Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design

The role of information systems in emergency department decision-making-a literature review

OBJECTIVES

Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED.

MATERIALS AND METHODS

We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes.

RESULTS

Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes.

DISCUSSION

Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation.

CONCLUSION

We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED.

Effectiveness of ChatGPT in clinical pharmacy and the role of artificial intelligence in medication therapy management

BACKGROUND

The use of artificial intelligence (AI) to optimize medication therapy management (MTM) in identifying drug interactions may potentially improve MTM efficiency. ChatGPT, an AI language model, may be applied to identify medication interventions by integrating patient and drug databases. ChatGPT has been shown to be effective in other areas of clinical medicine, from diagnosis to patient management. However, ChatGPT's ability to manage MTM related activities is little known.

OBJECTIVES

To evaluate the effectiveness of ChatGPT in MTM services in simple, complex, and very complex cases to understand AI contributions in MTM.

METHODS

Two clinical pharmacists rated and validated the difficulty of patient cases from simple, complex, and very complex. ChatGPT's response to the cases was assessed based on 3 criteria: the ability to identify drug interactions, precision in recommending alternatives, and appropriateness in devising management plans. Two clinical pharmacists validated the accuracy of ChatGPT's responses and compared them to actual answers for each complexity level.

RESULTS

ChatGPT 4.0 accurately solved 39 out of 39 (100 %) patient cases. ChatGPT successfully identified drug interactions, provided therapy recommendations and formulated general management plans, but it did not recommend specific dosages. Results suggest it can assist pharmacists in formulating MTM plans to improve overall efficiency.

CONCLUSION

The application of ChatGPT in MTM has the potential to enhance patient safety and involvement, lower healthcare costs, and assist healthcare providers in medication management and identifying drug interactions. Future pharmacists can utilize AI models such as ChatGPT to improve patient care. The future of the pharmacy profession will depend on how the field responds to the changing need for patient care optimized by AI and automation.

Learning From Clinical Supervisor Practice Variability: Exploring Medical Resident and Fellow Experiences and Interpretations

PURPOSE

Clinical practice variability is characterized by 2 or more clinicians making different treatment decisions despite encountering a similar case. This study explores how medical residents and fellows experience and interpret intersupervisor clinical practice variability and how these variations influence learning.

METHOD

Seventeen senior residents or fellows in internal medicine, hematology, or thrombosis medicine (postgraduate year 3 or above) participated in semistructured interviews after a clinical rotation in thrombosis medicine from December 2019 to March 2021. Data collection and analysis occurred iteratively and concurrently in a manner consistent with constructivist grounded theory. Variation theory was used to guide the development of some interview questions. A central tenet of this theory is that learning occurs by experiencing 3 sequential patterns of variation: contrast, generalization, and fusion. Participants were recruited purposively with respect to specialty until theoretical sufficiency was reached.

RESULTS

Clinical practice variability was experienced by all participants. Residents and fellows attributed practice variability to intrinsic differences among supervisors; interinstitutional differences; selection and interpretation of evidence; patient preferences, priorities, and fears; and their own participation in the decision-making process. Clinical practice variability helped residents and fellows discern key features of cases that influenced decision-making (contrast), group similar cases so that the appropriate evidence could be applied (generalization), and develop attitudes consistent with providing individualized patient care (fusion). Observing practice variability was more helpful for fifth- and sixth-year residents and less helpful for third- and fourth-year residents.

CONCLUSIONS

Clinical practice variability helped residents and fellows discern critical aspects, group similar patients, and practice individualized medicine. Future research should characterize how clinical practice variability influences learning across the spectrum of training, how supervisors could encourage learning from practice variability, and how curricula could be modified to allow learners greater opportunity to reflect on and consolidate the practice differences they observe.

Gamification to Improve Medication Adherence: A Mixed-method Usability Study for MedScrab

Medication non-adherence is a prevalent healthcare problem with poor health outcomes and added healthcare costs. MedScrab, a gamification-based mHealth app, is the first attempt to deliver crucial life-saving medication information to patients and increase their medication adherence. The paper presents the development of MedScrab and a two-phase mixed-method usability evaluation of MedScrab. Phase I qualitatively evaluated MedScrab using a think-aloud protocol for its usability. With 51 participants, qualitative data analysis of Phase I revealed two themes: positive functionality of the app and four areas of improvement. The improvement recommendations were incorporated into MedScrab's design. Phase I also validated a widely used mHealth App Usability Questionnaire (MAUQ). Quantitative data analysis of Phase I reduced the original 18-item MAUQ scale to a 15-item scale with two factors: ease of use (4 items) and usefulness and satisfaction (11 items). Phase II surveyed 83 participants from Amazon's Mechanical Turk using a modified MAUQ. The modified MAUQ scale showed strong internal consistency (Cronbach alpha = 0.959) and high factor loadings (between 0.623 and 0.987). The study design of the usability evaluation can serve as a methodological guide for designing, evaluating, and improving mHealth apps.The usability study showed that MedScrab was perceived as ease of use (6.24 out of 7) with high usefulness and satisfaction (5.72 out of 7). The quantitative data analysis results support the use of the modified MAUQ as a valid instrument to measure the usability of the MedScrab. However, the instrument should be used with adaptation based on the app's characteristics.

