Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality

Bates, David W.; Kuperman, Gilad J.; Wang, Samuel J.; Gandhi, Tejal K.; Kittler, Anne F.; Volk, Lynn A.; Spurr, Cynthia; Khorasani, Ramin; Tanasijevic, Milenko J.; Middleton, Blackford

doi:10.1197/jamia.m1370

Cited by 1,085 publications

(804 citation statements)

References 38 publications

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“…9,10,26,27,[31][32][33][34][35][36][37] These included issues related to: 1) workflow, and 2) usability/ flexibility of the reminder. Similar to previous studies, existing workflow often prevented completion of the screening reminder before patient appointments.…”

Section: Themes Anticipated a Priorimentioning

confidence: 99%

Factors Underlying Quality Problems with Alcohol Screening Prompted by a Clinical Reminder in Primary Care: A Multi-site Qualitative Study

et al. 2015

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Section: Themes Anticipated a Priorimentioning

confidence: 99%

Factors Underlying Quality Problems with Alcohol Screening Prompted by a Clinical Reminder in Primary Care: A Multi-site Qualitative Study

et al. 2015

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“…First, we used an EHR alert as the method by which to engage providers in the SDM process. Alerts are used extensively as a way to both notify and prompt providers, and this may lead to "alert fatigue" when alerts have little relevance to the clinical encounter [15][16][17]. This, in turn, can lead to alerts being ignored except in cases of "hard stop" alerts that require the provider to take a specific and immediate action.…”

Section: Decision Keymentioning

confidence: 99%

Shared decision making: using health information technology to integrate patient choice into primary care

Jones

Bruce

Shah

et al. 2011

Behav. Med. Pract. Policy Res.

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Advances in shared decision making (SDM) have not successfully translated to practice. We describe our experience and lessons learned in translating an SDM process for primary care cardiovascular disease management. The SDM process operationalized recognized SDM elements using workflow modifications, a computerized patient questionnaire, an automated risk calculator to identify at-risk patients, a web-based tool for patients to choose interventions, automated feedback on the personalized benefits of choices, and a web-based tool for providers to view patient risk, patient choice, and expert advice. Although medication was typically the intervention resulting in the greatest risk reduction, the majority of patients preferred dietary and other lifestyle changes. Patients generally favored the opportunity to make and communicate choices. However, providers only viewed patient choice data in 20% of the encounters. Translation of the SDM process was successful for patients and the difference between patient choice and optimal risk reduction points to the importance of engaging in an SDM process. Lack of engagement by providers may be due to "alert fatigue" or to the failure of the SDM process to improve efficiency in the office visit. KeywordsShared decision making, Translation, e-Technology BACKGROUND Shared decision making (SDM) is widely recognized as a desirable form of patient-provider interaction [1][2][3]. In theory, the SDM process seems straightforward. In practice, there is limited success in translating tested SDM protocols to routine clinical practice [4,5].SDM tools (e.g., a decision aid) and processes are intended to foster a consideration of the risk, benefits, and tradeoffs associated with a decision, and the way in which a patient's preferences are incorporated into the discussion and decision process [6,7]. There is growing interest in conceptual models, methods for communicating information, and tests of the utility and impact of tools and processes. There has been relatively little focus, however, on how SDM tools and processes can be successfully deployed and integrated into clinical practice. In general, SDM has not been

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“…This may lead to "alert fatigue", meaning that CDSS-triggered alerts are indiscriminately given little attention or even completely disregarded. [5,[14][15][16][17]] Therefore, the classification and subsequent selection of drug interactions according to their clinical relevance and management implications is a major challenge for CDSS. Furthermore, available CDSS show major differences in their system architecture, underlying knowledge databases and classification systems, and only few studies evaluated and compared different CDSS using real-life patient data.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance of Two Drug Interaction Screening Programmes Analysing a Cross-Sectional Prescription Dataset of 84,625 Psychiatric Inpatients

et al. 2013

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BACKGROUND: Clinical decision support software (CDSS) solutions can automatically identify drug interactions and thereby aim to improve drug safety. However, data on the comparative performance of different CDSS to detect and appropriately classify interactions in real-life prescription datasets is limited. OBJECTIVE: The aim of this study was to compare the results from two different CDSS analysing the pharmacotherapy of a large population of psychiatric inpatients for drug interactions. METHODS: We performed mass analyses of cross-sectional patient-level prescriptions from 84,625 psychiatric inpatients using two CDSS -MediQ and ID PHARMA CHECK(®). Interactions with the highest risk ratings and the most frequent ratings were reclassified according to the Zurich Interaction System (ZHIAS), a multidimensional classification that incorporates the OpeRational ClassificAtion of Drug Interactions (ORCA) and served as a reference standard. RESULTS: MediQ reported 6,133 unique interacting combinations responsible for 270,617 alerts affecting 63,454 patients. ID PHARMA CHECK(®) issued 5,400 interactions and 157,489 alerts in 48,302 patients. Only 2,154 unique interactions were identified by both programmes, but overlap increased with higher risk rating. MediQ reported high-risk interactions in 2.5 % of all patients, compared with 5 % according to ID PHARMA CHECK(®). The positive predictive value for unique major alerts to be (provisionally) contraindicated according to ORCA was higher for MediQ (0.63) than for either of the two ID PHARMA CHECK(®) components (0.42 for hospINDEX and 0.30 for ID MACS). MediQ reported more interactions, and ID PHARMA CHECK(®) tended to classify interactions into a higher risk class, but overall both programmes identified a similar number of (provisionally) contraindicated interactions according to ORCA criteria. Both programmes identified arrhythmia as the most frequent specific risk associated with interactions in psychiatric patients. CONCLUSIONS: CDSS can be used for mass-analysis of prescription data and thereby support quality management. However, in clinical practice CDSS impose an overwhelming alert burden on the prescriber, and prediction of clinical relevance remains a major challenge. Only a small subset of yet to be determined alerts appears suitable for automated display in clinical routine. DOI: https://doi.org/10.1007/s40264-013-0027-9Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-76756 Accepted Version Originally published at: Zorina, Olesya I; Haueis, Patrick; Greil, Waldemar; Grohmann, Renate; Kullak-Ublick, Gerd A; Russmann, Stefan (2013). Comparative performance of two drug interaction screening programmes analysing a cross-sectional prescription dataset of 84,625 psychiatric inpatients. Drug Safety, 36(4):247-258.

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Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality

Cited by 1,085 publications

References 38 publications

Factors Underlying Quality Problems with Alcohol Screening Prompted by a Clinical Reminder in Primary Care: A Multi-site Qualitative Study

Factors Underlying Quality Problems with Alcohol Screening Prompted by a Clinical Reminder in Primary Care: A Multi-site Qualitative Study

Shared decision making: using health information technology to integrate patient choice into primary care

Comparative Performance of Two Drug Interaction Screening Programmes Analysing a Cross-Sectional Prescription Dataset of 84,625 Psychiatric Inpatients

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