Patricia Strohm

Fetomaternal hemorrhage following trauma

Fetomaternal hemorrhage can result from different types of trauma and may be followed by fetal anemia, fetal death, or isoimmunization. We prospectively studied the frequency and volume of fetomaternal hemorrhage, fetal well-being, abruptio placentae, and fetal outcome in 32 pregnant patients suffering recent trauma. Fetomaternal hemorrhage occurred in nine of 32 trauma patients (28%) with a mean volume of 16 ml +/- 14.3(SD). There was a statistically significant difference in the frequency and mean volume of fetomaternal hemorrhage in this group over that in gestational-age-matched controls. Neither the nature of the trauma nor the gestational age was related to the frequency or volume of fetomaternal hemorrhage. The outcome in three of the nine trauma patients who sustained fetomaternal hemorrhage was poor; fetal anemia, paroxysmal atrial tachycardia, and fetal death occurred in each one. Maternal trauma remains a significant cause of maternal and fetal morbidity and death, and the use of the Kleihauer-Betke analysis is indicated to identify fetomaternal hemorrhage. Rh-immune globulin therapy should be given to Rh-negative patients with fetomaternal hemorrhage.

An empirical evaluation of the performance of antibody identification tasks.

Four empirical studies were conducted for better understanding of the nature of problem‐solving activities by medical technologists and medical technology students when performing antibody identification tasks. The results indicated the importance of strategies that ensure the collection of converging evidence, as these strategies protect against the fallibility of commonly used heuristics and against errors due to simple slips. The results also indicate that not only do students make significant numbers of errors, but so do practicing technologists. In one of the studies covering a 1‐year period, for instance, a group of 16 technologists made a total of 41 errors in 1057 cases. On the basis of these findings, several alternatives are proposed to reduce errors.

Successful Use of an Expert System to Teach Diagnostic Reasoning for Antibody Identification

A previously reported study indicated that, when used by an instructor as a tool to assist with tutoring in a class laboratory setting, use of the Transfusion Medicine Tutor (TMT) resulted in improvements in antibody identification performance of 87-93% (p<.001). Based on input from teachers requesting that TMT be designed for use without the presence of an instructor, a new study on the use of TMT without instructor assistance found that performance improved by 64-66% (p<.001). Finally, based on the results of these two studies, TMT was mailed to 7 sites for beta-testing. In exchange for a free copy of the kit, the instructors (and their students) were asked to fill out questionnaires. Results of these questionnaires are summarized.

Errors in abductive reasoning

Results of efforts to extract knowledge for an expert whose job is to detect the errors made by practicing technologies are examined. These errors are discussed in terms of possible cognitive biases. The example examined involves students, technologies, and experts performing antibody identification tasks in order to construct a critiquing and intelligent tutoring system.<<ETX>>

The Transfusion Medicine Tutor: Methods and Results from the Development of an Interactive Learning Environment for Teaching Problem-Solving Skills

Investigations of students and practicing medical technologists indicate that both groups make significant numbers of errors on tasks such as antibody identification. One potential solution to help with this problem is to provide access to a computerized learning environment in which users can get exposure to a larger and much broader set of problems than would otherwise be possible. This paper describes such a learning environment, the Transfusion Medicine Tutor, and discusses the ways in which it supports guided discovery learning.

The Transfusion Medicine Tutor: Using Expert Systems Technology to Teach Domain-Specific Problem-Solving Skills

This study provides data regarding the effectiveness of the expert system-based Transfusion Medicine Tutor (TMT) when used by medical technology students to learn an important problem-solving task, the identification of alloantibodies in a patients blood for the purpose of finding compatible blood for transfusion. The results show that the students who were taught by an instructor using TMT to provide the instructional environment went from 0% correct on a pre-test case to 87%–93% correct on post-tests (N=15). This compares with an improvement rate of 20% by a control group (N=15) who used a passive version of the system with the tutoring functions turned off. The results also demonstrate the importance of relying on objective performance data rather than questionnaire data to evaluate systems, as there was no difference in the subjective responses of the students to these two different versions of the system.

The transfusion medicine tutor: a case study in the design of an intelligent tutoring system

Empirical studies of medical technology students indicated that there is a considerable need for additional skill development in performing tasks such as antibody identification. The transfusion medicine tutor (TMT) explores the use of a model of expert problem-solving to direct the application of a variety of teaching strategies in such an environment. This tutoring system focuses on red cell antibody identification, which is a problem-solving task where multiple primitive solutions can be true simultaneously. The approach to tutoring is the design of a problem-solving environment in which the computer plays an active role in tutoring the student. TMT allows students to gain experience by exploring a wide range of patient cases. The design of TMT makes it easy for the computer to detect certain types of errors. TMT allows more advanced students to compare the conclusions they have drawn from a particular test result with the computers interpretation of the same data.<<ETX>>

Design concepts underlying the use of an expert system to teach diagnostic reasoning for antibody identification

Antibody identification is a laboratory task where medical technologists must select tests to run and interpret the results in order to determine the antibodies in a patients blood. It has the classical characteristics of an abduction task, including masking and problems with noisy data. Based on a cognitive analysis of both successful and error-producing performances, a tutoring system (the transfusion medicine tutor, TMT), was developed that uses expert systems technology to provide immediate, context-sensitive feedback as students solve actual patient cases. Development of this system required consideration of aspects of artificial intelligence, education, psychology, and human factors engineering, as well as the domain of study (i.e., allo-antibody identification). In a formal field evaluation, when used by an instructor as a tool to assist with tutoring in a class laboratory setting, use of TMT resulted in improvements in antibody identification performance of 87-93% (p<.001) as compared to a passive control version which improved performance by 20%.