IT Architectures and Implementations in Healthcare Environments Minitrack
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We invite papers that address IT architectures and implementations in healthcare environments, which range from the communication and dissemination of data/knowledge across healthcare systems, to pervasive healthcare settings, m-health, I-health, and intelligent, ubiquitous healthcare. We would also like to receive papers from a spectrum of disciplines involved in the IT implementations, which may include: personalized medicine, modeling patient behavioral change, and the management of online social-intensive healthcare environments which generate and disseminate healthcare knowledge. Modern IT architectures in healthcare often emphasize the pervasiveness of healthcare software solutions and proliferation of effective IT applications (Apps) which run on Android, iOS and other mobile operating environments.
Specific topics of interest may include:
- Integrated Solutions in Healthcare:
- Architectures and implementations of personal healthcare information systems, Software tools for empowering and educating patients, Applications of mining internet health information for health consumers, Personalized decision support systems for preventive healthcare.
- Architectures and frameworks for achieving interoperability, Deploying its standards and creating conceptual models; Medical vocabularies and terminology for manipulating semantics in heterogeneous health systems.
- Software tools and services in integrated healthcare: Architectures for and applications of healthcare web services and semantic web technologies and infrastructures in healthcare; Application architectures in public health protection, preventive healthcare and delivery of personalized health services.
- Complexities and challenges of addressing information overload and sharing data and practices across healthcare systems: Implications for patient records and the management of patient information and their accessibility and transparency; Healthcare application interoperability and e-health interoperability levels.
- Communicating heath-related data: exchange and integration of clinical data, documents and workflows; Dissemination of health related data to healthcare professionals and caregivers; Telemedicine, e-prescribing and medication management, e-health, m-health.
- Personalized medicine: Frameworks and software platforms for personalized medicine which includes science driven approaches to healthcare and the creation of targeted therapies, tailored medical interventions and selection of medicines; Intelligent software models for predictive development of diseases, disease prognosis and their prevention;
- Drug repositioning and ranking: computational models and solutions for evidence and network based drug repurposing; drug repositioning on the clouds, probabilistic models and algorithms for predicting drug repurposing.
- Smart healthcare: Health information portals; Educational and social models for self-managed healthcare and healthcare literacy; Internet and doctor-patient relationships; I-doctor; Moving health information amongst health consumers; Personalizing management of illnesses, treatments, rehabilitation regimes.
- Patient tracking: Tracking patients and analyzing their clinical outcomes and results of treatments; Creating statistics on shared healthcare data as a result of patient tracking within and across locations; tracking patients with similar conditions, or for those who had undergone similar treatments.
- Applications of social media and virtual environments in healthcare: social media analysis for healthcare and public healthcare management, virtual spaces for exchange of experiences and information; Infrastructures and architectures for implementing collaborative and virtual healthcare environments;
- Apps for creating innovative healthcare services: Apps for empowering health related mobile gadgets and smart phones; Apps for monitoring fitness, physical and cognitive activities, self-testing of health conditions in patients and for individual’s wellbeing goals;
- Apps for monitoring: Regular medicine intake and general use of certain types of medicine(s), the management of chronic diseases across population, in public health management and heath surveys.
- Apps for supporting: New medical practices of collecting, analyzing and interpreting health data in mobile and wireless environments, Generic and disease specific heath surveys across health domains and addressing cost saving issues in the delivery of quality healthcare.
- Apps stores for healthcare domain: experiences of using Android and iOS environment in m-healthcare, advantages and drawbacks of creating Apps for healthcare and incentives for investing in m-heath software solutions.
- Architectures for creating platforms for m-health: accommodating Apps for health related services, which support new medical practices, workflows and regulations dependent on wireless and mobile technologies; Securing healthcare Apps development environments; Supporting the creation of m-health start-up companies.
- Architectures for Integration: Specifying and placing mobile devices and their Apps in existing healthcare systems for various purposes: from demands for ubiquitous healthcare delivery to addressing relieving pressure on cash-strapped, poorly financed and ill-equipped healthcare institutions and environments.
Radmila Juric (Primary Contact)
Carnegie Melon University
ItemVirtual Articulator – Aid Simulator at Diagnosis, Pre-Surgical Planning and Monitoring of Bucomaxilofacial Treatment( 2017-01-04)This work presents a system for use in dentistry and medicine, that allows advance in diagnosis and planning of treatments and surgical procedures, in cases that involves the Temporomandibular Joint, TMJ. Construction of Virtual Articulator includes related research areas of computer graphics, virtual reality and medicine and tends to become a new paradigm as a tool because it will simulate and reproduce the movements of the TMJ in a realistic way, allowing a complete analysis of the case under treatment. It is a software which comes to replace and increase in an innovative way the work done by mechanical articulators. Initially Virtual Articulator reconstructs the TMJ virtually, generating a 3D model, starting from exams such as Computed Tomography and Magnetic Ressonance. Once it is obtained a virtual copy of the TMJ, software simulates real mandible movements, with great flexibility and facility of parameterization. Virtual joints model is based on points captured from the motion curve of lower incisor point. Contribution of each muscle in temporomandibular movement is approached from Hill actuators model and the new concept of curves of insertion. It will be possible to analyze in depth a particular case in a diagnostic phase or predict the results of the surgical procedure.
