On-site customer analytics and reporting (OSCAR):a portable clinical data warehouse for the in-house linking of hospital and telehealth data

This document conveys the results of the On-Site Customer Analytics and Reporting (OSCAR) project. This nine-month project started on January 2014 and was conducted at Philips Research in the Chronic Disease Management group as part of the H2H Analytics Project. Philips has access to telehealth data from their Philips Motiva tele-monitoring and other services. Previous projects within Philips Re-search provided a data warehouse for Motiva data and a proof-of-concept (DACTyL) solution that demonstrated the linking of hospital and Motiva data and subsequent reporting. Severe limitations with the DACTyL solution resulted in the initiation of OSCAR. A very important one was the unwillingness of hospitals to share personal patient data outside their premises due to stringent privacy policies, while at the same time patient personal data is required in order to link the hospital data with the Motiva data. Equally important is the fact that DACTyL considered the use of only Motiva as a telehealth source and only a single input interface for the hospitals. OSCAR was initiated to propose a suitable architecture and develop a prototype solution, in contrast to the proof-of-concept DACTyL, with the twofold aim to overcome the limitations of DACTyL in order to be deployed in a real-life hospital environment and to expand the scope to an extensible solution that can be used in the future for multiple telehealth services and multiple hospital environments. In the course of the project, a software solution was designed and consequently deployed in the form of a virtual machine. The solution implements a data warehouse that links and hosts the collected hospital and telehealth data. Hospital data are collected with the use of a modular service oriented data collection component by exposing web services described in WSDL that accept configurable XML data messages. ETL processes propagate the data, link, and load it on the OS-CAR data warehouse. Automated reporting is achieved using dash-boards that provide insight into the data stored in the data warehouse. Furthermore, the linked data is available for export to Philips Re-search in de-identified format

DACTyL:towards providing the missing link between clinical and telehealth data

This document conveys the findings of the Data Analytics, Clinical, Telehealth, Link (DACTyL) project. This nine-month project started at January 2013 and was conducted at Philips Research in the Care Management Solution group and as part of the Data Analysis for Home Healthcare (DA4HH) project. The DA4HH charter is to perform and support retrospective analyses of data from Home Healthcare products, such as Motiva telehealth. These studies will provide valid insights in actual clinical aspects, usage and behavior of installed products and services. The insights will help to improve service offerings, create clinical algorithms for better outcome, and validate and substantiate claims on efficacy and cost-effectiveness. The current DACTyL project aims at developing and implementing an architecture and infrastructure to meet the most demanding need from Motiva telehealth customers on return on investment (ROI). These customers are hospitals that offer Motiva telehealth to their patients. In order to provide the Motiva service cost-effectively, they need to have insight into the actual cost, benefit and resource utilization when it comes to Motiva deployment compared to their usual routine care. Additional stakeholders for these ROI-related data are Motiva customer consultants and research scientists from Philips for strengthening their messaging and service deliveries to arrive at better patient care

DACTyL:towards providing the missing link between clinical and telehealth data

This document conveys the findings of the Data Analytics, Clinical, Telehealth, Link (DACTyL) project. This nine-month project started at January 2013 and was conducted at Philips Research in the Care Management Solution group and as part of the Data Analysis for Home Healthcare (DA4HH) project. The DA4HH charter is to perform and support retrospective analyses of data from Home Healthcare products, such as Motiva telehealth. These studies will provide valid insights in actual clinical aspects, usage and behavior of installed products and services. The insights will help to improve service offerings, create clinical algorithms for better outcome, and validate and substantiate claims on efficacy and cost-effectiveness. The current DACTyL project aims at developing and implementing an architecture and infrastructure to meet the most demanding need from Motiva telehealth customers on return on investment (ROI). These customers are hospitals that offer Motiva telehealth to their patients. In order to provide the Motiva service cost-effectively, they need to have insight into the actual cost, benefit and resource utilization when it comes to Motiva deployment compared to their usual routine care. Additional stakeholders for these ROI-related data are Motiva customer consultants and research scientists from Philips for strengthening their messaging and service deliveries to arrive at better patient care

Sharing and viewing segments of electronic patient records service (SVSEPRS) using multidimensional database model

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The concentration on healthcare information technology has never been determined than it is today. This awareness arises from the efforts to accomplish the extreme utilization of Electronic Health Record (EHR). Due to the greater mobility of the population, EHR will be constructed and continuously updated from the contribution of one or many EPRs that are created and stored at different healthcare locations such as acute Hospitals, community services, Mental Health and Social Services. The challenge is to provide healthcare professionals, remotely among heterogeneous interoperable systems, with a complete view of the selective relevant and vital EPRs fragments of each patient during their care. Obtaining extensive EPRs at the point of delivery, together with ability to search for and view vital, valuable, accurate and relevant EPRs fragments can be still challenging. It is needed to reduce redundancy, enhance the quality of medical decision making, decrease the time needed to navigate through very high number of EPRs, which consequently promote the workflow and ease the extra work needed by clinicians. These demands was evaluated through introducing a system model named SVSEPRS (Searching and Viewing Segments of Electronic Patient Records Service) to enable healthcare providers supply high quality and more efficient services, redundant clinical diagnostic tests. Also inappropriate medical decision making process should be avoided via allowing all patients‟ previous clinical tests and healthcare information to be shared between various healthcare organizations. Multidimensional data model, which lie at the core of On-Line Analytical Processing (OLAP) systems can handle the duplication of healthcare services. This is done by allowing quick search and access to vital and relevant fragments from scattered EPRs to view more comprehensive picture and promote advances in the diagnosis and treatment of illnesses. SVSEPRS is a web based system model that helps participant to search for and view virtual EPR segments, using an endowed and well structured Centralised Multidimensional Search Mapping (CMDSM). This defines different quantitative values (measures), and descriptive categories (dimensions) allows clinicians to slice and dice or drill down to more detailed levels or roll up to higher levels to meet clinicians required fragment

