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 pa… Show more
“…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).…”
Section: Resultsmentioning
confidence: 99%
“…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 ).…”
Section: Resultsmentioning
confidence: 99%
“… 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%.…”
Objectives
To describe the application of nudges within electronic health records (EHRs) and their effects on inpatient care delivery, and identify design features that support effective decision-making without the use of interruptive alerts.
Materials and methods
We searched Medline, Embase, and PsychInfo (in January 2022) for randomized controlled trials, interrupted time-series and before–after studies reporting effects of nudge interventions embedded in hospital EHRs to improve care. Nudge interventions were identified at full-text review, using a pre-existing classification. Interventions using interruptive alerts were excluded. Risk of bias was assessed using the ROBINS-I tool (Risk of Bias in Non-randomized Studies of Interventions) for non-randomized studies or the Cochrane Effective Practice and Organization of Care Group methodology for randomized trials. Study results were summarized narratively.
Results
We included 18 studies evaluating 24 EHR nudges. An improvement in care delivery was reported for 79.2% (n = 19; 95% CI, 59.5–90.8) of nudges. Nudges applied were from 5 of 9 possible nudge categories: change choice defaults (n = 9), make information visible (n = 6), change range or composition of options (n = 5), provide reminders (n = 2), and change option-related effort (n = 2). Only one study had a low risk of bias. Nudges targeted ordering of medications, laboratory tests, imaging, and appropriateness of care. Few studies evaluated long-term effects.
Discussion
Nudges in EHRs can improve care delivery. Future work could explore a wider range of nudges and evaluate long-term effects.
Conclusion
Nudges can be implemented in EHRs to improve care delivery within current system capabilities; however, as with all digital interventions, careful consideration of the sociotechnical system is crucial to enhance their effectiveness.
“…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).…”
Section: Resultsmentioning
confidence: 99%
“…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 ).…”
Section: Resultsmentioning
confidence: 99%
“… 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%.…”
Objectives
To describe the application of nudges within electronic health records (EHRs) and their effects on inpatient care delivery, and identify design features that support effective decision-making without the use of interruptive alerts.
Materials and methods
We searched Medline, Embase, and PsychInfo (in January 2022) for randomized controlled trials, interrupted time-series and before–after studies reporting effects of nudge interventions embedded in hospital EHRs to improve care. Nudge interventions were identified at full-text review, using a pre-existing classification. Interventions using interruptive alerts were excluded. Risk of bias was assessed using the ROBINS-I tool (Risk of Bias in Non-randomized Studies of Interventions) for non-randomized studies or the Cochrane Effective Practice and Organization of Care Group methodology for randomized trials. Study results were summarized narratively.
Results
We included 18 studies evaluating 24 EHR nudges. An improvement in care delivery was reported for 79.2% (n = 19; 95% CI, 59.5–90.8) of nudges. Nudges applied were from 5 of 9 possible nudge categories: change choice defaults (n = 9), make information visible (n = 6), change range or composition of options (n = 5), provide reminders (n = 2), and change option-related effort (n = 2). Only one study had a low risk of bias. Nudges targeted ordering of medications, laboratory tests, imaging, and appropriateness of care. Few studies evaluated long-term effects.
Discussion
Nudges in EHRs can improve care delivery. Future work could explore a wider range of nudges and evaluate long-term effects.
Conclusion
Nudges can be implemented in EHRs to improve care delivery within current system capabilities; however, as with all digital interventions, careful consideration of the sociotechnical system is crucial to enhance their effectiveness.
“…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.…”
Section: Outcomes From Clinical Information Systems On Non-covid-19 Patient Carementioning
Summary
Objective: The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19.
Methods: PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced.
Results: CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes.
Conclusion: Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.
“…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].…”
Order sets that adhere to disease-specific guidelines have been shown to increase clinician efficiency and patient safety but curating these order sets, particularly for consistency across multiple sites, is difficult and time consuming. We created software called CDS-Compare to alleviate the burden on expert reviewers in rapidly and effectively curating large databases of order sets. We applied our clustering-based software to a database of NLP-processed order sets extracted from VA’s Electronic Health Record, then had subject-matter experts review the web application version of our software for clustering validity.
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