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

Abstract

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.

Effectiveness of non-interruptive nudge interventions in electronic health records to improve the delivery of care in hospitals: a systematic review

“…Five studies implemented multiple nudge interventions. 26 , 27 , 35 , 36 , 38 , 44 Twelve interventions aimed to improve medication or fluid use 26–28 , 35 , 40–44 ; 9 interventions to improve laboratory test ordering 29 , 31 , 32 , 34 , 36 , 38 , 39 ; 2 interventions to improve appropriate care 33 , 37 ; and 1 to improve imaging. 30 Overall, studies used 5 of the 9 nudge interventions listed in the taxonomy applied: (1) change choice defaults ( n = 9 interventions); (2) make information more visible ( n = 6 interventions); (3) change range or composition of options ( n = 5 interventions); (4) make information more or less salient ( n = 2 interventions); and (5) change option-related effort ( n = 2 interventions).…”

“…Four studies assessed the impact of 5 interventions that added 34 , 35 or removed medication orders or laboratory tests 38 , 40 from existing EHR displays. In 3 studies, this was done in existing orders sets, 35 , 38 , 40 while one study changed the options in the ‘frequently ordered tests’ window ( Table 3 presents details of interventions). 34 Four of 5 interventions showed an improvement on one or more of the measured outcomes ( Figure 2 ).…”

“… 34 Four of 5 interventions showed an improvement on one or more of the measured outcomes ( Figure 2 ). 34 , 35 , 38 , 40 Two studies were rated to be at serious risk of bias, 34 , 35 one at moderate risk, 40 and one did not provide enough information to assess risk of bias. 38 One intervention involving the addition of scheduled paracetamol (acetaminophen) to the orders set, showed no change in the percentage of patients with these orders from a baseline of 0%.…”

The Clinical Information Systems Response to the COVID-19 Pandemic

“…Chronic opioid addiction is complex and observed reductions in opioid overdoses have been less consistent highlighting the need for continued advancement to translate process improvements to measures like mortality. In the inpatient setting, CIS have been leveraged to successfully influence provider behavior with the aim of improving clinical outcomes [ 192 ]. Emergency medicine and critical care medicine are two areas that have embraced the use of data and CIS to support management of persistently challenging diseases.…”

CDS-Compare: A Web Application for Machine Learning Assisted Curation of Clinical Order Sets

“…However, creating order sets is time and resource intensive [2]. If we can reduce the difficulty in order set curation within and between facilities, then we can take steps towards a system-wide database of patient-centered order sets that adhere to conditionspecific guidelines, ultimately reducing unwanted variation in patient care across all VA medical facilities [4,5]. We further anticipate that this ability will help smooth VA's transition from 128 legacy CPRS installations to a single Cerner Millennium instance shared with the US Department of Defense by reducing multi-site order sets to a single, well-curated dataset (https://www.ehrm.va.gov/resources/factsheet) [6].…”