Skip to content

Advertisement

BMC Medical Research Methodology

Research article
Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Vignette studies of medical choice and judgement to study caregivers' medical decision behaviour: systematic review

  • Lucas M Bachmann1, 2Email author,
  • Andrea Mühleisen†1,
  • Annekatrin Bock†1,
  • Gerben ter Riet1, 3,
  • Ulrike Held1 and
  • Alfons GH Kessels1, 4
†Contributed equally
BMC Medical Research Methodology20088:50

https://doi.org/10.1186/1471-2288-8-50

©  Bachmann et al; licensee BioMed Central Ltd. 2008

Received: 14 May 2007

Accepted: 30 July 2008

Published: 30 July 2008

Open Peer Review reports

Abstract

Background

Vignette studies of medical choice and judgement have gained popularity in the medical literature. Originally developed in mathematical psychology they can be used to evaluate physicians' behaviour in the setting of diagnostic testing or treatment decisions. We provide an overview of the use, objectives and methodology of these studies in the medical field.

Methods

Systematic review. We searched in electronic databases; reference lists of included studies. We included studies that examined medical decisions of physicians, nurses or medical students using cue weightings from answers to structured vignettes. Two reviewers scrutinized abstracts and examined full text copies of potentially eligible studies. The aim of the included studies, the type of clinical decision, the number of participants, some technical aspects, and the type of statistical analysis were extracted in duplicate and discrepancies were resolved by consensus.

Results

30 reports published between 1983 and 2005 fulfilled the inclusion criteria. 22 studies (73%) reported on treatment decisions and 27 (90%) explored the variation of decisions among experts. Nine studies (30%) described differences in decisions between groups of caregivers and ten studies (33%) described the decision behaviour of only one group. Only six studies (20%) compared decision behaviour against an empirical reference of a correct decision. The median number of considered attributes was 6.5 (IQR 4–9), the median number of vignettes was 27 (IQR 16–40). In 17 studies, decision makers had to rate the relative importance of a given vignette; in six studies they had to assign a probability to each vignette. Only ten studies (33%) applied a statistical procedure to account for correlated data.

Conclusion

Various studies of medical choice and judgement have been performed to depict weightings of the value of clinical information from answers to structured vignettes of care givers. We found that the design and analysis methods used in current applications vary considerably and could be improved in a large number of cases.

Keywords

Discrete ChoiceCorrelate DataConjoint AnalysisDiscrete Choice ExperimentCare Giver

Background

Preferences and perceived similarities or differences between choice alternatives can be evaluated using structured vignettes. There are two prominent methods of constructing such models of medical judgements, each with their own literature and set of advocates. These are conjoint analysis, developed in the 1970s to study preference and choice [1], and judgement analysis, also called social judgement theory, developed in the 1950s from Brunswik's lens model [2, 3]. The two have developed along very different theoretical lines and have developed somewhat different methodology, although there is considerable overlap. Today there is a large number of marketing applications, where the joint effects of multiple product attributes on product choice have been studied. The types of choices include 'ranking', 'rating', and 'discrete choice'.

These methods can be carried forward to the analysis of medical decision making, as medical decisions require judgement under uncertainty. This uncertainty may concern a state, such as the presence of illness, the likelihood of future events, such as those in the natural course of an illness, or the likelihood with which such events may be averted, that is, treatment effects. For many years decision-making research has explored physicians' estimation of probabilities given clinical scenarios [4]. However, there have been concerns whether physicians' probability setting leads to consistent ratings [5]. Moreover, cognitive psychological research shows that physicians do not apply probabilities as suggested by decision-making theory but use their own heuristics to decide [6–8].

Studies of medical choice and judgement offer a way to elicit the public's, patients' and caregivers' views on healthcare that circumvents probability statements [9–11]. The technique is gaining widespread use in healthcare and has been applied in different areas for example to establish patients' preferences in the doctor-patient relationship [12], or to determine optimal treatments for patients [13]. Increasingly, discrete choice analyses are being employed to study how physicians weigh clinical information in the diagnostic work-up. In particular, respondents are asked to rank, rate, or choose between simulated clinical cases varying in values of different symptoms along the possibility that this case will have a certain illness or will need a certain treatment. Comparison with the results of clinical studies allows an analysis of potential discrepancies (e.g. undervaluation of signs and symptoms, overvaluation of test results). Moreover, such comparisons with reference data from clinical studies allow linking physicians' behaviour to illness probabilities and therefore allow examining (implicit) decision thresholds.

