A Holistic View of a Patient Medical Record

Abstract

A Holistic View of a Patient Medical Record Invited Speaker: Pattanasak Mongkolwat Faculty of Information and Communication Technology Mahidol University, Thailand [email protected] Medical records of a patient contain patient’s health concerns, medical diagnosis, treatments and outcomes. They create a longitudinal record of medical history that is the corner stone of patient care. They consist of various types of notes by healthcare professionals, medical related and drugs orders, laboratory test results, medical imaging reports, observation results, etc. In addition, advancements in modern medicine provide novel diagnosis methods and modalities, genomics, biomolecular informatics, imaging informatics, clinical informatics, and public health informatics. They lead to exuberant amount of medical data being generated from patients. This, in turn, gives to participatory, pre-emptive, personalized, and predictive medicine that benefit individual patient unlike any other before. With the tremendous amount of health information generated from each patient and from other relevant medical cases, the information is typically stored in proprietary and standard medical formats such as HL7, CDA, CCR, DICOM, and medical lexicons such as ICD-10, SNOMED CT© and RxNorm. This information must be presented to healthcare personnel and medical doctors in an informative and simplified manner. An electronic medical record (EMR) system was first used in early 1970s until today to display and record patient information. Most EMR software is cumbersome and users must spend a lot of time searching for health information to learn about the current and past medical treatments of a patient. All EMR applications are based on command-line, GUI design (e.g. menu-driven, tabs UI, form based), and direct manipulation interface (e.g. icons and objects). Healthcare providers must spend a lot of time searching and memorizing information found from multiple medical software applications. It is ineffective use of time. A new paradigm for collecting, storing and presenting a longitudinal patient medical record will be presented. A method to create a data warehouse for storing patients’ data for immediate display will be demonstrated alongside the use of AR and gaming technology to display a comprehensive view of patient past and current conditions. Pattanasak Mongkolwat is the Dean of the Faculty of ICT. He worked as a Research Assistant Professor at Northwestern University, USA, advancing to become a Research Associate Professor in 2004, and a Research Professor in 2012. In 2015, Dr. Mongkolwat returned to Thailand, becoming an instructor at the Faculty of Information and Communication Technology (ICT), Mahidol University. In 2016, he took on his first administrative position at the Faculty of ICT, as Deputy Dean for Academic Administration before becoming the Dean of ICT. Throughout his academic and administrative career, Dr. Mongkolwat has taken on additional responsibilities as a Scientific Reviewer, Expert Advisor, and Working Group and Committee Member for a wide variety of public, private, and academic organizations, including; IEEE Software, IEEE Transactions of Information Technology in 242 2019 IEEE World Congress on Services (SERVICES) 978-1-7281-3851-0/19/$31.00 ©2019 IEEE DOI 10.1109/SERVICES.2019.00068 Biomedicine, The U.S. National Cancer Institute, the Radiological Society of North America (RSNA) Radiographics and Artificial Intelligent, The Society for Imaging Informatics in Medicine, Digital Imaging and Communications in Medicine, and the Subcommittee of Information and Communication Technology, Social Security of Thailand. He has also been certified by the American Board of Imaging Informatics, through the Certified Imaging Informatics Program (CIIP) since 2013. Dr. Mongkolwat was a funded subject matter expert for the NIH National Cancer Institute’s Cancer (NCI) Biomedical Informatics Grid (caBIG®) In Vivo Imaging Workspace. He received NCI caBIG® Outstanding Achievement Award. Much of Dr. Mongkolwat’s previous research focuses on Medical Informatics and Imaging, with current research projects covering areas including; Machine Learning to Predict Appropriate Treatment for Stroke Cases using medical images; Automated Radiological Report Generation for Screening Mammography; A Robot and IoT for Senior Healthy Living; Applying Simulation and Gaming Technology in Outpatient Care; Health Data Collection and Mobile App for Patient; Mapping AIM to DICOM Structured Reporting (under the DICOM Working Group 8); the National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM).