Behavioral Economics Interventions in Clinical Decision Support Systems

An Integrative Scoping Review of Behavioral Economics Applications in Higher Education: Opportunities for Academic Pharmacy

OBJECTIVES

This integrative scoping review explores the applications of behavioral economics within higher education, particularly, through the lens of nudging (a concept that leverages insights from economics and psychology to guide individuals' decisions subtly). The primary objective is to provide a broad overview of interventions that use behavioral economics principles and, secondarily, discuss their potential to improve pharmacy education and create a foundation for future research in this area.

FINDINGS

The review analyzed 89 studies that applied behavioral economics principles in higher education settings. Reminders and prompts were the most frequently used principles, with generally positive outcomes, especially in enrollment management. Framing, incentives, and salience also demonstrated effectiveness, depending on the context. Social norms and simplification showed mixed results, whereas priming, loss aversion, feedback, comparison, and commitment had varying degrees of success in influencing behavior.

SUMMARY

Behavioral economics principles offer valuable insights and tools for enhancing various aspects of education. The review highlights the potential for using these principles to improve student engagement, enrollment processes, and health and well-being initiatives. However, it emphasizes the importance of context-specific design and careful implementation when applying these interventions. Future research opportunities exist to further explore the applications of behavioral economics in pharmacy education and beyond.

Self-management behavior strategy based on behavioral economics in patients with hypertension: a scoping review

Hypertensive patients often do not make the most favorable choices and behaviors for managing disease. Behavioral economics strategies offer new ideas for guiding patients toward health behavior. The scoping review aimed to summarize behavioral economics strategies designed to improve hypertension self-management behaviors. A literature search was conducted in September 2022 using the following electronic databases: Embase, Medline, CINAHL, PsycINFO, Web of Science, Cochrane Library, CNKI, Wan Fang Database for Chinese Periodicals, and CBM-SinoMed. We screened the literature for experimental studies written in Chinese or English reporting on BE strategies designed to improve self-management behavior in hypertension. We searched 17 820 records and included 18 articles in the final scoping review. We performed qualitative synthesis by the categories of choice architecture. The most common BE strategies were those targeting decision information and decision assistance, such as changing the presentation of information, making information visible, and providing reminders for actions. Most strategies targeted BP, diet, medication adherence, and physical activity behavior. Ten out of 18 studies reported statistically significant improvement in self-management behavior. Further research on BE strategies should focus on addressing the challenges, including changing the decision structure, encompassing a more comprehensive range of target behaviors, and examining the long-term effects of BE strategies.

Surgical appropriateness nudges: Developing behavioral science nudges to integrate appropriateness criteria into the decision making of spine surgeons

BACKGROUND

Substantial variation exists in surgeon decision making. In response, multiple specialty societies have established criteria for the appropriate use of spine surgery. Yet few strategies exist to facilitate routine use of appropriateness criteria by surgeons. Behavioral science nudges are increasingly used to enhance decision making by clinicians. We sought to design "surgical appropriateness nudges" to support routine use of appropriateness criteria for degenerative lumbar scoliosis and spondylolisthesis.

METHODS

The work reflected Stage I of the NIH Stage Model for Behavioral Intervention Development and involved an iterative, multi-method approach, emphasizing qualitative methods. Study sites included two large referral centers for spine surgery. We recruited spine surgeons from both sites for two rounds of focus groups. To produce preliminary nudge prototypes, we examined sources of variation in surgeon decision making (Focus Group 1) and synthesized existing knowledge of appropriateness criteria, behavioral science nudge frameworks, electronic tools, and the surgical workflow. We refined nudge prototypes via feedback from content experts, site leaders, and spine surgeons (Focus Group 2). Concurrently, we collected data on surgical practices and outcomes at study sites. We pilot tested the refined nudge prototypes among spine surgeons, and surveyed them about nudge applicability, acceptability, and feasibility (scale 1-5, 5 = strongly agree).

