Consolidated List of Requirements

This document is a consolidated catalogue of requirements for the Electronic Health Care Record (EHCR) and Electronic Health Care Record Architecture (EHCRA), gleaned largely from work done in the EU Framework III and IV programmes and CEN, but also including input from other sources including world-wide standardisation initiatives. The document brings together the relevant work done into a classified inventory of requirements to inform the on-going standardisation process as well as act as a guide to future implementation of EHCRA-based systems. It is meant as a contribution both to understanding of the standard and to the work that is being considered to improve the standard. Major features include the classification into issues affecting the Health Care Record, the EHCR, EHCR processing, EHCR interchange and the sharing of health care information and EHCR systems. The principal information sources are described briefly. It is offered as documentation that is complementary to the four documents of the ENV 13606 Parts I-IV produced by CEN Pts 26,27,28,29. The requirements identified and classified in this deliverable are referenced in other deliverables

Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases

Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine

Use of magnetic resonance angiography to select candidates with recently symptomatic carotid stenosis for surgery: systematic review

Objective To determine if sufficient evidence exists to support the use of magnetic resonance angiography as a means of selecting patients with recently symptomatic high grade carotid stenosis for surgery. Design Systematic review of published research on the diagnostic performance of magnetic resonance angiography, 1990-9. Main outcome measures Performance characteristics of diagnostic test. Results 126 potentially relevant articles were identified, but many articles failed to examine die performance of magnetic resonance angiography as a diagnostic test at the surgical decision thresholds used in major clinical trials on endarterectomy. 26 articles were included in a meta-analysis that showed a maximal joint sensitivity and specificity of 99% (95% confidence interval 98% to 100%) for identifying 70-99% stenosis and 90% (81% to 99%) for identifying 50-99% stenosis. Only four articles evaluated contrast enhanced magnetic resonance angiography. Conclusions Magnetic resonance angiography is accurate for selecting patients for carotid endarterectomy at the surgical decision thresholds established in the major endarterectomy trials, but the evidence is not very robust because of the heterogeneity of the studies included. Research is to determine the diagnostic performance of the most recent developments in magnetic resonance angiography, including contrast enhanced techniques, as well as to assess the impact of magnetic resonance angiography on surgical decision making and outcomes

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Implementing a hybrid cognitive-behavioural therapy for pain-related insomnia in primary care : lessons learnt from a mixed-methods feasibility study

Objectives: To test the feasibility of implementing a brief but intensive hybrid cognitive behavioural therapy (Hybrid CBT) for pain-related insomnia. Design: Mixed-methods, with qualitative process evaluation on a two-arm randomised controlled feasibility trial. Setting: Primary care. Participants: Twenty-five adult patients with chronic pain and insomnia. Intervention: Hybrid CBT or self-help control intervention. Primary and secondary outcome measures: Primary outcomes measures were the Insomnia Severity Index and interference scale of the Brief Pain Inventory (BPI). Secondary outcomes measures were the present pain intensity rating from the BPI, Multidimensional Fatigue Inventory, Hospital Anxiety and Depression Scale and EQ-5D-5L. Results: Fourteen participants were randomised to receive Hybrid CBT, 11 to receive the self-help control treatment. Of the 14 in the Hybrid CBT group, 9 (64%) completed all four treatment sessions (4 discontinued due to poor health; 1 due to time constraints). Adherence to the self-help control treatment was not monitored. The total number of participants completing the 12-week and 24-week follow-ups were 12 (6 in each group; Hybrid CBT: 43%; self-help: 55%) and 10 (5 in each group; Hybrid CBT: 36%; self-help: 45%). Based on the data available, candidate outcome measures appeared to be sensitive to changes associated with interventions. Thematic analysis of pre-postintervention interview data revealed satisfaction with treatment content among those who completed the Hybrid CBT, whereas those in the self-help control treatment wanted more contact hours and therapist guidance. Other practical suggestions for improvement included shortening the duration of each treatment session, reducing the amount of assessment paperwork, and minimising the burden of sleep and pain monitoring. Conclusion: Important lessons were learnt with regard to the infrastructure required to achieve better patient adherence and retention. Based on the qualitative feedback provided by a subset of treatment completers, future trials should also consider lowering the intensity of treatment and streamlining the data collection procedure. Trial registration number: ISRCTN17294365

Routine administration of Health Related Quality of Life (HRQoL) and needs assessment instruments to improve psychological outcome: a systematic review

Background. Routine administration of Health Related Quality of Life (HRQoL) and needs assessment instruments has been advocated as part of clinical care to aid the recognition of psychosocial problems, to inform clinical decision making, to monitor therapeutic response and to facilitate patient-doctor communication. However, their adoption is not without cost and the benefit of their use is unclear. Method. A systematic review was conducted. We sought experimental studies that examined the addition of routinely administered measures of HRQoL to care in both psychiatric and non-psychiatric settings. We searched the following databases: MEDLINE, EMBASE, CINAHL, PsycLIT and Cochrane Controlled Trials Register (to 2000). Data were extracted independently and a narrative synthesis of results was presented. Results. Nine randomized and quasi-randomized studies conducted in non-psychiatric settings were found. All the instruments used included an assessment of mental well-being, with specific questions relating to depression and anxiety. The routine feedback of these instruments had little impact on the recognition of mental disorders or on longer term psychosocial functioning. While clinicians welcomed the information these instruments imparted, their results were rarely incorporated into routine clinical decision making. No studies were found that examined the value of routine assessment and feedback of HRQoL or patient needs in specialist psychiatric care settings. Conclusions. Routine HRQoL measurement is a costly exercise and there is no robust evidence to suggest that it is of benefit in improving psychosocial outcomes of patients managed in non-psychiatric settings. Major policy initiatives to increase the routine collection and use of outcome measures in psychiatric settings are unevaluated

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems