Quality improvement Randomised controlled trial
Evidence-based clinical decision support improves the appropriate use of antibiotics and rapid strep testing 10.1136/eb-2013-101625
Jane Brokel Health Informatics, University of Iowa, Iowa City, Iowa, USA Correspondence to: Dr Jane Brokel, Health Informatics, University of Iowa, 1420 Willow Brook Ln, Riverside, Iowa City, IA 52327, USA; [email protected]
Commentary on: McGinn TG, McCullagh L, Kannry J, et al. Efficacy of an evidence-based clinical decision support in primary care practices: a randomised clinical trial. JAMA Intern Med 2013;173:1584–91.
Context Various forms of clinical decision support (CDS) involve the use of data entered within the electronic health record (EHR) containing components of clinical documentation and results. This study addresses the clinical impact of CDS for pharyngitis on evidence-based practices and evaluates challenges to achievement of large-scale adoption rates. The research findings include outcome and process measures that can be used to determine the efficacy of clinical prediction rules within the provider work flow.
Methods A randomised clinical trial evaluated two evidence-based clinical prediction rules (CPR) used in ambulatory primary care clinics by physicians, residents, fellows and nurse practitioners. The study used both clinical outcomes and process measures to determine the efficacy and adoption of the CPR in provider workflow. χ2 and t tests were used to evaluate the differences in performance between the control group and the intervention group within patient encounters by measuring frequency, rates and type of antibiotic prescribed; chest X-ray, rapid strep test and culture orders after using a risk score calculator; and the opening and signing of order sets. A generalised estimating equation model was used to compare the groups.
Findings Antibiotics were less likely to be prescribed by providers in the intervention group (age-adjusted RR: 0.74, 95% CI 0.60 to 0.92). Providers in the intervention group were also less likely to order rapid strep tests (RR 0.75, 95% CI 0.58 to 0.97). Chest X-ray and throat culture orders did not differ significantly between the intervention and control groups. For process measures, the CPR tool was triggered on 2.5% of the outpatient visits with 60% seen by the intervention group and 40% by the control group. The use of the CPR by providers in the intervention group was reported at 62.8% when the tool was opened. A greater acceptance was
118
Evid Based Med June 2014 | volume 19 | number 3 |
recorded for pharyngitis visits (74.2%) with 63.1% sets opened and 50.5% of orders completed on 374 encounters, while lower figures were recorded for pneumonia visits (42.5%) with 39.2% opening order sets and 26.9% of orders completed on 212 encounters.
Commentary Use of evidence-based CPRs appears to improve adherence to recommended practices and results in decreased antibiotic prescriptions and orders for rapid strep screening. The problem addressed in this article is the previously described delayed use and non-use of widely accepted clinical practice guidelines and the adoption of decision support tools.1 2 Access to data, information and knowledge resources is necessary to help professionals make wise choices in diagnosis and planning of treatment for patients.3 Commercial and proprietary EHRs require a substantial design work effort to use these data, information and evidence-based knowledge resources. The unique training and decision-making skills of providers add to the challenge in designing tools for all users. In addition, data accuracy and data absence can alter CPR triggers, the processing of inclusionary and exclusionary logic and the output message to providers. CDS interventions require forethought designs to overcome these challenges. In the past, the suitability of process measures used to evaluate adoption rates has been debated.2 The number of times a rule is triggered, whether the CPR is opened, and the number of resulting signed orders measure the planning of care but do not capture the actual care provided, such as tests or interventions actually performed. Execution of care orders is often delegated to other healthcare professionals and an assumption is made that these orders are completed. The evaluation of the adoption of clinical practice guidelines should include outcomes such as documented results from laboratory testing, documented nursing care interventions, administered medications and pharmacy-dispensed prescriptions. Workflows for clinical practice guidelines should include all processes of diagnosing, planning orders, interventions of care and the evaluation of patient outcomes. In the future, the actual documented data for care delivered must be obtained to determine the extent of the clinical guideline’s adoption. The adoption of evidence-based guidelines should be confirmed by measuring the interventions completed in the EHR and not merely by measuring the orders or order sets signed. This study offers methods to measure the adoption a few others have accomplished. The limitations within studies demand a better use of EHR data, which includes patient characteristics, diagnoses, interventions delivered and patient-sensitive assessment outcomes. CDS rules have high potential to assist providers in finding patient information and adhering to evidence-based guidelines to improve patient safety and quality outcomes. Competing interests None.
References 1. Arora D, Finley S. Implementing clinical decision support (CDS) systems: the experiences of AHRQ’s demonstration projects—second annual report. Rockville: Agency for Healthcare Research and Quality US Department of Health and Human Services, 2011. 2. Agency for Healthcare Research and Quality [homepage on the internet]. Clinical Decision Support. http://www.ahrq.gov/professionals/prevention-chronic-care/ decision/clinical/index.html 3. Nelson R. Revised nelson data to wisdom continuum. In: Nelson R, Staggers N, eds. Health informatics: an interprofessional approach. St Louis: Elsevier Mosby, 2014;25–28.