RESULTS

Fifteen surgeons participated in focus groups, giving substantive input and feedback on nudge design. Refined nudge prototypes included: individualized surgeon score cards (frameworks: descriptive social norms/peer comparison/feedback), online calculators embedded in the EHR (decision aid/mapping), a multispecialty case conference (injunctive norms/social influence), and a preoperative check (reminders/ salience of information/ accountable justification). Two nudges (score cards, preop checks) incorporated data on surgeon practices and outcomes. Six surgeons pilot tested the refined nudges, and five completed the survey (83%). The overall mean score was 4.0 (standard deviation [SD] 0.5), with scores of 3.9 (SD 0.5) for applicability, 4.1 (SD 0.5) for acceptability, and 4.0 (SD 0.5), for feasibility. Conferences had the highest scores 4.3 (SD 0.6) and calculators the lowest 3.9 (SD 0.4).

CONCLUSIONS

Behavioral science nudges might be a promising strategy for facilitating incorporation of appropriateness criteria into the surgical workflow of spine surgeons. Future stages in intervention development will test whether these surgical appropriateness nudges can be implemented in practice and influence surgical decision making.

Implementation Requires Evaluation of Adoption: Lessons From a Multimodal Pain Regimen Order Set

INTRODUCTION

Multimodal pain regimen (MMPR) protocols are the standard of care per the 2020 Trauma Quality Improvement Program guidelines. MMPR implementation methodology in trauma services has not been reported. The primary objective of this study was to evaluate the adoption of an MMPR order set at a level 1 trauma center and to describe its implementation. We hypothesized that order set utilization would be about 50%, and barriers to adoption would be related to personal biases.

METHODS

This was a mixed-methods study at a level 1 trauma center. We retrospectively evaluated MMPR utilization from July 1, 2021 to February 28, 2022. Agile implementation was the method used to implement a clinical decision support tool for the MMPR: a flow chart order set in the electronic medical record. This methodology utilizes short experiment sprints during which data are collected to guide the next iterations. During this process quantitative as well as qualitative data were collected. This included end user testing of the order set and a survey distributed to surgical residents about the order set. Manual thematic network analysis was employed to identify basic and organizing themes from the survey responses.

RESULTS

A total of 587 trauma patients were admitted during the study period and 95 patients (16.2%) had MMPR ordered through the order set. The survey response rate was 19% (13/68). We identified ease of use, desire for options, inadequate education, and assumption of personal expertise as the four basic themes from the survey. These basic themes were further analyzed to two organizing themes: heuristics and overconfidence bias.

CONCLUSIONS

The MMPR order set was easy to use but had low adoption at our center in the first 8 months of implementation. Agile implementation methodology provided an ideal framework to identify reasons for low adoption and guide the next sprint to address personal biases, improve heuristics, and provide effective education and dissemination. Evaluation of utilization and qualitative analysis are key components to ensuring clinical decision support tool adoption.

World Heart Federation Roadmap for Secondary Prevention of Cardiovascular Disease: 2023 Update

Background

Secondary prevention lifestyle and pharmacological treatment of atherosclerotic cardiovascular disease (ASCVD) reduce a high proportion of recurrent events and mortality. However, significant gaps exist between guideline recommendations and usual clinical practice.

Objectives

Describe the state of the art, the roadblocks, and successful strategies to overcome them in ASCVD secondary prevention management.

Methods

A writing group reviewed guidelines and research papers and received inputs from an international committee composed of cardiovascular prevention and health systems experts about the article's structure, content, and draft. Finally, an external expert group reviewed the paper.

Results

Smoking cessation, physical activity, diet and weight management, antiplatelets, statins, beta-blockers, renin-angiotensin-aldosterone system inhibitors, and cardiac rehabilitation reduce events and mortality. Potential roadblocks may occur at the individual, healthcare provider, and health system levels and include lack of access to healthcare and medicines, clinical inertia, lack of primary care infrastructure or built environments that support preventive cardiovascular health behaviours. Possible solutions include improving health literacy, self-management strategies, national policies to improve lifestyle and access to secondary prevention medication (including fix-dose combination therapy), implementing rehabilitation programs, and incorporating digital health interventions. Digital tools are being examined in a range of settings from enhancing self-management, risk factor control, and cardiac rehab.

