Information Engineering and Workflow Design in a Clinical Decision Support System for Colorectal Cancer Screening in Iran

Abstract

Background: Colorectal cancer is a major cause of morbidity and mortality throughout the world. Colorectal cancer screening is an optimal way for reducing of morbidity and mortality and a clinical decision support system (CDSS) plays an important role in predicting success of screening processes. DSS is a computer-based information system that improves the delivery of preventive care services. The aim of this article was to detail engineering of information requirements and work flow design of CDSS for a colorectal cancer screening program. Materials and Methods: In the first stage a screening minimum data set was determined. Developed and developing countries were analyzed for identifying this data set. Then information deficiencies and gaps were determined by check list. The second stage was a qualitative survey with a semi-structured interview as the study tool. A total of 15 users and stakeholders' perspectives about workflow of CDSS were studied. Finally workflow of DSS of control program was designed by standard clinical practice guidelines and perspectives. Results: Screening minimum data set of national colorectal cancer screening program was defined in five sections, including colonoscopy data set, surgery, pathology, genetics and pedigree data set. Deficiencies and information gaps were analyzed. Then we designed a work process standard of screening. Finally workflow of DSS and entry stage were determined. Conclusions: A CDSS facilitates complex decision making for screening and has key roles in designing optimal interactions between colonoscopy, pathology and laboratory departments. Also workflow analysis is useful to identify data reconciliation strategies to address documentation gaps. Following recommendations of CDSS should improve quality of colorectal cancer screening.

Integrated Colorectal Cancer Screening Based on Decision Support Information System

“…Wagholikar et al CRC screening workflow significantly improved the performance of decision systems and increased quality of information (29). Accurate design of work process and integration of information increases the effectiveness of the screening program (24). In the present system, there were no standardized work processes and there was no interactive between the goals of the screening program and the current information system.…”

“…Managers state more effective planning for a screening program can support with up-to-date and accurate information (24). The present information system is not capable of providing management reports, only limited to daily and routine reports.…”

“…While information systems follow the educational and research objectives of health care organizations and increase productivity (23). These technologies can transform the traditional interaction between clinicians and patients into a more productive and structured relationship (24). The educational objectives of the existing system are limited to the population covered by the screening program and the etraining of health care providers were not carried out.…”

Information construction of construction project management based on internet of things blockchain

“…Kosarak and retail datasets related to existing features will be applied to blockchain technology. 27,28 In identifying abnormal data of various functional departments, in order to be able to identify local and global anomalies, this article will use density-based algorithm (LOF) to detect abnormal data. This method can effectively identify the degree of outlier of the data, which does not require Assume a specific distribution model.…”

“…If the number of points N within its scanning radius is greater than the minimum included point MinPts, the point Is the core point, otherwise it is the boundary point; when point A is the core point within the range of the scanning radius, a cluster is established based on point A, and each point within the scanning radius where point A is located is identified, and these points are identified by the It is merged into the same cluster; when the points in the data set are all traversed, the algorithm ends. Kosarak and retail datasets related to existing features will be applied to blockchain technology 27,28 …”