A novel report generation approach for medical applications: The SISDS methodology and its applications

Abstract

Background
Despite exciting innovation in information system technologies, the medical reporting has remained static for a long time. Structured reporting was established to address the deficiencies in report content but has largely failed in its adoption due to concerns over workflow and productivity. The methods used in medical reporting are insufficient in providing with information for statistical processing and medical decision making as well as high quality healthcare.

Objective
The aim of this study is to introduce a novel method that enables professionals to efficiently produce medical reports that are less error-prone and can be used in decision support systems without extensive post-processing.

Methodology
We first present the formal definition of the proposed method, called SISDS, that provides a clear separation between the data, logic and presentation layers. It allows free-text like structured data entry in a structured form, and reduces the cognitive effort by inline editing and dynamically controlling the information flow based on the entered data. Then, we validate the usability and reliability of the method on a real-world testbed in the field of radiology. For this purpose, a sample esophagus report was constructed by a focus group of radiologists and real patient data have been collected using a web-based prototype; these data are then used to build a decision support system with off-the-shelf tools. The usability of the method is assessed by evaluating its acceptability by the users and the accuracy of the resulting decision support system. For reliability, we conducted a controlled experiment comparing the performance of the method to that of transcriptionist-oriented systems in terms of the rate of successful diagnosis and the total time required to enter the data.

Result
The most noticeable observation in the evaluation is that the rate of successful diagnosis improves significantly with the proposed method; in our case study, a success rate of 81.25% has been achieved by using the SISDS method compared to 43.75% for the transcriptionist-oriented system. In addition, the average time required to obtain the final approved reports decreased from 29 min to 14 min. Based on questionnaire responses, the acceptance rate of the SISDS methodology by users is also found to outperform the rates of the current methods.

Conclusion
The empirical results show that the method can effectively help to reduce medical errors, increase data quality, and lead to more accurate decision support. In addition, the dynamic hierarchical data entry model proves to provide a good balance between cognitive load and structured data collection.