Conclusions

Effective strategies for secondary prevention management exist, but there are barriers to their implementation. WHF roadmaps can facilitate the development of a strategic plan to identify and implement local and national level approaches for improving secondary prevention.

Sharpening clinical decision support alert and reminder designs with MINDSPACE: A systematic review

BACKGROUND

Clinical decision support (CDS) alerts and reminders aim to influence clinical decisions, yet they are often designed without considering human decision-making behaviour. While this behaviour is comprehensively described by behavioural economics (BE), the sheer volume of BE literature poses a challenge to designers when identifying behavioural effects with utility to alert and reminder designs. This study tackles this challenge by focusing on the MINDSPACE framework for behaviour change, which collates nine behavioural effects that profoundly influence human decision-making behaviour: Messenger, Incentives, Norms, Defaults, Salience, Priming, Affect, Commitment, and Ego.

METHOD

A systematic review searching MEDLINE, Embase, PsycINFO, and CINAHL Plus to explore (i) the usage of MINDSPACE effects in alert and reminder designs and (ii) the efficacy of those alerts and reminders in influencing clinical decisions. The search queries comprised ten Boolean searches, with nine focusing on the MINDSPACE effects and one focusing on the term mindspace.

RESULTS

50 studies were selected from 1791 peer-reviewed journal articles in English from 1970 to 2022. Except for ego, eight of nine MINDSPACE effects were utilised to design alerts and reminders, with defaults and norms utilised the most in alerts and reminders, respectively. Overall, alerts and reminders informed by MINDSPACE effects showed an average 71% success rate in influencing clinical decisions (alerts 73%, reminders 69%). Most studies utilised a single effect in their design, with higher efficacy for alerts (64%) than reminders (41%). Others utilised multiple effects, showing higher efficacy for reminders (28%) than alerts (9%).

CONCLUSION

This review presents sufficient evidence demonstrating the MINDSPACE framework's merits for designing CDS alerts and reminders with human decision-making considerations. The framework can adequately address challenges in identifying behavioural effects pertinent to the effective design of CDS alerts and reminders. The review also identified opportunities for future research into other relevant effects (e.g., framing).

Youth Trauma Histories are Associated with Under-diagnosis and Under-treatment of Co-occurring Youth Psychiatric Symptoms

OBJECTIVE

We examined whether in the presence of trauma exposure, non-traumatic stress-related symptoms are interpreted by mental health clinicians as less salient than the trauma exposure and are de-emphasized as a treatment target, consistent with a diagnostic overshadowing bias.

METHODS

Using an adapted version of a diagnostic overshadowing bias experimental paradigm, mental health clinicians (N = 266, M age = 34.4 years, 82% female) were randomly assigned to receive two of six clinical vignette variations. Vignette 1 described an adolescent with obsessive-compulsive disorder (OCD). Vignette 2 described a pre-adolescent with oppositional defiant disorder (ODD). Vignettes were identical except for whether the youth reported exposure to a potentially traumatic event (PTE; no PTE, sexual PTE, or physical PTE). Clinicians received one vignette with a PTE and one without, counterbalancing order. Clinicians rated the likelihood the youth met criteria for various diagnoses and the appropriateness of various treatments on 7-point scales.

RESULTS

Across both vignettes, clinicians rated the target diagnosis (OCD in Vignette 1, ODD in Vignette 2) as less likely for vignettes with a PTE than for the same vignettes without a PTE. Clinicians also rated evidence-based treatment modalities for target diagnoses as less appropriate in the presence of a PTE than when a PTE was present.

