Security and Privacy of Electronic Medical Records

Information Technology is being used in many ways to improve the quality and effectiveness of healthcare. Electronic Medical Record (EMR) is a medical record system that is computerized and delivers care in an institution, such as a physician\u27s office or a hospital. EMR tends to be a part of a local stand-alone health information system that allows storage, retrieval, and modification of records. Electronic Medical Records are critical, highly sensitive, and private information in healthcare; these records are frequently shared among health care providers. There are concerns and questions about the security and privacy of their health information on the EMR among all the stakeholders in healthcare - any person or party who provides, receives, manages or pays for healthcare. It is essential to ensure the security and privacy of Electronic Medical Records and protect them from cyberattack

Semantic Information on Electronic Medical Records (EMRs) through Ontologies

This work shows the development of ontology in the domain of Electronic Medical Records (EMRs). The ontology supports vocabulary and semantic information to patients. The ontology implemented begins with the, the exploration of semantic web applications, ontology design ,analysis and the use of ontological engineering in order information indexing and retrieval from and to electronic medical records. This ontology is one of other services to incorporate on current telemedicine systems.Sociedad Argentina de Informática e Investigación Operativ

Semantic Information on Electronic Medical Records (EMRs) through Ontologies

This work shows the development of ontology in the domain of Electronic Medical Records (EMRs). The ontology supports vocabulary and semantic information to patients. The ontology implemented begins with the, the exploration of semantic web applications, ontology design ,analysis and the use of ontological engineering in order information indexing and retrieval from and to electronic medical records. This ontology is one of other services to incorporate on current telemedicine systems.Sociedad Argentina de Informática e Investigación Operativ

DICOM Data from a Data Warehouse Design Perspective

Medical images play an essential role in electronic patient health care information for diagnosis, recommendation, and study. In radiology departments, the digitized imaging information for various modalities is stored in a standard Digital Imaging and Communication in Medicine (DICOM) record. These digitized imaging records create very large volumes of electronic files in the medical information system. The records have two parts: text and image. The text part describes the details and specifications of the digitized images. This paper provides a methodology for storing the text imaging information in an integrated and centralized data warehouse for multiple medical facilities for enhanced information retrieval and analysis

Clinical Text Mining: Secondary Use of Electronic Patient Records

This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields

A FRAMEWORK FOR A CLOUD-BASED ELECTRONIC HEALTH RECORDS SYSTEM FOR NIGERIA

  In most countries of the developed world, one of the integral components of Health Information System (HIS) is Electronic Health Records (EHR). With advances in Information and Communications Technology (ICT) and the rise in the adoption of cloud computing approaches in the health sector of these countries by a substantial number of health institutions, cloud servers are now remote repository of EHRs. However, in Nigeria and many other developing countries, health information of patients is still predominantly paper-based medical records. This manual method is not scalable in terms of storage, prone to error, insecure, susceptible to damage and degradation over time, highly unavailable, time consuming in accessing and with no visible audit trail and version history to mention but a few. In this paper, a framework for a cloud-based electronic health records system that is capable of storage, retrieval and updating of patients’ medical records for Nigeria is proposed. The framework provides for various medical stakeholders in a health institution and patients to access the EHR system via a web portal by using a variety of devices in the contextual scenario whereby the health institution is migrating from paper-based patient record documentation to an EHR system

Building realistic potential patient queries for medical information retrieval evaluation

To evaluate and improve medical information retrieval, benchmarking data sets need to be created. Few benchmarks have been focusing on patients’ information needs. There is a need for additional benchmarks to enable research into effective retrieval methods. In this paper we describe the manual creation of patient queries and investigate their automatic generation. This work is conducted in the framework of a medical evaluation campaign, which aims to evaluate and improve technologies to help patients and laypeople access eHealth data. To this end, the campaign is composed of different tasks, including a medical information retrieval (IR) task. Within this IR task, a web crawl of medically related documents, as well as patient queries are provided to participants. The queries are built to represent the potential information needs patients may have while reading their medical report. We start by describing typical types of patients’ information needs. We then describe how these queries have been manually generated from medical reports for the first two years of the eHealth campaign. We then explore techniques that would enable us to automate the query generation process. This process is particularly challenging, as it requires an understanding of the patients’ information needs, and of the electronic health records. We describe various approaches to automatically generate potential patient queries from medical reports and describe our future development and evaluation phase

Utilizing ChatGPT to Enhance Clinical Trial Enrollment

Clinical trials are a critical component of evaluating the effectiveness of new medical interventions and driving advancements in medical research. Therefore, timely enrollment of patients is crucial to prevent delays or premature termination of trials. In this context, Electronic Health Records (EHRs) have emerged as a valuable tool for identifying and enrolling eligible participants. In this study, we propose an automated approach that leverages ChatGPT, a large language model, to extract patient-related information from unstructured clinical notes and generate search queries for retrieving potentially eligible clinical trials. Our empirical evaluation, conducted on two benchmark retrieval collections, shows improved retrieval performance compared to existing approaches when several general-purposed and task-specific prompts are used. Notably, ChatGPT-generated queries also outperform human-generated queries in terms of retrieval performance. These findings highlight the potential use of ChatGPT to enhance clinical trial enrollment while ensuring the quality of medical service and minimizing direct risks to patients.Comment: Under Revie

CD ROMs for family doctors

These days more and more Maltese Family Doctors are using information technology in their practices. Electronic medical records offer many advantages over paper-based systems, including fast and efficient data retrieval and professional presentation of patient data as problem lists and medication lists. This has been the experience of many colleagues who chose to use the program Transhis for their clinical records, and this has the added advantage that data is classified with ICPC and can be used for research purposes. More details were given in the article about the project published in the June issue of the Journal.peer-reviewe