Improving lung transplantation referral time, indication and patient selection with designing a mobile based CDSS

Background:Due to difficulty and complexity to determine the optimal time of patient referral, we decided to create the novel approach to help physicians with clinical decision support system (CDSS) to decide with no hesitate to refer patients to the transplantation center. Timely referral is too important that it can affect the final result and patient survival.Material and Methods: Since specifying this gold time is very complicated, we determined required data set from LTX guideline.Then, we applied decision tree with expert consultation to achieve the best way in determining optimal referral time and selecting an appropriate patient with considering different diseases, symptoms, life styleand different other related factors. This application designed in the Android platform with use of the Android studio software. Results: We extracted more than 25 essential data sets and finally we can be designed more than 30 rules to design our knowledgebase. The physician should consider different factors in complex decision making process fortransplantation. This application can guide and assist the physician in decision making process and all factors available in the form of check boxes, lists, suggestion and comments.Thus; he will be sure no factors will be unregarded and can make decisions with high confidence. Therefore, this application would be favored by physicians in the future. Conclusion: Referring and selecting patients in the best and appropriate time in selected patients for lung transplantation could improve survival and quality of life. As guidelines can assist pulmonologists in defining the optimal referral time but using guidelines are complex and time consuming, with using this expert system which is available any time, we can improve the quality of selection appropriate patients for lung transplantation and decrease mortality in transplant centers.

Keywords:

lung transplantation , CDSS , M-health , referral