A Novel Approach to Facilitate Interoperability between Clinical Decision Support Systems and Electronic Health Records
We are trying to view this problem in different way. Compared to conventional approaches, we suggest more fundamental change – uniform clinical terminology should be used by EHR and HIS (with its integrated CDSS). Facilitated data exchange will increase overall data loading efficacy for analysis. Furthermore, this updated CDSS should be machine-learning based, which dynamically update knowledge according to constantly incoming training dataset.
As the last section of this research, a case study will be done to examine feasibility of our concepts in EHR and HIS design. First we will test whether data can be transferred between two sides unambiguously. The second task is to compare diagnosis and therapy recommendation generated by our suggested algorithm to conclusions made by clinicians. In order to better simulate clinical practice, we hereby request NLST datasets for this study. Usage of datasets will be cited in our work and will be restricted to current proposed research plan. This study is proposed for paper call of Journal of Medical Systems.
Aim 1. Unify the data format of CDSS and EHR. In order to facilitate interoperability between CDSS and EHR, one solution is to define the same data format standard and protocol for these two systems. In this study, new data format protocol will be raised to prevent the misinterpretation due to different definitions taken by different systems for the same clinical concepts.
Aim 2. Design a machine-learning based CDSS algorithm. Currently the majority of CDSS are knowledge-based, i.e. CDSS make decisions following pre-set rules defined by committee of experts. These rules should be updated frequents to keep up with the novel findings in clinical practice. Here we suggest a machine-learning based CDSS, which can update the clinical knowledge dynamically to save the labor of maintenance and update. Primary structure of algorithm for this novel system will be given.
Aim 3. Check the function of this new system in a case study. In this aim this newly built system will be tested using data obtained from NLST. Certain diagnostic and therapeutic data sets will be used as training data, and the system will try to diagnose and give treatment suggestions using other data, and decisions made by system will be compared with those from clinicians to determine the reliability of algorithm.
Shuo Yang
University of Macau E-Commerce Technology Laboratory. E11, Avenida da Universidade, Taipa, Macau, China.