A considerable number of studies have been published recently. We provide an overview of existing reports, present an inventory of their objectives and methods, and evaluate them using systematic review methodology.

Methods

We defined a study of medical choice and judgement as an investigation in which preferences were elicited in physicians, nurse practitioners or medical students and that allowed the estimation of the relative importance of different characteristics.

Search strategy

We performed electronic searches in Medline, PsychINFO, CINAHL (Ovid®-version). Web of Science (ISI web of Science®) was used to locate studies that cited four key papers [14–17]. The last update search was performed on 25/3/2005. The exact search strategy may be obtained from the authors.

Inclusion criteria

Eligible articles for this review had to infer cue or attribute weighting from answers to structured vignettes and had to report on caregivers' decision making.

Data extraction strategy

We developed a data extraction form based on the assessment of three articles [17–19]. The form contained twelve items describing a study's salient features of context, design and analysis (for details see Table 1).
Table 1

Salient features of studies included in the systematic review.

Author

Year of publication

Clinical problem

Type1 of decision

Aim2 + reference2

Number of participants

Type3 of participants

Number of vignettes

Number of attributes

Source4 of attributes

Type5 of outcome

Type6 of analysis

Kirwan

1983

Rheumatoid arthritis

3

3

2

4

17

5

2

2

2

Wigton

1986

Pulmonary embolism

1

5 → B

55

4, 1

27

8

1

6

1

Smith

1987

Tube feeding

2

2

222

4, 1

12

6

1

2

2

Holmes

1989

Hypertension

1

3

98

3

16

4

1

2

1

Von Preyss-Friedman

1992

Tube feeding

2

2

141

4, 4

16

6

1

2

1

Lee

1994

Surgical patients

2

2

34

4, 3, 2

30

8

4

2

1

Harries

1996

Diversity of diseases

2

3

32

4

130

13

3

2

1

McKinlay

1997

Breast cancer

4

1

128

4

32

6

1

2, 4

1

Shea

1997

Bile duct stones

4

2

624

4, 4

27

8

1

1, 2, 4

1

Skaner

1998

Heart failure

1

1

27

4

40

10

1

1

2

Timmermans

1997

Colonic emergency

2

2

102

4

16

3

1

2

1

VanMilten-burg-Van Zijl

1997

Unstable angina

2

2

18

4, 4

12

7

2

2

2

Ross

1999

Depression

4

1

407

4

6

2

3

2, 4

1

Backlund

2000

Hypercholesterolemia

2

5 → A

38

4

40

8

2

2

1

Haggerty

2000

Fetal risk situation

3

1

573

2

32

10

1

2

1

Skaner

2000

Heart failure

1

5 → C

70

4, 4, 1

40

8

2

1

2

Bouma

2001

Aortic stenosis

2

2

275

4, 4

32

10

9

2

1

Engelsbel

2001

Ectopic pregnancy

1

4

27

4

16

6

9

2

2

Kee

2002

Renal disease

2

1

8

4

50

11

2

2, 3

1

Sorum

2002

Acute otitis media

4

2

75

4, 4

46

15

3

1, 2

2

Sorum

2002

Acute otitis media

4

4

75

4, 4

46

15

1

1, 4

2

Wahlström

2002

Asthma

2

5 → A

314

4, 4, 4

18

5

3

2

1

Bouma

2004

Aortic stenosis

2

5 → B

34

4

32

9

9

2

1

Sorum

2003

Prostate cancer

4

2

65

4, 4, 4

32

5

1

1, 2

2

Tamayo-Sarver

2003

Opioid analgesic

2

1

2872

4

3

3

3

4

1

Mays

2004

Vaccine program

2

1

224

2

13

4

9

2

1

Raley

2004

Papilloma vaccine

2

1

181

4

13

4

9

2

1

Arnold

2005

Resp. tract infection

2

1

257

4, 4

16

4

1

2

1

Lee

2005

Postoperat. recovery

5

1

60

4, 3, 2

8

3

3

5

2

Tiemeier

2002

Depression

2

5 → A

449

4, 4, 4

22

7

3

4, 6

1

1) 1 = diagnosis, 2 = treatment, 3 = risk, prognosis, 4 = diagnosis & treatment, 5 = other

