Nudging strategies to influence prescribers’ behavior toward reducing opioid prescriptions: a systematic scoping review

Abstract

Objective

This systematic scoping review aimed to map the literature on the use of various nudging strategies to influence prescriber behavior toward reducing opioid prescriptions across diverse healthcare settings.

Methods

A systematic database search was conducted using seven electronic databases. Only articles published in English were included. A total of 2234 articles were identified, 35 of which met the inclusion criteria. Two independent dimensions were used to describe nudging strategies according to user action and the timing of their implementation.

Results

Six nudging strategies were identified. The most common strategy was default choices, followed by increasing salience of information or incentives and providing feedback. Moreover, 32 studies used the electronic health record as an implementation method, and 29 reported significant results. Most of the effective interventions were multicomponent interventions (i.e., combining nudge strategies and non-nudge components).

Conclusions

Most nudging strategies used a passive approach, such as defaulting prescriptions to generics and requiring no action from the prescriber. Although reported as effective, this approach often operates under the prescriber’s radar. Future research should explore the ethical implications of nudging strategies.

INPLASY registration number: 202420082.

Keywords

Nudging choice architecture prescriber behavior change opioid prescription healthcare professional healthcare setting electronic health record multicomponent intervention

Introduction

Despite growing awareness of the opioid epidemic, opioid prescription rates remain high in regions such as North America and Europe; this constitutes a worrying trend that may not be isolated to these areas.^1–4 Pain management has evolved into a complex and costly task that is further complicated by the increasing prevalence of chronic pain and the reduced availability of specialized care, which generates a greater reliance on opioids for pain control.^5–7 The current challenge is characterized by the need to manage uncontrolled chronic and acute pain and reduce the negative consequences of indiscriminate opioid prescribing, an issue that affects not just individual patients but also healthcare systems and communities.⁸ Opioid analgesics are associated with undesirable side effects, such as constipation, respiratory depression and the risk of addiction, and their misuse places a heavy burden on healthcare resources.^5,7,9,10 As excessive prescribing contributes to a surplus of medication in circulation, it raises concerns about both the misuse of opioids and the challenges associated with their disposal.¹¹

Recent studies indicate that efforts to curb opioid overprescribing have led to mixed results across different regions. For instance, opioid analgesic sales decreased in North America and Oceania between 2015 and 2019 but increased in South America and parts of Europe, highlighting the diverse effects of regulatory measures and educational efforts.¹² In addition, from 2009 to 2019, global prescription opioid consumption declined, particularly in high-income countries like the USA and Germany, reflecting the success of recent restrictive regulatory and educational interventions.¹³ Conversely, over-the-counter codeine product sales have increased in some countries, highlighting the ongoing challenges in controlling opioid availability and misuse.¹⁴ These trends suggest that although considerable progress has been made in certain areas, continued vigilance and tailored strategies are necessary to address the complex dynamics of global opioid prescribing and misuse.

In response to these challenges, advocacy for strategies that minimize or eliminate reliance on opioids for pain management has been increasing.¹⁵ The discrepancy between clinicians’ opioid prescription practices and their awareness of the opioid epidemic, opioid-associated side effects and the limited effectiveness of opioids in treating chronic pain is highlighted by the large quantity of prescribed but unused opioid pills.^10,16 This indicates that prescribing is not always grounded in rational decision-making.¹⁷ Prescribing patterns frequently reflect habits rather than patient-centered needs or empirical efficacy.¹⁸ Furthermore, even informed clinicians can make cognitive errors, particularly in areas fraught with uncertainty about treatment risks and benefits, such as opioid prescribing.¹⁹

Thus, efforts to change clinician prescribing behaviors for medications with potential for misuse, such as opioids, psychotropics and antibiotics, have had limited success, largely owing to the dependence on conventional approaches such as guidelines, continuing medical education and incentives.^15,20,21 Behavioral science, especially the use of behavioral economic strategies, has gained recognition as an effective means to improve decision-making to overcome these limitations.^22,23 Such strategies, known as “nudges,” are promising cost-effective mechanisms to positively influence prescriber behavior and promote safe and effective medication use without compromising autonomy of choice.^24,25 The concept of nudging was introduced in the landmark publication Nudge: improving decisions about health, wealth, and happiness by Richard Thaler and Cass Sunstein,²⁶ and presents a strategic approach to decision-making. Described as “libertarian paternalism,” nudging upholds individual liberty to choose while subtly steering choices toward those that enhance well-being. This concept highlights two modes of thinking: the automatic system and the reflective system. These cognitive modes have been referred to as System 1 and System 2 by the cognitive scientist Daniel Kahneman.²⁷ System 1 thinking involves rapid, intuitive decision-making. System 2 thinking is characterized by slower, more deliberate and effortful cognitive processes.

In several countries, nudging has rapidly become a prominent approach recognized for its innovation and effectiveness in guiding people toward making choices that bolster health and well-being,²⁸ with extensive and varied applications spanning diverse sectors, such as financial markets,²⁹ education policies³⁰ and healthcare.³¹ Most research on nudging strategies in healthcare has focused on modifying patient behavior (e.g., reminders to encourage vaccinations, enhancement of malaria testing and self-management of chronic diseases).^32–35 Nudging strategies specifically targeted toward healthcare professionals (HCPs) have been used to increase guideline adherence.³⁶ However, the effect of nudging on opioid prescription practices remains less clear. The aim of this systematic scoping review was to map the evidence in the literature for the use of various nudging strategies to influence prescriber behavior toward reducing opioid prescriptions across diverse healthcare settings.

Method

Objectives

This systematic scoping review was conducted in accordance with the Joanna Briggs Institute methodological framework.^37,38 It adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR).³⁹ In error, we did not prospectively register this scoping review, but we have registered it retrospectively at INPLASY (https://inplasy.com; registration number 202420082).

The aim of the review was to provide a comprehensive overview of the available evidence on the reported outcomes of various nudging strategies in influencing prescriber behavior toward reducing opioid prescriptions across diverse healthcare settings. As this was a scoping review, we did not focus on the quality of the reviewed studies.³⁷ Scoping reviews are particularly suitable for exploring broader topics by synthesizing studies that use various designs and methodologies.⁴⁰

The following research questions were addressed in this scoping review:

What nudging strategies have been used in healthcare settings to influence prescribers’ behavior toward reducing opioid prescriptions?

What outcomes have been reported for these nudging strategies in reducing the number of opioid prescriptions among HCPs?

Search strategy and study selection

A literature search was conducted in April 2022 by an information specialist at a university library. Seven electronic databases were searched: PubMed, PsycINFO, SocIndex, CINAHL, Cochrane Library, Web of Science and Embase. The search terms used included “nudging,” “nudges,” and “nudge,” with related terms such as “behavioral economics” and “choice architecture,” combined with variants of “opioids” or “analgesics,” and terms related to “health care professionals,” “clinicians,” or “prescribers.” The complete search strategy is provided as supplementary material (Supplementary File 1. Search Strategy).

Studies were selected for inclusion based on several criteria: (a) interventions carried out in clinical settings aimed at prescribers; (b) behavioral interventions described as nudging; (c) studies with a randomized controlled trial, quasi-experimental or longitudinal (before–after) design; and (d) original research articles written in English and published in peer-reviewed journals. No date restrictions were applied to ensure comprehensive coverage.

Screening and selection process

At the initial stage of the screening and selection process, duplicate articles were removed. Subsequently, two independent researchers used the Rayyan QCRI systematic review software tool to conduct a blinded review of titles and abstracts for potential inclusion.⁴¹ The blind was then lifted, any discrepancies between the reviewers were resolved through discussion to achieve consensus and the same two researchers independently assessed the full texts using the same systematic review tool, consulting a third team member as necessary to reach a consensus in case of disagreements.

