Order Set to Improve the Care of Patients Hospitalized for an Exacerbation of Chronic Obstructive Pulmonary Disease

Brown, Kirstin E.; Johnson, Kara; DeRonne, Beth; Parenti, Connie M.; Rice, Kathryn

doi:10.1513/annalsats.201507-466oc

Cited by 20 publications

(42 citation statements)

References 22 publications

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“…QI methodologies are utilized in many facets of medicine. This model has been used to implement CPOE for the administration of chemotherapy agents, care of patients with chronic obstructive pulmonary disease, and reduction of excessive laboratory test orders . To our knowledge, this study is the first to utilize QI methodology to integrate CDSTs to prompt users to initiate early EN in pediatrics.…”

Section: Discussionmentioning

confidence: 99%

Clinical Decision Support Tools and a Standardized Order Set Enhances Early Enteral Nutrition in Critically Ill Children

et al. 2019

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Background: Critically ill children in a pediatric intensive care unit (PICU) have unique nutrition needs that are challenging to achieve and thus are at high risk of malnutrition. There is increasing evidence that children who reach caloric goals early have improved outcomes. The purpose of this initiative was to implement an enteral nutrition (EN) algorithm in a tertiary care PICU utilizing clinical decision support tools (CDSTs) and a standardized order set within an electronic health record. Methods: A quality improvement initiative was undertaken to implement an EN feeding protocol using electronic CDSTs, including a new standardized order set. Results: In a historical cohort of 376 patients, only 18% met goal EN in the first 48 hours of admission. The EN protocol was implemented in 272 patients who met 88% goal feed volume within 48 hours of intensive care unit admission. Median time to start EN (1.7 vs 1.3 days, P < 0.0001) and time to goal nutrition (2.8 vs 2.2 days, P < 0.001) improved after project implementation. Length of stay in the PICU was significantly reduced following protocol implementation (202 hours pre-implementation vs 156 hours post implementation, P < 0.0001). Conclusions: We used CDSTs and standardized order sets to implement a nutrition algorithm to facilitate and likely improve the nutrition care of critically ill children. (Nutr Clin Pract. 2019;34:916-921)

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Section: Discussionmentioning

confidence: 99%

Clinical Decision Support Tools and a Standardized Order Set Enhances Early Enteral Nutrition in Critically Ill Children

et al. 2019

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“…2 Healthcare systems have sought to improve both the quality of patient care and the EHR experience by providing standardized order sets. [30][31][32][33] However, order sets may not align with individual cases with many extraneous, irrelevant, or contextual order options. (Li et al 2019;Kumar and Allaudeen 2016) They are also static: for instance, the manually-authored static order sets available in our own institution for deep venous thrombosis treatment still recommend warfarin therapy, despite direct oral anticoagulants largely becoming the current standard of practice.…”

Section: Discussionmentioning

confidence: 99%

Usability of a Machine-Learning Clinical Order Recommender System Interface for Clinical Decision Support and Physician Workflow

Kumar

Chiang

et al. 2020

Preprint

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Objective: To determine whether clinicians will use machine learned clinical order recommender systems for electronic order entry for simulated inpatient cases, and whether such recommendations impact the clinical appropriateness of the orders being placed. Materials and Methods: 43 physicians used a clinical order entry interface for five simulated medical cases, with each physician-case randomized whether to have access to a previously-developed clinical order recommendation system. A panel of clinicians scored determined whether orders placed were clinically appropriate. Outcomes assessed included The primary outcome was the differences in clinical appropriateness scores, of orders for cases randomized to the recommender system. Secondary outcomes included usage metrics, and physician opinions. Results: Clinical appropriateness scores for orders were comparable for cases randomized to the recommender system (mean difference -0.1 order per score, 95% CI:[-0.4, 0.2]). Physicians using the recommender placed more orders (mean 17.3 vs. 15.7 orders; incidence ratio 1.09, 95% CI:[1.01-1.17]). Case times were comparable with the recommender system. Order suggestions generated from the recommender system were more likely to match physician needs than standard manual search options. Approximately 95% of participants agreed the system would be useful for their workflows. Discussion: Machine-learned clinical order options can meet physician needs better than standard manual search systems. This may increase the number of clinical orders placed per case, while still resulting in similar overall clinically appropriate choices. Conclusions: Clinicians can use and accept machine learned clinical order recommendations integrated into an electronic order entry interface. The clinical appropriateness of orders entered was comparable even when supported by automated recommendations.

