Digital Health Data Quality Issues: Systematic Review.

BACKGROUND: The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. OBJECTIVE: The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ? METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework. RESULTS: The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes. CONCLUSIONS: The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

A meta-synthesis of behavioral outcomes from telemedicine clinical trials for type 2 diabetes and the Clinical User-Experience Evaluation (CUE).

A worldwide demographic shift is in progress and the aged population proportion is projected to more than double across the next four decades. Our current healthcare models may not be adequate to handle this shift in demography, which may have serious consequences for the ageing population who are more prone to chronic diseases. One proposed remediation is to provide in-home assisted healthcare with technology-intervened approaches. Telemedicine, telehealth, e-health are paradigms found in scientific literature that provide clinical treatment through a technology intervention. In evidence-based medical science, these technology interventions are evaluated through clinical trials, which are targeted to measure improvements in medical conditions and the treatment's cost effectiveness. However, effectiveness of a technology also depends on the interaction pattern between the technology and its' users, especially the patients. This paper presents (1) a meta-synthesis of clinical trials for technology-intervened treatments of type 2 diabetes and (2) the Clinical User-Experience Evaluation (CUE). CUE is a recommendation for future telemedicine clinical trials that focuses on the patient as the user from Human-Computer Interaction (HCI) perspective and was developed as part of this research. The clinical trials reviewed were interpreted from a technology perspective and the non-medical or non-biological improvements of the users (patients) rather than the medical outcome. Results show that technology-intervened treatments provide positive behavior changes among patients and are potentially highly beneficial for chronic illness management such as type 2 diabetes. The results from the CUE method show how it complements clinical trials to capture patients' interaction with a technology.

SEMAT--the next generation of inexpensive marine environmental monitoring and measurement systems.

There is an increasing need for environmental measurement systems to further science and thereby lead to improved policies for sustainable management. Marine environments are particularly hostile and extremely difficult for deploying sensitive measurement systems. As a consequence the need for data is greatest in marine environments, particularly in the developing economies/regions. Expense is typically the most significant limiting factor in the number of measurement systems that can be deployed, although technical complexity and the consequent high level of technical skill required for deployment and servicing runs a close second. This paper describes the Smart Environmental Monitoring and Analysis Technologies (SEMAT) project and the present development of the SEMAT technology. SEMAT is a "smart" wireless sensor network that uses a commodity-based approach for selecting technologies most appropriate to the scientifically driven marine research and monitoring domain/field. This approach allows for significantly cheaper environmental observation systems that cover a larger geographical area and can therefore collect more representative data. We describe SEMAT's goals, which include: (1) The ability to adapt and evolve; (2) Underwater wireless communications; (3) Short-range wireless power transmission; (4) Plug and play components; (5) Minimal deployment expertise; (6) Near real-time analysis tools; and (7) Intelligent sensors. This paper illustrates how the capacity of the system has been improved over three iterations towards realising these goals. The result is an inexpensive and flexible system that is ideal for short-term deployments in shallow coastal and other aquatic environments.

Community Led Co-Design of a Social Networking Platform with Adolescents with Autism Spectrum Disorder.

Adolescents with ASD face challenges in forming positive friendships due to their ASD condition. This study developed a social networking platform based on the needs of a small group of ASD adolescents and their parents/carers and examined what potential benefits such a system could provide. We conducted seven co-design workshops with six adolescents with ASD over eight months. The team exchanged ideas and communicated through group discussions and drawings. The findings suggest that: (1) participants demonstrated self-advocacy skills through an iterative co-design process; (2) a safe and familiar environment encourages active participation from adolescents with ASD as co-designers; and (3) parents, community group and fellow participants play a pivotal role in engaging adolescents with ASD on a social-network.

Complementing a Clinical Trial With Human-Computer Interaction: Patients' User Experience With Telehealth.

BACKGROUND: The use of telehealth to monitor patients from home is on the rise. Telehealth technology is evaluated in a clinical trial with measures of health outcomes and cost-effectiveness. However, what happens between a technology and the patients is not investigated during a clinical trial-the telehealth technology remains as a "black box." Meanwhile, three decades of research in the discipline of human-computer interaction (HCI) presents design, implementation, and evaluation of technologies with a primary emphasis on users. HCI research has exposed the importance of user experience (UX) as an essential part of technology development and evaluation. OBJECTIVE: This research investigates the UX of patients with type 2 diabetes mellitus (T2D) with a telehealth in-home monitoring device to manage T2D from home. We investigate how the UX during a clinical trial can be researched and what a clinical trial can learn from HCI research. METHODS: We adopted an ethnographic philosophy and conducted a contextual inquiry due to time limitations followed by semistructured interviews of 9 T2D patients. We defined the method as Clinical User-experience Evaluation (CUE). The patients were enrolled in a telehealth clinical trial of T2D; however, this research was an independent study conducted by information technologists and health researchers for a user-centered evaluation of telehealth. RESULTS: Key analytical findings were that patients valued the benefits of in-home monitoring, but the current device did not possess all functionalities that patients wanted. The results include patients' experiences and emotions while using the device, patients' perceived benefits of the device, and how patients domesticated the device. Further analysis showed the influence of the device on patients' awareness, family involvement, and design implications for telehealth for T2D. CONCLUSIONS: HCI could complement telehealth clinical trials and uncover knowledge about T2D patients' UX and future design implications. Through HCI we can look into the "black box" phenomenon of clinical trials and create patient-centered telehealth solutions.

Optimizing spatial healthcare assets with Internet of Things.

Six percent of the total cost of healthcare delivery in Australia is from buying, building and maintaining physical assets. Current practice does not measure the efficient use of existing clinical spaces prior to making funding decisions for service expansion, remodeling or relocation. Healthcare service delivery can be increased through existing assets by optimizing the use of clinical space. The wait times for healthcare service consumers and capital expenditure pressures could be reduced, which would result in increased funds available for frontline services. Sensor technology has been used to study aspects of time in ambulatory outpatient clinics using Infra-red Tags or Radio Frequency Identification tags. This paper proposes the use of Internet of Things (IoT) technology to assist in the optimization of high-value clinical spaces and presents phase one of the project where a trial was held in a non-clinical location to evaluate sensor performance. In Phase two, sensors will be installed to count people across an ambulatory outpatient clinic in a live public healthcare environment to understand clinical space utilization and inform decision-makers. The data produced by the sensors on room use is processed for visualization in "dashboard" format so frontline and executive staff have evidence-based decision-making support for space optimization strategies. This paper presents the phase one trial and preliminary results that show the disparity space utilization patterns between the IoT sensed occupancy data with the current room reservation system in a non-clinical space.