CONCLUSIONS

Consistent with possible bias, clinicians may under-recognize and under-treat non-traumatic stress-related mental health symptoms in youth with a co-occurring trauma history. Future work to validate this bias in real-world practice is indicated.

Preventing Teen Pregnancies on Teratogenic Drugs by Quality Improvement and Behavioral Economics

BACKGROUND

Adolescents with chronic disease engage in sexual activity similar to their healthy peers, with generally low utilization of contraception. Adolescents with rheumatic diseases prescribed teratogenic medications may be at risk for unplanned pregnancy.

METHODS

Using structured quality improvement (QI) methods with behavior economic (BE) principles, a multidisciplinary team aimed to implement pregnancy prevention processes for females on high-risk medications. We leveraged BE-inspired interventions including improved accessibility of consents, utilizing distinctly colored consent forms, real-time reminders, peer comparison, and audit and feedback. Our primary aim was to increase the number of days between pregnancies for postmenarcheal females followed in rheumatology clinics who were taking teratogenic medications. Phase 1 focused on annual consenting of female adolescents prescribed teratogenic drugs. Phase 2 emphasized sexual history screening and pregnancy prevention planning at every clinic visit for females ≥12 years on teratogenic medications.

RESULTS

We increased the days between pregnancies for female adolescents prescribed teratogenic medications from 52 days to >900 days by using QI methodology with BE strategies. In phase 1, annual consents for postmenarcheal patients on teratogenic medications improved from 0% in 2017 to 95% in 2021. In phase 2, sexual history screening and pregnancy prevention planning at every clinic visit improved from 2% in 2019 to over 78% in 2021.

CONCLUSIONS

A multiphase, multidisciplinary QI project with integration of behavior economic strategies can improve patient and caregiver counseling to prevent unplanned pregnancies for adolescents on teratogenic medications.

A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center

Background: Diabetes mellitus (DM) is a major public health problem worldwide. It involves dysfunction of blood sugar regulation resulting from insulin resistance, inadequate insulin secretion, or excessive glucagon secretion. Methods: This study collated 971,401 drug usage records of 51,009 DM patients. These data include patient identification code, age, gender, outpatient visiting dates, visiting code, medication features (included items, doses, and frequencies of drugs), HbA1c results, and testing time. We apply a random forest (RF) model for feature selection and implement a regression model with the bidirectional long short-term memory (Bi-LSTM) deep learning architecture. Finally, we use the root mean square error (RMSE) as the evaluation index for the prediction model. Results: After data cleaning, the data included 8,729 male and 9,115 female cases. Metformin was the most important feature suggested by the RF model, followed by glimepiride, acarbose, pioglitazone, glibenclamide, gliclazide, repaglinide, nateglinide, sitagliptin, and vildagliptin. The model performed better with the past two seasons in the training data than with additional seasons. Further, the Bi-LSTM architecture model performed better than support vector machines (SVMs). Discussion & Conclusion: This study found that Bi-LSTM models is a well kernel in a CDSS which help physicians' decision-making, and the increasing the number of seasons will negative impact the performance. In addition, this study found that the most important drug is metformin, which is recommended as first-line treatment OHA in various situations for DM patients.

Context-Dependent Risk & Benefit Sensitivity Mediate Judgments About Cognitive Enhancement

Opinions about cognitive enhancement (CE) are context-dependent. Prior research has demonstrated that factors like peer pressure, the influence of authority figures, competition, moral relevance, familiarity with enhancement devices, expertise, and the domain of CE to be enhanced can influence opinions. The variability and malleability of patient, expert, and public attitudes toward CE is important to describe and predict because these attitudes can influence at-home, clinical, research, and regulatory decisions. If individual preferences vary, they could influence opinions about practices and regulations due to disagreements about the desirable levels of risks and benefits. The study of attitudes about CE would benefit from psychological theories that explain judgments. In particular, we suggest that variability in risk and benefit sensitivity could psychologically mediate judgments about CE in many contexts. Drawing from prospect theory, which originated in behavioral economics, it is likely that framing effects, shifted reference points, and the tendency to weigh losses (risks) more heavily than gains (benefits) predict decisions about CE. We suggest that public policy could benefit from a shared conceptual framework, such as prospect theory, that allows us to describe and predict real-world decisions about CE by patients, experts, and the public.