Data charting

The two primary reviewers collaboratively developed a data-charting form to identify key variables for extraction to systematically capture and organize the study data. Independently, each reviewer charted the data and refined the results through discussion. This data-charting form was subjected to continuous updates throughout an iterative review process.^37,40,42

The extracted data encompassed various study characteristics, including (a) author(s), (b) year of publication, (c) objectives and aims of the study, (d) population and sample size, (e) design of the study, (f) underlying logic of the effect, (g) types of nudges applied, (h) implementation method (e.g., physical or digital platforms), (i) specific details of implementation (e.g., clinical settings, hospitals, emergency departments), (j) geographic location of the study, (k) duration of the study and (l) primary outcomes and findings. To further analyze the data, we created a coding system that summarized the primary outcomes and findings according to their significance. Each outcome was categorized as significant, mixed, or non-significant. We calculated the number and percentage of studies that reported significant, mixed, or non-significant results for each type of nudging strategy. These results were presented descriptively in terms of numbers and percentages to obtain a clear summary of the findings.

Classification of nudge-based strategies

The strategies used in the included studies were systematically classified based on the categorization established in the seminal work of Thaler and Sunstein,²⁶ and reported in earlier studies.^36,43 These nudge categories encompass a range of implementation techniques that are accessible to choice architects, including increasing the salience of information or incentives, understanding mapping, default choices, providing feedback, error reduction and structuring complex choices.^26,44

The increasing salience strategy focuses on making information or incentives associated with decision alternatives more noticeable and prominent; for example, through the strategic use of text, color, animation or alerts. These techniques increase the salience of particular decision options, making them more likely to be chosen. Understanding mapping involves simplifying the understanding of the connections between choices and their potential outcomes, often through the use of visual aids like flowcharts or decision trees. These aids can effectively summarize critical guidelines for various therapeutic approaches and practices. The default choices strategy leverages the human tendency to follow the path of least resistance by setting the most beneficial option as the default. In practical terms, this may mean that physicians are automatically enrolled in an opioid default order set unless they choose to opt out. Providing feedback involves informing decision-makers of the outcomes of their behaviors. This practice could manifest as systems that give physicians feedback on their opioid prescribing rates relative to their peers, fostering an environment of informed decision-making and peer comparison. Error reduction incorporates prompts and “forcing functions” to reduce the frequency of common mistakes. Structuring complex choices involves breaking down choices and their attributes into clear, distinct categories, which helps in situations involving simultaneous choices. For example, structuring opioid prescribing guidelines by categorizing pain severity, opioid potency and risk of dependency enables physicians to make informed and responsible decisions.⁴⁴

Drawing on the definitions of System 1 and System 2,^26,27 the nudging interventions were further categorized according to two independent dimensions to create four quadrants, as described in the scientific literature^45,46 and illustrated in Figure 1. These dimensions are:

Figure 1.

Illustrates a two-dimensional view of nudging.

Synchronous vs. Asynchronous: A strategy is defined as synchronous when its implementation aligns with the timing of the decision or behavior it aims to influence. In contrast, an asynchronous strategy is not bound by specific timing and can be executed at any moment.

Active vs. Passive: An intervention is considered active if it necessitates direct action from the targeted clinician for its completion. Conversely, a passive strategy does not require any action from the clinician.

Thus, asynchronous and passive strategies are characterized by their independence from the clinician’s actions and timing. In contrast, synchronous and active strategies demand the clinician’s involvement concurrent with the presentation of the nudge.

Results

Study selection

From the initial database search, 2234 articles were retrieved. After removing duplicates, 2175 articles underwent relevance assessment. Further screening led to the evaluation of 103 full-text articles for eligibility and resulted in the exclusion of 68 articles for various reasons (37 were unrelated to nudging or opioids, 12 were abstract-only articles, 7 were study protocols, 10 were excluded based on type of outcome (e.g., outcomes focusing on general interventions for pain management, medication alerts without nudging components, or only educational programs), 1 was excluded based on publication type (commentary) and 1 was identified as an duplicate). Consequently, 35 articles were included in the review; the selection process is shown in Figure 2.

Figure 2.

PRISMA-ScR flow chart. PRISMA-ScR, Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

General study characteristics

Table 1 provides a descriptive summary of the characteristics of the 35 studies included in the review; the entire dataset can be found in Supplementary File 2. Dataset, which provides a more comprehensive description of the different interventions. Most of the studies (n = 23) were conducted after 2020; 32 were conducted in the USA, and 1 study each was reported from Finland, the UK and Argentina. The research settings were diverse and encompassed various HCPs (including physicians, physician assistants, nurses and nurse practitioners). Their distribution included emergency departments (n = 11), academic environments (n = 6), ambulatory care (n = 5), primary care (n = 4) and tertiary centers (n = 3). Two studies were conducted in health centers, and the remainder were conducted across diverse locations, such as multisite hospital healthcare systems, outpatient clinics, maternity units and regional health services. Among the included studies, 34 targeted prescriptions or orders and 1 targeted procedural aspects. The studies analyzed used a wide range of study designs; the most common design was quality improvement studies (n = 6), followed by retrospective observational studies (n = 2). The study duration varied substantially from 1 month to 10 years and 5 months. Furthermore, 32 studies used electronic health records (EHRs) as an implementation method; of these, 5 used EHRs in combination with other modes of delivery, such as informational or educational handouts and seminars and meetings. The remaining three studies used letters and media, staff meetings and email messages.

Table 1.

Characteristics of the articles included in the review.

