Automated reminders increase adherence to guidelines for administration of prophylaxis for postoperative nausea and vomiting

Kooij, Fabian O.; Klok, Toni; Hollmann, Markus W.; Kal, Jasper E.

doi:10.1097/eja.0b013e32832d6a76

Cited by 59 publications

(30 citation statements)

References 15 publications

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“…In a similar vein, Kooij found that in the preoperative setting, CDS could increase adherence to prescribing PONV prophylactic treatment in high‐risk PONV patients from 38% to 73% 58. A follow‐up study has demonstrated that automated reminders could increase adherence to guidelines for the actual administration of PONV prophylaxis 59. Gawande, the World Health Organization and others have done a significant amount of work establishing the importance and value of a Surgical Safety Checklist, otherwise known as a “pre‐incision timeout,” to avoid preventable surgical adverse events that have been found to be preventable 54% of cases 60.…”

Section: Current Usage Of Decision Support In Practice Of Anesthesiamentioning

confidence: 88%

Future of Electronic Health Records: Implications for Decision Support

Rothman

Leonard²,

Vigoda

2012

Mount Sinai J Medicine

103

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The potential benefits of the electronic health record over traditional paper are many, including cost containment, reductions in errors, and improved compliance by utilizing real-time data. The highest functional level of the electronic health record (EHR) is clinical decision support (CDS) and process automation, which are expected to enhance patient health and healthcare. The authors provide an overview of the progress in using patient data more efficiently and effectively through clinical decision support to improve health care delivery, how decision support impacts anesthesia practice, and how some are leading the way using these systems to solve need-specific issues. Clinical decision support uses passive or active decision support to modify clinician behavior through recommendations of specific actions. Recommendations may reduce medication errors, which would result in considerable savings by avoiding adverse drug events. In selected studies, clinical decision support has been shown to decrease the time to follow-up actions, and prediction has proved useful in forecasting patient outcomes, avoiding costs, and correctly prompting treatment plan modifications by clinicians before engaging in decision-making. Clinical documentation accuracy and completeness is improved by an electronic health record and greater relevance of care data is delivered. Clinical decision support may increase clinician adherence to clinical guidelines, but educational workshops may be equally effective. Unintentional consequences of clinical decision support, such as alert desensitization, can decrease the effectiveness of a system. Current anesthesia clinical decision support use includes antibiotic administration timing, improved documentation, more timely billing, and postoperative nausea and vomiting prophylaxis. Electronic health record implementation offers data-mining opportunities to improve operational, financial, and clinical processes. Using electronic health record data in real-time for decision support and process automation has the potential to both reduce costs and improve the quality of patient care.

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Section: Current Usage Of Decision Support In Practice Of Anesthesiamentioning

confidence: 88%

Future of Electronic Health Records: Implications for Decision Support

Rothman

Leonard²,

Vigoda

2012

Mount Sinai J Medicine

103

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“…In this study, the researchers compared prophylactic antiemetic prescription rates after the reminder was implemented and discovered a 38% pre‐intervention increase in prophylaxis. After the reminder was discontinued, there was a decrease of 41% in prescribing prophylactic antiemetics 10 . As a follow‐up, Kooij et al 10 re‐implemented the guidelines; in the pre‐admission testing phase of care, when patients were determined to need intraoperative PONV prophylaxis, adherence to prescribing guidelines increased from 37% to 79%.…”

Section: Guideline Description and Rationalementioning

confidence: 99%

“…After the reminder was discontinued, there was a decrease of 41% in prescribing prophylactic antiemetics 10 . As a follow‐up, Kooij et al 10 re‐implemented the guidelines; in the pre‐admission testing phase of care, when patients were determined to need intraoperative PONV prophylaxis, adherence to prescribing guidelines increased from 37% to 79%. This research supports the integration of individualized clinical decision support tools into electronic systems as a reminder for perioperative staff members to order antiemetic medications based on the patient's level of risk.…”

Section: Guideline Description and Rationalementioning

confidence: 99%

Standardizing Documentation for Postoperative Nausea and Vomiting in the Electronic Health Record

DeBlieck¹,

LaFlamme²,

Rivard³

et al. 2013

AORN Journal

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Postoperative nausea and vomiting (PONV) remains a common postoperative complication that causes patient discomfort and increases health care costs. Clinicians use the American Society of PeriAnesthesia Nurses (ASPAN) guideline to help prevent and treat PONV. However, the lack of standardized terminology in the electronic health record (EHR) and the lack of clinical decision support tools make it difficult for clinicians to document guideline implementation and to determine the effects of nursing care on PONV. To address this, we created a concept map of the Perioperative Nursing Data Set (PNDS) that illustrates the relationship between elements of this standardized nursing terminology and the ASPAN guideline, using the Systematized Nomenclature of Medicine-Clinical Terms multidisciplinary terminology to fill any gaps. This mapping results in a standardized dataset specific to PONV for use in an EHR, which links nursing care to nursing diagnoses, interventions, and outcomes. The mapping and documentation in the EHR also allows standardized data collection for research, evaluation, and benchmarking, which makes perioperative nursing care of patients who are at risk for or experiencing PONV measureable and visible. Distributing this information to perioperative and perianesthesia nursing personnel, in addition to implementing risk assessment tools for PONV and clinical support alerts in electronic documentation systems, will help support implementation of the PONV clinical practice guideline in the EHR.

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“…Daher ist eine großzügigere generelle Prophylaxe zu überdenken, die dann noch einmal bei besonders gefährdeten Patienten (z. B. Kieferverdrahtung) ausgedehnt werden könnte, um ein noch wirksameres Regime zur Anwendung zu bringen [14,15].…”

Section: Statistische Analyseunclassified