Behavioral Economics and Ambulatory Antibiotic Stewardship: A Narrative Review

PURPOSE

Behavioral economics recognizes that contextual, psychological, social, and emotional factors powerfully influence decision-making. Behavioral economics has the potential to provide a better understanding of, and, through subtle environmental changes, or "nudges," improve persistent quality-of-care challenges, like ambulatory antibiotic overprescribing. Despite decades of admonitions and educational initiatives, in the United States, up to 50% of ambulatory antibiotic prescriptions remain inappropriate or not associated with a diagnosis.

METHODS

We conducted a Medline search and performed a narrative review that examined the use of behavioral economics to understand the rationale for, and improvement of, ambulatory antibiotic prescribing.

FINDINGS

Clinicians prescribe antibiotics inappropriately because of perceived patient demand, to maintain patient satisfaction, diagnostic uncertainty, or time pressure, among other reasons. Behavioral economics-informed approaches offer additional improvements in antibiotic prescribing beyond clinician education and communication training. Precommitment, in which clinicians publicize their intent to prescribe antibiotics "only when they are absolutely necessary," leverages clinicians' self-conception and a desire to act in a manner consistent with public statements. Precommitment was associated with a 20% absolute reduction in the inappropriate antibiotic prescribing for acute respiratory infections. Justification alerts, in which clinicians must provide a brief written rationale for prescribing antibiotics, leverages social accountability, redefines the status quo as an active choice, and helps clinicians to shift from fast to slow, careful thinking. With justification alerts, the absolute rate of inappropriate antibiotic prescribing decreased from 23% to 5%. Peer comparison, in which clinicians receive feedback comparing their performance to their top-performing peers, provides evidence of improved performance and leverages peoples' desire to conform to social norms. Peer comparison decreased absolute inappropriate antibiotic prescribing rates from 20% to 4%, a decrease that persisted for 12 months after the end of the intervention. Also, a one-time peer-comparison letter from a high-profile messenger to primary care practices with high rates of prescribing antibiotics, there was a 6-month, 3% decrease inantibiotic prescribing. Future directions in applying behavioral economics to the inappropriate antibiotic prescribing include paying careful attention to design details; improving intervention effectiveness and durability; making harms salient; participants' involvement in the development of interventions (the "Ikea effect"); factoring in patient satisfaction; and patient-facing nudges about antibiotic use and care-seeking. In addition, the COVID pandemic could aid in ambulatory antibiotic prescribing improvements due to changing cognitive frames around respiratory symptom evaluation and antibiotic prescribing.

IMPLICATIONS

To improve ambulatory antibiotic prescribing, several behavioral economics-informed approaches-especially precommitment, justification alerts, and peer comparison-have reduced the rates of inappropriate prescribing of antibiotics to low levels.

Improving Guideline-Congruent Care for Chemotherapy-Induced Nausea and Vomiting Prophylaxis in Pediatric Oncology Patients

PURPOSE

Chemotherapy-induced nausea and vomiting (CINV) is a very common side effect of pediatric cancer therapy. High-quality, evidence-based, pediatric-specific guidelines for prophylaxis and treatment of CINV are available. At many centers, guideline-concordant care is uncommon. We formed a multidisciplinary quality improvement team to implement guideline-concordant care for CINV prophylaxis at our center. We present the results following the first year of our interventions.

METHODS

We planned and implemented a multipronged approach in three key phases: (1) developing and publishing an acute CINV prophylaxis pathway, (2) education of providers, and (3) updating the computerized provider order entry system. We used iterative, sequential Plan-Do-Study-Act cycles and behavioral economic strategies to improve adherence to guideline-concordant CINV prophylaxis. We focused on aprepitant usage as a key area for improvement.