2) 1st digit describes aim: 1 = descriptive, 2 = group comparison, 3 = consistency, 4 = change over time, 5 = comparison with reference

2nd digit describes reference: A = guidelines, B = actual patients, C = clinical study

3) 1 = student, 2 = paramedic, 3 = physician in training, 4 = expert

4) 1 = literature, 2 = patients, 3 = expert, 4 = guidelines, 5 = no information

5) 1 = probability, 2 = rating, 3 = ranking, 4 = yes/no choice, 5 = discrete choice, 6 = >2 alternatives

6) 1 = no adjustment for correlated data, 2 = adjustment for correlated data

Besides some study descriptors such as first author and year of publication, we extracted information on the studies' objectives, the clinical problem, who the decision-maker was, the type of decision/preference (diagnosis, treatment, risk, prognosis, diagnosis & treatment, and other), the number of participants and the authors' aims. The objectives were extracted into five categories: description of preferences in one group of caregivers (1), comparison of two or more groups such as different professions or different levels of competence. (2), assessment of the consistency within caregivers with their actual decisions or their direct rating of the attributes (3), assessment of changes in preferences over time, e.g. after attending a course (4), and comparison of caregivers with guidelines (5a), actual patients' preferences (5b), or the findings of one or more clinical studies (5c). We also registered the number of vignettes, the number of attributes of each vignette and the rationale behind the selection of the attributes. Finally, we documented how participants were asked to respond to the vignettes: rating (yes/no, otherwise), ranking, probability estimates, or discrete choice and the way, if any, in which authors accounted for correlated data in the analysis. We extracted this item because observations resulting from these experiments are typically not independent. Each respondent evaluates each of the vignettes. This makes the data from one respondent more alike than one would expect under the assumption of independence, and therefore standard deviations of the attributes could be underestimated. We searched for any statistical method that allows to adjust the standard errors for the intra-group correlation.

All studies were assessed in duplicate. Discordant scores based on reading errors were corrected. Discordant scores based on real differences in interpretation were discussed and resolved through consensus.

Results

The searches retrieved 2001 records. Full papers of 81 potentially relevant studies were obtained. In total 51 articles did not meet the inclusion criteria and were excluded after reading the full reports, leaving 30 reports published between 1983 and 2005 for evaluation. (See flowchart in the Figure 1) The salient features of included studies are shown in the Table 1.
Figure 1
Figure 1

Study flow.

General aspects

Although the first study was published in 1983, 24 studies (84%) were published after 1995.

Twenty-seven out of thirty studies examined decision behaviour of medical experts [15, 17–42]. In half of the studies more than one type of respondent was surveyed.

Twenty-eight different medical problems were addressed. Twenty-two (73%) studies examined treatment decisions. Eleven studies (37%) asked for a preferred diagnostic decision, sometimes (6 studies) in combination with a treatment decision.

Objectives

Ten studies (33 percent) aimed at describing decision preferences of specific groups of participants [20, 24, 26, 27, 29, 30, 32, 38, 43, 44] and nine studies (30 percent) described decision preference differences between groups [18, 25, 28, 31, 34, 36, 37, 40, 41]. Three studies explored the consistency of decisions between groups of experts [15, 23, 45] and two studies examined change of preferences after an intervention [22, 35]. Only six studies (20 percent) compared decision behaviour against some sort of empirical reference such as a guideline [21, 39, 42] (n = 3), actual patient data [17, 19] (n = 2) or the result of a clinical study [33].

Design

The median number of attributes was 6.5 (inter quartile range IQR 4–9, range 2–15). In 20 studies (67%) the selection of attributes was based on information like the literature [17, 20, 27, 28, 31, 32, 34, 35, 37, 40, 43, 45] (12 studies), expert opinion (7 studies) or guidelines (1 study). In five studies patient files [15, 21, 24, 33, 41] were used to construct the vignettes.