"What brought you in today?": Modeling patient-provider clinic visits to characterize rural providers' antibiotic treatment decisions

BACKGROUND

Designing clinical decision support (CDS) tools is challenging because clinical decision-making must account for an invisible task load: incorporating non-linear objective and subjective factors to make an assessment and treatment plan. This calls for a cognitive task analysis approach.

OBJECTIVES

The objectives of this study were to 1.) understand healthcare providers' decision making during a typical clinic visit, and 2.) explore how antibiotic treatment decisions are made when they arise.

METHODS

Two cognitive task analysis methods were applied - Hierarchical Task Analysis (HTA) and Operations Sequence Diagramming (OSD) - to 39 h of observational data collected at family medicine, urgent care, and emergency medicine clinical sites.

RESULTS

The resulting HTA models included a coding taxonomy detailing ten cognitive goals and associated sub-goals and demonstrated how the goals occur as interactions between the provider and electronic health record, the patient, and the physical clinic environment. Although the HTA detailed resources for antibiotic treatment decisions, antibiotics were a minority of drug classes ordered. The OSD shows the sequence of events and when decisions are made solely at the provider level and when shared decision making occurs with the patient. Qualitative data from the observations informed a constructed vignette case example portraying select tasks from the HTA.

CONCLUSIONS

These findings emphasize that the scope of disease states presenting to a generalist clinical setting is broad and could include acute exacerbations of rare diseases within a time-pressured environment. CDS must be accessible, time efficient, and fit within the resource gathering task before treatment decisions are made.

Opportunities to integrate nutrigenomics into clinical practice and patient counseling

BACKGROUND

Little progress has been made in translating nutrigenomics knowledge into clinical counseling in the past decade. Currently, clinicians are overwhelmed by nutrigenomics information without the proper scientific guidelines on patient counseling.

METHODS

In this study, we conducted a scoping review of the primary literature to assess the current evidence of nutrigenomics counseling. A literature search using PRISMA guidelines identified the current challenges and opportunities facing nutrigenomics counseling in clinical practice.

RESULTS

We identified four main themes: inadequate training, lack of awareness, underdeveloped nutrigenomics counseling skills, and unreliable evidence-based practice information. Many clinicians did not have the necessary knowledge to perform nutrigenomic counseling and were unaware of the available scientific information source. Moreover, there are no guidelines in the scientific community to counsel patients on nutrigenomics testing.

CONCLUSION

Opportunities exist for government and non-government entities to create an evidence-based information platform using clinical guidelines to integrate nutrigenomics knowledge from bench to bedside successfully.

Artificial Intelligent Context-Aware Machine-Learning Tool to Detect Adverse Drug Events from Social Media Platforms

INTRODUCTION

Pharmacovigilance (PV) has proven to detect post-marketing adverse drug events (ADE). Previous research used the natural language processing (NLP) tool to extract unstructured texts relevant to ADEs. However, texts without context reduce the efficiency of such algorithms. Our objective was to develop and validate an innovative NLP tool, aTarantula, using a context-aware machine-learning algorithm to detect existing ADEs from social media using an aggregated lexicon.

METHOD

aTarantula utilized FastText embeddings and an aggregated lexicon to extract contextual data from three patient forums (i.e., MedHelp, MedsChat, and PatientInfo) taking warfarin. The lexicon used warfarin package inserts and synonyms of warfarin ADEs from UMLS and FAERS databases. Data was stored on SQLite and then refined and manually checked by three clinical pharmacists for validation.

RESULTS

Multiple organ systems where the most frequent ADE were reported at 1.50%, followed by CNS side effects at 1.19%. Lymphatic system ADEs were the least common side effect reported at 0.09%. The overall Spearman rank correlation coefficient between patient-reported data from the forums and FAERS was 0.19. As determined by pharmacist validation, aTarantula had a sensitivity of 84.2% and a specificity of 98%. Three clinical pharmacists manually validated our results. Finally, we created an aggregated lexicon for mining ADEs from social media.

CONCLUSION

We successfully developed aTarantula, a machine-learning algorithmn based on artificial intelligence to extract warfarin-related ADEs from online social discussion forums automatically. Our study shows that it is feasible to use aTarantula to detect ADEs. Future researchers can validate aTarantula on the diverse dataset.

Exploring Uniformity of Clinical Judgment: A Vignette Approach to Understanding Healthcare Professionals' Suicide Risk Assessment Practices

OBJECTIVES

Suicide risk assessment often requires health professionals to consider a complex interplay of multiple factors, with a significant reliance on judgment, which can be influenced by factors such as education and experience. Our study aimed at assessing the uniformity of decision making around suicide risk within healthcare professionals.

