current position for 3.5 years. Most worked fulltime. Table ii shows how subjects as a group ranked the five decision alternatives. The vast majority, 69.51%, ranked the "wait-and-see" alternative in

HEART & LUNG voL. 26, NO. 3

fifth or last place. Notifying the b o a r d of nursing was ranked in fourth place b y m o s t (57.32%) subjects. The decision alternative that 63.41% subjects ranked in third place was to call the p e e r assistance network of the SNA. A similar n u m b e r ranked "reporting the incident to the h e a d nurse" in second place. The majority, 67%, indicated that "confronting the nurse" was the b e s t or first-ranking alternative. The s a m p l e ' s aggregate e x p e c t e d utilities for each of the five decision alternatives are shown in Table II1. T h e s e e x p e c t e d utilities were calculated using probabilities and utilities elicited from the subjects and range from a m e a n of 551.1 for the "wait-and-see" alternative, to a m e a n of 1143.1 for

245

Table VI

Decision-making characteristics of critical care nurses (N = 82) Characteristic

Frequency

%

Educational exposure to decision-making strategies None Small amo u n t Some of the time Quite a bit

23 30 18 11

28.1 36.6 21.9 13.4

Consider probability w h e n making decisions Very rarely Some of the time Most of the time Nearly always

3 41 26 12

3.7 50.0 31.7 14.6

Frequency with which values influence decisions Very rarely 6 Some of the time 40 Most of the time 29

7.3 48.8 35.4

:i~i! ¸ ~!~ii ¸

;~i~:iii!

~ii!i;i;i'~

!iiii~!ii !i!i i!

!i

;i i¸I ii

the alternative of reporting the incident to the board of nursing. The wide ranges and standard deviations also suggest considerable variability among subjects with respect to the utility of each alternative. As for which alternative is the most rational or logical, expected utility theory argues that the alternative with the highest expected utility is most optimal, that the alternative with the second highest expected is the next "best" alternative, and so on. Therefore, according to decision analysis, "reporting the incident to the board of nursing" is, on average, the most optimal alternative and is ranked first, "calling the peer assistance network" is ranked second, and so on. The extent to which the sample's informal ranking of decision alternatives coincides with that of the decision analytic model is shown in Table IV, which is a composite of Tables II and III. The results indicate that, as a group, the nurses' least optimal (fifth ranked) alternative was the same as that "recommended" by the model. That is, the model and nurses agreed on average that "wait-and-see" was the worst alternative. However, there was no agreement as to the rank order of the other four decision alternatives. Stated differently, the nurses' first, second, third, and fourth choices did not

246

coincide with the model; that is, when the average nurse-ranked alternatives are compared to the average model-ranked alternatives, there is little similarity. The findings are mixed as to the degree of consistency that exists between individual nurse's decisions and those recommended by the individual decision analytic model (Table V). Specifically, the results indicate that 50% (n = 41) of all nurses agreed with the model as to which decision alternative was the worst or least optimal alternative. Individual nurses did not agree with the model, however, as to which alternative was best or most optimal. Table V also indicates little difference between educational groups with respect to consistency. Diploma, associate degree, baccalaureate-prepared nurses did not differ in the extent to which they made decisions that coincided with the model (p > 0.05). Nor was there a difference among nurses with varying amounts of clinical experience (p > 0.05). Finally, Table VI indicates that most subjects received little or no education in clinical decision making during their basic nursing education programs. Almost two thirds (64.7%) reportedly received no educational exposure to decisionmaking strategies, or only a small amount. Only 11, or 13.4%, reportedly received a lot of exposure. Almost 54% said they consider probability rarely, or only some of the time, whereas 46% consider probability most or nearly all of the time. As for the frequency with which their values are influenced by their personal or professional values, 46--or 56. l%--indicated they are influenced by these values very rarely, or only some of the time. When these responses were examined by type of education, there were no significant differences among diploma, associate degree, or baccalaureate-prepared nurses. However, exposure to decision-mak~ ing strategies was related to years of nursing experience (X2 -~ 14.96 (1, 6), p < 0.05). Almost half (9 or 47.4%) of all subjects with less than 2 years of experience reported that they had received no exposure to decision-making strategies, compared to 22.9% (n = 8) of nurses with 3 through 9 years and 21.4% (n = 6) of nurses with 10 or more years of experience.