The median number of vignettes was 25 (IQR 16–32), ranging from 3 to 130.

Authors used several response modes for the vignettes. In eight cases they used more than one response mode. In 23 cases authors used a rating procedure [15, 18, 20–25, 27–31, 34–37, 40–45], where respondents had to rate the relative importance of a given vignette or assign a probability (n = 6) to a diagnosis or outcome [31–33, 35–37]. One study used a ranking design, where respondents had to arrange each of the attributes in descending order of importance [24]. In six studies respondents could reply with a yes/no choice [27, 30, 31, 35, 38, 39]. One study used a conventional discrete choice mode, where respondents, given two or more vignettes, had to select one with the highest likelihood of postoperative recovery [26].

Analysis

Twenty (67%) studies did not correct for correlated data. Consequently, only ten studies applied some statistical procedure to account for this correlation within the data [15, 22, 26, 32–37, 41].

Discussion

This review has two main findings. First, studies of medical choice and judgement are regularly used in the medical field to explore healthcare providers' decision behaviour or preferences. Second, we found a broad spectrum of different methods, and both design and analysis were suboptimal in some cases.

Cognitive burden/complexity

One fourth of our studies either contained vignettes with more than nine attributes or compiled sets of over forty vignettes in the same experiment. Empirical evidence showing that these figures are too high is scarce and there is much controversy particularly about the number of vignettes [46]. From a cognitive psychological point of view both figures appear to be very high and could bias the results. This bias typically occurs because respondents are unable to integrate and process large information quantities provided simultaneously, or because respondents lose attention when sifting through too many vignettes. However, evidence suggests that more attributes, more choice options and more vignettes decrease response reliability, but do not bias mean responses [46]. As a rule of thumb, the number of attributes per vignette should not exceed six to eight [47–49]. There is much opinion and controversy about maximally allowed number of vignettes, but little rigorous evidence [46]. A re-analysis of 21 commercial studies suggests a maximum of 20 vignettes [48] and a review of discrete choice experiments evaluating healthcare shows that the number of vignettes seldom exceeds 16 [49]. Furthermore, the majority of studies either used a ranking or rating response mode. These two modes imply very strong assumptions about human cognitive abilities making it more likely that measures will be biased and invalid [50]. Consequently, we therefore recommend the choice based approach.

Validity, usefulness of study objectives

In contrast to applications in marketing research where the main topic of a study is to identify opinions regarding a new product, we would be particularly interested to learn about the correctness of care givers' weighting of the value of clinical information in decisions. While there is no normative benchmark for a "correct" product there is usually one in medical judgement if clinical studies are available. For example, if the results of a study on medical choice and judgement showed that physicians consistently attribute high weights to relatively uninformative lab test but instead undervalue the informativeness of cues from clinical examination they would hint at something that needed to be improved perhaps with an educational intervention. Also the method would allow assessing the change in preferences after intervening with educational measures.

Most studies did not compare the attributed weights to some sort of normative benchmark such as the results of a clinical study. We only found one out of 30 studies that actually examined this and another five that used a further normative reference (guidelines or patient files). In absence of a normative benchmark these studies leave it to the reader to approve or disapprove the results. Moreover, assessment of discrepancies between different groups of participants has the problem that these could be explained by different clinical circumstances or other factors rather than group specific differences. On the other hand there are medical situations in which views about optimal choices are controversial. In these situations studies that do not compare caregivers' decision behaviour (or preferences) to some norm may still be useful in that they allow the examination of present opinions.

Statistical model

The majority of studies did not account for correlated data in the analysis. Correlated data occur because each respondent assesses different vignettes. Not accounting for this leads to too small estimates of the standard deviations for an attribute and can mimic a statistically significant association where in fact there is none. Unfortunately, guidelines on the conduct of conjoint analyses have not yet reached consensus about the optimal way to analyse correlated data.

