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Usability of a computerised drug monitoring programme to detect adverse drug events and non-compliance in outpatient ambulatory care

Abstract

Objective To assess the usability of a computerised drug monitoring programme for ambulatory patients receiving outpatient prescriptions.

Materials and methods A prospective cohort of 200 patients received two automated calls after a new drug prescription (day 3 and day 17) to screen for unfilled prescriptions and medication problems. Usability was assessed objectively and subjectively with the coding of technical (eg, voice recognition problems) and respondent burden (eg, failing to follow instructions) problems observed during the calls, and with an interview 21 days after the prescription. Associations between personal factors, usability and call outcome were examined with logistic regression models.

Results The automated calls successfully reached 70.0% of enrolled patients. Older age increased the likelihood of experiencing technical (OR 2.18, 95% CI 1.22 to 3.88) and respondent burden problems (OR 3.32, 95% CI 1.88 to 5.87), as well as unsuccessful calls (OR 2.16, 95% CI 1.19 to 3.91). Patients with higher education experienced less respondent burden problems (OR 0.44, 95% CI 0.21 to 0.91), but they were more prone to have unsuccessful calls (OR 2.65, 95% CI 1.07 to 6.56) and less likely to find them useful (OR 0.23 95% CI 0.08 to 0.68). Older adults perceived the calls as easy to use and useful, although they reported lower intention to use the automated calls in the future (OR 0.32, 95% CI 0.15 to 0.70).

Discussion As reported in previous studies, we found that older adults tend to have more difficulty when interacting with automated calls. Evidence about the association between education and usability was mixed.

Conclusions Our results highlight practical suggestions to improve the feasibility and usability of automated calls in primary care screening programmes.

  • Ambulatory care
  • Human factors
  • Quality improvement
  • Adverse events, epidemiology and detection
  • Compliance

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