METHODS

We used a factorial survey approach to gather information on healthcare professionals' demographics, clinical experience, and their decision on 3 vignettes of patients with suicidal ideation. We used Kruskal-Wallis tests for determining if there were significant differences between groups for continuous variables and Spearman rank correlation for measuring the association between continuous variables. Content analysis was used for analyzing free-text comments.

RESULTS

Responses were gathered from 79 healthcare professionals (nurses, nurse practitioners, physicians) who worked in primary care, mental health, or emergency department settings. Median suicide risk rates across all respondents were 90%, 50%, and 53% for vignettes 1, 2, and 3, respectively. Confidence in healthcare professionals' decisions was significantly associated with the clinical designation and personal risk profile of the healthcare professional in certain vignettes, with nurses and those willing to take more risks having a higher confidence in their decisions for vignettes 1 and 3, respectively. Treatment decision was significantly associated with mental health experience (i.e., those with lengthier mental health experiences were less likely to choose "admit to psychiatry ward" for vignette 2), clinical designation (i.e., nurses were more likely to "admit to psychiatry ward" for vignette 1), and practice setting. It should be noted that these associations were not consistent across all 3 vignettes, and results for each association were only specific to 1 of the 3 vignettes.

DISCUSSION

Findings compare decision-making practices for suicide risk assessment across several types of healthcare professions over a range of practice settings, with the high-risk vignette showing the least variability. Insights from this study are relevant when building clinical decision support systems for suicide risk assessment. Designers should think about incorporating tailored messaging and alerts to health professionals' mental health experience and/or designation.

CONCLUSIONS

Within our Canadian sample, there was considerable variability among healthcare professionals assessing the risk of suicide, with important implications for tailoring education and decision support.

Investigating pharmacy students' therapeutic decision-making with respect to antimicrobial stewardship cases

BACKGROUND

Antimicrobial stewardship programs (ASPs) play a big role in minimizing antimicrobial resistance. Pharmacists are essential members of the health care team and in order for them to fulfill roles on ASP teams and become antimicrobial stewards, they must be prepared adequately by pharmacy schools prior to entry into actual practice. Although programming has been implemented into entry-to-practice programs worldwide, little is known about how students interpret antimicrobial stewardship (AMS) data and arrive at clinical decisions. We aimed to explore students' cognitive processes and determine how they formulate therapeutic decisions when presented with AMS cases.

METHODS

This was a qualitative study conducted using a case study approach, in which a sample (n=20) of pharmacy students was recruited to interpret AMS cases. Semi-structured 1-on-1 interviews were arranged with each participant. A think-aloud procedure with verbal protocol analysis was adopted to determine students' decision-making processes. Thematic analysis was used to interpret themes from the interview data.

RESULTS

Two themes were interpreted from the data: students' focus and students' approach to case interpretation. Students' focus relates to external factors students consider when interpreting AMS case data and use to make and justify therapeutic decisions including patient-centered factors, drug-related factors, AMS interventions, and pharmacist's role. Students' clinical reasoning describes the approach that students use to interpret the data and the decision-making processes they employ to arrive at a clinical decision including a systematic approach versus non-systematic approach.

CONCLUSIONS

Students vary in their focus and the cognitive strategies used to interpret AMS cases. Findings support the notion that clinical reasoning and decision-making should be explicitly taught in pharmacy curricula, in order to help students become aware of their own cognitive processes and decision-making abilities.

The promise of digital health in healthcare equity and medication adherence in the disadvantaged dementia population

Tweetable abstract Pharmacogenomics cascade testing in a digital health solution can improve medication adherence in dementia care for disadvantaged populations.

PGxKnow: a pharmacogenomics educational HoloLens application of augmented reality and artificial intelligence

Aim: To develop and assess an augmented reality tool for pharmacogenomics (PGx) education based on artificial intelligence. Materials & methods: A HoloLens application was developed using feedback from three clinical PGx-trained pharmacists. 15 Participants independently reviewed the application and assessed usability using the system usability scale (SUS). Results & conclusion: Eighteen different frames were developed. Each video module was 2-3 min for the education. The application included textual information and 3D structures of PGx concepts. The mean SUS score for 15 participants (11 pharmacy students and four pharmacists) was 83, with a standard deviation of 6.6. Results suggest that PGxKnow has the potential to bridge the gap in PGx education, further widespread utilization of PGx and boost its impact on precision medicine.

Ground truth labels challenge the validity of sepsis consensus definitions in critical illness

BACKGROUND

Sepsis is the leading cause of death in the intensive care unit (ICU). Expediting its diagnosis, largely determined by clinical assessment, improves survival. Predictive and explanatory modelling of sepsis in the critically ill commonly bases both outcome definition and predictions on clinical criteria for consensus definitions of sepsis, leading to circularity. As a remedy, we collected ground truth labels for sepsis.