Limitations

What are the limitations of this review? We think that the search and appraisal procedures were reliable. However, sometimes classifications were difficult to make because of unclear descriptions in the article. We did not contact authors to clarify these uncertainties. Second, there have been two prominent methods of constructing linear models of medical judgements, each with their own literature and set of advocates. These are conjoint analysis, developed in the 1970s to study preference and choice[1], and judgement analysis, also called social judgement theory, developed in the 1950s from Brunswik's lens model[2, 3]. In this review we did not make a distinction between the two methods because there is substantial overlap in methodology. Arguably this is a weakness of our study. However, since we were interested in providing an overview of all studies that examined medical decisions of care givers using cue weightings from answers to structured vignettes applying all sorts of different methods, we feel that our approach has its own merit.

Future research

Our review indicates that current applications of conjoint and judgment analysis in the medical field remain suboptimal in some instances. We think that researchers should consider our propositions to ensure internal validity. Moreover we believe that studies investigating care givers' judgements are most valuable if they allow comparisons with some norm and if they include an assessment of deviations from that norm. Our review only found few such investigations. From a more methodological point of view we agree with a statement in a recent editorial that research is required to learn whether individuals do behave in reality as they state in a hypothetical context. [51]

Conclusion

We believe that studies of medical choice and judgement offer many attractive and new insights into medical action. Provided that both methods and application evolve they offer a unique opportunity to improve quality of care.

Notes

Declarations

Acknowledgements

We thank Dr. Pius Estermann (information specialist, University Hospital Zurich) for doing the literature searches. This work was supported by the Swiss National Science Foundation (grants no. 3233B0-103182 and 3200B0-103183). The funding body played no role in study design; in the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication.

Authors’ Affiliations

(1)
Horten Centre, University of Zurich, Zurich, Switzerland
(2)
Division of Epidemiology and Biostatistics, Department of Social and Preventive Medicine, University of Bern, Switzerland
(3)
Department of General Practice, Academic Medical Center, Amsterdam, The Netherlands
(4)
Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Hospital, Maastricht, The Netherlands

