A Decision Support System for Therapy Prescription in a Hospital Centre

Several cases are reported every year where the prescribed therapy results incompatible with the patient’s medical history, leading to worsening of clinical condition or death. Some technologies and processes to prevent this misbehaviour already exist, but a concrete solution is not available in hospitals yet. This paper presents a Decision Support System (DSS) that can be easily integrated into a typical health workflow at hospitals and provides feedback on the possible prescription of drugs at a patient with specific diseases. The DSS is based on a Big Data analysis algorithm able to check drugs and diseases relationships and detect possible failures in drugs prescriptions. We developed a prototype of the proposed solution, implementing the DSS system and setting up the necessary Big Data management tools for the effective adoption of the DSS system. We performed some evaluations to assess the efficacy and the response time of the DSS algorithm.

[1]  Seungok Lee,et al.  Organization of Maximum Surgical Blood Order Schedule (MSBOS) according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) , 2008 .

[2]  R. Weiss,et al.  Steroid‐induced diabetes: a clinical and molecular approach to understanding and treatment , 2014, Diabetes/metabolism research and reviews.

[3]  Maria Fazio,et al.  Blockchain-Based Healthcare Workflow for Tele-Medical Laboratory in Federated Hospital IoT Clouds , 2020, Sensors.

[4]  Hideki Origasa,et al.  Major cardiovascular and bleeding events with long-term use of aspirin in patients with prior cardiovascular diseases: 1-year follow-up results from the Management of Aspirin-induced Gastrointestinal Complications (MAGIC) study , 2019, Heart and Vessels.

[5]  Pierangelo Veltri,et al.  Modeling and application of aorta coarctation: support system for pre-operative decision , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[6]  Maria Fazio,et al.  An OAIS-Based Hospital Information System on the Cloud: Analysis of a NoSQL Column-Oriented Approach , 2018, IEEE Journal of Biomedical and Health Informatics.

[7]  Jesús Rodríguez-Marín,et al.  International Classification of Diseases (WHO) , 2004 .

[8]  Stephen Billings,et al.  NARMAX Model as a Sparse, Interpretable and Transparent Machine Learning Approach for Big Medical and Healthcare Data Analysis , 2019, 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).

[9]  Lawrence Mbuagbaw,et al.  Antiplatelet therapy and coronary artery bypass grafting , 2019, Medicine.

[10]  J. James A New, Evidence-based Estimate of Patient Harms Associated with Hospital Care , 2013, Journal of patient safety.

[11]  Jouhaina Chaouachi Siala,et al.  A Decision Support System for Drug Inventory Management within an Emergency Department: A Case Study , 2019, 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT).

[12]  Hui Wang,et al.  Intelligent Clinical Decision Support Systems for Patient-Centered Healthcare in Breast Cancer Oncology , 2018, 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom).

[13]  Frank Piontek,et al.  DSS in Healthcare: Advances and Opportunities , 2008 .

[14]  Albert Y. Zomaya,et al.  The Next Grand Challenges: Integrating the Internet of Things and Data Science , 2018, IEEE Cloud Computing.