Electronic medical records concepts and data management

Healthcare information (Clinical Data) is associated with every individual, young or old, rich or poor, belonging to any country. Clinical data is very extensive. Everyday some new diseases and new symptoms are being seen and the human race is struggling to find cures. There are many diseases whose diagnosis, symptoms, and possible treatment are known but unfortunately that rare knowledge is not available to every individual in the world. This initiates all the vision behind presenting a paper on EMR/ EHR and its Data Management. The thesis reviews the concept of EMR/ EHR thus explaining its concepts, importance, market need etc. Thesis will also explain privacy and security related to clinical data in electronic format which is a very important as any electronic data is prone to hacks and data loss. To manage and utilize such amount of data, there is need of extensive data management and so the thesis explains the concepts of Datawarehouse, its importance, ETL, Schemas etc. As part of explaining these concepts a mini EMR/EHR Datawarehouse is designed which explains various subject areas possible in any EMR Datawarehouse. Last but not the least, the thesis comments on the Future of EMR/ EHR and the World Vision on this revolutionary change

Automated Injection of Curated Knowledge Into Real-Time Clinical Systems: CDS Architecture for the 21st Century

abstract: Clinical Decision Support (CDS) is primarily associated with alerts, reminders, order entry, rule-based invocation, diagnostic aids, and on-demand information retrieval. While valuable, these foci have been in production use for decades, and do not provide a broader, interoperable means of plugging structured clinical knowledge into live electronic health record (EHR) ecosystems for purposes of orchestrating the user experiences of patients and clinicians. To date, the gap between knowledge representation and user-facing EHR integration has been considered an “implementation concern” requiring unscalable manual human efforts and governance coordination. Drafting a questionnaire engineered to meet the specifications of the HL7 CDS Knowledge Artifact specification, for example, carries no reasonable expectation that it may be imported and deployed into a live system without significant burdens. Dramatic reduction of the time and effort gap in the research and application cycle could be revolutionary. Doing so, however, requires both a floor-to-ceiling precoordination of functional boundaries in the knowledge management lifecycle, as well as formalization of the human processes by which this occurs. This research introduces ARTAKA: Architecture for Real-Time Application of Knowledge Artifacts, as a concrete floor-to-ceiling technological blueprint for both provider heath IT (HIT) and vendor organizations to incrementally introduce value into existing systems dynamically. This is made possible by service-ization of curated knowledge artifacts, then injected into a highly scalable backend infrastructure by automated orchestration through public marketplaces. Supplementary examples of client app integration are also provided. Compilation of knowledge into platform-specific form has been left flexible, in so far as implementations comply with ARTAKA’s Context Event Service (CES) communication and Health Services Platform (HSP) Marketplace service packaging standards. Towards the goal of interoperable human processes, ARTAKA’s treatment of knowledge artifacts as a specialized form of software allows knowledge engineers to operate as a type of software engineering practice. Thus, nearly a century of software development processes, tools, policies, and lessons offer immediate benefit: in some cases, with remarkable parity. Analyses of experimentation is provided with guidelines in how choice aspects of software development life cycles (SDLCs) apply to knowledge artifact development in an ARTAKA environment. Portions of this culminating document have been further initiated with Standards Developing Organizations (SDOs) intended to ultimately produce normative standards, as have active relationships with other bodies.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Bringing Business Intelligence to Health Information Technology Curriculum

Business intelligence (BI) and healthcare analytics are the emerging technologies that provide analytical capability to help healthcare industry improve service quality, reduce cost, and manage risks. However, such component on analytical healthcare data processing is largely missed from current healthcare information technology (HIT) or health informatics (HI) curricula. In this paper, we took an initial step to fill this gap. We investigated the current HIT educational programs, BI industry, and healthcare BI job listings, and students’ perceptions of BI and how BI could be incorporated into HIT programs. The student survey results showed strong interests from students in a HIT course containing BI components or a BI course specialized in the healthcare context. Based on the student survey and investigation of BI industry and job market, as well as HIT educational programs, we developed a general curriculum framework and exemplar implementation strategies to demonstrate how BI can be incorporated into an HI or HIT program. To the best of our knowledge, this research is the first of its kind. Our approach of integrating information from students, the HIT industry and other HIT programs can also be used as a model for general HIT curriculum development and improvement