References

  1. Green PE: On the Design of Choice Experiments Involving Multifactor Alternatives. Journal of Consumer Research: An Interdisciplinary Quarterly. 1974, 1: 61-68.View ArticleGoogle Scholar
  2. Cooksey RW: Judgment Analysis: Theory, Methods, and Applications. Academic Press; 1 edition (January 16, 1996). 2007, ISBN 012187575X-Google Scholar
  3. Hammond KR, Hursch CJ, Todd FJ: Analyzing the Components of Clinical Inference. Psychol Rev. 1964, 71: 438-456. 10.1037/h0040736.View ArticlePubMedGoogle Scholar
  4. Poses RM, Cebul RD, Wigton RS: You can lead a horse to water--improving physicians' knowledge of probabilities may not affect their decisions. Med Decis Making. 1995, 15: 65-75. 10.1177/0272989X9501500110.View ArticlePubMedGoogle Scholar
  5. Puhan MA, Steurer J, Bachmann LM, ter RG: Variability in diagnostic probability estimates. Ann Intern Med. 2004, 141: 578-579.View ArticlePubMedGoogle Scholar
  6. Gigerenzer G, Goldstein DG: Reasoning the fast and frugal way: models of bounded rationality. Psychol Rev. 1996, 103: 650-669. 10.1037/0033-295X.103.4.650.View ArticlePubMedGoogle Scholar
  7. Hoffrage U, Gigerenzer G: Using natural frequencies to improve diagnostic inferences. Acad Med. 1998, 73: 538-540. 10.1097/00001888-199805000-00024.View ArticlePubMedGoogle Scholar
  8. Hoffrage U, Lindsey S, Hertwig R, Gigerenzer G: Medicine. Communicating statistical information. Science. 2000, 290: 2261-2262. 10.1126/science.290.5500.2261.View ArticlePubMedGoogle Scholar
  9. Elstein AS, Schwarz A: Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ. 2002, 324: 729-732. 10.1136/bmj.324.7339.729.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Ratcliffe J, Buxton M: Patients' preferences regarding the process and outcomes of life-saving technology. An application of conjoint analysis to liver transplantation. Int J Technol Assess Health Care. 1999, 15: 340-351.PubMedGoogle Scholar
  11. Ratcliffe J, Buxton M, Young T, Longworth L: Determining Priority for Liver Transplantation: A Comparison of Cost per QALY and Discrete Choice Experiment-Generated Public Preferences. Appl Health Econ Health Policy. 2005, 4: 249-255. 10.2165/00148365-200504040-00007.View ArticlePubMedGoogle Scholar
  12. Ryan M: Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation. Soc Sci Med. 1999, 48: 535-546. 10.1016/S0277-9536(98)00374-8.View ArticlePubMedGoogle Scholar
  13. Ryan M, Hughes J: Using conjoint analysis to assess women's preferences for miscarriage management. Health Econ. 1997, 6: 261-273. 10.1002/(SICI)1099-1050(199705)6:3<261::AID-HEC262>3.0.CO;2-N.View ArticlePubMedGoogle Scholar
  14. Addelman S: Orthogonal main-effect plans for asymmetrical factorial experiments. Technometrics. 1962, 4: 47-58. 10.2307/1266171.View ArticleGoogle Scholar
  15. Kirwan JR, Chaputdesaintonge DM, Joyce CRB, Currey HLF: Clinical Judgment in Rheumatoid-Arthritis .2. Judging Current Disease-Activity in Clinical-Practice. Annals of the Rheumatic Diseases. 1983, 42: 648-651. 10.1136/ard.42.6.648.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Kirwan JR, Desaintonge DMC, Joyce CRB, Currey HLF: Clinical Judgment in Rheumatoid-Arthritis .3. British Rheumatologists Judgments of Change in Response to Therapy. Annals of the Rheumatic Diseases. 1984, 43: 686-694. 10.1136/ard.43.5.686.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Wigton RS, Hoellerich VL, Patil KD: How physicians use clinical information in diagnosing pulmonary embolism: an application of conjoint analysis. Medical Decision Making. 1986, 6: 2-11. 10.1177/0272989X8600600102.View ArticlePubMedGoogle Scholar
  18. Bouma BJ, van der Meulen JHP, van den Brink RBA, Arnold AER, Smidts A, Teunter LH, Lie KI, Tijssen JGP: Variability in treatment advice for elderly patients with aortic stenosis: a nationwide survey in the Netherlands. Heart. 2001, 85: 196-201. 10.1136/heart.85.2.196.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Bouma BJ, van der Meulen JH, van den Brink RB, Smidts A, Cheriex EC, Hamer HP, Arnold AE, Zwinderman AH, Lie KI, Tijssen JG: Validity of conjoint analysis to study clinical decision making in elderly patients with aortic stenosis. Journal of Clinical Epidemiology. 2004, 57: 815-823. 10.1016/j.jclinepi.2003.12.014.View ArticlePubMedGoogle Scholar