Author/year	Country	Study design	Study duration	Objective	Population/sample size	Setting	Nudging type(s)	Nudging method	P-valueSignificance
Ahomäki et al., 2020	Finland	Registry study (pharmacy records). Controlled, before–after	1.5 years	Prescriptions/orders	4,357 physicians who had issued a prescription for 100 tablets or more of paracetamol-codeine. 146,178 new patient paracetamol-codeine purchases	Letter sent to physician’s home address (primary care physicians in urban, semi-urban or rural municipalities)	PF, IS	Letter, media	Sign.
Alderson et al., 2021	UK	A quasi-experimental controlled interrupted time series	1 year	Prescriptions/orders	Primary care providers serve 3.2 million residents	Primary practices	PF, IS	Electronic reports letter	Sign.
Alley et al., 2022	USA	Retrospective quality improvement study	Interrupted time series, pre- and post-February 2016	Prescriptions/orders	Prescribers, 5,826 records of uncomplicated vaginal deliveries	TCC and teaching hospital	DF, ER	EHR order sets	Sign.
Ancker et al., 2021	USA	Pre–post registry study	2 years, 9 months	Prescriptions/orders	821 family practice physicians and advanced practice nurses serving 22,113 patients	Academic medical center and federally qualified health center	DF	EHR order sets	Sign.
Anderson et al., 2015	USA	Pre/postimplementation evaluation	2 years	Prescriptions/orders	Providers serving 40,629 patients	Multisite federally qualified health center	PF, UM	EHR	Sign.
Bachhuber et al., 2021	USA	Cluster RCT	2 years	Prescriptions/orders	490 prescribers serving 21,331 patients	Primary care and ED	DF	EHR	Sign.
Bernstein et al., 2022	USA	Quality improvement study	3 years	Prescriptions/orders	Urologic oncology attendings, inpatient nurses and post-operative teams serving 1,295 patients	TCC	DF, UM, ER +Education	EHR order sets	Sign.
Bestha et al., 2022	USA	Retrospective observational study	3 years	Prescriptions/orders	Prescribers seeing 3,121 patients with a diagnosis of PTSD. 37,443 encounters	Urgent and emergency care, inpatient, and ambulatory care settings	IS	EHR	Sign.
Bryl et al., 2021	USA	Quality improvement study	1 year, 7 months	Prescriptions/orders	220 primary care and 370 specialty physicians	ED	IS, DF, PF + Guidelines and education	EHR order sets, handouts, information at seminars, meetings, email	N/A
Carlson et al., 2021	USA	Retrospective review, before and after	10 years, 5 months	Prescriptions/orders	Attending physicians, resident physicians, nurse practitioners, and physician assistants serving 125,000 patients	ED	DF	EHR	Sign.
Chiu et al., 2018	USA	Pre–post intervention study	6 months	Prescriptions/orders	Prescribers. 11,447 procedures of 1 of the 10 most common operations	Multihospital healthcare system	DF	EHR	Sign.
Chua et al., 2022	USA	Retrospective study	2 years, 7 months	Prescriptions/orders	Prescribers serving 1,107 patients	Tertiary care children’s hospital	DF, SC	EHR order sets	Mixed (partly sign.) for primary outcome*
Guarisco & Salup, 2018	USA	Quality improvement study	1 year	Prescriptions/orders	Prescribers, sample size not available	ED	IS, PF + Education	EHR, handouts	Sign.
Gugelmann et al., 2013	USA	Prospective study	8 months	Prescriptions/orders	Nurses, residents, nurse practitioners, and attending physicians serving a total of 116,655 patients	ED	IS, SC + Education	EHR order sets, handouts, lectures	Sign.
Gupta, 2020	USA	Quality improvement study	3 years	Procedure	165 primary care providers	Outpatient clinics	IS, PF, ER + Education	EHR order sets, handouts, information at seminars, meetings	Sign.
Jones et al., 2021	USA	Quality improvement study	2 years, 4 months	Prescriptions/orders	Providers, sample size not available	Academic children’s hospital	IS, DF, PF + Guidelines and education	EHR order sets	Sign.
Kim et al., 2017	USA	Before-and-after intervention study	8 months	Prescriptions/orders	Physicians serving 1,946 unique patients	Academic ED	DF	EHR order sets	Sign.
Landau et al., 2021	USA	Retrospective cohort study	Unclear, however not less than 7 months	Prescriptions/orders	Obstetric and anesthesia providers serving 3,453 cesarean deliveries	Labor and delivery units in academic hospitals	IS, DF + Education	EHR order sets	Sign.
Liu et al., 2021	USA	Prospective cohort study	1 year	Prescriptions/orders	Surgeons serving 934 patients	Post-ambulatory surgery pain management at a rural hospital	IS, PF + Guidelines and education	Information shared at staff meetings, hand-over meetings	Sign.
Lowenstein et al., 2020	USA	Difference-in-differences study	1 year, 10 months	Prescriptions/orders	Prescribers, 5,316 new prescriptions in total	Ambulatory practices	IS, ER	Prompt in the EHR	Sign.
Luk et al., 2016	USA	Case series with chart review	2 years, 11 months	Prescriptions/orders	Physicians serving 437 patients	Ambulatory care within a health maintenance organization	DF, ER	EHR order sets	Sign.
Meisenberg et al., 2018	USA	Quality improvement study	1 year, 10 months	Prescriptions/orders	Physicians, sample size not available	Regional health system including an acute care hospital, same-day surgery, and outpatient clinics. ED, primary care, and the orthopedic clinic	IS, DF, PF + Education	EHR order sets and in-person notification	Sign.
Mittal et al., 2020	USA	Quality improvement cohort study	3 years	Prescriptions/orders	Physicians and prescribing physician, nurses and physician assistants serving 6,684 consecutive outpatient urologic cases	Tertiary children’s hospital	IS, DF, PF, ER + Education	EHR redesign order sets	Sign.
Montoy et al., 2020	USA	RCT	8 months	Prescriptions/orders	104 physicians, nurse practitioners, and physician assistants writing 4,320 opioid prescriptions	ED	DF	EHR order sets	Not sign.
Nanji et al., 2019	USA	Retrospective, before-and-after, clinical practice	11 months	Prescriptions/orders	Maternity unit nurses serving 1,050 women	Maternity unit at an academic tertiary obstetric service	IS, DF + Education	EHR order sets	Not sign.
Navarro et al., 2022	USA	Ecological study	2.5 years	Prescriptions/orders	39 orthopedic surgeons, 872 shoulder arthroplasties performed on 840 patients	Integrated healthcare system	IS, DF, PF + Education	EHR order sets	Sign.
Rathlev et al., 2016	USA	Randomized, non-blinded, two-group parallel design study	2 years	Prescriptions/orders	Primary care physicians serving 40 patients	ED at academic medical center	IS	Alerts in the EHR	Sign.
Rogers et al., 2019	USA	Cohort study	1 year, 10 months	Prescriptions/orders	Healthcare providers serving 6,892 women who delivered	Large healthcare system	IS, DF, SC, ER	EHR order sets	Sign.
Santistevan et al., 2018	USA	Retrospective observational study	1 year, 10 months	Prescriptions/orders	Clinicians, prescribing opioids to 4,104 adult patients at discharge	Single academic, urban ED	DF	EHR order sets	Sign.
Schapira et al., 2021	Argentina	before/after, single-center, open-label study	2 years	Prescriptions/orders	140 primary care physicians overseeing 879 patients	Ambulatory setting of an integrated healthcare network	DF, IS + Education	EHR order sets	Sign.
Sigal et al., 2021	USA	Observational study	1.5 years	Prescriptions/orders	Emergency physicians and advanced practice providers. 117,776 opioid-eligible discharges of which 15,549 discharges included an opioid prescription	ED	IS, DF, SC + Education	EHR order sets	Sign.
Smalley et al., 2020	USA	Retrospective multicenter study	1 year	Prescriptions/orders	361 physicians and physician assistants serving 80,209 patients	ED	DF, PF	EHR order sets	Sign.
Suffoletto & Landau, 2020	USA	Pilot RCT	1 month	Prescriptions/orders	37 emergency medicine providers from 16 hospitals prescribing opioids at least 10 times	ED	PF	Email messages	Not sign.
Wang et al., 2021	USA	Before and after, ongoing quality improvement initiative	1 year, 4 months	Prescriptions/orders	18 providers, 5 clinical sites, and a patient volume of approximately 25,000 visits per year	Academic setting	IS, PF + Guidelines and education	EHR order sets	Sign.
Zivin et al., 2019	USA	Mixed-methods pilot study	3 months	Prescriptions/orders	448 prescribers, 6,390 opioid prescriptions	Health centers	DF, IS	EHR order sets	Not sign.

The primary outcome of interest in this study was the effect on opioid prescriptions. There was a significant reduction in the median number of doses prescribed and high-dose prescriptions (30+ doses), but no significant change in the overall opioid prescribing rates.

DF, default choices; ED, emergency department; EHR, electronic health record; ER, error reduction; ICU, intensive care unit; IS, increasing salience of information or incentives; PCC, primary care clinic; PF, providing feedback; PTSD, posttraumatic stress disorder; RCT, randomized controlled trial; SC, structuring complex choices; TCC, tertiary care center; UM, understanding mapping; N/A, not available; + non-nudge component.

