Exchanging personal health data with electronic health records: A standardized information model for patient generated health data and observations of daily living

Objective: The development of a middleware information model to facilitate better interoperability between Personal and Electronic Health Record systems in order to allow exchange of Patient Generated Health Data and Observations of Daily Leaving between patients and providers in order to encourage patient self-management. Materials and methods: An information model based on HL7 standards for interoperability has been extended to support PGHD and ODL data types. The new information models uses HL7 CDA to represent data, is instantiated as a Protégé ontology and uses a set of mapping rules to transfer data between Personal and Electronic Health Record systems. Results: The information model was evaluated by executing a set of use case scenarios containing data exported from three consumer health apps, transformed to CDA according to developed mapping rules and validated against a CDA schema. This allowed various challenges to emerge as well as revealed gaps in current standards in use and the information model has been refined accordingly. Discussion and conclusion: Our proposed middleware solution offers a number of advantages. When modifications are made to either a Personal or Health Electronic Health Record system or any integrated consumer app, they can be incorporated by altering only the instantiation of the information model. Our proposition uses current standards in use such as CDA. The solution is applicable to any EHR system with HL7 CDA support

Check Your Tech – Considering the Provenance of Data Used to Build Digital Products and Services: Case Studies and an Ethical CheckSheet

Digital products and services are producing unprecedented amounts of data worldwide. These products and services have broad reach and include many users and consumers in the developing world. Once data is collected it is often used to create large and valuable datasets. A lack of data protection regulation in the developing world has led to concerns about digital colonization and a lack of control of their data on the part of citizens in the developing world. The authors of this paper are developing a new digital ethics curriculum for the instruction of computer science students. In this paper we present two case studies we have developed with a focus on data ethics in a developing world context. Each case study is accompanied by a list of specific questions to be used by the instructor to allow students to evaluate the implications of introducing new digital products and services in a developing world context as well as a generic case studies checksheet that allow deeper reflection on the intended and unintended consequences of introducing new technologies

Nurse-led Design and Development of an Expert System for Pressure Ulcer Management

The use of Clinical Practice Guidelines (CPGs) is known to enable better care outcomes by promoting a consistent way of treating patients. This paper describes a user-centered design approach involving nurses, to develop a prototype expert system for modelling CPGs for Pressure Ulcer management. The system was developed using Visirule, a software tool that uses a graphical approach to modeling knowledge. The system was evaluated by 5 staff nurses and compared nurses’ time and accuracy to assess a wound using CPGs accessed via the Intranet of an NHS Trust and the expert system. A post task qualitative evaluation revealed that nurses found the system useable with a systematic design, that it increased access to CPGs by reducing time and effort required by other usual methods of access, that it provided opportunities for learning due to its interactive nature, and that its recommendations were more actionable that those provided by usual static CPG documents

What\u27s in a Name?”: The Use of Instructional Design in Overcoming Terminology Barriers Associated with Dark Patterns

Many users experience a phenomena when they are shopping on-line where they feel they are being pressured to either spend more money than they had intended, or to share more personal data than they wanted. In academic circles we use the term “Dark Patterns” to describe these deceptive practices, and categorize them as being within the discipline of User Experience (Narayanan, 2020). As academics it is important to name phenomena, and to categorize them, so that we can discuss and analyze these issues. However, this particular topic is one that all users should be made aware of when interacting online, and therefore these ideas should be explained in a manner to ensure that the terminology does not prove to be a barrier to understanding these concepts (Gordon, et al., 2014). To overcome the terminological barriers, this research proposes the use of a model of instructional design called Elaboration Theory, developed by Charles Reigeluth (1999). Crucially this model proposes that when explaining a new concept, the last thing the instructor should do is to mention the name of the concept, they should first explain the concept, and at the very end of an instructional session, say “And by the way, this concept is called…”. This model also contends that the instructor should explain the concept in simple terms first, and then continue to elaborate on that explanation throughout the teaching process (adhering to the notion of a Spiral Curriculum). It also suggests that the content should be summarized at each level of explanation, and analogies should be used to help clarify concepts. Therefore, this research proposes the redevelopment of existing teaching content about Dark Patterns, where these patterns are retitled as “Online Shopping Tricks”, and the teaching content is redesigned to begin with a simple explanation of Dark Patterns and to elaborate with more complexity at a number of levels of explanation, and including summarizers and analogies at the end of each stage. This content will be subsequently piloted on a number of non-academic participants to determine whether or not this redesign process has been effective