  20. Arnold SR, To T, McIsaac WJ, Wang FEL: Antibiotic prescribing for upper respiratory tract infection: The importance of diagnostic uncertainty. Journal of Pediatrics. 2005, 146: 222-226. 10.1016/j.jpeds.2004.09.020.View ArticlePubMedGoogle Scholar
  21. Backlund L, Danielsson B, Bring J, Strender LE: Factors influencing GPs' decisions on the treatment of hypercholesterolaemic patients. Scandinavian Journal of Primary Health Care. 2000, 18: 87-93. 10.1080/028134300750018963.View ArticlePubMedGoogle Scholar
  22. Engelsbel S, Mol BWJ, Hajenius PJ, Ankum WM, van der Veen F, Hemrika DJ, Bossuyt PMM: Non-invasive management of patients with suspected ectopic pregnancy: a survey among Dutch gynaecologists. European Journal of Obstetrics Gynecology and Reproductive Biology. 2001, 95: 81-85. 10.1016/S0301-2115(00)00369-9.View ArticleGoogle Scholar
  23. Harries C, Evans J, Dennis I, Dean J: A clinical judgement analysis of prescribing decisions in general practice. Travail Humain. 1996, 59: 87-109.Google Scholar
  24. Kee F, Patterson CC, Wilson AE, McConnell JM, Wheeler SM, Watson JD: Judgment analysis of prioritization decisions within a dialysis program in one United Kingdom region. Medical Decision Making. 2002, 22: 140-151. 10.1177/02729890222063026.View ArticlePubMedGoogle Scholar
  25. Lee A, Lum ME, Hillman KM, Bauman A: Referral of Surgical Patients to an Anesthetic Clinic - a Decision-Making Analysis. Anaesth Intensive Care. 1994, 22 (5): 562-567.PubMedGoogle Scholar
  26. Lee A, Gin T, Lau AS, Ng FF: A comparison of patients' and health care professionals' preferences for symptoms during immediate postoperative recovery and the management of postoperative nausea and vomiting. Anesthesia & Analgesia. 2005, 100: 87-93. 10.1213/01.ANE.0000140782.04973.D9.View ArticleGoogle Scholar
  27. McKinlay JB, Burns RB, Durante R, Feldman HA, Freund KM, Harrow BS, Irish JT, Kasten LE, Moskowitz MA: Patient, physician and presentational influences on clinical decision making for breast cancer: results from a factorial experiment. Journal of Evaluation in Clinical Practice. 1997, 3: 23-57. 10.1111/j.1365-2753.1997.tb00067.x.View ArticlePubMedGoogle Scholar
  28. Preyss-Friedman SM, Uhlmann RF, Cain KC: Physicians' attitudes toward tube feeding chronically ill nursing home patients. Journal of General Internal Medicine. 1992, 7: 46-51. 10.1007/BF02599102.View ArticleGoogle Scholar
  29. Raley JC, Followwill KA, Zimet GD, Ault KA: Gynecologists' attitudes regarding human papilloma virus vaccination: a survey of Fellows of the American College of Obstetricians and Gynecologists. Infectious Diseases in Obstetrics & Gynecology. 2004, 12: 127-133. 10.1080/10647440400020661.View ArticleGoogle Scholar
  30. Ross S, Moffat K, McConnachie A, Gordon J, Wilson P: Sex and attitude: a randomized vignette study of the management of depression by general practitioners. British Journal of General Practice. 1999, 49: 17-21.PubMedPubMed CentralGoogle Scholar
  31. Shea JA, Asch DA, Johnson RF, Staroscik RN, Malet PF, Pollack BJ, Clarke JR, Green PE, Schwartz JS, Williams SV: What predicts gastroenterologists' and surgeons' diagnosis and management of common bile duct stones?. Gastrointestinal Endoscopy. 1997, 46: 40-47. 10.1016/S0016-5107(97)70208-6.View ArticlePubMedGoogle Scholar
  32. Skaner Y, Strender LE, Bring J: How do GPs use clinical information in their judgements of heart failure? A Clinical Judgement Analysis study. Scandinavian Journal of Primary Health Care. 1998, 16: 95-100. 10.1080/028134398750003241.View ArticlePubMedGoogle Scholar
  33. Skaner Y, Bring J, Ullman B, Strender LE: The use of clinical information in diagnosing chronic heart failure: A comparison between general practitioners, cardiologists, and students. Journal of Clinical Epidemiology. 2000, 53: 1081-1088. 10.1016/S0895-4356(00)00241-9.View ArticlePubMedGoogle Scholar
  34. Smith DG, Wigton RS: Modeling Decisions to Use Tube-Feeding in Seriously Ill Patients. Archives of Internal Medicine. 1987, 147: 1242-1245. 10.1001/archinte.147.7.1242.View ArticlePubMedGoogle Scholar
