Automation bias in electronic prescribing.

BACKGROUND: Clinical decision support (CDS) in e-prescribing can improve safety by alerting potential errors, but introduces new sources of risk. Automation bias (AB) occurs when users over-rely on CDS, reducing vigilance in information seeking and processing. Evidence of AB has been found in other clinical tasks, but has not yet been tested with e-prescribing. This study tests for the presence of AB in e-prescribing and the impact of task complexity and interruptions on AB. METHODS: One hundred and twenty students in the final two years of a medical degree prescribed medicines for nine clinical scenarios using a simulated e-prescribing system. Quality of CDS (correct, incorrect and no CDS) and task complexity (low, low + interruption and high) were varied between conditions. Omission errors (failure to detect prescribing errors) and commission errors (acceptance of false positive alerts) were measured. RESULTS: Compared to scenarios with no CDS, correct CDS reduced omission errors by 38.3% (p < .0001, n = 120), 46.6% (p < .0001, n = 70), and 39.2% (p < .0001, n = 120) for low, low + interrupt and high complexity scenarios respectively. Incorrect CDS increased omission errors by 33.3% (p < .0001, n = 120), 24.5% (p < .009, n = 82), and 26.7% (p < .0001, n = 120). Participants made commission errors, 65.8% (p < .0001, n = 120), 53.5% (p < .0001, n = 82), and 51.7% (p < .0001, n = 120). Task complexity and interruptions had no impact on AB. CONCLUSIONS: This study found evidence of AB omission and commission errors in e-prescribing. Verification of CDS alerts is key to avoiding AB errors. However, interventions focused on this have had limited success to date. Clinicians should remain vigilant to the risks of CDS failures and verify CDS.

Prevalence, management and control of hypertension in older adults on admission to hospital.

INTRODUCTION: The aim of this study was to explore the prevalence and management of hypertension among older adults on admission to hospital and to assess the choice of antihypertensive pharmacotherapy in light of relevant comorbid conditions using the national treatment guideline. MATERIALS AND METHODS: A retrospective cross sectional study of 503 patients aged 65 years or older admitted to a large metropolitan teaching hospital in Sydney Australia was conducted. The main outcome measures were prevalence of hypertension, blood pressure (BP) control, antihypertensive medication use and the appropriateness of antihypertensive medications. RESULTS: Sixty-nine percent (n = 347) of the study population had a documented diagnosis of hypertension and of these, approximately one third were at target BP levels on admission to hospital. Some concerns regarding choice of antihypertensive noted with 51% of those with comorbid diabetes and 30% of those with comorbid heart failure receiving a potentially inappropriate antihypertensive agent. CONCLUSIONS: Despite the use of antihypertensive pharmacotherapy, many older adults do not have optimal BP control and are not reaching target BP levels. New strategies to improve blood pressure control in older populations especially targeting women, those with a past history of myocardial infarction and those on multiple antihypertensive medications are needed.

Relationship between guideline treatment and health-related quality of life in asthma.

The aim of this study was to compare the health-related quality of life (HRQL) of asthma patients treated according to the 1997 National Institute of Health (NIH) international asthma guideline and that of asthmatics receiving non-guideline treatment. The suitability of 146 asthmatics' medication regimes was determined according to the 1997 NIH asthma guideline. Quality of life was assessed on a seven-point scale using the Asthma Quality of Life questionnaire. Just over half of the patients were not currently using the treatment considered necessary for controlling their asthma. Patients treated according to the guideline (n=72) had a significantly higher overall HRQL than patients with non-guideline treatment (5.7 versus 5.3). The differences were also significant for the subscales measuring symptoms and environmental exposure, but not for activities or emotional function. An association between non-guideline treatment and a poorer health-related quality of life in asthma patients treated in general practice was observed. This study supports the role of evidence-based guidelines in daily practice. Further studies are needed to determine if guideline treatment is responsible for the increase in health-related quality of life observed in this work.

Comparison of indicators assessing the quality of drug prescribing for asthma.

OBJECTIVE: To compare different indicators for assessing the quality of drug prescribing and establish their agreement in identifying doctors who may not adhere to treatment guidelines. DATA SOURCES/STUDY SETTING: Data from 181 general practitioners (GPs) from The Netherlands. The case of asthma is used as an example because, in this area, different quality indicators exist whose validity is questioned. The study is part of the European Drug Education Project. STUDY DESIGN: Spearman rank correlations were assessed among the GPs' scores on self-report instruments, aggregated prescribing indicators, and individualized prescribing indicators. Kappa values were calculated as agreement measures for identifying low adherence to the guidelines. DATA COLLECTION: Prescribing data from GPs were collected through pharmacies, public health insurance companies, or computerized GP databases. Two self-report instruments were mailed to the GPs. The GPs first received a questionnaire assessing their competence regarding the treatment of asthma patients. Three months later they received a series of 16 written asthma cases asking for their intended treatment for each case. PRINCIPAL FINDINGS: Correlations between scores based on self-report instruments and indicators based on actual prescribing data were mostly nonsignificant and varied between 0 and 0.21. GPs identified as not adhering to the guidelines by the prescribing indicators often had high scores on the self-report instruments. Correlations between 0.20 and 0.55 were observed among indicators based on aggregated prescribing data and those based on individualized data. The agreement for identifying low adherence was small, with kappa values ranging from 0.19 to 0.30. CONCLUSIONS: Indicators based on self-report instruments seem to overestimate guideline adherence. Indicators assessing prescribing quality at an aggregated level give clearly different results, as compared to indicators evaluating prescribing data on an individual patient level. Caution is needed when using such prescribing indicators to identify low adherence to guidelines. Further validation studies using a gold standard comparison are needed to define the best possible indicator.