RESULTS

At the beginning of the study period, < 50% of patients were receiving guideline-concordant CINV prophylaxis and < 15% of eligible patients were receiving aprepitant. After 1 year, more than 60% of patients were receiving guideline-concordant care and 50% of eligible patients were receiving aprepitant.

CONCLUSION

We describe the development and implementation of a standardized pathway for prevention of acute CINV in pediatric oncology patients. With a multidisciplinary, multifaceted approach, we demonstrate significant improvements to guideline-congruent CINV prophylaxis.

Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing

Background

A systematic approach to MRI protocol assignment is essential for the efficient delivery of safe patient care. Advances in natural language processing (NLP) allow for the development of accurate automated protocol assignment. We aim to develop, evaluate, and deploy an NLP model that automates protocol assignment, given the clinician indication text.

Methods

We collected 7139 spine MRI protocols (routine or contrast) and 990 head MRI protocols (routine brain, contrast brain, or other) from a single institution. Protocols were split into training (n = 4997 for spine MRI; n = 839 for head MRI), validation (n = 1071 for spine MRI, fivefold cross-validation used for head MRI), and test (n = 1071 for spine MRI; n = 151 for head MRI) sets. fastText and XGBoost were used to develop 2 NLP models to classify spine and head MRI protocols, respectively. A Flask-based web app was developed to be deployed via Heroku.

Results

The spine MRI model had an accuracy of 83.38% and a receiver operator characteristic area under the curve (ROC-AUC) of 0.8873. The head MRI model had an accuracy of 85.43% with a routine brain protocol ROC-AUC of 0.9463 and contrast brain protocol ROC-AUC of 0.9284. Cancer, infectious, and inflammatory related keywords were associated with contrast administration. Structural anatomic abnormalities and stroke/altered mental status were indicative of routine spine and brain MRI, respectively. Error analysis revealed increasing the sample size may improve performance for head MRI protocols. A web version of the model is provided for demonstration and deployment.

Conclusion

We developed and web-deployed two NLP models that accurately predict spine and head MRI protocol assignment, which could improve radiology workflow efficiency.

Behavioral Economics: A Primer and Applications to the UN Sustainable Development Goal of Good Health and Well-Being

Behavioral economics (BE) is a relatively new field within economics that incorporates insights from psychology that can be harnessed to improve economic decision making with the potential to enhance good health and well-being of individuals and societies, the third of the United Nations Sustainable Development Goals. While some of the psychological principles of economic decision making were described as far back as the 1700s by Adam Smith, BE emerged as a discipline in the 1970s with the groundbreaking work of psychologists Daniel Kahneman and Amos Tversky. We describe the basic concepts of BE, heuristics (decision-making shortcuts) and their associated biases, and the BE strategies framing, incentives, and economic nudging to overcome these biases. We survey the literature to identify how BE techniques have been employed to improve individual choice (focusing on childhood obesity), health policy, and patient and healthcare provider decision making. Additionally, we discuss how these BE-based efforts to improve health-related decision making can lead to sustaining good health and well-being and identify additional health-related areas that may benefit from including principles of BE in decision making.

Decision fatigue in low-value prostate cancer screening

BACKGROUND

Low-value prostate-specific antigen (PSA) testing is common yet contributes substantial waste and downstream patient harm. Decision fatigue may represent an actionable target to reduce low-value urologic care. The objective of this study was to determine whether low-value PSA testing patterns by outpatient clinicians are consistent with decision fatigue.

METHODS

Outpatient appointments for adult men without prostate cancer were identified at a large academic health system from 2011 through 2018. The authors assessed the association of appointment time with the likelihood of PSA testing, stratified by patient age and appropriateness of testing based on clinical guidelines. Appointments included those scheduled between 8:00 am and 4:59 pm, with noon omitted. Urologists were examined separately from other clinicians.