  35. Sorum PC, Stewart TR, Mullet E, Gonzalez-Vallejo C, Shim J, Chasseigne G, Sastre MTM, Grenier B: Does choosing a treatment depend on making a diagnosis? US and French physicians' decision making about acute otitis media. Medical Decision Making. 2002, 22: 394-402. 10.1177/027298902320556082.PubMedGoogle Scholar
  36. Sorum PC, Shim J, Chasseigne G, Mullet E, Sastre MTM, Stewart T, Gonzalez-Vallejo C: Do parents and physicians differ in making decisions about acute otitis media?. Journal of Family Practice. 2002, 51: 51-57.PubMedGoogle Scholar
  37. Sorum PC, Shim J, Chasseigne G, Bonnin-Scaon S, Cogneau J, Mullet E: Why do primary care physicians in the United States and France order prostate-specific antigen tests for asymptomatic patients?. Medical Decision Making. 2003, 23: 301-313. 10.1177/0272989X03256010.View ArticlePubMedGoogle Scholar
  38. Tamayo-Sarver JH, Dawson NV, Hinze SW, Cydulka RK, Wigton RS, Albert JM, Ibrahim SA, Baker DW: The effect of race/ethnicity and desirable social characteristics on physicians' decisions to prescribe opioid analgesics. Academic Emergency Medicine. 2003, 10: 1239-1248. 10.1111/j.1553-2712.2003.tb00608.x.View ArticlePubMedGoogle Scholar
  39. Tiemeier H, de Vries WJ, van het LM, Kahan JP, Klazinga N, Grol R, Rigter H: Guideline adherence rates and interprofessional variation in a vignette study of depression.[see comment]. Quality & Safety in Health Care. 2002, 11: 214-218. 10.1136/qhc.11.3.214.View ArticleGoogle Scholar
  40. Timmermans DRM, Gooszen AW, Geelkerken RH, Tollenaar R, Gooszen HG: Analysis of the variety in surgeons' decision strategies for the management of left colonic emergencies. Medical Care. 1997, 35: 701-713. 10.1097/00005650-199707000-00004.View ArticlePubMedGoogle Scholar
  41. vanMiltenburgvanZijl AJM, Bossuyt PMM, Nette RW, Simoons ML, Taylor TR: Cardiologists' use of clinical information for management decisions for patients with unstable angina: A policy analysis. Medical Decision Making. 1997, 17: 292-297. 10.1177/0272989X9701700305.View ArticleGoogle Scholar
  42. Wahlstrom R, Hummers-Pradier E, Lundborg CS, Muskova M, Lagerlov P, Denig P, Oke T, de Saintonge DMC: Variations in asthma treatment in five European countries - judgement analysis of case simulations. Family Practice. 2002, 19: 452-460. 10.1093/fampra/19.5.452.View ArticlePubMedGoogle Scholar
  43. Haggerty LA, Nuttall RL: Experienced obstetric nurses' decision-making in fetal risk situations. JOGNN - Journal of Obstetric, Gynecologic, & Neonatal Nursing. 2000, 29: 480-490. 10.1111/j.1552-6909.2000.tb02769.x.View ArticleGoogle Scholar
  44. Mays RM, Zimet GD: Recommending STI vaccination to parents of adolescents: the attitudes of nurse practitioners. Sexually Transmitted Diseases. 2004, 31: 428-432. 10.1097/01.OLQ.0000130536.71812.E5.View ArticlePubMedGoogle Scholar
  45. Holmes MM, Rovner DR, Rothert ML, Schmitt N, Given CW, Ialongo NS: Methods of Analyzing Physician Practice Patterns in Hypertension. Medical Care. 1989, 27: 59-68. 10.1097/00005650-198901000-00006.View ArticlePubMedGoogle Scholar
  46. Louviere JJ, Eagle CE, Cohen SH: Conjoint Analysis: Methods, Myths and Much More. CEnSoc Working Paper No 05-001: 31, University of Technology, Sydney (2005). 2005Google Scholar
  47. Green PE, Sirnivasan V: Conjoint analysis in marketing: new developments with implications for research and practice. J Marketing. 1990, 54: 3-19. 10.2307/1251756.View ArticleGoogle Scholar
  48. Johnson RM, Orme BK: How many questions should you ask in choice-based conjoint studies. Sawtooth Softwarte Research Papers Sawtooth Software, Inc. 1996Google Scholar
  49. Ryan M, Gerard K: Using discrete choice experiments to value health care programmes: current practice and future research reflections. Appl Health Econ Health Policy. 2003, 2 (1): 55-64.PubMedGoogle Scholar
  50. Louviere JJ, Henscher DA, Swait J: Stated choice methods: Analysis and application. Cambridge University Press (2000) chapter 2 : 30. 2000Google Scholar
  51. Ryan M: Discrete choice experiments in health care. BMJ. 2004, 328: 360-361. 10.1136/bmj.328.7436.360.View ArticlePubMedPubMed CentralGoogle Scholar

    52. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/8/50/prepub

Copyright

© Bachmann et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement

BMC Medical Research Methodology

ISSN: 1471-2288

Contact us