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“…2 Implementation of disease-specific orders sets has been shown to improve compliance with standards of care for the management of asthma exacerbations, acute coronary syndrome, septic shock, community acquired pneumonia, and chronic obstructive pulmonary disease. [3][4][5][6][7][8] In a study conducted at a large academic medical center, application of a standardized acute coronary syndrome order set significantly increased the number of patients who received aspirin within the first 24 hours of hospitalization. 4 Similarly, initiation of a septic shock order set in the emergency department of a large academic medical center significantly increased the number of patients receiving adequate fluid resuscitation and an appropriate empiric antibiotic regimen, while also reporting decreased 28-day mortality.…”

Section: Background and Significancementioning

confidence: 99%

“…Implementation of disease-specific orders sets has been shown to improve compliance with standards of care for the management of asthma exacerbations, acute coronary syndrome, septic shock, community acquired pneumonia, and chronic obstructive pulmonary disease. [3][4][5][6][7][8] 2. Which of the following order set changes has significantly influenced prescribing behavior in numerous studies?…”

Section: Clinical Relevance Statementmentioning

confidence: 99%

The Impact of Changes to an Electronic Admission Order Set on Prescribing and Clinical Outcomes in the Intensive Care Unit

2020

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Background Implementation of disease-specific order sets has improved compliance with standards of care for a variety of diseases. Evidence of the impact admission order sets can have on care is limited. Objective The main purpose of this article is to evaluate the impact of changes made to an electronic critical care admission order set on provider prescribing patterns and clinical outcomes. Methods A retrospective, observational before-and-after exploratory study was performed on adult patients admitted to the medical intensive care unit using the Inpatient Critical Care Admission Order Set. The primary outcome measure was the percentage change in the number of orders for scheduled acetaminophen, a histamine-2 receptor antagonist (H2RA), and lactated ringers at admission before implementation of the revised order set compared with after implementation. Secondary outcomes assessed clinical impact of changes made to the order set. Results The addition of a different dosing strategy for a medication already available on the order set (scheduled acetaminophen vs. as needed acetaminophen) had no impact on physician prescribing (0 vs. 0%, p = 1.000). The addition of a new medication class (an H2RA) to the order set significantly increased the number of patients prescribed an H2RA for stress ulcer prophylaxis (0 vs. 20%, p < 0.001). Rearranging the list of maintenance intravenous fluids to make lactated ringers the first fluid option in place of normal saline significantly decreased the number of orders for lactated ringers (17 vs. 4%, p = 0.005). The order set changes had no significant impact on clinical outcomes such as incidence of transaminitis, gastrointestinal bleed, and acute kidney injury. Conclusion Making changes to an admission order set can impact provider prescribing patterns. The type of change made to the order set, in addition to the specific medication changed, may have an effect on how influential the changes are on prescribing patterns.

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Order Set to Improve the Care of Patients Hospitalized for an Exacerbation of Chronic Obstructive Pulmonary Disease

Abstract: Computerized multidisciplinary admission order set implementation for patients hospitalized for a COPD exacerbation improved physicians' adherence to evidence-based pharmacologic treatment, and they were associated with reductions in length of hospital stay.

Cited by 20 publications

References 22 publications

Clinical Decision Support Tools and a Standardized Order Set Enhances Early Enteral Nutrition in Critically Ill Children

Clinical Decision Support Tools and a Standardized Order Set Enhances Early Enteral Nutrition in Critically Ill Children

Usability of a Machine-Learning Clinical Order Recommender System Interface for Clinical Decision Support and Physician Workflow

The Impact of Changes to an Electronic Admission Order Set on Prescribing and Clinical Outcomes in the Intensive Care Unit

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