Guideline-based decision support for the mobile patient incorporating data streams from a body sensor network

We present a mobile decision support system (mDSS) which helps patients adhere to best clinical practice by providing pervasive and evidence-based health guidance via their smartphones. Similar to some existing clinical DSSs, the mDSS is designed to execute clinical guidelines, but it operates on streaming data from, e.g., body sensor networks instead of persistent data from clinical databases. Therefore, we adapt the typical guideline-based architecture by basing the mDSS design on existing data stream management systems (DSMSs); during operation, the mDSS instantiates from the guideline knowledge a network of concurrent streaming processes, avoiding the resource implications of traditional database approaches for processing patient data which may arrive at high frequencies via multiple channels. However, unlike typical DSMSs, we distinguish four types of streaming processes to reflect the full disease management process: Monitoring, Analysis, Decision and Effectuation. A prototype of the mDSS has been developed and demonstrated on an Android smartphone

A quality-of-data aware mobile decision support system for patients with chronic illnesses

We present a mobile decision support system (mDSS) which runs on a patient Body Area Network consisting of a smartphone and a set of biosensors. Quality-of-Data (QoD) awareness in decision making is achieved by means of a component known as the Quality-of-Data Broker, which also runs on the smartphone. The QoD-aware mDSS collaborates with a more sophisticated decision support system running on a fixed back-end server in order to provide distributed decision support. This distributed decision support system has been implemented as part of a larger system developed during the European project MobiGuide. The MobiGuide system is a guideline-based Patient Guidance System designed to assist patients in the management of chronic illnesses. The system, including the QOD-aware mDSS, has been validated by clinicians and is being evaluated in patient pilots against two clinical guidelines

Emerging Insights of Health Informatics Research: A Literature Analysis for Outlining New Themes

This paper presents a contemporary literature review to provide insights into the current health informatics literature. The objective of this study is to identify emerging directions of current health informatics research from the latest and existing studies in the health informatics domain. We analyse existing health informatics studies using a thematic analysis, so that justified sets of research agenda can be outlined on the basis of these findings. We selected articles that are published in the Science Direct online database. The selected 73 sample articles (published from 2014 to 2018 in premier health informatics journals) are considered as representative samples of health informatics studies. The analysis revealed ten topic areas and themes that would be of paramount importance for researchers and practitioners to follow. The findings provide an important foundational understanding for new health informatics studies

Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus

Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted beforePartial funding for open access charge: Universidad de Málag

A Decision Support Framework for Public Healthcare: An approach to Follow-up Support Service

The literature on the development of measurement help researchers consider two broad issues: the qualitative tasks of conceptually modelling constructs and operationalizing them in a set of indicators and the more quantitative issues of using estimation procedures to convert their data into estimates. While these issues are interdependent, these two streams of literature have been largely separate. With this paper, we aim to contribute toward the establishment of integral guidance on measurement development. We propose a conceptual framework that should connect more explicitly the qualitative issues of measurement design with its quantitative issues. By analysing common sources of error, we show how it can be used to identify the sources of error specific to a measurement. We further provide initial guidelines on how problematic indicators can be remedied. Finally, we suggest how future research could take a next step in synthesizing qualitative with quantitative issues to provide more integral guidelines

Designing User-Centered Interfaces to Support Clinical Decision-Making and Patient Engagement

The delivery of most psychotherapies has been constrained by data collected from patient self-report and clinician intuition for the last century. Clinicians who use evidence-based treatments need methods, tools, and data to efficiently track, assess, and respond to mental health needs throughout the treatment process. Patients need tools that provide feedback to optimize their therapeutic exercises and increase engagement. In this dissertation, I explore how interfaces shared by clinicians and patients can be used to support this aim in the context of prolonged exposure (PE) therapy, an evidence-based treatment used in treating post-traumatic stress disorder (PTSD). I focus on the case of designing for United States (US) veterans as well as the clinicians who treat them as US Veterans are disproportionately affected by PTSD due to the nature of their work. In this dissertation, I investigate how to design shared, user-centered interfaces which seek to support clinical decision-making and patient engagement in the context of veterans with post-traumatic stress disorder (PTSD). To lay the groundwork for design, I detail the care ecologies of veterans with PTSD, identifying the human and non-human intermediaries involved in their circles of care as well as barriers to care and future design opportunities. Leveraging this information, I explore how a clinician dashboard for PTSD, sensor-captured patient generated data, and feedback gathered via text message from trusted others (e.g., friends, family) can be designed into a shared interface and support clinical decision-making and/or patient engagement.Ph.D

Applying machine learning for healthcare: A case study on cervical pain assessment with motion capture

Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources