An application of an EHR based on conceptual modeling to integrate clinical and genomic data and guide therapeutic strategy

Currently, data management in oncology department is complex and requires advanced Information Systems (ISs) to process data where “omic” information should be integrated together with patient’s clinical data to improve data analysis and decision-making process. This research paper reports a practical experience in this context. A Conceptual Model (CM) has been designed to develop an Information System (IS) in order to manage clinical, pathological, and molecular data in a holistic way at the oncology department of two main Hospitals in Paraguay. Additionally, model-based archetypes have been proposed to specify the selected user interaction strategy. The CM and its associated archetypes are the basis to develop a clinical IS in order to load -firstly- and manage -secondly- all the clinical data that the domain requires, showing how feasible the approach is in practice, and how much the corresponding clinical data management is improved. In this work, we want to reinforce with this real experience how using a CM along with archetypes correctly helps to design, develop and manage better information systems, emphasizing the relevance of the selected clinical domain

A Radiation Oncology Based Electronic Health Record in an Integrated Radiation Oncology Network

Purpose: The goal of this ongoing project is to develop and integrate a comprehensive electronic health record (EHR) throughout a multi-facility radiation oncology network to facilitate more efficient workflow and improve overall patient care and safety. Methodology: We required that the EHR provide pre-defined record and verify capability for radiation treatment while still providing a robust clinical health record. In 1996, we began to integrate the Local Area Network Treatment Information System (LANTIS®) across the West Penn Allegheny Radiation Oncology Network (currently including 9 sites). By 2001, we began modifying and expanding the assessment components and creating user-defined templates and have developed a comprehensive electronic health record across our network. Results: In addition to access to the technical record and verify information and imaging obtained for image-guided therapy, we designed and customized 6 modules according to our networks needs to facilitate information acquisition, tracking, and analysis as follows: 1) Demographics/scheduling; 2) Charge codes; 3) Transcription/clinical documents; 4) Clinical/technical assessments; 5) Physician orders 6) Quality assurance pathways. Each module was developed to acquire specific technical/clinical data prospectively in an efficient manner by various staff within the department in a format that facilitates data queries for outcomes/statistical analyses and promotes standardized quality guidelines resulting in a more efficient workflow and improved patient safety and care. Conclusions: Development of a comprehensive EHR across a radiation oncology network is feasible and can be customized to promote clinical/technical standards, facilitate outcomes studies, and improve communication and peer review. The EHR has improved patient care and network integration across a multi-facility radiation oncology system and has markedly reduced the flow and storage of paper across the network

An Application of an Electronic Health Record System in order to integrate clinical and molecular data and guide therapeutic strategy in Paraguay.

Improve data management in two public hospitals in Paraguay - Hospital de Clínicas and Instituto Nacional del Cáncer (INCAN). Currently, data management in oncology department is complex and requires advanced Information System to process data where "omic" information should be integrated together with patient's clinical data to improve data analysis and decision-making process. Conceptual Modelling is an important and essential activity that helps us not only to design an abstract model of an advanced Information System but also facilitates the development process.CONACYT - Consejo Nacional de Ciencias y TecnologíaPROCIENCI

Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning

Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety

Development of a Strategy to Implement an Oncology Clinical Research Program at a Rural Hospital

The purpose of this Capstone Project was to design a strategy for implementing an oncology clinical research program at a rural hospital cancer center. The rural cancer center is part of a large healthcare system (Healthcare System) that encompasses several hospitals located throughout northern Illinois. Healthcare System administrators prioritized development of a research program at the rural hospital as part of an institution initiative to expand access to oncology clinical trials in the community and rural settings. The author of this project was tasked with the responsibility of developing a strategy for building this research program at the rural cancer center. The project was accomplished by conducting a literature review, completing a needs assessment, and reviewing hospital analytic data. The literature review was used to identify best practices for opening and managing clinical research programs and to identify concerns specific to rural hospitals. The needs assessment was completed with key individuals in the oncology and research departments in the Healthcare System to gather information to ensure that the proposed strategy met the requirements of the oncology physicians and oncology and research leadership. The information from the literature review was then combined with feedback from the needs assessment and hospital analytic data to create a strategy that will provide a foundation for an oncology research program at the rural hospital that meets the needs of the patients, physicians, and Healthcare System administrators

PROsaiq: A Smart Device-Based and EMR-Integrated System for Patient-Reported Outcome Measurement in Routine Cancer Care

The PROsaiq prototype, which is based on the use of smart devices, was developed to show the technical feasibility of a lean, low-cost ePRO system that integrated with the oncology information system MOSAIQ to provide the potential for benefits in routine patient care, and improved data for clinical research. The system was built with Free & Open Source Software and trialled for a limited number of assessments. The report describes the components used, the decisions made and the hurdles met during the project. An on-line demonstration system is available to showcase PROsaiqs functionality

Artificial neural network-statistical approach for PET volume analysis and classification

Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

Establishing an Internet Based Paediatric Cancer Registration and Communication System for the Hungarian Paediatric Oncology Network

Cancer registration has developed in Europe over the last 50 years, and in the last decade intensive joint activities between the European Cancer Registries, in response to the need of pan-European harmonization of registration practices, have taken place. The Hungarian Paediatric Cancer Registry has been functioning as the database of the Hungarian Paediatric Oncology Network since 1971, aiming to follow the incidence and the treatment efficacy of malignant diseases.The goals of this globally unique open source information system are the following: 1) to raise the quality of the registration system to the European level by developing an Internet-based registration and communication system, modernizing the database, establishing automatic statistical analyses and adding an Internet website, 2) to support clinical epidemiological studies that we conduct with international collaborators on detailed analyses of the characteristics of patients and their diseases, evaluation of new diagnostic and therapeutic methods, prevention programs, and long-term quality of life and side effects.The benefits of the development of the Internet-based registration and communication system are as follows: a) introduction of an Internet-based case reporting system, b) modernization of the registry database according to international recommendations, c) automatic statistical summaries, encrypted mail systems, document repository, d) application of data security and privacy standards, e) establishment of a website and compilation of educational materials.The overall objective of this scientific project is to contribute towards the improvement of cancer prevention and cancer care for the benefit of the public in general and of cancer patients in particular

The Molecular Tumor Board Portal supports clinical decisions and automated reporting for precision oncology

Cancer; Cancer geneticsCáncer; Genética del cáncerCàncer; Genètica del càncerThere is a growing need for systems that efficiently support the work of medical teams at the precision-oncology point of care. Here, we present the implementation of the Molecular Tumor Board Portal (MTBP), an academic clinical decision support system developed under the umbrella of Cancer Core Europe that creates a unified legal, scientific and technological platform to share and harness next-generation sequencing data. Automating the interpretation and reporting of sequencing results decrease the need for time-consuming manual procedures that are prone to errors. The adoption of an expert-agreed process to systematically link tumor molecular profiles with clinical actions promotes consistent decision-making and structured data capture across the connected centers. The use of information-rich patient reports with interactive content facilitates collaborative discussion of complex cases during virtual molecular tumor board meetings. Overall, streamlined digital systems like the MTBP are crucial to better address the challenges brought by precision oncology and accelerate the use of emerging biomarkers.Open access funding provided by Karolinska Institut