Classification of nudge strategies to influence prescribers’ behavior toward reducing opioid prescriptions

The nudge strategies used as interventions in the included studies were categorized according to the classification by Thaler and Sunstein²⁶ as well as other scientific literature^36,43 using the above-mentioned nudge categories of increasing the salience of information or incentives, understanding mapping, default choices, providing feedback, error reduction, and structuring complex choices. The present review showed that the included studies used all six nudging strategies to influence prescriber behavior toward reducing opioid prescriptions in diverse healthcare settings. Furthermore, 25 of the 35 studies used a combination of nudge strategies (e.g., increasing salience combined default choices or providing feedback),^47–71 and 15 of these were combined with non-nudge components such as guidelines and educational features.^{51,52,54–58,61–64,66,67,69,71} Ten studies used the various nudge strategies as a single-component intervention.^72–81

The most prevalent nudge strategy used was default choices (n = 23). Through the design of default options presented to decision-makers, this strategy allows choice architects to subtly influence prescribers. Default choices was the sole strategy in 7 studies (i.e., a single-component intervention),^{72,73,75–78,80} and was used in combination with other interventions (i.e., multicomponent interventions) in 16 studies,^{49,51–53,56,57,60–68,70} 10 of which also involved non-nudge components (guidelines and education).^{51,52,56,57,61–64,66,67}

Increasing the salience of information was another frequently explored strategy aimed at increasing the prominence of information or incentives to guide the subject’s attention. This strategy was used in 21 studies,^{47,48,52,54–59,61–67,69–71,74,79} 14 of which incorporated non-nudge components (guidelines and education).^{52,54–58,61–64,66,67,69,71} Increasing salience was used as the sole strategy in only two studies.^74,79

Providing feedback, the strategy of providing information on behavioral outcomes to individuals, was also examined. Providing feedback was used in 14 studies,^{47,48,50,52,54–56,58,61,62,64,68,69,81} 9 of which incorporated non-nudge components (guidelines and education).^{52,54–56,58,61,62,64,69} Only one study used this strategy as the sole strategy.⁸¹

In contrast, understanding mapping, error reduction, and structuring complex choices were used less, and never as the sole strategy. Error reduction featured in seven studies,^{49,51,55,59,60,62,65} of which three involved non-nudge components (guidelines and education).^51,55,62 Structuring complex choices was paired with other nudges in four studies,^53,65,67,71 and two of these studies incorporated education as a non-nudge component.^67,71 Understanding mapping was paired with other nudges in two studies,^50,51 one of which incorporated education as a non-nudge component.⁵¹

Reported outcomes based on nudge strategies

Twenty-nine of 35 studies reported significant results,^{47–51,54–62,64–69,71–77,79,80} 4 reported non-significant results,^63,70,78,81 1 reported mixed results⁵³ and 1 reported insufficient information.⁵² Detailed information about the results of the study interventions can be found in the findings section in Supplementary File 2. Dataset. Table 2 shows an aggregation of the reported outcomes for the nudge strategies according to the nudge strategy or strategies used (i.e., default choices, providing feedback, or both) and the objective (i.e., prescriptions/orders or procedure) and the setting (i.e., emergency department, urgent and emergency care). Of note, a nudge strategy could be classified under different categories. For instance, an intervention that used a combination of default choices and providing feedback would appear in both the “default choices in combination” and “providing feedback in combination” categories.

Table 2.

Aggregation of reported outcomes for nudging strategies based on strategy type, objective and setting.

Nudge strategy	Objective	Setting	Sign. (N) %	Mixed results* (N) %	Not sign. (N) %	N/A (N) %
DF	Prescriptions/orders	Diverse settings	(6/7) 85.71%	–	(1/7) 14.28%	–
DF in combination	Prescriptions/orders	Diverse settings	(12/16) 75%	(1/16) 6.25%	(2/16) 12.50%	(1/16) 6.25%
IS	Prescriptions/orders	Urgent and emergency care & academic ED	(2/2) 100%	–	–	–
IS in combination	Prescriptions/ordersProcedure	Diverse settings	(16/19) 84.21%	–	(2/19) 10.52%	(1/19) 5.26%
PF	Prescriptions/orders	ED	–	–	(1) 100%	–
PF in combination	Prescriptions/orders	Diverse settings	(12/13) 92.30%	–	–	(1/13) 7.69%
Other choice architecture strategies: UM, ER and SC in combination	Prescriptions/orders	Diverse settings	(10/11) 90.90%	(1/11) 9.09%	–	–

Mixed (partly sign.) on the primary.

DF, default choices; ED, emergency department; ER, error reduction; IS, increasing salience of information or incentives; PF, providing feedback; SC, structuring complex choices; UM, understanding mapping; (N), number of studies; N/A, not available.

Among the 23 studies that used default choices as a choice architecture strategy to influence prescriber behavior toward reducing opioid prescriptions in diverse healthcare settings, 18 reported significant results,^{49,51,56,57,60–62,64–68,72,73,75–77,80} either for default choices as a sole strategy or in combination with other interventions. However, one study reported mixed results,⁵³ three reported non-significant results^63,70,78 and one lacked information.⁵² When combined with other interventions, the default choices strategy yielded significant results in 75% of the studies, whereas when used as the sole strategy, significance was reported in 85.71% of the studies.

Moreover, 18 of the 21 studies that implemented the increasing salience strategy reported significant results in diverse settings.^{47,48,54–59,61,62,64–67,69,71,74,79} Two studies reported non-significant results^63,70 and one reported insufficient information.⁵² Increasing salience was used as the sole nudge strategy in two studies, both of which reported significant results.^74,79 A total of 84.21% of the studies that combined increasing salience with other interventions reported significant results.

Among the 14 studies that implemented providing feedback,^{47,48,50,52,54–56,58,61,62,64,68,69,81} only 1 used it as a sole strategy, and that study reported no significant results.⁸¹ When providing feedback was used with other interventions, 92.30% of the studies reported significant results, and one study lacked information.⁵²

The understanding mapping, error reduction, and structuring complex choices strategies were used less and never as the sole choice. These strategies yielded significant results in 90.90% of the studies.^{49–51,53,59,60,62,65,67,71} One study reported mixed results.⁵³

Two-dimensional view of nudging

Among the 35 studies analyzed, 8 specifically targeted the automatic system (System 1) in their nudging interventions,^{70,72–74,77–80} whereas 5 targeted the reflective system (System 2).^{47,48,55,57,81} A substantial proportion of studies (n = 22) used a hybrid strategy that combined nudges designed to affect Systems 1 and 2 in their interventional approaches.^{49–54,56,57,59–69,71,75,76}

Figure 3 illustrates how the included studies fit into a two-dimensional view of nudging (constructed using the definitions of Systems 1 and 2). Most studies (n = 14) were based on synchronous and passive interventions,^{49,53,60,68,70,72–80} indicating that the nudge strategy or strategies used occurred simultaneously with the clinician’s decision-making process but did not necessitate their active engagement. Four studies investigated synchronous and active interventions,^51,59,63,65 which were concomitant with the clinician’s decision-making process and required their direct involvement. Eight studies examined asynchronous and passive interventions independent of clinical decision-making and did not require active HCP participation.^{47,48,50,54,55,66,69,81} Six studies examined passive interventions that offered flexibility in timing (synchronous and asynchronous).^{52,56,57,64,68,71} Three studies^61,62,67 used a combination of synchronous, asynchronous, active and passive interventions, demonstrating various applications.²²

Figure 3.

How included studies fit into a two-dimensional view of nudging.

Nudge strategies with reported significant results in a two-dimensional view of nudging

Figure 4 shows how nudge strategies that demonstrated significant results fit into a two-dimensional view of nudging. Among the 29 studies that reported significant results, 11 used a synchronous and passive approach.^{49,55,60,72–77,79,80} This finding demonstrates that although these nudge strategies were aligned with the clinicians’ decision-making process, they did not require any active engagement. The most prevalent nudge strategy was default choices, which was used in six of these studies. Seven studies used an asynchronous and passive approach^{47,48,50,54,58,66,69} that did not align with the decision-making situation and did not require any active engagement; five of these used a combination of providing feedback and increasing salience.

Figure 4.

How nudge strategies that demonstrated significant results fit into a two-dimensional view of nudging.

In contrast, three studies used a synchronous and active approach^51,59,65 that was concomitant with the clinician’s decision-making process and required their direct involvement. Five studies used a passive approach that offered flexibility in timing (synchronous and asynchronous).^{56,57,64,68,71} Three studies used a versatile approach that was both synchronous and asynchronous, and both active and passive.^61,62,67 None of the studies used an active and asynchronous approach.

Discussion

The present review showed that researchers have studied all six nudge strategies identified in the Thaler and Sunstein framework²⁶ to influence prescriber behavior toward reducing opioid prescriptions. In decreasing order, the frequency of interventions used was default choices, increasing the salience of information or incentives, providing feedback, error reduction, structuring complex choices, and understanding mapping. We found compelling evidence for the efficacy of nudging strategies and identified substantial variation in the application of these strategies in diverse healthcare settings, which reflects the complexity of prescribing practices. These findings highlight the importance of integrating nudge strategies such as default choices, increasing salience and providing feedback into EHR systems to effectively reduce opioid prescriptions and improve patient safety.

It should be noted that most of the evidence for the use of nudge strategies came from studies that focused on adjusting defaults, increasing the salience of information presented and providing feedback strategies, either individually or in combination (all the included studies used increasing salience, providing feedback, and default choices, either individually or in combination with other nudges). These nudges are likely more straightforward to integrate into workflow processes, clinical decision-support systems and EHR applications,³⁶ which facilitates their incorporation into practice.⁸² Furthermore, the preference for default choices, increasing salience and providing feedback strategies in the reviewed studies, which has been noted in other reviews,^36,43 indicates the potential effectiveness of these strategies in diverse settings. In addition, the findings indicate that the effectiveness of error reduction, structuring complex choices, and understanding mapping nudges for influencing behavior remains unconfirmed. These results identify a research opportunity to explore the efficacy of these lesser-studied nudges in influencing prescriber behavior toward reducing opioid prescriptions.

Most of the studies (32/35) included in the present review used the EHR as the main method of intervention delivery. This may limit the generalizability of our findings to settings where such systems are not as robust or uniformly implemented. This technological dependency highlights a digital divide that may exclude certain healthcare environments from benefiting from these interventions. Nonetheless, considering the widespread adoption of digital documentation in current healthcare systems, the use of EHR as an implementation method may reflect an increasing transition toward digitization in clinical environments.⁸³

The most prevalent nudge strategy was default choices, primarily delivered through the EHR. This approach aligns with the principles of choice architecture and indicates that subtle changes in prescribing options can influence decision-making in meaningful ways.²⁶ For example, several studies examined the effect of default choices by changing aspects of the EHR, such as removing the default opioid selection,⁸⁰ reducing the default number of opioid pills from 30 to 12⁷⁶ and using autocompletion to select the frequency and quantity of opioid pills prescribed.⁷² The success of default choice nudges may be attributed to the “default effect” – a cognitive bias that predisposes individuals to choose an option presented as the standard and to frequently perceive divergence from established norms as a potential loss or a choice that may demand additional effort.²⁶ This cognitive bias can be leveraged to steer prescribers toward more conservative prescribing practices without impinging on their autonomy or clinical judgment. However, the use of the default choices strategy raises ethical concerns, particularly the potential to improperly influence HCPs without their knowledge or explicit consent.^28,84–86 The default choices strategy may compromise the prescriber’s autonomy by steering decisions toward a default that may not align with individual preferences. Thus, it is essential to balance the intention to influence prescriber behavior toward reducing opioid prescriptions and guide positive health outcomes with safeguarding HCPs’ autonomy of choice.

Another frequently used nudge strategy was increasing the salience of information or incentives, which was mostly used in combination with default choices and providing feedback and primarily delivered through EHR. This strategy showed a promising trend in influencing prescriber behavior toward reducing opioid prescriptions. For example, some researchers used EHR-based alerts to “push” care plan recommendations to HCPs as prominent visible “pop-up” screens when accessing patient records.⁷⁹ In contrast, other researchers used an active choice to acknowledge an alert, with a recommended prescribed quantity of opioid pills consistent with the law, to either continue with the original order or adjust the prescribed quantity.⁵⁹ The increasing salience strategy emphasizes increasing the visibility and prominence of information or incentives linked to decision alternatives and facilitating specific actions, such as strategic alerts or pop-ups.⁸⁷ However, it is important to consider the issue of alert fatigue. This phenomenon is characterized by cognitive overload caused by excessive alerts, and frequently results in alerts being overridden, thus hindering the prescriber’s ability to recognize the relevance of an alert to the situation.⁸⁸

A relatively high number of studies (25/35) investigated the efficacy of multicomponent interventions, and 15 of these studies combined non-nudge interventions. For example, providing feedback was a frequently adopted strategy that was mostly used as part of multicomponent interventions, and showed a promising trend in influencing prescriber behavior toward reducing opioid prescriptions. As part of multicomponent interventions, providing feedback seemed to outperform other nudge strategies; however, fewer studies used providing feedback as a sole strategy or in combination with other interventions, which could have influenced the results. Providing feedback was used to provide information about behavioral outcomes to influence future behavior; for example, sending a personal information letter on overprescribing practice with recommendations,⁴⁷ giving feedback on prescribing rates and pills with a pocket card created to include the 10 most prescribed opioid pills⁵⁸ and providing feedback on individual deidentified prescribing patterns and an opioid dashboard page within the EHR.⁶⁹

Although many of the reviewed studies used multicomponent interventions, caution is needed in interpreting the superiority of such interventions in influencing prescriber behavior toward reducing opioid prescriptions. As most individual interventions tended to be successful, the use of multicomponent interventions may increase the likelihood of a positive effect. However, this makes it difficult to determine which specific elements of a multicomponent intervention are responsible for its effect. Thus, designing and implementing single-component or multicomponent nudge interventions should be based on organizational characteristics and resource constraints (e.g., financial and time constraints), strategic goals and timelines, and prescribers’ readiness to adopt/implement interventions.³⁶

Furthermore, our analysis demonstrated that the timing and nature of healthcare nudge interventions greatly affect their efficacy. Synchronous nudges are particularly effective in inducing immediate behavioral changes when rapid decisions are required. In contrast, asynchronous nudges are conducive to gradual changes, which makes them ideal for changing entrenched prescribing habits. Figure 4 illustrates this by showing that the most successful nudges were predominantly passive and either synchronous or asynchronous, emphasizing their smooth incorporation into clinicians’ workflows without requiring active engagement. This trend indicates a tendency to favor subtle nudges that support, rather than disrupt, medical practice.⁸² However, the passive nature of these nudges, which operate subtly or “under the radar,” raises ethical concerns about autonomy and informed consent. The challenge lies in navigating the fine line between beneficially guiding behavior and unduly manipulating choices without clear consent. The nuanced influence of such nudges warrants a critical examination of the transparency and informed nature of clinicians’ decision-making processes. Future research should explore these ethical dimensions by assessing how to balance the benefits of nudges with respect for prescriber autonomy to ensure that interventions remain transparent and informative.^28,84–86 Whereas passive nudges are subtly effective, the role of active nudges (those that require clinicians to engage more directly) should not be overlooked as these may induce more conscious changes to clinical practices. The variety of effective nudge strategies highlights the importance of customizing approaches while considering the timing and extent of clinician involvement to maximize the effect of interventions.

Weighing these interventional outcomes against the realities of patient care is essential. Rogers et al. present a cautionary case of how a successful reduction in postpartum opioid prescriptions was associated with a concurrent reduction in acceptable levels of pain management.⁶⁵ Therefore, although nudges may successfully influence prescriber behaviors, they must be balanced with the need to maintain patient-centered care to ensure that any reduction in opioid prescriptions does not negatively affect pain management. Widespread adoption of these nudge strategies could lead to a reduction in opioid misuse and dependency, ultimately alleviating the public health burden.

Limitations

There were several study limitations. Our restriction to English language studies likely excluded relevant research conducted in other languages, which may have provided insights from a wider range of countries. In addition, most of the included studies did not isolate individual nudge interventions but examined them within the context of multicomponent strategies. This approach complicates the independent evaluation of each nudge type. Moreover, our exclusive focus on nudging as a behavioral intervention may have overlooked a wide range of other methods aimed at influencing prescriber behavior toward reducing opioid prescriptions. Despite the numerous behavioral change theories applicable to healthcare, our inquiry was specifically tailored to map nudging strategies, as described by Thaler and Sunstein.²⁶ Furthermore, we did not conduct a formal risk of bias assessment, which is a common limitation in scoping reviews. This omission highlights the need for additional research to include more rigorous quality assessments and provide a comprehensive evaluation of the evidence.

