Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Email for communicating results of diagnostic medical investigations to patients

<p>Background: As medical care becomes more complex and the ability to test for conditions grows, pressure on healthcare providers to convey increasing volumes of test results to patients is driving investigation of alternative technological solutions for their delivery. This review addresses the use of email for communicating results of diagnostic medical investigations to patients.</p> <p>Objectives: To assess the effects of using email for communicating results of diagnostic medical investigations to patients, compared to SMS/ text messaging, telephone communication or usual care, on outcomes, including harms, for health professionals, patients and caregivers, and health services.</p> <p>Search methods: We searched: the Cochrane Consumers and Communication Review Group Specialised Register, Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library, Issue 1 2010), MEDLINE (OvidSP) (1950 to January 2010), EMBASE (OvidSP) (1980 to January 2010), PsycINFO (OvidSP) (1967 to January 2010), CINAHL (EbscoHOST) (1982 to February 2010), and ERIC (CSA) (1965 to January 2010). We searched grey literature: theses/dissertation repositories, trials registers and Google Scholar (searched July 2010). We used additional search methods: examining reference lists and contacting authors.</p> <p>Selection criteria: Randomised controlled trials, quasi-randomised trials, controlled before and after studies and interrupted time series studies of interventions using email for communicating results of any diagnostic medical investigations to patients, and taking the form of 1) unsecured email 2) secure email or 3) web messaging. All healthcare professionals, patients and caregivers in all settings were considered.</p> <p>Data collection and analysis: Two review authors independently assessed the titles and abstracts of retrieved citations. No studies were identified for inclusion. Consequently, no data collection or analysis was possible.</p> <p>Main results: No studies met the inclusion criteria, therefore there are no results to report on the use of email for communicating results of diagnostic medical investigations to patients.</p> <p>Authors' conclusions: In the absence of included studies, we can draw no conclusions on the effects of using email for communicating results of diagnostic medical investigations to patients, and thus no recommendations for practice can be stipulated. Further well-designed research should be conducted to inform practice and policy for communicating patient results via email, as this is a developing area.</p&gt

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

Using electronic health records to support clinical trials: a report on stakeholder engagement for EHR4CR

Background. The conduct of clinical trials is increasingly challenging due to greater complexity and governance requirements as well as difficulties with recruitment and retention. Electronic Health Records for Clinical Research (EHR4CR) aims at improving the conduct of trials by using existing routinely collected data, but little is known about stakeholder views on data availability, information governance, and acceptable working practices. Methods. Senior figures in healthcare organisations across Europe were provided with a description of the project and structured interviews were subsequently conducted to elicit their views. Results. 37 structured interviewees in Germany, UK, Switzerland, and France indicated strong support for the proposed EHR4CR platform. All interviewees reported that using the platform for assessing feasibility would enhance the conduct of clinical trials and the majority also felt it would reduce workloads. Interviewees felt the platform could enhance trial recruitment and adverse event reporting but also felt it could raise either ethical or information governance concerns in their country. Conclusions. There was clear support for EHR4CR and a belief that it could reduce workloads and improve the conduct and quality of trials. However data security, privacy, and information governance issues would need to be carefully managed in the development of the platform

Measuring patient-perceived quality of care in US hospitals using Twitter

BACKGROUND: Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. OBJECTIVE: To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. DESIGN: 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. KEY RESULTS: Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003). CONCLUSIONS: Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators

Using colocation to support human memory

The progress of health care in the western world has been marked by an increase in life expectancy. Advances in life expectancy have meant that more people are living with acute health problems, many of which are related to impairment of memory. This paper describes a pair of scenarios that use RFID to assist people who may suffer frommemory defects to extend their capability for independent living. We present our implementation of an RFID glove, describe its operation, and show how it enables the application scenarios

Visualizing pain data for wheelchair users: A ubiquitous approach

Copyright @ 2005 Rinton PressWe describe a wireless enabled solution for the vizualisation of pain data. Our approach uses pain drawings to record spatial location and type of pain and enables data collection with appropriate time stamping, thus providing a means for the seldom-recorded (but often attested) time-varying nature of pain, with consequential impact on monitoring the effectiveness of patient treatment regimes. Moreover, since the implementation platform of our solution is that of a Personal Digital Assistant (PDA), data collection takes place ubiquitously, providing back pain sufferers with mobility problems (such as wheelchair users) with a convenient means of logging their pain data and of seamlessly uploading it to a hospital server using WiFi technology. Stakeholder results show that, notwithstanding problems related to PDA data input, our approach is generally perceived to be an easy to use and convenient solution to the challenges of anywhere/anytime data collection

Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing

Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies

Integrating New Zealand census mortality study and New Zealand longitudinal census: privacy impact assessment

This paper outlines the processes for managing the privacy risks raised by integrating two particular census datasets. Overview Integrating&nbsp;New Zealand Census Mortality&nbsp;Study and New Zealand Longitudinal Census: Privacy impact assessment provides a systematic evaluation of the privacy risks associated with integrating datasets from the New Zealand Census Mortality Study (NZCMS) and the New Zealand Longitudinal Census (NZLC). It outlines the processes for managing these risks. The NZCMS comprise datasets of mortality records linked to the 1981–2006 Censuses and the NZLC datasets comprise linked census pairs covering the same period