An Empirical Study of Home Healthcare Robots Adoption Using the UTUAT Model

Home healthcare initiatives are aimed to reduce readmission costs, transportation costs, and hospital medical errors, and to improve post hospitalization healthcare quality, and enhance patient home independency. Today, it is almost unimaginable to consider this initiative without information technology. Home healthcare robots are one type of the emerging technologies that hold promise for making clinical information available at the right place and right time. Several robots have been developed to facilitate home healthcare such as remote presence robots (e.g., RP2) and Paro. Most previous research focus on technical and implementation issues of home healthcare robots, there is a need to understand the factors that influence their adoption. This research aims to fill this knowledge gap by applying the UTAUT model. The model was tested using survey questionnaire. The empirical results confirm that performance expectancy, social influence, and facilitating condition directly affect usage intention of home healthcare robots, while effort expectancy indirectly affects usage intention through performance expectancy. Several practical and theoretical implications are also discussed

Developing Metadata Categories as a Strategy to Mobilize Computable Biomedical Knowledge

A work by a group of volunteer members drawn from the Mobilizing Computable Biomedical Knowledge community's Standards Workgroup. See mobilizecbk.org for more information about this community and workgroup.Computable biomedical knowledge artifacts (CBKs) are digital objects or entities representing biomedical knowledge as machine-independent data structures that can be parsed and processed by different information systems. The breadth of content represented in CBKs spans all biomedical knowledge related to human health and so it includes knowledge about molecules, cells, organs, individual people, human populations, and the environment. CBKs vary in their scope, purpose, and audience. Some CBKs support biomedical research. Other CBKs help improve health outcomes by enabling clinical decision support, health education, health promotion, and population health analytics. In some instances, CBKs have multiple uses that span research, education, clinical care, or population health. As the number of CBKs grows large, producers must describe them with structured, searchable metadata so that consumers can find, deploy, and use them properly. This report delineates categories of metadata for describing CBKs sufficiently to enable CBKs to be mobilized for various purposes.https://deepblue.lib.umich.edu/bitstream/2027.42/155655/1/MCBK.Metadata.Paper.June2020.f.pdfDescription of MCBK.Metadata.Paper.June2020.f.pdf : MCBK 2020 Virtual Meeting version of Standards Workgroup's Working Paper on CBK Metadat

De-identifying a public use microdata file from the Canadian national discharge abstract database

<p>Abstract</p> <p>Background</p> <p>The Canadian Institute for Health Information (CIHI) collects hospital discharge abstract data (DAD) from Canadian provinces and territories. There are many demands for the disclosure of this data for research and analysis to inform policy making. To expedite the disclosure of data for some of these purposes, the construction of a DAD public use microdata file (PUMF) was considered. Such purposes include: confirming some published results, providing broader feedback to CIHI to improve data quality, training students and fellows, providing an easily accessible data set for researchers to prepare for analyses on the full DAD data set, and serve as a large health data set for computer scientists and statisticians to evaluate analysis and data mining techniques. The objective of this study was to measure the probability of re-identification for records in a PUMF, and to de-identify a national DAD PUMF consisting of 10% of records.</p> <p>Methods</p> <p>Plausible attacks on a PUMF were evaluated. Based on these attacks, the 2008-2009 national DAD was de-identified. A new algorithm was developed to minimize the amount of suppression while maximizing the precision of the data. The acceptable threshold for the probability of correct re-identification of a record was set at between 0.04 and 0.05. Information loss was measured in terms of the extent of suppression and entropy.</p> <p>Results</p> <p>Two different PUMF files were produced, one with geographic information, and one with no geographic information but more clinical information. At a threshold of 0.05, the maximum proportion of records with the diagnosis code suppressed was 20%, but these suppressions represented only 8-9% of all values in the DAD. Our suppression algorithm has less information loss than a more traditional approach to suppression. Smaller regions, patients with longer stays, and age groups that are infrequently admitted to hospitals tend to be the ones with the highest rates of suppression.</p> <p>Conclusions</p> <p>The strategies we used to maximize data utility and minimize information loss can result in a PUMF that would be useful for the specific purposes noted earlier. However, to create a more detailed file with less information loss suitable for more complex health services research, the risk would need to be mitigated by requiring the data recipient to commit to a data sharing agreement.</p

KC1

<p><strong>Overview of Data</strong></p> <p>The data is a weka .arff file. It contains 94 independent variables and 1 dependent variable.</p> <p><strong>Paper Abstract</strong></p> <p>Modern requirements tracing tools employ information retrieval methods to automatically generate candidate links. Due to the inherent trade-off between recall and precision, such methods cannot achieve a high coverage without also retrieving a great number of false positives, causing a significant drop in result accuracy. In this paper, we propose an approach to improving the quality of candidate link generation for the requirements tracing process. We base our research on the cluster hypothesis which suggests that correct and incorrect links can be grouped in high-quality and low-quality clusters respectively. Result accuracy can thus be enhanced by identifying and filtering out low-quality clusters. We describe our approach by investigating three open-source datasets, and further evaluate our work through an industrial study. The results show that our approach outperforms a baseline pruning strategy and that improvements are still possible.</p