14,309 research outputs found
Usability and feasibility of consumer-facing technology to reduce unsafe medication use by older adults
Background
Mobile health technology can improve medication safety for older adults, for instance, by educating patients about the risks associated with anticholinergic medication use.
Objective
This study's objective was to test the usability and feasibility of Brain Buddy, a consumer-facing mobile health technology designed to inform and empower older adults to consider the risks and benefits of anticholinergics.
Methods
Twenty-three primary care patients aged ā„60 years and using anticholinergic medications participated in summative, task-based usability testing of Brain Buddy. Self-report usability was assessed by the System Usability Scale and performance-based usability data were collected for each task through observation. A subset of 17 participants contributed data on feasibility, assessed by self-reported attitudes (feeling informed) and behaviors (speaking to a physician), with confirmation following a physician visit.
Results
Overall usability was acceptable or better, with 100% of participants completing each Brain Buddy task and a mean System Usability Scale score of 78.8, corresponding to āGoodā to āExcellentā usability. Observed usability issues included higher rates of errors, hesitations, and need for assistance on three tasks, particularly those requiring data entry. Among participants contributing to feasibility data, 100% felt better informed after using Brain Buddy and 94% planned to speak to their physician about their anticholinergic related risk. On follow-up, 82% reported having spoken to their physician, a rate independently confirmed by physicians.
Conclusion
Consumer-facing technology can be a low-cost, scalable intervention to improve older adultsā medication safety, by informing and empowering patients. User-centered design and evaluation with demographically heterogeneous clinical samples uncovers correctable usability issues and confirms the value of interventions targeting consumers as agents in shared decision making and behavior change
Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening
Socio-technical systems play an important role in public health screening
programs to prevent cancer. Cervical cancer incidence has significantly
decreased in countries that developed systems for organized screening engaging
medical practitioners, laboratories and patients. The system automatically
identifies individuals at risk of developing the disease and invites them for a
screening exam or a follow-up exam conducted by medical professionals. A triage
algorithm in the system aims to reduce unnecessary screening exams for
individuals at low-risk while detecting and treating individuals at high-risk.
Despite the general success of screening, the triage algorithm is a
one-size-fits all approach that is not personalized to a patient. This can
easily be observed in historical data from screening exams. Often patients rely
on personal factors to determine that they are either at high risk or not at
risk at all and take action at their own discretion. Can exploring patient
trajectories help hypothesize personal factors leading to their decisions? We
present Portinari, a data exploration tool to query and visualize future
trajectories of patients who have undergone a specific sequence of screening
exams. The web-based tool contains (a) a visual query interface (b) a backend
graph database of events in patients' lives (c) trajectory visualization using
sankey diagrams. We use Portinari to explore diverse trajectories of patients
following the Norwegian triage algorithm. The trajectories demonstrated
variable degrees of adherence to the triage algorithm and allowed
epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software
Engineering in Society Track. 10 pages, 5 figure
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
Exercise and the microbiota
The authors are supported in part by research grants from Science Foundation Ireland including a centre grant (Alimentary Pharmabiotic Centre; Grant Numbers SFI/12/RC/2273 and 12/RC/2273). Dr. Orla OāSullivan is funded by a Starting Investigator Research Grant from Science Foundation Ireland (Grant number 13/SIRG/2160). Dr. Paul Cotter is funded by a Principal Investigator Award from Science Foundation Ireland P.D.C are supported by a SFI PI award (Grant number 11/PI/1137).peer-reviewedSedentary lifestyle is linked with poor health, most commonly obesity and associated disorders, the corollary being that exercise offers a preventive strategy. However, the scope of exercise biology extends well beyond energy expenditure and has emerged as a great āpolypillā, which is safe, reliable and cost-effective not only in disease prevention but also treatment. Biological mechanisms by which exercise influences homeostasis are becoming clearer and involve multi-organ systemic adaptations. Most of the elements of a modern lifestyle influence the indigenous microbiota but few studies have explored the effect of increased physical activity. While dietary responses to exercise obscure the influence of exercise alone on gut microbiota, professional athletes operating at the extremes of performance provide informative data. We assessed the relationship between extreme levels of exercise, associated dietary habits and gut microbiota composition, and discuss potential mechanisms by which exercise may exert a direct or indirect influence on gut microbiota.The authors are supported in part by research grants from Science Foundation Ireland including a centre grant (Alimentary Pharmabiotic Centre; Grant Numbers SFI/12/RC/2273 and 12/RC/2273). Dr. Orla OāSullivan is funded by a Starting Investigator Research Grant from Science Foundation Ireland (Grant number 13/SIRG/2160). Dr. Paul Cotter is funded by a Principal Investigator Award from Science Foundation Ireland P.D.C are supported by a SFI PI award (Grant number 11/PI/1137)
Drug prescription support in dental clinics through drug corpus mining
The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our modelās association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients
Drug prescription support in dental clinics through drug corpus mining
The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our modelās association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients
- ā¦