Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays

Assessing organizational health-analytics readiness: artifacts based on elaborated action design method

Purpose

While the adoption of health-analytics (HA) is expanding, not every healthcare organization understands the factors impacting its readiness for HA. An assessment of HA-readiness helps guide organizational strategy and the realization of business value. Past research on HA has not included a comprehensive set of readiness-factors and assessment methods. This study’s objective is to design artifacts to assess the HA-readiness of hospitals.

Design/methodology/approach

The information-systems (IS) theory and methodology entail the iterative Elaborated Action Design Research (EADR)method, combined with cross-sectional field studies involving 14 healthcare organizations and 27 participants. The researchers determine factors and leverage multi-criteria decision-making techniques to assess HA-readiness.

Findings

The artifacts emerging from this research include: (1) a map of readiness factors, (2) multi-criteria decision-making techniques that assess the readiness levels on the factors, the varying levels of factor-importance and the inter-factor relationships and (3) an instantiated system. The in-situ evaluation shows how these artifacts can provide insights and strategic direction to an organization through collective knowledge from stakeholders.

Originality/value

This study finds new factors influencing HA-readiness, validates the well-known and details their industry-specific nuances. The methods used in this research yield a well-rounded HA readiness-assessment (HARA) approach and offer practical insights to hospitals.

Development of a paper-based analytical device for the colourimetric detection of alanine transaminase and the application of deep learning for image analysis

A paper-based colourimetric assay for the detection of alanine transaminase has been developed. In the presence of alanine transaminase, 2,4-dinitrophenyl hydrazine changes to pyruvate hydrazone leading to a colour change from pale yellow to dark yellow. Reaction conditions were optimized using absorption spectroscopic studies. Hydrophobic patterns on the Whatman chromatographic paper were created by wax printing, and the reagents were drop cast at the reagent zone. On the paper device, the intensity of the yellow colour increases with ALT concentration in the range of 20-140 U/L in human serum. For the quantification of ALT, coloured images were captured using a digital camera and were processed with Image J software. The machine learning approach was also explored for the ALT analysis by training with colour images of the paper device and testing using a cross-validation procedure. The results obtained with real clinical samples on the paper device showed good accuracy of less than 5% relative error with the clinical lab results. Furthermore, the paper device shows high selectivity to ALT in the presence of various interfering species in blood serum with a sensitivity of 0.261 a.u/(U/L), a detection limit of 4.12 U/L, and precise results with an RSD of less than 7%. For the testing of whole blood, a plasma separation membrane was integrated with the patterned paper.