Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial

Wright, Adam; Pang, Justine E.; Feblowitz, Joshua; Maloney, Francine L.; Wilcox, Alison; McLoughlin, Karen Sax; Ramelson, Harley Z.; Schneider, Louise I.; Bates, David W.

doi:10.1136/amiajnl-2011-000521

Cited by 75 publications

(61 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previously, we found that approximately 40% of unique alerts were accepted by providers [10]. This aggregate acceptance rate might reasonably be explained by one of two usage patterns: (1) most providers accepting alerts at a similar frequency or (2) some providers accepting most alerts with others accepting very few.…”

Section: Discussionmentioning

confidence: 70%

“…We designed the current study as a follow-up study to the trial described above [10]. In order to assess provider experience with the CDS intervention, we aggregated final data for each provider from the intervention arm clinics including: the total number of alerts, the number of times alerts were ignored, the number of times alerts were accepted, the number overridden, and the number of unique alerts (excluding alerts that were shown multiple times for the same provider and patient).…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we carried out a secondary analysis of a completed randomized, controlled trial that examined whether a CDS alert system that employs inference rules could improve problem list completeness [9,10]. We characterized individual providers' use of this system and compared it with their demographic information and self-reported assessment of the CDS tool.…”

Section: Introductionmentioning

confidence: 99%

“…We then created an active clinical alerting system based on these rules and conducted a randomized, controlled trial of the alerts in BWH-affiliated primary care practices [10]. When the rule criteria were met, an alert was displayed to provider suggesting problems to be added to the problem list (▶ Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Target problems were roughly three times more likely to be documented when the alerts were shown, with significant increases in problem documentation for 13 of 17 conditions. Complete results of the trial are reported in a prior publication [10].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Provider Use of and Attitudes Towards an Active Clinical Alert

Feblowitz¹,

Henkin²,

Pang³

et al. 2013

Appl Clin Inform

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 70%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Provider Use of and Attitudes Towards an Active Clinical Alert

Feblowitz¹,

Henkin²,

Pang³

et al. 2013

Appl Clin Inform

View full text Add to dashboard Cite

show abstract

The effects of on-screen, point of care computer reminders on processes and outcomes of care

Shojania

Jennings²,

Mayhew

et al. 2009

Cochrane Database of Systematic Reviews

357

339

View full text Add to dashboard Cite

Background The opportunity to improve care by delivering decision support to clinicians at the point of care represents one of the main incentives for implementing sophisticated clinical information systems. Previous reviews of computer reminder and decision support systems have reported mixed effects, possibly because they did not distinguish point of care computer reminders from e-mail alerts, computer-generated paper reminders, and other modes of delivering ‘computer reminders’. Objectives To evaluate the effects on processes and outcomes of care attributable to on-screen computer reminders delivered to clinicians at the point of care. Search methods We searched the Cochrane EPOC Group Trials register, MEDLINE, EMBASE and CINAHL and CENTRAL to July 2008, and scanned bibliographies from key articles. Selection criteria Studies of a reminder delivered via a computer system routinely used by clinicians, with a randomised or quasi-randomised design and reporting at least one outcome involving a clinical endpoint or adherence to a recommended process of care. Data collection and analysis Two authors independently screened studies for eligibility and abstracted data. For each study, we calculated the median improvement in adherence to target processes of care and also identified the outcome with the largest such improvement. We then calculated the median absolute improvement in process adherence across all studies using both the median outcome from each study and the best outcome. Main results Twenty-eight studies (reporting a total of thirty-two comparisons) were included. Computer reminders achieved a median improvement in process adherence of 4.2% (interquartile range (IQR): 0.8% to 18.8%) across all reported process outcomes, 3.3% (IQR: 0.5% to 10.6%) for medication ordering, 3.8% (IQR: 0.5% to 6.6%) for vaccinations, and 3.8% (IQR: 0.4% to 16.3%) for test ordering. In a sensitivity analysis using the best outcome from each study, the median improvement was 5.6% (IQR: 2.0% to 19.2%) across all process measures and 6.2% (IQR: 3.0% to 28.0%) across measures of medication ordering. In the eight comparisons that reported dichotomous clinical endpoints, intervention patients experienced a median absolute improvement of 2.5% (IQR: 1.3% to 4.2%). Blood pressure was the most commonly reported clinical endpoint, with intervention patients experiencing a median reduction in their systolic blood pressure of 1.0 mmHg (IQR: 2.3 mmHg reduction to 2.0 mmHg increase). Authors’ conclusions Point of care computer reminders generally achieve small to modest improvements in provider behaviour. A minority of interventions showed larger effects, but no specific reminder or contextual features were significantly associated with effect magnitude. Further research must identify design features and contextual factors consistently associated with larger improvements in provider behaviour if computer reminders are to succeed on more than a trial and error basis.

show abstract

Decision-support tools via mobile devices to improve quality of care in primary healthcare settings

Agarwal

Glenton

Tamrat

et al. 2021

Cochrane Database of Systematic Reviews

View full text Add to dashboard Cite

Background The ubiquity of mobile devices has made it possible for clinical decision‐support systems (CDSS) to become available to healthcare providers on handheld devices at the point‐of‐care, including in low‐ and middle‐income countries. The use of CDSS by providers can potentially improve adherence to treatment protocols and patient outcomes. However, the evidence on the effect of the use of CDSS on mobile devices needs to be synthesized. This review was carried out to support a World Health Organization (WHO) guideline that aimed to inform investments on the use of decision‐support tools on digital devices to strengthen primary healthcare. Objectives To assess the effects of digital clinical decision‐support systems (CDSS) accessible via mobile devices by primary healthcare providers in the context of primary care settings. Search methods We searched CENTRAL, MEDLINE, Embase, Global Index Medicus, POPLINE, and two trial registries from 1 January 2000 to 9 October 2020. We conducted a grey literature search using mHealthevidence.org and issued a call for papers through popular digital health communities of practice. Finally, we conducted citation searches of included studies. Selection criteria Study design: we included randomized trials, including full‐text studies, conference abstracts, and unpublished data irrespective of publication status or language of publication. Types of participants: we included studies of all cadres of healthcare providers, including lay health workers and other individuals (administrative, managerial, and supervisory staff) involved in the delivery of primary healthcare services using clinical decision‐support tools; and studies of clients or patients receiving care from primary healthcare providers using digital decision‐support tools. Types of interventions: we included studies comparing digital CDSS accessible via mobile devices with non‐digital CDSS or no intervention, in the context of primary care. CDSS could include clinical protocols, checklists, and other job‐aids which supported risk prioritization of patients. Mobile devices included mobile phones of any type (but not analogue landline telephones), as well as tablets, personal digital assistants, and smartphones. We excluded studies where digital CDSS were used on laptops or integrated with electronic medical records or other types of longitudinal tracking of clients. Data collection and analysis A machine learning classifier that gave each record a probability score of being a randomized trial screened all search results. Two review authors screened titles and abstracts of studies with more than 10% probability of being a randomized trial, and one review author screened those with less than 10% probability of being a randomized trial. We followed standard methodological procedures expected by Cochrane and the Effective Practice and Organisatio...

show abstract

Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial

Cited by 75 publications

References 11 publications

Provider Use of and Attitudes Towards an Active Clinical Alert

Provider Use of and Attitudes Towards an Active Clinical Alert

The effects of on-screen, point of care computer reminders on processes and outcomes of care

Decision-support tools via mobile devices to improve quality of care in primary healthcare settings

Contact Info

Product

Resources

About