METHODS

In the Ground Truth for Sepsis Questionnaire (GTSQ), senior attending physicians in the ICU documented daily their opinion on each patient's condition regarding sepsis as a five-category working diagnosis and nine related items. Working diagnosis groups were described and compared and their SOFA-scores analyzed with a generalized linear mixed model. Agreement and discriminatory performance measures for clinical criteria of sepsis and GTSQ labels as reference class were derived.

RESULTS

We analyzed 7291 questionnaires and 761 complete encounters from the first survey year. Editing rates for all items were > 90%, and responses were consistent with current understanding of critical illness pathophysiology, including sepsis pathogenesis. Interrater agreement for presence and absence of sepsis was almost perfect but only slight for suspected infection. ICU mortality was 19.5% in encounters with SIRS as the "worst" working diagnosis compared to 5.9% with sepsis and 5.9% with severe sepsis without differences in admission and maximum SOFA. Compared to sepsis, proportions of GTSQs with SIRS plus acute organ dysfunction were equal and macrocirculatory abnormalities higher (p < 0.0001). SIRS proportionally ranked above sepsis in daily assessment of illness severity (p < 0.0001). Separate analyses of neurosurgical referrals revealed similar differences. Discriminatory performance of Sepsis-1/2 and Sepsis-3 compared to GTSQ labels was similar with sensitivities around 70% and specificities 92%. Essentially no difference between the prevalence of SIRS and SOFA ≥ 2 yielded sensitivities and specificities for detecting sepsis onset close to 55% and 83%, respectively.

CONCLUSIONS

GTSQ labels are a valid measure of sepsis in the ICU. They reveal suspicion of infection as an unclear clinical concept and refute an illness severity hierarchy in the SIRS-sepsis-severe sepsis spectrum. Ground truth challenges the accuracy of Sepsis-1/2 and Sepsis-3 in detecting sepsis onset. It is an indispensable intermediate step towards advancing diagnosis and therapy in the ICU and, potentially, other health care settings.

Scoping review: the empowerment of Alzheimer's Disease caregivers with mHealth applications

Alzheimer's Disease (AD) is one of the most prevalent neurodegenerative chronic diseases. As it progresses, patients become increasingly dependent, and their caregivers are burdened with the increasing demand for managing their care. Mobile health (mHealth) technology, such as smartphone applications, can support the need of these caregivers. This paper examines the published academic literature of mHealth applications that support the caregivers of AD patients. Following the PRISMA for scoping reviews, we searched published literature in five electronic databases between January 2014 and January 2021. Twelve articles were included in the final review. Six themes emerged based on the functionalities provided by the reviewed applications for caregivers. They are tracking, task management, monitoring, caregiver mental support, education, and caregiver communication platform. The review revealed that mHealth applications for AD patients' caregivers are inadequate. There is an opportunity for industry, government, and academia to fill the unmet need of these caregiver.

Life or limb: an international qualitative study on decision making in sarcoma surgery during the COVID-19 pandemic

OBJECTIVES

The COVID-19 pandemic is unprecedented as a global crisis over the last century. How do specialist surgeons make decisions about patient care in these unprecedent times?

DESIGN

Between April and May 2020, we conducted an international qualitative study. Sarcoma surgeons from diverse global settings participated in 60 min interviews exploring surgical decision making during COVID-19. Interview data were analysed using an inductive thematic analysis approach.

SETTING

Participants represented public and private hospitals in 14 countries, in different phases of the first wave of the pandemic: Australia, Argentina, Canada, India, Italy, Japan, Nigeria, Singapore, Spain, South Africa, Switzerland, Turkey, UK and USA.

PARTICIPANTS

From 22 invited sarcoma surgeons, 18 surgeons participated. Participants had an average of 19 years experience as a sarcoma surgeon.

RESULTS

17/18 participants described a decision they had made about patient care since the start of the pandemic that was unique to them, that is, without precedence. Common to 'unique' decisions about patient care was uncertainty about what was going on and what would happen in the future (theme 1: the context of uncertainty), the impact of the pandemic on resources or threat of the pandemic to overwhelm resources (theme 2: limited resources), perceived increased risk to self (theme 3: duty of care) and least-worst decision making, in which none of the options were perceived as ideal and participants settled on the least-worst option at that point in time (theme 4: least-worst decision making).

CONCLUSIONS

In the context of rapidly changing standards of justice and beneficence in patient care, traditional decision-making frameworks may no longer apply. Based on the experiences of surgeons in this study, we describe a framework of least-worst decision making. This framework gives rise to actionable strategies that can support decision making in sarcoma and other specialised fields of surgery, both during the current crisis and beyond.

Re-visioning clinical reasoning, or stepping out from the skull

THE PROBLEM

Progress in teaching and learning clinical reasoning depends upon more sophisticated modelling of the reasoning process itself. Current accounts of clinical reasoning, grounded in experimental psychology, show a bias towards situating reasoning inside the skull, further reduced to neural processes signified by imaging. Such a model is necessary but not sufficient to explain the clinical reasoning process where it fails to embrace cognition extended to the environment and social contexts.