ItemSuitability of Fast Healthcare Interoperability Resources (FHIR) for Wellness Data( 2017-01-04)Wellness data generated by patients using smart phones and portable devices can be a key part of Personal Health Record (PHR) data and offers healthcare service providers (healthcare providers) patient health information on a daily basis. Prior research has identified the potential for improved communication between healthcare provider and patient. However the practice of sharing patient generated wellness data has not been widely adopted by the healthcare sector; one of the reasons being the lack of interoperability preventing successful integration of such device generated data into the PHR and Electronic Health Record (EHR) systems. To address the interoperability issue it is important to make sure that wellness data can be supported in healthcare information exchange standards. Fast Healthcare Interoperability Resources (FHIR) is used in the current research study to identify the technical feasibility for patient generated wellness data. FHIR is expected to be the future healthcare information exchange standard in the healthcare industry. \ A conceptual data model of wellness data was developed for evaluation using FHIR standard. The conceptual data model contained blood glucose readings, blood pressure readings and Body Mass Index (BMI) data and could be extended to accept other types of wellness data. The wellness data model was packaged in an official FHIR resource called Observation. The research study proved the flexibility of adding new data elements related to wellness in Observation. It met the requirements in FHIR to include such data elements useful in self-management of chronic diseases. It also had the potential in sharing it with the healthcare provider system. \
ItemPersonalized Drug Administration to Patients with Parkinson’s Disease: Manipulating Sensor Generated Data in Android Environments( 2017-01-04)This paper illustrates the application of mobile and wireless technologies for estimating the severity of Parkinson Disease symptoms, and performing a personalized drug administration to PD patients. The measurements of patient finger pressures on the screen of a smart phone, translated into analogue voltage and digital bits, are taken by an Android App. The computations performed through Fast Fourier Transformations (FFT) and Reaction and Movement time, enable the calculation of the severity of the PD symptoms, which results in an appropriate drug administration for that patient, at the moment when the measurement of patient finger pressures is taken. The novelty of this research is twofold. It allows a high level of personalization in PD treatment and uses modern technologies to bring new solutions in the field of drug administration to PD patients.
ItemKey Performance Indicators across the Perioperative Process: Holistic Opportunities for Improvement via Business Process Management( 2017-01-04)This study examines the development and use of multiple scorecard metrics within each stage of the perioperative process as key performance indicators to enable business process management practices across the entire process to target and measure continuous improvement. This paper identifies how dynamic technological activities of analysis, evaluation, and synthesis applied to internal and external organizational data can highlight complex relationships within integrated hospital processes to target opportunities for improvement and ultimately yield improved process capabilities. The identification of existing limitations, potential capabilities, and the subsequent contextual understanding are contributing factors that yield measured improvement. This case study investigates the impact of integrated information systems to identify, qualify, and quantify perioperative improvement based on a 154-month longitudinal study of a large, 1.046 registered-bed teaching hospital. The theoretical and practical implications and/or limitations of this study’s results are also discussed with respect to practitioners and researchers alike.
ItemArchitecture Enabling Service-oriented Digital Biobanks( 2017-01-04)In Finland, the Biobank Act entered into effect in 2013. The primary motivation for the act is to enable the utilization of collected biological sample material for medical research. However, in order to effectively utilize this data, there exists a need to develop new technological solutions to support the collection and management of potentially large sets of sensitive data through multiple stages of processing. The cumulative data stored within biobanks will enable multi-disciplinary research and new innovations. We propose an architecture that addresses several challenges involved in defining and deploying a biobank infrastructure including consent management, data management and data transfer. Our architecture expedites the development of this important area within the research and industrial communities, and enables the deployment of service-oriented biobanks.
ItemA Sensor-based Learning Public Health System( 2017-01-04)New smartphone technologies for the first time provide a platform for a new type of on-person, public health data collection and also a new type of informational public health intervention. In such interventions, it is the device via automatically collecting data relevant to the individual’s health that triggers the receipt of an informational public health intervention relevant to that individual. This will enable far more targeted and personalized public health interventions than previously possible. However, furthermore, sensor-based public health data collection, combined with such informational public health interventions provides the underlying platform for a novel and powerful new form of learning public health system. In this paper we provide an architecture for such a sensor-based learning public health system, in particular one which maintains the anonymity of its individual participants, we describe its algorithm for iterative public health intervention improvement, and examine and provide an evaluation of its anonymity maintaining characteristics.
ItemA Lightweight App Distribution Strategy to Generate Interest in Complex Commercial Apps: Case Study of an Automated Wound Measurement System( 2017-01-04)Tablet-based healthcare technologies automating clinical triage procedures hold exciting promise for increased precision and expediency. These point-of-care (POC) solutions are often complex, and their introduction to the marketplace may encounter cost and usability barriers. One example triage procedure is wound measurement. This paper demonstrates an innovative approach to POC wound measurement by introducing a free “light” version of a wound measurement mobile app that serves as a teaser for a full-featured commercial offering. We first describe the commercial offering; a 3D wound assessment tablet application. Then we present the smartphone app that inherits features from the tablet app. The smartphone app adopts a simple scaling algorithm to address the lack of a highly advanced computer vision system for the automated wound measurement task that exists in the tablet app. This paper describes the design process for developing this smartphone app, provides a detailed exposition of the scaling algorithm, and discusses the significance of this approach to app development and distribution.