No abstract

The adoption of blockchain shows a variety of benefits owing to an incorruptible digital ledger and a decentralized database. This has eliminated the need for a gatekeeper to oversee all associated transactions. Blockchain, the underlying technology behind Bitcoin and other crypto-currencies, has found use in many industries besides finance, such as healthcare, where it is used for verifying medical licensing and credentialing, for tracking medical equipment (or consumables) from production to usage, and in cases associated with high levels of privacy and security. Patient data is collected using a plethora of patient-generated data devices, such as Internet of Things (IoT)-enabled wearables, health trackers, and home use medical devices. As a result, the data is siloed amongst several applications and/or vendors’ proprietary solutions. Of all this data, only some of it is transmitted to Electronic Medical Records. This produces the risk that not all data collected will be reviewed at the point of care due to the abundance of data collected. This article explains the areas within healthcare where blockchain could address data usability challenges and analyzes new ways in which patient health data can be collected to address the increasing number of challenges associated with the amount of data being generated over time. It also describes the drivers behind this data collection trend, the associated challenges and the subsequent ramifications. It concludes with a review of previous studies on data usability challenges and the means by which blockchain can be used to overcome these challenges.

This paper aims to compare agreeance of four of most commonly used ergonomics assessment tools as well as introduce the Agriculture Cumulative Risk Evaluation Systems (ACRES), a tool that assesses both lifting and posture. ACRES discretizes factors to evaluate ergonomic risk. Participants lacked substantial experience with ergonomic tools, but were given instruction guides for each tool and tasked with evaluating various lifting and repetitive tasks to help evaluate initial perceptions. Results showed that REBA, QEC, and ACRES all had significant correlations with a more linear relationship between REBA and ACRES than REBA and QEC. The NIOSH lifting equation and the Snook and Cirello tables were too different to have significant correlation whereas ACRES was able to correlate with RNLE. In all cases REBA and the RNLE were found to be more difficult to use to novices and ACRES was perceived to be more appropriate for the lifting tasks.

The goal of purchasing hand tools is to improve financial outcomes for buyers. These financialgains can come from multiple aspects of hand tools, such as increased productivity, improved product quality, and decreased injury rate. Based on a literature review, current hand tool purchasingdecisions are frequently made by professionals without specialized scientific backgrounds; however, the hand tool selection methods they receive are solely based on ergonomic evaluations. Heuristic evaluation, created by Nielsen & Molich (1990), has been proven effective in discoveringusability issues quickly. Combined with the knowledge from hand tool evaluation literature andNielsen & Molich’s (1990) heuristic evaluation platform, two versions of financially orientatedhand tool heuristic evaluations were created: a buyer version and a seller version. The buyer versionhelps hand tool buyers make financially orientated purchasing decisions; the designer version helpsquickly assess the financial viability of the hand tool design under review.