DISCUSSION The findings indicate a clear majority consensus among critical care nurses as to the best and worst alternatives regarding a chemically impaired colleague. More than two thirds (67%) agreed that "confrontation" was the best alternative or course of action and almost 70% agreed that a "do nothing

MAY/JUNE 1997 HEART & LUNG

and adopt a wait-and-see" approach was the worst course of action. Both the nurses and their decision analytic models were in agreement, on average, as to which was the worst alternative. On average, though, they did not agree with one another as to which alternative was best. While somewhat mixed, these findings fail to support previous studies that found a moderate degree of consistency between nurses and deci~ sion analytic models for clinical decision tasks, especially when those tasks were less complex.17" 19 However, it should be noted that these studies included very few critical care nurses. Because these findings partially coincide with those of Baumann and Deber, 21 who found that decision analysis was not particularly well*suited to the critical care environment, it may be that critical care nurses use markedly different decision*making processes. However, it might be interesting to speculate that nurses make decisions that coin* cide with decision analytic recommendations, but that this consistency disappears when the decision task takes on ethical overtones. Additional research is needed to determine whether critical care nurses differ from other nurses in the methods they use to make ethical versus clinical decisions. Further investigation is also needed to clarify the effects of decision task on nurses' decision making. In the meantime, it should not be assumed that methods used to study nurses' clinical decision making may not be applicable for all nurses or all types of decisions. -The major discrepancies between the nurses and their respective decision analytic models were with respect to where each ranked the alternatives of confrontation and board notification. When nurses' probabilities and utilities are inserted into the decision analytic models, the most optimal decision alternative, on average, was "reporting the incident to the board of nursing," followed closely by "calling the peer assistance network." That most decision analytic models recommended board notification as the first course of action, while nurses' informally ranked it in last place, deserves further investigation, especially because the state currently requires nurse administrators to report chemically impaired nurses who do not voluntarily agree to treatment. The results also suggest that the model was more consistent with the views of many chemical dependency experts in that its second-best alternative was to contact the peer assistance program of the SNA, an approach that has been endorsed by many professional nursing organizations. 33 In contrast, nurses informally ranked this alternative in third place, behind both con-

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frontation and reporting the incident to the head nurse. As for the alternative of doing nothing, there is considerable consensus among chemical dependency experts that failing to respond to an impaired nurse is an exceedingly poor course of action. 34-36 In this instance, then, the nurses and decision analytic models agreed both with each other and with the prevailing standard. However, there was considerable disagreement between the nurses and models as to the relative merits of confrontation in that nurses ranked it as the best alternative, whereas the model ranked it in fourth place. Taken together, this suggests that the recommendations of decision analytic models may coincide with informal decisions in matters for which there is significant consensus, but that such models "fail" under circumstances that are less clear-cut. The finding that nurses strongly favored the confrontation alternative supports observation that nurses may be willing to individually confront impaired colleagues in an effort to "help" without being too intrusive.37 However, it is somewhat disconcerting in that many experts believe that individual confrontation of an impaired colleague can be potentially problematic and less likely to lead to a successful outcome, i.e., entry into an employee assistance program, 12-step program, and so on. Organized interventions conducted by management, it is thought, are more likely to lead to successful outcomes, 33,35 whereas informal confrontations by individual coworkers are thought to simply encourage more secretive behavior. That most nurses in our study chose confrontation as their first course of action is disturbing in that it suggests that many nurses may not know how to respond to chemically impaired colleagues. In this instance, then, the models' low ranking of con~ frontation (second-to-last place) is more conso~ nant with prevailing expert opinion about how to manage chemically impaired nurses. Additional research is n e e d e d to gauge nurses' understanding of their roles and responsibilities in this area so that academic and continuing educational programs can be d e v e l o p e d to address the problem of chemical d e p e n d e n c e in the workplace. Along similar lines, nurse managers need to assess their staff nurses' particular understandings of their eth~ ical and legal obligations. Finally, it is disturbing that most nurses reported receiving little or no education in decision making during their basic nursing education programs, regardless of the type of program. Moreover, a large percentage admitted that they rarely consid-

