Biomedical subjects

Frank Puppe

Publications and source records attributed to Frank Puppe.

4 recordsLinked to original sources

PubMed2006

Assessment of a case-based training system (d3web.Train) in rheumatology.

To improve the learning experiences for third year medical students, a case-based training program based on real patient records has been introduced as a supplement to lectures. It was built with the novel training environment d3web.Train. Optimization of the learning environment d3web.Train for both teachers and learners has been considered as equally important and offers several trade-offs. The training program has been evaluated during three subsequent rheumatology courses, the first one voluntary and the latter two mandatory. For each course, we tracked how often and intensive each student used the system. In addition, the students answered questionnaires about their expectations and experiences. In the first voluntary course, 39/92 students finished at least one case and solved 187 cases in total (average: 4.8 cases from 12 cases available). In the second and third mandatory courses, all students (60 resp. 56) finished at least the required 20 cases with an average usage of 22.5 resp. 29.3 out of 31 available cases. Most of the cases were processed twice by the students in preparation for the final examinations. A positive correlation between processed cases per student and the exam score could be shown (30, 73 and 95% in the first, second and third courses, respectively). The findings clearly exceeded the expectations of the students concerning its usefulness as a supplement to lectures and as good preparation for their job as physician, as well as the learning period to use the program. For working through one case, students needed about 9 min on average. The case-based training system d3web.Train offers a new tool for medical education in rheumatology. The main advantage of the system is the relatively low effort needed to create a case-based program starting from the available dismissal records.

Computer-Assisted Instruction↗

PubMed2005

Evaluation of a novel case-based training program (d3web.Train) in hematology.

The new media such as the internet and digital imaging offer new opportunities in medical education. In addition to conventional lectures, we developed a case-based simulation training program of 17 hematology cases using the novel training system d3web.Train. We evaluated the assessment of this internet course by medical students, as well as their results in the hematology exam. From a group of 150 students, 47 worked through at least one case and solved 435 cases in total; in average, these students solved 9.5 cases. Eighteen different students filled in a questionnaire about the training system and 68 questionnaires about individual cases. The main results were the students found the cases very helpful (1.5+/-0.6 on a scale from 1=very helpful to 5=not at all), the training system very good (1.4+/-0.5 on a scale from 1 to 6), and want to work with it further (1.2+/-0.4 on a scale from 1 to 5). During the final examination, those 16 students who answered that they had solved more than 5 from the 17 cases scored significantly better (two-sided t test, p<0.01) in the hematological part of the exam than those 34 students solving 0 to 5 cases. To our knowledge, this is the first student evaluation of a case-based training program in general hematology. The d3web.Train system offers a new and great tool for creating a training program in a reasonable amount of time, because it is able to process available patient records.

Computer-Assisted Instruction↗

PubMed2005

Semi-automatic learning of simple diagnostic scores utilizing complexity measures.

OBJECTIVE: Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semi-automatic learning methods can be used to support the domain specialists. They are usually not only interested in the accuracy of the learned knowledge: the understandability and interpretability of the learned models is of prime importance as well. Then, often simple models are more favorable than complex ones. METHODS AND MATERIAL: We propose diagnostic scores as a promising approach for the representation of simple diagnostic knowledge, and present a method for inductive learning of diagnostic scores. It can be incrementally refined by including background knowledge. We present complexity measures for determining the complexity of the learned scores. RESULTS: We give an evaluation of the presented approach using a case base from the fielded system SonoConsult. We further discuss that the user can easily balance between accuracy and complexity of the learned knowledge applying the presented measures. CONCLUSIONS: We argue that semi-automatic learning methods can support the domain specialist efficiently when building (diagnostic) knowledge systems from scratch. The presented complexity measures allow for an intuitive assessment of the learned patterns.

Algorithms↗

PubMed2004

A diagnostic expert system for structured reports, quality assessment, and training of residents in sonography.

BACKGROUND: The quality of medical reports on diagnostic procedures has a considerable impact on the quality of medical care. Handwritten or otherwise unstructured reports tend to be incomplete, whereas structured questionnaires are of limited flexibility and not considered case-adequate. Thus, medical reports of this kind may promote an incomplete and misleading documentation and, therefore, be problematic with respect to their reliability. METHODS: SonoConsult (SC), an expert system for structured and case-adequate documentation of sonographic findings with an additional diagnostic component, was evaluated with respect to user acceptance and suitability for enhancing the quality of reports and supporting sonographic beginners. The expectations and the attitudes of the users toward the program were evaluated by anonymous questionnaires. The documentation of findings and the diagnostic conclusions in 103 free text reports made by experienced examiners were evaluated by subjecting their information to a subsequent input into SC. Free text reports were checked for information that was asked by SC but not mentioned in the reports. In a series of 150 cases, the system diagnoses were blinded during input of findings into SC-questionnaires and the examiners' diagnostic conclusions were compared with the uncovered SC-diagnoses with respect to forgotten diagnoses. RESULTS: The structured and data-driven acquisition of information by the program was well accepted by the users. However, only a medium interest in the system-delivered diagnoses was noted. The program-generated reports were characterized by a more detailed description of the findings and a higher number of diagnoses in comparison to the unstructured reports before introduction of SC as the only documentation system. When unaware of the system diagnoses, information was entered into the questionnaires, and SC generated some diagnoses which were not mentioned by the examiners in their conclusions. The possibility to inspect the system diagnoses led to an enhancement of the number of diagnoses the examiners mentioned in their conclusions. By contrast, the examiners meant that the influence of the program on their conclusions was minimal or dispensable. Beginners in sonography acknowledged that the program led them to perform a complete examination in an adequate sequence. CONCLUSION: An expert system for the data-driven, case-adequate information acquisition of abdominal ultrasound examinations may enhance the quality of the reports and, potentially, of the examinations at the same time. In addition, it may help beginners to learn a structured problem- and finding-adequate examination sequence.

Abdomen↗