RESULTS

In 1,581,826 outpatient appointments identified, the median patient age was 54 years (interquartile range, 37-66 years), 1,256,152 participants (79.4%) were White, and 133,693 (8.5%) had family history of prostate cancer. PSA testing would have been appropriate in 36.8% of appointments. Clinicians ordered testing in 3.6% of appropriate appointments and in 1.8% of low-value appointments. Appropriate testing was most likely at 8:00 am (reference group). PSA testing declined through 11:00 am (odds ratio [OR], 0.57; 95% CI, 0.50-0.64) and remained depressed through 4:00 pm (P < .001). Low-value testing was overall less likely (P < .001) and followed a similar trend, declining steadily from 8:00 am (OR, 0.48; 95% CI, 0.42-0.56) through 4:00 pm (P < .001; OR, 0.23; 95% CI, 0.18-0.30). Testing patterns in urologists were noticeably different.

CONCLUSIONS

Among most clinicians, outpatient PSA testing behaviors appear to be consistent with decision fatigue. These findings establish decision fatigue as a promising, actionable target for reducing wasteful and low-value practices in routine urologic care.

LAY SUMMARY

Decision fatigue causes poorer choices to be made with repetitive decision making. This study used medical records to investigate whether decision fatigue influenced clinicians' likelihood of ordering a low-value screening test (prostate-specific antigen [PSA]) for prostate cancer. In more than 1.5 million outpatient appointments by adult men without prostate cancer, the chances of both appropriate and low-value PSA testing declined as the clinic day progressed, with a larger decline for appropriate testing. Testing patterns in urologists were different from those reported by other clinicians. The authors conclude that outpatient PSA testing behaviors appear to be consistent with decision fatigue among most clinicians, and interventions may reduce wasteful testing and downstream patient harms.

The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World

Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. However, in the real-world clinical setting, the clinician will work in collaboration with AI. Existing evidence regarding the integration of such AI diagnostic tools into clinical practice is limited. Human factors, such as cognitive style, personality, experience, preferences, and attitudes may influence clinicians' use of AI. In this review, we consider these human factors and the potential cognitive errors, biases, and unintended consequences that could arise when using an AI skin cancer diagnostic tool in the real world. Integrating this knowledge in the design and implementation of AI technology will assist in ensuring that the end product can be used effectively. Dermatologist leadership in the development of these tools will further improve their clinical relevance and safety.

Economic impact of clinical decision support interventions based on electronic health records

Background

Unnecessary healthcare utilization, non-adherence to current clinical guidelines, or insufficient personalized care are perpetual challenges and remain potential major cost-drivers for healthcare systems around the world. Implementing decision support systems into clinical care is promised to improve quality of care and thereby yield substantial effects on reducing healthcare expenditure. In this article, we evaluate the economic impact of clinical decision support (CDS) interventions based on electronic health records (EHR).

Methods

We searched for studies published after 2014 using MEDLINE, CENTRAL, WEB OF SCIENCE, EBSCO, and TUFTS CEA registry databases that encompass an economic evaluation or consider cost outcome measures of EHR based CDS interventions. Thereupon, we identified best practice application areas and categorized the investigated interventions according to an existing taxonomy of front-end CDS tools.

Results and discussion

Twenty-seven studies are investigated in this review. Of those, twenty-two studies indicate a reduction of healthcare expenditure after implementing an EHR based CDS system, especially towards prevalent application areas, such as unnecessary laboratory testing, duplicate order entry, efficient transfusion practice, or reduction of antibiotic prescriptions. On the contrary, order facilitators and undiscovered malfunctions revealed to be threats and could lead to new cost drivers in healthcare. While high upfront and maintenance costs of CDS systems are a worldwide implementation barrier, most studies do not consider implementation cost. Finally, four included economic evaluation studies report mixed monetary outcome results and thus highlight the importance of further high-quality economic evaluations for these CDS systems.