Evidence-based clinical decision support improves the appropriate use of antibiotics and rapid strep testing 10.1136/eb-2013-101625
Jane Brokel Health Informatics, University of Iowa, Iowa City, Iowa, USA Correspondence to: Dr Jane Brokel, Health Informatics, University of Iowa, 1420 Willow Brook Ln, Riverside, Iowa City, IA 52327, USA; [email protected]
Commentary on: McGinn TG, McCullagh L, Kannry J, et al. Efficacy of an evidence-based clinical decision support in primary care practices: a randomised clinical trial. JAMA Intern Med 2013;173:1584–91.
Context Various forms of clinical decision support (CDS) involve the use of data entered within the electronic health record (EHR) containing components of clinical documentation and results. This study addresses the clinical impact of CDS for pharyngitis on evidence-based practices and evaluates challenges to achievement of large-scale adoption rates. The research findings include outcome and process measures that can be used to determine the efficacy of clinical prediction rules within the provider work flow.
Methods A randomised clinical trial evaluated two evidence-based clinical prediction rules (CPR) used in ambulatory primary care clinics by physicians, residents, fellows and nurse practitioners. The study used both clinical outcomes and process measures to determine the efficacy and adoption of the CPR in provider workflow. χ2 and t tests were used to evaluate the differences in performance between the control group and the intervention group within patient encounters by measuring frequency, rates and type of antibiotic prescribed; chest X-ray, rapid strep test and culture orders after using a risk score calculator; and the opening and signing of order sets. A generalised estimating equation model was used to compare the groups.
Findings Antibiotics were less likely to be prescribed by providers in the intervention group (age-adjusted RR: 0.74, 95% CI 0.60 to 0.92). Providers in the intervention group were also less likely to order rapid strep tests (RR 0.75, 95% CI 0.58 to 0.97). Chest X-ray and throat culture orders did not differ significantly between the intervention and control groups. For process measures, the CPR tool was triggered on 2.5% of the outpatient visits with 60% seen by the intervention group and 40% by the control group. The use of the CPR by providers in the intervention group was reported at 62.8% when the tool was opened. A greater acceptance was
118
Evid Based Med June 2014 | volume 19 | number 3 |
recorded for pharyngitis visits (74.2%) with 63.1% sets opened and 50.5% of orders completed on 374 encounters, while lower figures were recorded for pneumonia visits (42.5%) with 39.2% opening order sets and 26.9% of orders completed on 212 encounters.
Commentary Use of evidence-based CPRs appears to improve adherence to recommended practices and results in decreased antibiotic prescriptions and orders for rapid strep screening. The problem addressed in this article is the previously described delayed use and non-use of widely accepted clinical practice guidelines and the adoption of decision support tools.1 2 Access to data, information and knowledge resources is necessary to help professionals make wise choices in diagnosis and planning of treatment for patients.3 Commercial and proprietary EHRs require a substantial design work effort to use these data, information and evidence-based knowledge resources. The unique training and decision-making skills of providers add to the challenge in designing tools for all users. In addition, data accuracy and data absence can alter CPR triggers, the processing of inclusionary and exclusionary logic and the output message to providers. CDS interventions require forethought designs to overcome these challenges. In the past, the suitability of process measures used to evaluate adoption rates has been debated.2 The number of times a rule is triggered, whether the CPR is opened, and the number of resulting signed orders measure the planning of care but do not capture the actual care provided, such as tests or interventions actually performed. Execution of care orders is often delegated to other healthcare professionals and an assumption is made that these orders are completed. The evaluation of the adoption of clinical practice guidelines should include outcomes such as documented results from laboratory testing, documented nursing care interventions, administered medications and pharmacy-dispensed prescriptions. Workflows for clinical practice guidelines should include all processes of diagnosing, planning orders, interventions of care and the evaluation of patient outcomes. In the future, the actual documented data for care delivered must be obtained to determine the extent of the clinical guideline’s adoption. The adoption of evidence-based guidelines should be confirmed by measuring the interventions completed in the EHR and not merely by measuring the orders or order sets signed. This study offers methods to measure the adoption a few others have accomplished. The limitations within studies demand a better use of EHR data, which includes patient characteristics, diagnoses, interventions delivered and patient-sensitive assessment outcomes. CDS rules have high potential to assist providers in finding patient information and adhering to evidence-based guidelines to improve patient safety and quality outcomes. Competing interests None.
References 1. Arora D, Finley S. Implementing clinical decision support (CDS) systems: the experiences of AHRQ’s demonstration projects—second annual report. Rockville: Agency for Healthcare Research and Quality US Department of Health and Human Services, 2011. 2. Agency for Healthcare Research and Quality [homepage on the internet]. Clinical Decision Support. http://www.ahrq.gov/professionals/prevention-chronic-care/ decision/clinical/index.html 3. Nelson R. Revised nelson data to wisdom continuum. In: Nelson R, Staggers N, eds. Health informatics: an interprofessional approach. St Louis: Elsevier Mosby, 2014;25–28.