Modulation of Medical Condition Likelihood by Patient History Similarity

Introduction: We describe an analysis that modulates the simple population prevalence derived likelihood of a particular condition occurring in an individual by matching the individual with other individuals with similar clinical histories and determining the prevalence of the condition within the matched group. Methods: We have taken clinical event codes and dates from anonymised longitudinal primary care records for 25,979 patients with 749,053 recorded clinical events. Using a nearest neighbour approach, for each patient, the likelihood of a condition occurring was adjusted from the population prevalence to the prevalence of the condition within those patients with the closest matching clinical history. Results: For conditions investigated, the nearest method performed well in comparison with standard logistic regression. Conclusions: Results indicate that it may be possible to use histories to identify \u27similar\u27 patients and thus to modulate future likelihoods of a condition occurring

The Development of Teaching Case Studies to Explore Ethical Issues Associated with Computer Programming

In the past decade software products have become pervasive in many aspects of people’s lives around the world. Unfortunately, the quality of the experience an individual has interacting with that software is dependent on the quality of the software itself, and it is becoming more and more evident that many large software products contain a range of issues and errors, and these issues are not known to the developers of these systems, and they are unaware of the deleterious impacts of those issues on the individuals who use these systems. The authors of this paper are developing a new digital ethics curriculum for the instruction of computer science students. In this paper we present case studies that were explored to demonstrate programming issues to First Year Computer Science students. Each case study outlines key issues associated with a particular scenario and is accompanied by specific questions to be used by the instructor to allow students to begin to reflect on, and evaluate, the implications of these issues. The objective of this teaching content is to ensure that the students are presented with, and engage with, ethical considerations early in their studies and well before they encounter them in an employment setting

Modulation of medical condition likelihood by patient history similarity

Introduction: We describe an analysis that modulates the simple population prevalence derived likelihood of a particular condition occurring in an individual by matching the individual with other individuals with similar clinical histories and determining the prevalence of the condition within the matched group. Methods: We have taken clinical event codes and dates from anonymised longitudinal primary care records for 25,979 patients with 749,053 recorded clinical events. Using a nearest neighbour approach, for each patient, the likelihood of a condition occurring was adjusted from the population prevalence to the prevalence of the condition within those patients with the closest matching clinical history. Results: For conditions investigated, the nearest method performed well in comparison with standard logistic regression. Conclusions: Results indicate that it may be possible to use histories to identify ‘similar’ patients and thus to modulate future likelihoods of a condition occurring

Towards Exchanging Wearable-PGHD with EHRs: Developing a Standardized Information Model for Wearable-Based Patient Generated Health Data

Wearables have become commonplace for tracking and making sense of patient lifestyle, wellbeing and health data. Most of this tracking is done by individuals outside of clinical settings, however some data from wearables may be useful in a clinical context. As such, wearables may be considered a prominent source of Patient Generated Health Data (PGHD). Studies have attempted to maximize the use of the data from wearables including integrating with Electronic Health Records (EHRs). However, usually a limited number of wearables are considered for integration and, in many cases, only one brand is investigated. In addition, we find limited studies on integration of metadata including data quality and provenance, despite such data being very relevant for clinical decision making. This paper describes a proposed design and development of a generic information model for wearable based PGHD integration with EHRs. We propose a vendor-neutral model that can work with a wider range of wearables and discuss our proposed method to employ an ontology-based approach and provide insights to future work

Pothole Detection under Diverse Conditions using Object Detection Models

One of the most important tasks in road maintenance is the detection of potholes. This process is usually done through manual visual inspection, where certified engineers assess recorded images of pavements acquired using cameras or professional road assessment vehicles. Machine learning techniques are now being applied to this problem, with models trained to automatically identify road conditions. However, approaching this real-world problem with machine learning techniques presents the classic problem of how to produce generalisable models. Images and videos may be captured in different illumination conditions, with different camera types, camera angles, and resolutions. In this paper, we present our approach to building a generalized learning model for pothole detection. We apply four datasets that contain a range of image and environment conditions. Using the Faster RCNN object detection model, we demonstrate the extent to which pothole detection models can generalise across various conditions. Our work is a contribution to bringing automated road maintenance techniques from the research lab into the real-worl

Ideating XAI: An Exploration of User’s Mental Models of an AI-Driven Recruitment System Using a Design Thinking Approach

Artificial Intelligence (AI) is playing an important role in society including how vital, often life changing decisions are made. For this reason, interest in Explainable Artificial Intelligence (XAI) has grown in recent years as a means of revealing the processes and operations contained within what is often described as a black box, an often-opaque system whose decisions are difficult to understand by the end user. This paper presents the results of a design thinking workshop with 20 participants (computer science and graphic design students) where we sought to investigate users\u27 mental models when interacting with AI systems. Using two personas, participants were asked to empathise with two end users of an AI driven recruitment system, identify pain points in a user’s experience and ideate on possible solutions to these pain points. These tasks were used to explore the user’s understanding of AI systems, the intelligibility of AI systems and how the inner workings of these systems might be explained to end users. We discovered that visual feedback, analytics, and comparisons, feature highlighting in conjunction with factual, counterfactual and principal reasoning explanations could be used to improve user’s mental models of AI systems