Another limitation is that the geographical distribution of the reviewed studies was skewed; 32 of the 35 studies were conducted in the USA. This concentration indicates a contextual bias that may affect the generalizability of our findings. Although the consistency of healthcare systems within the USA may yield reliable data on nudge strategy efficacy, these data may not reflect the diversity of global healthcare practices. Given that opioid prescription rates remain high in other regions as well,^1–4 there is a need for broader research in various international contexts that encompasses diverse healthcare environments to fully understand the universal applicability of nudge interventions in influencing prescriber behavior toward reducing opioid prescriptions.

Conclusions

There is compelling evidence for the efficacy of nudging strategies for influencing prescriber behavior toward reducing opioid prescriptions. Moreover, there is substantial variation in how these strategies have been applied in diverse healthcare settings, reflecting the complexity of prescribing practices. However, most of this evidence relies on studies that predominantly focused on default choices, increasing salience and providing feedback strategies individually or in combination. Although nudges may successfully influence prescriber behavior, they must be balanced with the need to maintain patient-centered care to ensure that any reduction in opioid prescriptions does not negatively affect pain management. Furthermore, most nudging strategies used a passive approach, which required no active prescriber choice. Although effective, this approach often operates under the prescriber’s radar. Future research should explore the ethical implications and the efficacy of nudging strategies that target healthcare providers and patients/relatives.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605241272733 - Supplemental material for Nudging strategies to influence prescribers’ behavior toward reducing opioid prescriptions: a systematic scoping review

Supplemental material, sj-pdf-1-imr-10.1177_03000605241272733 for Nudging strategies to influence prescribers’ behavior toward reducing opioid prescriptions: a systematic scoping review by Salwan Diwan, Andreas Vilhelmsson, Axel Wolf and Pether Jildenstål in Journal of International Medical Research

Supplemental Material

sj-pdf-2-imr-10.1177_03000605241272733 - Supplemental material for Nudging strategies to influence prescribers’ behavior toward reducing opioid prescriptions: a systematic scoping review

Supplemental material, sj-pdf-2-imr-10.1177_03000605241272733 for Nudging strategies to influence prescribers’ behavior toward reducing opioid prescriptions: a systematic scoping review by Salwan Diwan, Andreas Vilhelmsson, Axel Wolf and Pether Jildenstål in Journal of International Medical Research

Footnotes

Acknowledgements

The authors acknowledge Krister Aronsson (Lund University) for developing search strategies and conducting the searches and Editage () for English language editing.

Author contributions

Conceptualization and methodology: AV, AW and SD. Data collection: AV and SD. Formal analysis and writing—original draft preparation: SD and AV. Writing—review and editing: SD, AV, AW and PJ. Supervision: PJ. All authors have read and agreed to the published version of the manuscript.

Declaration of conflicting interest

The authors declare that there is no conflict of interest.

Funding

This study was funded by the Swedish Research Council (project 2021-01166) and by the University of Gothenburg Centre for Person-Centred Care (GPCC), Sweden. The GPCC is funded by a grant from the Swedish Government for Strategic Research Areas (Care Sciences) and the University of Gothenburg, Sweden.

ORCID iDs

Salwan Diwan

Pether Jildenstål

Supplemental material

Supplemental material for this article is available online.

References

Berterame

Erthal

Thomas

, et al. Use of and barriers to access to opioid analgesics: a worldwide, regional, and national study. Lancet 2016; 387: 1644–1656. DOI: 10.1016/S0140-6736(16)00161-6.

Jani

Birlie Yimer

Sheppard

, et al. Time trends and prescribing patterns of opioid drugs in UK primary care patients with non-cancer pain: a retrospective cohort study. PLOS Med 2020; 17: e1003270. DOI: 10.1371/journal.pmed.1003270.

Schieber

Guy

Seth

, et al. Trends and patterns of geographic variation in opioid prescribing practices by state, United States, 2006-2017. JAMA Netw Open 2019; 2: e190665. DOI: 10.1001/jamanetworkopen.2019.0665.

Verhamme

KMC

Bohnen

AM.

Are we facing an opioid crisis in Europe?

Lancet Public Health 2019; 4: e483–e484. DOI: 10.1016/s2468-2667(19)30156-2.

Merrill

Von Korff

Banta-Green

, et al. Prescribed opioid difficulties, depression and opioid dose among chronic opioid therapy patients. Gen Hosp Psychiatry 2012; 34: 581–587. DOI: 10.1016/j.genhosppsych.2012.06.018.

Services

Results from the 2013 national survey on drug use and health: summary of national findings. Report no. (SMA) 14-4863, 2014. Department of Health and Human Services.

Wan

Corman

Gao

, et al. Economic burden of opioid-induced constipation among long-term opioid users with noncancer pain. Am Health Drug Benefits 2015; 8: 93–102.

Ray

Chung

Murray

, et al. Prescription of long-acting opioids and mortality in patients with chronic noncancer pain. Jama 2016; 315: 2415–2423. DOI: 10.1001/jama.2016.7789.

Bhamb

Brown

Hariharan

, et al. Survey of select practice behaviors by primary care physicians on the use of opioids for chronic pain. Curr Med Res Opin 2006; 22: 1859–1865. DOI: 10.1185/030079906x132398.

10.

Morasco

Yarborough

Smith

, et al. Higher prescription opioid dose is associated with worse patient-reported pain outcomes and more health care utilization. J Pain 2017; 18: 437–445. 20161218. DOI: 10.1016/j.jpain.2016.12.004.

11.

Bicket

Long

Pronovost

, et al. Prescription opioid analgesics commonly unused after surgery. JAMA Surg 2017; 152: 1066–1071. DOI: 10.1001/jamasurg.2017.0831.

12.

Wei

Man

KKC

, et al. Global, regional, and national trends in opioid analgesic consumption from 2015 to 2019: a longitudinal study. Lancet Public Health 2022; 7: e335–e346. DOI: 10.1016/s2468-2667(22)00013-5.

13.

Jayawardana

Forman

Johnston-Webber

, et al. Global consumption of prescription opioid analgesics between 2009-2019: a country-level observational study. EClinicalMedicine 2021; 42: 101198. 20211113. DOI: 10.1016/j.eclinm.2021.101198.

14.

Richards

Aronson

MacKenna

, et al. Sales of over-the-counter products containing codeine in 31 countries, 2013-2019: a retrospective observational study. Drug Saf 2022; 45: 237–247. 20220122. DOI: 10.1007/s40264-021-01143-2.

15.

Shanthanna

Ladha

Kehlet

, et al. Perioperative opioid administration. Anesthesiology 2021; 134: 645–659. DOI: 10.1097/aln.0000000000003572.

16.

Schirle

Stone

Morris

, et al. Leftover opioids following adult surgical procedures: a systematic review and meta-analysis. Syst Rev 2020; 9: 139. DOI: 10.1186/s13643-020-01393-8.

17.

Potthoff

Rasul

Sniehotta

, et al. The relationship between habit and healthcare professional behaviour in clinical practice: a systematic review and meta-analysis. Health Psychol Rev 2019; 13: 73–90. DOI: 10.1080/17437199.2018.1547119.

18.

Foy

Leaman

McCrorie

, et al. Prescribed opioids in primary care: cross-sectional and longitudinal analyses of influence of patient and practice characteristics. BMJ Open 2016; 6: e01027620160513. DOI: 10.1136/bmjopen-2015-010276.

19.

Hoffmann

Del Mar

Clinicians’ expectations of the benefits and harms of treatments, screening, and tests. JAMA Intern Med 2017; 177: 407–419. DOI: 10.1001/jamainternmed.2016.8254.

20.

