Provider Use of and Attitudes Towards an Active Clinical Alert

Abstract

SummaryBackground: In a previous study, we reported on a successful clinical decision support (CDS) intervention designed to improve electronic problem list accuracy, but did not study variability of provider response to the intervention or provider attitudes towards it. The alert system accurately predicted missing problem list items based on health data captured in a patient's electronic medical record. Objective: To assess provider attitudes towards a rule-based CDS alert system as well as heterogeneity of acceptance rates across providers. Methods: We conducted a by-provider analysis of alert logs from the previous study. In addition, we assessed provider opinions of the intervention via an email survey of providers who received the alerts (n = 140). Results: Although the alert acceptance rate was 38.1%, individual provider acceptance rates varied widely, with an interquartile range (IQR) of 14.8%-54.4%, and many outliers accepting none or nearly all of the alerts they received. No demographic variables, including degree, gender, age, assigned clinic, medical school or graduation year predicted acceptance rates. Providers' self-reported acceptance rate and perceived alert frequency were only moderately correlated with actual acceptance rates and alert frequency. Conclusions: Acceptance of this CDS intervention among providers was highly variable but this heterogeneity is not explained by measured demographic factors, suggesting that alert acceptance is a complex and individual phenomenon. Furthermore, providers' self-reports of their use of the CDS alerting system correlated only modestly with logged usage.

Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis

“…38–43 A CDS system that uses an active interaction model, such as generating clinical alerts or reminders based on clinicians’ data entry and interaction, is the most effective in improving clinical practice. 39,44–46 In our study, the nurse preference to receive recommendations in the alert was refreshing, indicating they would adopt suggested interventions. In alert motivation, the finding that priorities of the patient, unit, and hospital were placed ahead of federal mandates and accreditation, which are most often more influential to physicians who may experience consequences of not meeting guidelines, was not surprising.…”

“…40 However, fewer published studies 41–43 have been assessments of attitudes and perception toward clinical alerts and CDS systems. Feblowitz et al 44 found that among health care providers, alert acceptance was 38.1% (interquartile ratio, 14.8%–54.4%), indicating the mass complexities of alerts. Studies on alarm fatigue indicated that most nurses (~95%) considered that false alarms reduced trust in alerts, disrupted clinical care, and desensitized members of the nursing staff.…”

Determinants of a successful problem list to support the implementation of the problem-oriented medical record according to recent literature

“… • Users should be trained in using the problem list, its features and policies on the problem list. This increases the correct use of the problem list [ 12 , 14 , 25 , 40 , 42 ] • Experience in medicine and medical profession also influences the use of the problem list this should be taken into account in training of staff [ 17 , 25 , 41 ] • Training can increase the correct use of the problem list [ 12 , 14 , 25 , 40 , 42 ] Usability The definition of usability in this article is user-friendliness and user interface. • Custom views and filters should be possible [ 2 , 8 , 18 , 39 ] • The interface should be intuitive and efficient [ 8 , 13 , 39 ] • The encoded dictionary should be able to identify synonyms, misspelled entries and handle free text [ 2 , 6 , 12 , 31 , 32 ] • System support has to be integrated at logical moments in the notes registry (e.g.…”

Primary Care Providers’ Opening of Time-Sensitive Alerts Sent to Commercial Electronic Health Record InBaskets

“…Review of audit logs provides information on clinician behavior and on interactions between team members. 22,28 Previous studies have used audit logs to describe physician and care team behaviors including medical information queries, [29][30][31] team communication, 32 viewing of EHR notes, 23 response to *A total of 630 alerts were delivered to the InBaskets of PCPs who were logged into the EHR at the time of alert delivery or within 24 h of delivery; of these, 375 were opened within 24 h of delivery † Adjusted odds ratios account for clustering by PCP using a GEE model and control for PCP characteristics, patient characteristics and contextual factors clinical decision support tools, 33 and patient interactions through tethered portals. 34 This study has several limitations.…”