Sunday, July 21, 2013

Medication Adherence

Non-adherence to medications is a growing concern among health care providers and payers as this is associated with adverse outcomes and consequently higher healthcare costs. Several factors contribute to non-adherence; the significant issues as identified by the World Health Organization are outlined here.

Complexity of the medication regimen is a key factor contributing to non-adherence. This includes the inability of patients to understand how and when to take a particular drug. Reduced cognitive functioning (especially for the elderly patients or patients with mental disorders) and a long or complicated medication list (e.g., multiple doses a day) can make medication planning and adherence a daunting task.

Do you think the dosage  instructions below can help?

Monday, July 1, 2013

Do you trust the machine?

Several factors such as automation reliability, automation consistency, and operator workload have been shown to affect automation use. However, there are very few empirical studies examining the role of user personality on trust in automation.

This article describes the importance of examining individual differences when studying trust and automation use. The key-takeaways from the article are:
  • Individuals with higher propensity to trust machines are likely to expect the automation to perform correctly. Therefore, when the machine makes errors,      these individuals are likely to notice these errors and remember these errors more strongly. This experience then influences subsequent perceptions of trust. Conversely, if the machine has high reliability, these individuals will trust the system to a dangerously high level.
  • The authors differentiate between two types of trust: dispositional trust and history-based trust.  Dispositional trust is based on an individual’s personality characteristics. For example, extraverts are more likely to trust automation (just as extraverts are more willing to trust other people). History-based trust, on the other hand, is created as a result of interactions with the machine.
  • Don’t view trust as a static concept. Trust evolves over time from dispositional trust to history-based trust. This means that researchers should measure trust at multiple times to get a clear picture of how trust evolves from the beginning, based on automation characteristics and performance.
  • Operator training should take into consideration individual characteristics. For example, training should be customized based on the level of extraversion, wherein extraverts are cautioned about the dangers of exhibiting overreliance on automation.

Photo credit: Salvagnini, via Wikimedia Commons.