A SOLUTION

Sufficiency for a model of clinical reasoning must include dialogues between doctor, patient, and colleagues, including the complex influences of history and culture, where artefacts and semiotics such as computers, testing, and narrative structures augment cognition. Here, 'extended' cognition is configured as an outside-in process of 'sensemaking' or 'adaptive expertise'.

THE FUTURE

Current 'predictive processing' cognition models place emphasis on anticipatory cognition, where memory is reconfigured as active reconstruction rather than recall and recognition. Such an 'ecological perception' or 'externalistic' model provides a counter to the current dominant paradigm of 'ego-logical' cognitive reasoning - the latter, again, abstracted from context and located inside the skull. New models of clinical reasoning as an open, dynamic, nonlinear, complex system are called for.

Unravelling the polyphony in clinical reasoning research in medical education

RATIONALE

Clinical reasoning lies at the heart of medical practice and has a long research tradition. Nevertheless, research is scattered across diverse academic disciplines with different research traditions in a wide range of scientific journals. This polyphony is a source of conceptual confusion.

AIMS AND OBJECTIVES

We sought to explore the underlying theoretical assumptions of clinical reasoning aiming to promote a comprehensive conceptual and theoretical understanding of the subject area. In particular, we asked how clinical reasoning is defined and researched and what conceptualizations are relevant to such uses.

METHODS

A scoping review of the clinical reasoning literature was undertaken. Using a "snowball" search strategy, the wider scientific literature on clinical reasoning was reviewed in order to clarify the different underlying conceptual assumptions underlying research in clinical reasoning, particularly to the field of medical education. This literature included both medical education, as well as reasoning research in other academic disciplines outside medical education, that is relevant to clinical reasoning. A total of 124 publications were included in the review.

RESULTS

A detailed account of the research traditions in clinical reasoning research is presented. In reviewing this research, we identified three main conceptualisations of clinical reasoning: "reasoning as cognitive activity," "reasoning as contextually situated activity," and "reasoning as socially mediated activity." These conceptualisations reflected different theoretical understandings of clinical reasoning. Each conceptualisation was defined by its own set of epistemological assumptions, which we have identified and described.

CONCLUSIONS

Our work seeks to bring into awareness implicit assumptions of the ongoing clinical reasoning research and to hopefully open much needed channels of communication between the different research communities involved in clinical reasoning research in the field.

Pharmacogenomics cascade testing (PhaCT): a novel approach for preemptive pharmacogenomics testing to optimize medication therapy

The implementation of pharmacogenomics (PGx) has come a long way since the dawn of utilizing pharmacogenomic data in clinical patient care. However, the potential benefits of sharing PGx results have yet to be explored. In this paper, we explore the willingness of patients to share PGx results, as well as the inclusion of family medication history in identifying potential family members for pharmacogenomics cascade testing (PhaCT). The genetic similarities in families allow for identifying potential gene variants prior to official preemptive testing. Once a candidate patient is determined, PhaCT can be initiated. PhaCT recognizes that further cascade testing throughout a family can serve to improve precision medicine. In order to make PhaCT feasible, we propose a novel shareable HIPAA-compliant informatics platform that will enable patients to manage not only their own test results and medications but also those of their family members. The informatics platform will be an external genomics system with capabilities to integrate with patients' electronic health records. Patients will be given the tools to provide information to and work with clinicians in identifying family members for PhaCT through this platform. Offering patients the tools to share PGx results with their family members for preemptive testing could be the key to empowering patients. Clinicians can utilize PhaCT to potentially improve medication adherence, which may consequently help to distribute the burden of health management between patients, family members, providers, and payers.

Simulated Adoption of 2019 Community-Acquired Pneumonia Guidelines Across 114 Veterans Affairs Medical Centers: Estimated Impact on Culturing and Antibiotic Selection in Hospitalized Patients

BACKGROUND

The 2019 American Thoracic Society/Infectious Diseases Society of America guidelines for community-acquired pneumonia (CAP) revised recommendations for culturing and empiric broad-spectrum antibiotics. We simulated guideline adoption in Veterans Affairs (VA) inpatients.

METHODS

For all VA acute hospitalizations for CAP from 2006-2016 nationwide, we compared observed with guideline-expected proportions of hospitalizations with initial blood and respiratory cultures obtained, empiric antibiotic therapy with activity against methicillin-resistant Staphylococcus aureus (anti-MRSA) or Pseudomonas aeruginosa (antipseudomonal), empiric "overcoverage" (receipt of anti-MRSA/antipseudomonal therapy without eventual detection of MRSA/P. aeruginosa on culture), and empiric "undercoverage" (lack of anti-MRSA/antipseudomonal therapy with eventual detection on culture).