247

er p r o b a b i l i t i e s o r p r e f e r e n c e for certain o u t c o m e s w h e n m a k i n g c l i n i c a l o r e t h i c a l d e c i s i o n s . Because t h e r e c o m m e n d a t i o n s of d e c i s i o n a n a l y t i c m o d e l s reflect d e c i s i o n makers' u n d e r s t a n d i n g of clinical p r o b a b i l i t y , as w e l l as t h e i r p r e f e r e n c e s for specific o u t c o m e s , it m a k e s sense t h a t d e c i s i o n makers who do not take these concepts into consideration would not make decisions that coincide with decision a n a l y t i c r e c o m m e n d a t i o n s . T h a t is, nurses' lack of e x p o s u r e to s y s t e m a t i c d e c i s i o n - m a k i n g strategies m a y p a r t i a l l y e x p l a i n t h e r e l a t i v e lack of consistency between informal decisions and those r e c o m m e n d e d b y d e c i s i o n a n a l y t i c processes. W h e t h e r o r n o t nurses w o u l d m a k e m o r e consist e n t d e c i s i o n s w i t h a d d i t i o n a l e x p o s u r e to formal d e c i s i o n strategies is u n k n o w n . Clearly, research a i m e d at d e t e r m i n i n g t h e effects of such e x p o s u r e of d e c i s i o n - m a k i n g o u t c o m e s is n e e d e d . In t h e m e a n t i m e , nurse e d u c a t o r s n e e d to d e t e r m i n e w h e t h e r t h e y d e v o t e sufficient t i m e to t h e d e v e l o p m e n t of d e c i s i o n - m a k i n g skills. We thank research assistants Barbara Harris, MS, RN, and Elizabeth Carlson, MS, RN, for their hard work, enthusiasm, and attention to detail.

REFERENCES 1. Benner P. Uncovering the knowledge embedded in clinical practice, image ] 983;115:36-41. 2. Benner P. From novice to expert: excellence and power in clin~ ical nursing. Menlo Park (CA): Addison-Wesley, 1984. 3. Benner P, Wrubel J. Skilled clinical knowledge: the value of perceptual awareness. J Nurs Admin ] 982;] 2:28-33. 4. Py]es S, Stern iF Discovery of nursing gestalt in critical care nursing. Image 1983;]5:51-7. 5. SchraederBD, Fischer DK. Using intuitive knowledge to make clinical decisions. ] Matern~Child Nuts 1986;11:161-2. 6. Schraeder BD, Fischer DK. Using intuitive knowledge in the neonatal intensive care nursery. J Matern-Child Nurs 1987;11:161-2. 7. Smith SK. An analysis of the phenomenon of deterioration in the critically ill. Image 1988;20:12~5. 8. Benjamin M, Curtis I. Ethics in nursing. New York: Oxford Press, 1992. 9. Smith KV. Ethical decision-making in nursing: implications for continuing education. I Contin Educ Nurs 1996; 27:42-5. /0. DavisAJ, Aroskar MA. Ethical dilemmas and nursing practice. Norwalk (CT): Appleton-Century-Crofts; I983. l 1. Morreim EH. Am 1 my brother's warden?: responding to the unethical or incompetent colleague. Hastings Cent Rep I983;23:19-27. 12. Elstein AS, Holzman GB, Ravitch MM, Metheny WA, Holmes MM, Hoppe RB, eta]. Comparisons of physicians' decisions regarding estrogen replacement therapy for menopausal women and decisions derived from a decision analytic model. Am J Med 1986;80:246~58.

