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The challenges of developing and delivering a computer-based simulation of the patient management environment were signi cant, but this was only part of the effort required to produce a useful assessment tool In order for the system to be useful for licensure evaluation, it had to be possible to quantify examinee responses as an indication of pro ciency: it was not only necessary to collect meaningful examinee responses; it was necessary to score them During much of the period in which Primum was being developed, effort was also underway to create scoring procedures The evolution of these procedures was associated with dramatic changes; four conceptually different systems were developed Each of these scoring procedures required that the simulation system be able to capture examinee responses and compare them to a key The procedures differed in which information was used to calculate the scores and how that information was aggregated The simplest and earliest procedures were based on the scoring approaches that had been applied to PMPs administered on paper Although there were many variations on this theme, the basic approach is that examinees are given points for selecting keyed actions that were identi ed as bene cial for management and points are lost for ordered actions that are non-indicated and may be dangerous to the patient The main problem with this approach was that test-wise examinees could sometimes achieve adequate scores by ordering non-intrusive procedures and avoiding potentially risky procedures Adequate scores did not necessarily re ect adequate management A second scoring procedure that was examined applied the Rasch model to the response patterns based on the key (Julian and Wright, 1988) More than any of the others, this approach is purely data driven It provided an implicit weighting for items based on the likelihood that examinees of a given pro ciency level would order those actions Both of these approaches were limited by the fact that the resulting scores may not show a strong correspondence to the judgments that expert clinicians would make if they reviewed the associated examinee performances The nal two scoring approaches that were studied share in common the fact that they are both explicit efforts to model that expert judgment Again, both approaches begin with items that could be quanti ed based on a kind of key Content experts reviewed case material and identi ed actions that would be bene cial for the given case presentation This de nition of bene cial was process rather than outcomes based in that bene cial items were those that would be appropriate for patients with a given presentation For example, diagnostic tests that would rule out reasonable differential diagnoses may be considered as bene cial as those that would identify the speci c problem that would ultimately prove to be the correct answer In addition to identifying those actions that would be considered bene cial, content experts categorized the non-indicated actions based on their level of potential risk to the patient (or level of invasiveness) These latter two procedures differed in terms of how the scored actions were combined to create an aggregate score The rst of these procedures used linear regression For this
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COMPUTER-BASED TESTING AND THE INTERNET
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approach, content experts categorized the bene cial actions in terms of their importance in the context of the case Actions were considered essential for adequate care , important for optimal care , or appropriate for optimal care Non-indicated actions were similarly placed in one of three categories de ned in terms of the associated level of risk and/or intrusiveness Finally, a variable was created that represented the time frame in which the essential actions were completed To produce scores, counts of actions were produced for examinees These counts, along with the variable associated with timing, were then used as the dependent measures in a multiple regression The independent measure was a rating of the examinee s performance on the case In effect, the regression procedure allowed for the estimation of optimal weights for predicting the rating of the examinee s performance based on the seven scoring variables (counts of actions in each of the three categories representing bene cial actions, counts of actions in each of the three categories representing non-indicated actions, and the timing variable) The resulting weights could then be applied to produce predicted ratings for performances that had not been reviewed by experts This regression-based procedure produced scores that were highly correlated with the actual ratings The one potential limitation of the procedure was that although resulting scores had a high correspondence to the expert ratings the structure of the scoring procedure was at best an approximation of the logic that experts used in making their judgments In response to this limitation, a procedure was developed in which an effort was made to combine the scorable items using logic statements that directly approximated the rules that experts used when rating examinee performance These logical statements de ned the combinations of diagnostic studies, therapeutic interventions and follow-up required for a given score The logic also included requirements about timing and sequencing (eg treatment must be initiated prior to the end of the rst day or credit is given for treatment only if the results of a diagnostic study are seen prior to initiating treatment ) These latter two procedures were evaluated in detail during the years immediately preceding operational implementation of the simulation as part of USMLE An obvious rst step in that evaluation was to compare the correlations between the scores produced using these methods with the expert rating these scores were intended to approximate Several published studies report correlations between one or both of these scores and ratings of the same performances (Clauser, Margolis, Clyman, & Ross, 1997a; Clauser et al, 1997b; Clauser, Subhiyah, Nungester, Ripkey, Clyman, & McKinley, 1995) The results lead to two general conclusions: (1) the resulting scores have a generally high correlation with the related ratings and (2) scores from the regression-based procedure are generally more highly correlated with the ratings than are those from the rule-based procedure Table 91 provides one set of these correlations (taken from Clauser et al, 1997b) Subsequent studies compared the reliability (or generalizability) of scores produced using the two methods, as well as the correlation between the observed scores produced using these methods and the pro ciency measured
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HEALTH PROFESSIONAL LICENSING AND CERTIFICATION Table 91 Correlations between ratings and regression-based and rule-based scores Case 1 2 3 4 5 6 7 8 Regression-based score 081 091 089 088 084 086 079 095 Rule-based score 077 085 087 084 069 087 079 086
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by the ratings of performance (ie the correlation between the observed scores produced with the automated systems and the true scores based on raters) (Clauser, Swanson, & Clyman, 1999; Clauser, Harik, & Clyman, 2000) Results suggested that the regression-based scores were more reliable that the rulebased scores The reliability analysis additionally indicated that using the regression-based procedure produced a score that was approximately as reliable as that which would have been obtained if two experts had rated each examinee performance The scores produced using both procedures were also shown to have an essentially perfect correlation with the true score associated with the rating These studies were important because (1) all else being equal, a more reliable score is to be preferred and (2) increasing the reliability at the expense of decreasing the correlation with the criterion may be an unwise trade-off between reliability and validity Based on the results of these and other related studies, a decision was made to implement the regression-based procedure for operational scoring of Primum Since November of 1999 this procedure has been used to score approximately 100,000 examinations The experience of the NBME has demonstrated that this type of automated scoring procedure can be psychometrically defensible and logistically feasible Clearly, the effort can be considered a success Nonetheless, there were lessons to learn in the process One important lesson is that complexity has a cost Even with the reasonably straightforward regression-based scoring approach, each case and each key require many hours of staff time not only for development but for the many important steps in the quality control process A second, but related, lesson is that decisions about the intended interpretation of the scores should be made early on in the development process Development of the simulation and the scoring system should then proceed in tandem Complexity in either part of the system should be justi ed by its relevance to the inferences that will be made based on the scores After more than three years of testing, examinees express strong approval of the test format In post-test surveys, examinees express appreciation for a test format that allows demonstration of their clinical management skills
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