RESULTS

Of 115 036 CAP hospitalizations over 11 years, 17 877 (16%) were admitted to an intensive care unit (ICU). Guideline adoption would slightly increase respiratory culture (30% to 36%) and decrease blood culture proportions (93% to 36%) in hospital wards and increase both respiratory (40% to 100%) and blood (95% to 100%) cultures in ICUs. Adoption would decrease empiric selection of anti-MRSA (ward: 27% to 1%; ICU: 61% to 8%) and antipseudomonal (ward: 25% to 1%; ICU: 54% to 9%) therapies. This would correspond to greatly decreased MRSA overcoverage (ward: 27% to 1%; ICU: 56% to 8%), slightly increased MRSA undercoverage (ward: 0.6% to 1.3%; ICU: 0.5% to 3.3%), with similar findings for P. aeruginosa. For all comparisons, P < .001.

CONCLUSIONS

Adoption of the 2019 CAP guidelines in this population would substantially change culturing and empiric antibiotic selection practices, with a decrease in overcoverage and slight increase in undercoverage for MRSA and P. aeruginosa.

Use of Acute Psychiatric Hospitalisation: A Study of the Factors Influencing Decisions to Arrange Acute Admission to Inpatient Mental Health Facilities

Background: Human decision-making involves a complex interplay of intra- and inter-personal factors. The decisions clinicians make in practise are subject to a wide range of influences. Admission to a psychiatric hospital is a major clinical intervention, but the decision-making processes involved in admissions remain unclear. Aims: To delineate the range of factors influencing clinicians' decisions to arrange acute psychiatric admissions. Methods: We undertook six focus groups with teams centrally involved in decisions to admit patients to hospital (crisis resolution home treatment, liaison psychiatry, approved mental health practitioners and consultant psychiatrists). The data were analysed using qualitative thematic analysis. Results: Our analysis of the data show a complex range of factors influencing decision-making that were categorised as those related to: (i) clinical and risk factors; (ii) fear/threat factors; (iii) interpersonal dynamics; (iv) contextual factors. Conclusions: Decisions to arrange acute admission to hospital are not just based on an appraisal of clinical and risk-related information. Emotional, interpersonal and contextual factors are also critical in decision-making. Delineating the breadth of factors that bear on clinical decision-making can inform approaches to (i) clinical decision-making research, (ii) the training and supervision of clinicians, and (iii) service delivery models.

Artificial Intelligence-Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study

BACKGROUND

Medication Guides consisting of crucial interactions and side effects are extensive and complex. Due to the exhaustive information, patients do not retain the necessary medication information, which can result in hospitalizations and medication nonadherence. A gap exists in understanding patients' cognition of managing complex medication information. However, advancements in technology and artificial intelligence (AI) allow us to understand patient cognitive processes to design an app to better provide important medication information to patients.

OBJECTIVE

Our objective is to improve the design of an innovative AI- and human factor-based interface that supports patients' medication information comprehension that could potentially improve medication adherence.

METHODS

This study has three aims. Aim 1 has three phases: (1) an observational study to understand patient perception of fear and biases regarding medication information, (2) an eye-tracking study to understand the attention locus for medication information, and (3) a psychological refractory period (PRP) paradigm study to understand functionalities. Observational data will be collected, such as audio and video recordings, gaze mapping, and time from PRP. A total of 50 patients, aged 18-65 years, who started at least one new medication, for which we developed visualization information, and who have a cognitive status of 34 during cognitive screening using the TICS-M test and health literacy level will be included in this aim of the study. In Aim 2, we will iteratively design and evaluate an AI-powered medication information visualization interface as a smartphone app with the knowledge gained from each component of Aim 1. The interface will be assessed through two usability surveys. A total of 300 patients, aged 18-65 years, with diabetes, cardiovascular diseases, or mental health disorders, will be recruited for the surveys. Data from the surveys will be analyzed through exploratory factor analysis. In Aim 3, in order to test the prototype, there will be a two-arm study design. This aim will include 900 patients, aged 18-65 years, with internet access, without any cognitive impairment, and with at least two medications. Patients will be sequentially randomized. Three surveys will be used to assess the primary outcome of medication information comprehension and the secondary outcome of medication adherence at 12 weeks.

RESULTS

Preliminary data collection will be conducted in 2021, and results are expected to be published in 2022.

CONCLUSIONS

This study will lead the future of AI-based, innovative, digital interface design and aid in improving medication comprehension, which may improve medication adherence. The results from this study will also open up future research opportunities in understanding how patients manage complex medication information and will inform the format and design for innovative, AI-powered digital interfaces for Medication Guides.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/21659.

The inclusion of health data standards in the implementation of pharmacogenomics systems: a scoping review

Background: Despite potential benefits, the practice of incorporating pharmacogenomics (PGx) results in clinical decisions has yet to diffuse widely. In this study, we conducted a review of recent discussions on data standards and interoperability with a focus on sharing PGx test results among health systems. Materials & methods: We conducted a literature search for PGx clinical decision support systems between 1 January 2012 and 31 January 2020. Thirty-two out of 727 articles were included for the final review. Results: Nine of the 32 articles mentioned data standards and only four of the 32 articles provided solutions for the lack of interoperability. Discussions: Although PGx interoperability is essential for widespread implementation, a lack of focus on standardized data creates a formidable challenge for health information exchange. Conclusion: Standardization of PGx data is essential to improve health information exchange and the sharing of PGx results between disparate systems. However, PGx data standards and interoperability are often not addressed in the system-level implementation.

Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework

Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.

ENHANCING USEFULNESS AND USABILITY OF A CLINICAL DECISION SUPPORT PROTOTYPE FOR ANTIBIOTIC STEWARDSHIP

Human factors engineering can enhance software usefulness and usability. We describe a multi-method approach to improve clinical decision support (CDS) for antibiotic stewardship. We employed a heuristic review to generate recommendations to improve the usability of a prototype CDS to support empiric antibiotic prescribing in the hospital setting. We then engaged in a design improvement cycle in collaboration with software programmers, which resulted in additional enhancements to our prototype. Finally, we used the revised prototype during three walkthrough demonstration interviews with physician and pharmacist subject matter experts. These walkthrough interviews generated recommendations to improve the interface, functionality, and tailoring for groups of users. We discuss common elements of the recommendations for models for using clinical decision support in general.

Choice and consequence: A naturalistic analysis of least-worst decision-making in critical incidents

Individuals responsible for decision-making during critical incidents must wrestle with uncertainty, complexity, time pressure, and accountability. Critical incidents are defined as rare events where demand outstrips resources and where there are high stakes, uncertainty, and dynamic and ever-shifting elements that frustrate clear predictions. This paper argues that critical-incident decision-making is highly complex because many critical incidents have no such analogue, and thus there is no prior experience to draw upon. Further, while prescriptive models argue for a selection of a "best" outcome, rarely in critical incidents is there a "best" outcome and, instead, more likely a "least-worst" one. Most options are high risk, most will carry negative consequences, and many will be immutable and irreversible once committed to. This paper analyzes data collected from critical decision method interviews with members of the United States Armed Forces to explore the psychological processes of making (or not making) least-worst decisions in high-consequence situations. Specifically, and based on thematic analysis of interviews with those who have made least-worst decisions while serving as part of the Armed Forces, we identify a host of exogenous (external to the incident such as resources, political agendas) and endogenous factors (features of the incident itself-size, scale, duration) that affect the decision-making process. These factors have, to date, not been factored into theoretical models of how high-stakes decisions are made under conditions of uncertainty.

Novel displays of patient information in critical care settings: a systematic review

OBJECTIVE

Clinician information overload is prevalent in critical care settings. Improved visualization of patient information may help clinicians cope with information overload, increase efficiency, and improve quality. We compared the effect of information display interventions with usual care on patient care outcomes.

MATERIALS AND METHODS

We conducted a systematic review including experimental and quasi-experimental studies of information display interventions conducted in critical care and anesthesiology settings. Citations from January 1990 to June 2018 were searched in PubMed and IEEE Xplore. Reviewers worked independently to screen articles, evaluate quality, and abstract primary outcomes and display features.

RESULTS

Of 6742 studies identified, 22 studies evaluating 17 information displays met the study inclusion criteria. Information display categories included comprehensive integrated displays (3 displays), multipatient dashboards (7 displays), physiologic and laboratory monitoring (5 displays), and expert systems (2 displays). Significant improvement on primary outcomes over usual care was reported in 12 studies for 9 unique displays. Improvement was found mostly with comprehensive integrated displays (4 of 6 studies) and multipatient dashboards (5 of 7 studies). Only 1 of 5 randomized controlled trials had a positive effect in the primary outcome.

CONCLUSION

We found weak evidence suggesting comprehensive integrated displays improve provider efficiency and process outcomes, and multipatient dashboards improve compliance with care protocols and patient outcomes. Randomized controlled trials of physiologic and laboratory monitoring displays did not show improvement in primary outcomes, despite positive results in simulated settings. Important research translation gaps from laboratory to actual critical care settings exist.

Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design

Background

According to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis.

Objective

The overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management.

Methods

To achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3).

Results

Between 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021.

Conclusions

The results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics.

International Registered Report Identifier (IRRID)

DERR1-10.2196/16047

Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design

Background

Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications.

Objective

The objective of this study was to explore the feasibility of designing an interactive visualization-based mobile app using an HTA approach through a mobile prototype.

Methods

Two pharmacists constructed the HTA for the drug risperidone. Later, the specific requirements of the design were translated using infographics. We transferred the wireframes of the prototype into an interactive user interface. Finally, a usability evaluation of the mobile health app was conducted.

Results

A mobile app prototype using HTA and infographics was successfully created. We reiterated the design based on the specific recommendations from the usability evaluations.

Conclusions

Using HTA methodology, we successfully created a mobile prototype for delivering PPI on the drug risperidone to patients. The hierarchical goals and subgoals were translated into a mobile prototype.