Adams

Hastings

Maidment

, et al. Deprescribing psychotropic medicines for behaviours that challenge in people with intellectual disabilities: a systematic review. BMC Psychiatry 2023; 23: 202. DOI: 10.1186/s12888-022-04479-w.

21.

Qureshi

Kroger

Zangwill

, et al. Changes in perceptions of antibiotic stewardship among neonatal intensive care unit providers over the course of a learning collaborative: a prospective, multisite, mixed-methods evaluation. J Perinatol 2024; 44: 62–70. DOI: 10.1038/s41372-023-01823-0.

22.

Avorn

The psychology of clinical decision making - implications for medication use. N Engl J Med 2018; 378: 689–691. DOI: 10.1056/NEJMp1714987.

23.

Faria

Barbieri

Light

, et al. The economics of medicines optimization: policy developments, remaining challenges and research priorities. Br Med Bull 2014; 111: 45–61. DOI: 10.1093/bmb/ldu021.

24.

Lamprell

Tran

Arnolda

, et al. Nudging clinicians: a systematic scoping review of the literature. J Eval Clin Pract 2021; 27: 175–192. DOI: 10.1111/jep.13401.

25.

Talat

Schmidtke

Khanal

, et al. A systematic review of nudge interventions to optimize medication prescribing. Front Pharmacol 2022; 13: 798916. DOI: 10.3389/fphar.2022.798916.

26.

Thaler

Sunstein

CR.

Nudge: improving decisions about health, wealth, and happiness. New Haven and London: Yale University Press, 2008.

27.

Kahneman

Thinking, Fast and Slow. New York: Farrar, Strauss and Giroux, 2011.

28.

Vallgarda

Nudge: a new and better way to improve health?

Health Policy 2012; 104: 200–203. 20111121. DOI: 10.1016/j.healthpol.2011.10.013.

29.

Cai

CW.

Nudging the financial market? A review of the nudge theory. Accounting & Finance 2020; 60: 3341–3365. DOI: 10.1111/acfi.12471.

30.

Damgaard

Nielsen

HS.

Nudging in education. Economics of Education Review 2018; 64: 313–342. DOI: 10.1016/j.econedurev.2018.03.008.

31.

Yoong

Hall

Stacey

, et al. Nudge strategies to improve healthcare providers’ implementation of evidence-based guidelines, policies and practices: a systematic review of trials included within Cochrane systematic reviews. Implement Sci 2020; 15: 50. DOI: 10.1186/s13012-020-01011-0.

32.

Dai

Saccardo

Han

, et al. Behavioural nudges increase COVID-19 vaccinations. Nature 2021; 597: 404–409. DOI: 10.1038/s41586-021-03843-2.

33.

Milkman

Patel

Gandhi

, et al. A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor’s appointment. Proc Natl Acad Sci USA 2021; 118: e2101165118. DOI: 10.1073/pnas.2101165118.

34.

Modrek

Schatzkin

De La Cruz

, et al. SMS messages increase adherence to rapid diagnostic test results among malaria patients: results from a pilot study in Nigeria. Malar J 2014; 13: 69. DOI: 10.1186/1475-2875-13-69.

35.

Mollenkamp

Zeppernick

Schreyogg

The effectiveness of nudges in improving the self-management of patients with chronic diseases: a systematic literature review. Health Policy 2019; 123: 1199–1209. 20191002. DOI: 10.1016/j.healthpol.2019.09.008.

36.

Nwafor

Singh

Collier

, et al. Effectiveness of nudges as a tool to promote adherence to guidelines in healthcare and their organizational implications: a systematic review. Soc Sci Med 2021; 286: 114321. 20210818. DOI: 10.1016/j.socscimed.2021.114321.

37.

Peters

Godfrey

Khalil

, et al. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc 2015; 13: 141–146. DOI: 10.1097/XEB.0000000000000050.

38.

Peters

MDJ

Gc McInerney

Munn

, et al. Chapter 11: Scoping Reviews (2020 version). JBI, 2020.

39.

Tricco

Lillie

Zarin

, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018; 169: 467–473. DOI: 10.7326/m18-0850.

40.

Arksey

O'Malley

Scoping studies: towards a methodological framework. International Journal of Social Research Methodology 2005; 8: 19–32. DOI: 10.1080/1364557032000119616.

41.

Ouzzani

Hammady

Fedorowicz

, et al. Rayyan—a web and mobile app for systematic reviews. Syst Rev 2016; 5: 210. DOI: 10.1186/s13643-016-0384-4.

42.

Lenzen

Daniëls

Van Bokhoven

, et al. Disentangling self-management goal setting and action planning: a scoping review. Plos One 2017; 12: e0188822. DOI: 10.1371/journal.pone.0188822.

43.

Nurchis

Di Pumpo

Perilli

, et al. nudging interventions on alcohol and tobacco consumption in adults: a scoping review of the literature. Int J Environ Res Public Health 2023; 20: 1675. DOI: 10.3390/ijerph20031675.

44.

Thaler

Sunstein

Balz

JP.

Choice architecture. Epub Ahead of Print April 2010; 2. DOI: 10.2139/ssrn.1583509.

45.

Sant'Anna

Vilhelmsson

Wolf

Nudging healthcare professionals in clinical settings: a scoping review of the literature. BMC Health Serv Res 2021; 21: 543. DOI: 10.1186/s12913-021-06496-z.

46.

Vilhelmsson

Sant'Anna

Wolf

Nudging healthcare professionals to improve treatment of COVID-19: a narrative review. BMJ Open Qual 2021; 10: e001522. DOI: 10.1136/bmjoq-2021-001522.

47.

Ahomäki

Pitkänen

Soppi

, et al. Impact of a physician-targeted letter on opioid prescribing. J Health Econ 2020; 72: 102344. DOI: 10.1016/j.jhealeco.2020.102344.

48.

Alderson

Farragher

Willis

, et al. The effects of an evidence- and theory-informed feedback intervention on opioid prescribing for non-cancer pain in primary care: a controlled interrupted time series analysis. PLOS Med 2021; 18: e1003796. DOI: 10.1371/journal.pmed.1003796.

49.

Alley

Cowles

Rangan

, et al. The effect of an automated order on postpartum opioid use after uncomplicated vaginal deliveries. J Womens Health (Larchmt) 2022; 31: 842–847. DOI: 10.1089/jwh.2021.0378.

50.

Anderson

Zlateva

Khatri

, et al. Using health information technology to improve adherence to opioid prescribing guidelines in primary care. Clin J Pain 2015; 31: 573–579. DOI: 10.1097/AJP.0000000000000177.

51.

Bernstein

Nourian

Strother

, et al. Harnessing choice architecture in urologic practice: implementation of an opioid-sparing protocol grounded in cognitive behavioral theory. Urol Oncol 2022; 40: 95–102. DOI: 10.1016/j.urolonc.2021.10.011.

52.

Bryl

Demartinis

Etkin

, et al. Reducing opioid doses prescribed from a pediatric emergency department. Pediatrics 2021; 147: e20201180. DOI: 10.1542/peds.2020-1180.

53.

Chua

Groenewald

Varakitsomboon

, et al. Can use of default dispensing quantities in electronic medical record lower opioid prescribing? Pediatr Emerg Care 2022; 38: e600–e604. DOI: 10.1097/PEC.0000000000002411.

54.

Guarisco

Salup

Reducing opioid prescribing rates in emergency medicine. Ochsner J 2018; 18: 42–45.

55.

Gupta

Lindstrom

Shevatekar

Reducing opioid overprescribing by educating, monitoring and collaborating with clinicians: a quality improvement study. Cureus 2020; 12: e7778. DOI: 10.7759/cureus.7778.

56.

Jones

Engler

Fonte

, et al. Opioid reduction through postoperative pain management in pediatric orthopedic surgery. Pediatrics 2021; 148: DOI: 10.1542/peds.2020-001487.