248

i3. Gorry G, Kassier J, Essig A, SchwartzW. Decision analysis as the basis for computer~aidedmanagement of acute renal failure. Am J Med 1973;55:473-84. 14. Kassier J. The principles of clinical decision making: an introduction to decision analysis. Yale J Biol Med 1976;49:149-64. ] 5. Weinstein M, Feinberg H, E]stein A, Frazier H, Neuhauser D, Neutra R, et al. Clinical decision analysis. Philadelphia: WB Saunders, 1980. 16. Aspinall M. Use of a decision tree to improve accuracy of diagnosis. Nurs Res ] 979;28:182-5. 17. Grier M. Decision making about patient care. Nuts Res 1976; 25:]05-10. I8. Hughes CA, Hughes KK. N-way sensitivity analysis: a verification and stability assessment technique for completely subiective decision analysis tress. Med Deeis Making i 990; I 0:68-74. 19. Hughes KK, Young WB. The relationship between task complexity and decision making consistency. Res Nurs Health ] 990;13:189-97. 20. Shamian J. Effect of teaching decision analysis on student nurses' clinical intervention decision making. Res Nurs Health 199I;14:59-66. 21. Baumann A, Deber R. The limits of decision analysis for rapid decision making in [CU nursing. Image 1989;21:69-71. 22. HughesKK, E[stein AS. Dialogue [letter]. Image 1990; 22:]. 23. Paulker SP, Paulker SG. Prenatal diagnosis: a directive approach to genetic counseling using decision analysis. Yale ] Bio] Med ]977;50:275~89. 24. Pollster P. Decision analysis and clinical judgement. Med Decis Making ]981;1:36]-89. 25. Ensor], Giovinco G. Ethical issues related to chemical dependency, imprint 1991;38:85-7. 26. Moore G, Hogan RL. Substance abuse and the nurse: a legal and ethical dilemma. J Prof Nurs ]987;3:5. 27. Boyd NF, Sutherland Hi, Heasman KZ, Tritchler DL, Cummings BJ. Whose utilities for decision analysis. Med Decis Making 1990;l 0:58-67. 28. Llewellyn-Thomas H, Sutherland HJ, Tibshirani R, Ciampi A, Till IE, Boyd NE Describing health states: methodologic issues in obtaining values for health states. Med Care 1984;22:542-3. 29. Torrence GW. Multiattribute utility theory as a method of measuring social preferences for health states in long-term care. In: Kane RL, Kane RA, editors. Values and long-term care. Lexington (MA): Heath; 1982.p. 127-49. 30. Sox HC, Blatt MA, Higgins MC, Marton KI. Medical decision making. Boston: Butterworths, ]988. 31. Erdman HP, Klein MH, Greist JH. Direct patient computer interviewing. ] Consult Clin Psychol 1985;53:760~73. 32. Greist JH, VanCura LJ, Kneppreth NR A computer interview for emergency room patients. Comput Biomed Res 1973; 6:257-65. 33. Naegle MA. Creative management of impaired nursing practice. Nuts Admin Q 1985;9:16~26. 34. Hughes T. Managing problem employees. In: Decker Pl, Sullivan E], editors. Nursing administration: a micro/macro approach for effective nurse executives. Norwalk (CT): Appleton & Lange, 1992:359-86. 35. Sullivan E]. Managing the chemically dependent nurse. In: Sullivan E], DeckerPJ, editors. Effective nursing management. 2nd ed. Menlo-Park (CA): Addison-Wesley, 1988:137-50. 36. Cannon BL, Brown IS. Nurses' attitudes toward impaired colleagues, image 1988;20:96-I01. 37. Abbott CA. The impaired nurse: part I. Am Oper Room Nurse J 1987;46:870-6.

MAY/JUNE 1997 HEART & L U N G

DISEASES

Bacillus species pseudomeningitis Burke A. Cunha, MD, Paul E. Schoch, PhD, and Jos6 T. Bonoan, MD, Mineola and Stony Brook, N. Y.

Bacillus species are aerobic gram-positive bacilli that are usually found in nature in the soil and dust. Except for B. anthracis, Bacillus spedes are organisms of low virulence, and only rarely cause infections in i m m u n o c o m p r o m i s e d hosts. The recovery of Bacillus species from body fluids in healthy patients would suggest a Bacillus species pseudoinfection. Bacillus species has been associated with both pseudobacteremia and least commonly, pseudomeningitis. The Bacillus organisms usually contaminate liquid culture media, which have been implicated in Bacillus pseudoinfections of the blood and cerebrospinal fluid. We report a case of Bacillus pseudomeningifis in a normal host. To our knowledge, this is the third case of Bacillus pseudomeningitis reported in the literature. (Heart Lung ® 1997;26:249-51)

pseudomeningitis may be defined as the demonstration or recovery of an organism from the cerebrospinal fluid (CSF) by smear or culture that does not correlate with the patient's clinical condition, or is not a usual neuropathogen. Pseudomeningitis has been infrequently reported in the literature since 1973, and to date there have been only 19 cases before this case (Table). Furthermore, this is only the second reported case of pseudomeningitis due to Bacillus to be reported in the literature. H 9

remained negative. The patient continued to have fevers of up to 104° g and a second computed tomography scan of the head showed an increase in ventricular dilatation; an Ommaya reservoir was implanted. The CSF obtained revealed 21,000 red blood cells, 5 white blood cells with 85% lymphocytes, and a lactic acid level of 2.65 with normal glucose and protein of 107. The Gram stain showed no organisms. After several days, he again experienced high-grade fevers and the incision site for the Ommaya reservoir was found to have dehisced. Empiric vancomycin therapy was started, the shunt incision was closed, and the CSF sample taken grew Bacillus species on liquid media.