Conclusion

Current research studies lack consideration of comparative cost-outcome metrics as well as detailed cost components in their analyses. Nonetheless, the positive economic impact of EHR based CDS interventions is highly promising, especially with regard to reducing waste in healthcare.

Designing, Conducting, and Reporting Clinical Decision Support Studies: Recommendations and Call to Action

By enabling more efficient and effective medical decision making, computer-based clinical decision support (CDS) could unlock widespread benefits from the significant investment in electronic health record (EHR) systems in the United States. Evidence from high-quality CDS studies is needed to enable and support this vision of CDS-facilitated care optimization, but limited guidance is available in the literature for designing and reporting CDS studies. To address this research gap, this article provides recommendations for designing, conducting, and reporting CDS studies to: 1) ensure that EHR data to inform the CDS are available; 2) choose decision rules that are consistent with local care processes; 3) target the right users and workflows; 4) make the CDS easy to access and use; 5) minimize the burden placed on users; 6) incorporate CDS success factors identified in the literature, in particular the automatic provision of CDS as a part of clinician workflow; 7) ensure that the CDS rules are adequately tested; 8) select meaningful evaluation measures; 9) use as rigorous a study design as is feasible; 10) think about how to deploy the CDS beyond the original host organization; 11) report the study in context; 12) help the audience understand why the intervention succeeded or failed; and 13) consider the financial implications. If adopted, these recommendations should help advance the vision of more efficient, effective care facilitated by useful and widely available CDS.

Can nudge-interventions address health service overuse and underuse? Protocol for a systematic review

INTRODUCTION

Nudge-interventions aimed at health professionals are proposed to reduce the overuse and underuse of health services. However, little is known about their effectiveness at changing health professionals' behaviours in relation to overuse or underuse of tests or treatments.

OBJECTIVE

The aim of this study is to systematically identify and synthesise the studies that have assessed the effect of nudge-interventions aimed at health professionals on the overuse or underuse of health services.

METHODS AND ANALYSIS

We will perform a systematic review. All study designs that include a control comparison will be included. Any qualified health professional, across any specialty or setting, will be included. Only nudge-interventions aimed at altering the behaviour of health professionals will be included. We will examine the effect of choice architecture nudges (default options, active choice, framing effects, order effects) and social nudges (accountable justification and pre-commitment or publicly declared pledge/contract). Studies with outcomes relevant to overuse or underuse of health services will be included. Relevant studies will be identified by a computer-aided search of the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library), MEDLINE, CINAHL, Embase and PsycINFO databases. Two independent reviewers will screen studies for eligibility, extract data and perform the risk of bias assessment using the criteria recommended by the Cochrane Effective Practice and Organisation of Care (EPOC) group. We will report our results in a structured synthesis format, as recommended by the Cochrane EPOC group.

ETHICS AND DISSEMINATION

No ethical approval is required for this study. Results will be presented at relevant scientific conferences and in peer-reviewed literature.

Contributions from the 2017 Literature on Clinical Decision Support

Objectives:  To summarize recent research and select the best papers published in 2017 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook.

Methods:  A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation.

Results:  Among the 1,194 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper studies the impact of recency and of longitudinal extent of electronic health record (EHR) datasets used to train a data-driven predictive model of inpatient admission orders. The second paper presents a decision support tool for surgical team selection, relying on the history of surgical team members and the specific characteristics of the patient. The third paper compares three commercial drug-drug interaction knowledge bases, particularly against a reference list of highly-significant known interactions. The fourth paper focuses on supporting the diagnosis of postoperative delirium using an adaptation of the “anchor and learn” framework, which was applied in unstructured texts contained in EHRs.

Conclusions:  The conducted review illustrated also this year that research in the field of CDSS is very active. Of note is the increase in publications concerning data-driven CDSSs, as revealed by the review process and also reflected by the four papers that have been selected. This trend is in line with the current attention that “Big Data” and data-driven artificial intelligence have gained in the domain of health and CDSSs in particular.