57.

Landau

Romanelli

Daoud

, et al. Effect of a stepwise opioid-sparing analgesic protocol on in-hospital oxycodone use and discharge prescription after cesarean delivery. Reg Anesth Pain Med 2021; 46: 151–156. DOI: 10.1136/rapm-2020-102007.

58.

Liu

Anderson

Franklin

, et al. Nudging patients and surgeons to change ambulatory surgery pain management: results from an opioid buyback program. Surgery 2021; 170: 485–492. DOI: 10.1016/j.surg.2021.01.016.

59.

Lowenstein

Hossain

Yang

, et al. Impact of a state opioid prescribing limit and electronic medical record alert on opioid prescriptions: a difference-in-differences analysis. J Gen Intern Med 2020; 35: 662–671. DOI: 10.1007/s11606-019-05302-1.

60.

Luk

Mosen

Macarthur

, et al. Implementation of a Pediatric Posttonsillectomy Pain Protocol in a Large Group Practice. Otolaryngol Head Neck Surg 2016; 154: 720–724. DOI: 10.1177/0194599815627810.

61.

Meisenberg

Grover

Campbell

, et al. Assessment of opioid prescribing practices before and after implementation of a health system intervention to reduce opioid overprescribing. JAMA Netw Open 2018; 1: e182908. DOI: 10.1001/jamanetworkopen.2018.2908.

62.

Mittal

Shukla

Sahadev

, et al. Reducing post-operative opioids in children undergoing outpatient urologic surgery: a quality improvement initiative. J Pediatr Urol 2020; 16: 846.e1–846 e847. 20200928. DOI: 10.1016/j.jpurol.2020.09.022.

63.

Nanji

Guo

Riley

, et al. Evaluation of opioid use with split doses of oral opioids in a postcesarean delivery analgesia order set. Obstet Gynecol 2019; 134: 120–127. DOI: 10.1097/AOG.0000000000003305.

64.

Navarro

Lin

Prentice

, et al. System-wide opioid prescription reduction after shoulder arthroplasty. Seminars in Arthroplasty: JSES 2022; 32: 375–381. DOI: 10.1053/j.sart.2021.12.001.

65.

Rogers

Nix

Chipman

, et al. Decreasing opioid use postpartum. Obstet Gynecol 2019; 134: 932–940. DOI: 10.1097/aog.0000000000003512.

66.

Schapira

Calabró

Montero-Odasso

, et al. A multifactorial intervention to lower potentially inappropriate medication use in older adults in Argentina. Aging Clin Exp Res 2021; 33: 3313–3320. DOI: 10.1007/s40520-020-01582-4.

67.

Sigal

Shah

Onderdonk

, et al. Alternatives to opioid education and a prescription drug monitoring program cumulatively decreased outpatient opioid prescriptions. Pain Med 2021; 22: 499–505. DOI: 10.1093/pm/pnaa278.

68.

Smalley

Willner

Muir

, et al. Electronic medical record-based interventions to encourage opioid prescribing best practices in the emergency department. Am J Emerg Med 2020; 38: 1647–1651. 20191014. DOI: 10.1016/j.ajem.2019.158500.

69.

Wang

Helgesen

Johr

, et al. Targeted program in an academic rheumatology practice to improve compliance with opioid prescribing guidelines for the treatment of chronic pain. Arthritis Care Res (Hoboken) 2021; 73: 1425–1429. DOI: 10.1002/acr.24354.

70.

Zivin

White

Chao

, et al. Implementing electronic health record default settings to reduce opioid overprescribing: a pilot study. Pain Med 2019; 20: 103–112. DOI: 10.1093/pm/pnx304.

71.

Gugelmann

Shofer

Meisel

, et al. Multidisciplinary intervention decreases the use of opioid medication discharge packs from 2 urban EDs. Am J Emerg Med 2013; 31: 1343–1348. 20130730. DOI: 10.1016/j.ajem.2013.06.002.

72.

Ancker

Gossey

Nosal

, et al. Effect of an electronic health record “nudge” on opioid prescribing and electronic health record keystrokes in ambulatory care. J Gen Intern Med 2021; 36: 430–437. DOI: 10.1007/s11606-020-06276-1.

73.

Bachhuber

Nash

Southern

, et al. Effect of changing electronic health record opioid analgesic dispense quantity defaults on the quantity prescribed. JAMA Netw Open 2021; 4: e217481. DOI: 10.1001/jamanetworkopen.2021.7481.

74.

Bestha

Tomatsu

Hutcheson

, PRIMUM Groupet al. Impact of an opioid prescribing alert system on patients with posttraumatic stress disorder. Am J Addict 2022; 31: 123–131. DOI: 10.1111/ajad.13261.

75.

Carlson

Nelson

Patel

Longitudinal impact of a pre‐populated default quantity on emergency department opioid prescriptions. J Am Coll Emerg Physicians Open 2021; 2: e12337. DOI: 10.1002/emp2.12337.

76.

Chiu

Jean

Hoag

, et al. Association of lowering default pill counts in electronic medical record systems with postoperative opioid prescribing. JAMA Surg 2018; 153: 1012–1019. DOI: 10.1001/jamasurg.2018.2083.

77.

Kim

Kaplan

Mitchell

, et al. The effect of computerized physician order entry template modifications on the administration of high-risk medications in older adults in the emergency department. Drugs Aging 2017; 34: 793–801. DOI: 10.1007/s40266-017-0489-z.

78.

Montoy

JCC

Coralic

Herring

, et al. Association of default electronic medical record settings with health care professional patterns of opioid prescribing in emergency departments. JAMA Intern Med 2020; 180: 487–493. DOI: 10.1001/jamainternmed.2019.6544.

79.

Rathlev

Almomen

Deutsch

, et al. Randomized controlled trial of electronic care plan alerts and resource utilization by high frequency emergency department users with opioid use disorder. West J Emerg Med 2016; 17: 28–34. DOI: 10.5811/westjem.2015.11.28319.

80.

Santistevan

Sharp

Hamedani

, et al. By default: the effect of prepopulated prescription quantities on opioid prescribing in the emergency department. West J Emerg Med 2018; 19: 392–397. DOI: 10.5811/westjem.2017.10.33798.

81.

Suffoletto

Landau

Nudging emergency care providers to reduce opioid prescribing using peer norm comparison feedback: a pilot randomized trial. Pain Med 2020; 21: 1393–1399. DOI: 10.1093/pm/pnz314.

82.

Schnell

Currie

Addressing the opioid epidemic: is there a role for physician education?

Am J Health Econ 2018; 4: 383–410. DOI: 10.1162/ajhe_a_00113.

83.

Borges Do Nascimento

Abdulazeem

Vasanthan

, et al. The global effect of digital health technologies on health workers’ competencies and health workplace: an umbrella review of systematic reviews and lexical-based and sentence-based meta-analysis. Lancet Digit Health 2023; 5: e534–e544. DOI: 10.1016/s2589-7500(23)00092-4.

84.

Hagman

Andersson

Västfjäll

, et al. Public views on policies involving nudges. RevPhilPsych 2015; 6: 439–453. DOI: 10.1007/s13164-015-0263-2.

85.

Hofmann

Stanak

Nudging in screening: literature review and ethical guidance. Patient Educ Couns 2018; 101: 1561–1569. DOI: 10.1016/j.pec.2018.03.021.

86.

Reisch

Sunstein

CR.

Do Europeans like nudges?

Ssrn Journal 2016; 11: 310–325. DOI: 10.2139/ssrn.2739118.

87.

Dexter

Perkins

Overhage

, et al. A computerized reminder system to increase the use of preventive care for hospitalized patients. N Engl J Med 2001; 345: 965–970. DOI: 10.1056/nejmsa010181.

88.

Van Der Sijs

Aarts

Vulto

, et al. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006; 13: 138–147. DOI: 10.1197/jamia.m1809.

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