CASE HISTORY A 57-year-old man with hydrocephalus since birth was admitted with a 5-day history of falling and an unsteady gait. There was no associated loss of consciousness, nausea, vomiting, mental status changes, or fever. He had a medical history significant for depression, and cerebral palsy with residual left-sided weakness. On physical examination, he was afebrile, responsive but mildly confused, and had a large head. Several hours after admission, a low-grade temperature developed, he became more drowsy, and experienced a stiff neck. Empiric ceftriaxone and ampicillin were started, and were discontinued after 7 days when all cultures

From the Infectious Disease Division and the Department of Pathology, Winthrop-University Hospital, Mineola, and the State University of New York School of Medicine, Stony Brook. Reprint requests: BurkeA. Cunha, MD, Chief, Infectious Disease Division, Winthrop-Un!versity Hospital, Mineola, NY 11501. Copyright © 1997by Mosby-YearBook, Inc. 0147-9563/97/$5.00+ 0 2/1//9401

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DISCUSSION Pseudomeningitis used to be reported sporadically in the literature and is second only to p s e u d o b a c t e r e m i a as a r e p o r t e d cause of pseudoinfection.l,2 The clinical clue to the recognition of pseudomeningitis is the discrepancy between the clinical presentation of the patient and the organisms found in the CSF. The most frequent microorganisms associated with p s e u d o m e n i n g i t i s are aerobic gram-negative bacilli that usually gain access to the CSF as part of the specimen handling or staining procedures. The two cases of Bacillus species pseudomeningitis previously reported were both associated with contaminated culture media. The largest outbreak, r e p o r t e d by Lettau et al. 13 in 1988, involved 16 patients, three of whom received unnecessary antibiotic therapy. In the patient we present, fortunately antibiotic therapy was not instituted. A n o t h e r clue to the presence of 249

Table. Pseudomeningitis ( 1973-1996) No.

Author (year)

Reference

Microorganism contaminant

S o u r c e of contamination

Positive CSF Gram stain

Positive CSF culture

patients No. receiving p a t i e n t s treatinvolved ment

M u s h e r and Schell (1973)

3

Gram (-) cocci

Specimen tubes

4

0

4

0

J o y n e r et al. (1974)

4

Gram (+) cocci

Slides

1

0

i

i

Weinstein et al. (1975)

5

Gram (-) cocci Gram (+) cocci

Specimen tubes

5

0

5

1

Coyle-Gilchrist et al. (1976)

6

Flavobacterium meningosepticum

Skin preparation soap

0

1

1

1

Ericsson et al. ( 1978)

7

Gram (-) bacilli

Slides

l0

0

l0

5

Hoke et al. ( 1979)

8

Gram (-) bacilli

Transport media

2

0

2

2

Batt and Reller (1978)

9

Gram (-) bacilli

Transport media

1

0

1

1

Harris et al. (1983)

10

Salmonella typhimurium

Pipette

0

2

2

1

Ullman et al. (1985)

11

Acinetobacter

Culture media

1

1

1

1

Strampfer et al. (1987)

12

Aspergillus

Extrinsic culture

0

1

1

0

Lettau et al. (1988)

13

Bacillus

TSB broth

3

13

16

3

P e n n et al. (1988)

14

Gram (-) bacilli

Specimen tubes

15

0

15

3

C u n h a and C o h e n (1989)

15

Bacillus

Culture media

0

1

1

I

Gelfand et al. (1990)

16

Fungal elements

Airborne containination of staining reagent

1

0

1

1

Mitrani- Schwartz et al. (1991)

17

Pseudomonas paucimobilis

Specimen tubes

0

1

I

0

Bignardi et al. (1994)

18

Mycobacterium Crossaviumcontamination intracellulare of culture media

0

NA

NA

0

Gill et al. (1994)

19

Cunha et al. (1996)

CDC group VE-I

Gram (+) diplococci

Culture media

1

0

0

1

Bacillus

Contaminated culture media?

0

1

1

0

TSB, Trypticase soy

250

MAY/JUNE 1997 HEART & LUNG

pseudomeningitis in the Winthrop-University Hospital case was the fact that Bacillus is not a usual neuropathogen. Furthermore, the rnicrobi~ ology laboratory reported that the organism was growing only in broth--not on solid subculture media. Pseudoinfections are reported nearly every month in medical journals from around the world. Clinicians must be ever vigilant to suspect pseudoinfections and to clearly delineate them from actual infectious disease processes. Pseudomeningitis is the second most common cause of pseudoinfection after pseudobacteremia, and is usually the result of aerobic gram-negative organisms) "~9 We report the third known case of pseudomeningitis due to Bacillus species in the literature. We were not able to demonstrate the mechanism by which Bacillus gained access to the broth used to culture the CSF, and can only speculate that the broth may have been contaminated in some way--as has been the casein previously reported cases of Bacillus pseudomeningitis.

REFERENCES 1. Cunha BA, Klein NC. Pseudoinfections. Infect Dis Clin Pract 1995;4:95-103. 2. Cunha BA. Pseudomeningitis--another nosocomial headache. infect Control Hosp Epidemiol 1988;9:391-3. 3. Musher DM, Schell RE False-positive Gram stains of cerebrospinal fluid [letter]. Ann Intern Med 1973;79:603-4. 4. loyner RW, Idriss ZH, Wilfert CM. Misinterpretation of cerebrospinal fluid Gram stain. Pediatrics 1974;54:360-2.

5. Weinstein RA, Bauer FW, Hoffman RD, Tyler PG, Anderson RL, Stature WE. Factitious meningitis diagnostic error due to nonviable bacteria in commercial lumbar puncture trays. lAMA 1975;233:878-9. 6. Coyle-Gilchrist MM, Crewe P, Roberts G. Flavobacterium meningosepticum in the hospital environment. ] Cfin Pathol 1976;29:824-6. 7. Ericsson CB, Carmiehael M, Pickering LK, Mussett R. Kohl S. Erroneous diagnosis of meningitis due to false-positive Gram stains. South Med I 1978;71:1524-5. 8. Hoke CH, Batt JM, Mirrett S, Cox RL, Relier LB. False-positive Gram-stained smears, lAMA 1979;241:478-80 9. Batt JM, Relier LB. False-positive Gram stain due to nonviable organisms in sterile commercial transport medium. MMWR [978;27:23. 10. Harris A, Pottage IC It, Fliegelman R, Goodman L], Levin S, Vetter M, Kaplan RL. A pseudoepidemic due to Salmonella typhimurium. Diagn Mierobiol Infect Dis 1983;1:335-7. ii. Ullman R, Schoch PE, Cunha BA. Pseudomeningitis due to Acinetobacter/CDC Group VE-I organisms. Medical Journal of Winthrop-University Hospital 1985;7:38-41. 12. Strampfer MI, Twist PF, Greensher I, Schoch PE, Cunha BA. Hemophilus pseudomeningitis in a neonate. Gin Microbiol Newsletter 1987;9:22-3. 13. Lettau LA, Beniamin D, Cantrell HT, Potts DW, Boggs IM, Bacillus species pseudomeningitis. Infect Control Hosp Epidemiol 1988;9:394-7. 14. Penn RL, Normand R, Klotz SA. Factitious meningitis: a recurring problem. Infect Control Hosp Epidemiol 1988;9:501-4. !5. Cunha BA, Cohen S. Pseudomeningitis: report of a case caused by Bacillus and review of the literature. Heart Lung 1989; 18:418-20. 16. Gelfand MS, Sehoch PE, Cunha BA. Fungal pseudomeningitis superimposed on Escherichia coli meningitis. Heart Lung 1990; 19:53-6. 17. Mitrani-Schwartz A, Schoch PE, Cunha BA. Pseudomeningitis caused by Pseudomonas paucimobilis. Heart Lung 1991;20:305-7. |g. Bignardi GE, Barrett SP, Hinkins R, Jenkins PA, Rebec MP. False~positive Mycobacterium avium-intracellulare cultures with the Bactec 460 TB system. I Hosp Infect 1994;26:203~10. 19~ Gill MV, Shea KW, Klein NC, Cunha BA. Pseudomeningitis due to gram-positive diplococci, infect Dis Pract 1994;18:9I.

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