Five decades of research and theorization on clinical reasoning: a critical review

Clinical reasoning is a complex cognitive process that is essential to evaluate and manage a patient's medical problem. The aim of this paper was to provide a critical review of the research literature on clinical reasoning theories and models. To conduct our study, we applied the process of conducting a literature review in four stages in accordance with the approach of Carnwell and Daly. First, we defined the scope of the review as being limited to clinical reasoning theories and models in medical education. In the second stage, we conducted a search based on related words in PubMed, Google Scholar, PsycINFO, ERIC, ScienceDirect and Web of Science databases. In the third stage, we classified the results of the review into three categories, and in the fourth stage, we concluded and informed further studies. Based on the inclusion and exclusion criteria, 31 articles were eligible to be reviewed. Three theories and two models were recognized and classified into three categories. Several theories and models have been proposed in relation to clinical reasoning, but it seems that these theories and models could only explain part of this complex process and not the whole process. Therefore, to fulfill this gap, it may be helpful to build a Meta-model or Meta-theory, which unified all the models, and theories of clinical reasoning.

Automated symptom and treatment side effect monitoring for improved quality of life among adults with diabetic peripheral neuropathy in primary care: a pragmatic, cluster, randomized, controlled trial

AIMS

To evaluate the effectiveness of automated symptom and side effect monitoring on quality of life among individuals with symptomatic diabetic peripheral neuropathy.

METHODS

We conducted a pragmatic, cluster randomized controlled trial (July 2014 to July 2016) within a large healthcare system. We randomized 1834 primary care physicians and prospectively recruited from their lists 1270 individuals with neuropathy who were newly prescribed medications for their symptoms. Intervention participants received automated telephone-based symptom and side effect monitoring with physician feedback over 6 months. The control group received usual care plus three non-interactive diabetes educational calls. Our primary outcomes were quality of life (EQ-5D) and select symptoms (e.g. pain) measured 4-8 weeks after starting medication and again 8 months after baseline. Process outcomes included receiving a clinically effective dose and communication between individuals with neuropathy and their primary care provider over 12 months. Interviewers collecting outcome data were blinded to intervention assignment.

RESULTS

Some 1252 participants completed the baseline measures [mean age (sd): 67 (11.7), 53% female, 57% white, 8% Asian, 13% black, 20% Hispanic]. In total, 1179 participants (93%) completed follow-up (619 control, 560 intervention). Quality of life scores (intervention: 0.658 ± 0.094; control: 0.653 ± 0.092) and symptom severity were similar at baseline. The intervention had no effect on primary [EQ-5D: -0.002 (95% CI -0.01, 0.01), P = 0.623; pain: 0.295 (-0.75, 1.34), P = 0.579; sleep disruption: 0.342 (-0.18, 0.86), P = 0.196; lower extremity functioning: -0.079 (-1.27, 1.11), P = 0.896; depression: -0.462 (-1.24, 0.32); P = 0.247] or process outcomes.

CONCLUSIONS

Automated telephone monitoring and feedback alone were not effective at improving quality of life or symptoms for people with symptomatic diabetic peripheral neuropathy.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT02056431).

Physician Information Needs in Managing Delirium

Delirium has the highest occurrence rate of any complication in hospitalized adults over the age of 65. The study objective was to determine physician information needs for use in the development of electronic clinical decision support for physicians managing the care of patients with delirium. Critical incident interviews were conducted with 8 experienced internal medicine physicians and 1 cardiologist. Thematic analysis revealed the following 6 themes: 1) Clinician's experience an impoverished information field for mental status, 2) Uncertainty is pervasive, 3) Extensive information foraging effort is required for cohesive story building, 4) Goal Conflict leads to missed diagnosis and early closure, 5) Diffusion of Responsibility for treating delirium is common, and 6) Use of structured delirium resources is minimal. Elicited information needs were identified and physician recommendations for improving access to information needed in managing the care of patient's with delirium are reported. Information elicited in this study is useful for designing delirium clinical decision support that supports physician cognition.

Information needs of physicians, care coordinators, and families to support care coordination of children and youth with special health care needs (CYSHCN)

Objectives

Identify and describe information needs and associated goals of physicians, care coordinators, and families related to coordinating care for medically complex children and youth with special health care needs (CYSHCN).

Materials and Methods

We conducted 19 in-depth interviews with physicians, care coordinators, and parents of CYSHCN following the Critical Decision Method technique. We analyzed the interviews for information needs posed as questions using a systematic content analysis approach and categorized the questions into information need goal types and subtypes.

Results

The Critical Decision Method interviews resulted in an average of 80 information needs per interview. We categorized them into 6 information need goal types: (1) situation understanding, (2) care networking, (3) planning, (4) tracking/monitoring, (5) navigating the health care system, and (6) learning, and 32 subtypes.

Discussion and Conclusion

Caring for CYSHCN generates a large amount of information needs that require significant effort from physicians, care coordinators, parents, and various other individuals. CYSHCN are often chronically ill and face developmental challenges that translate into intense demands on time, effort, and resources. Care coordination for CYCHSN involves multiple information systems, specialized resources, and complex decision-making. Solutions currently offered by health information technology fall short in providing support to meet the information needs to perform the complex care coordination tasks. Our findings present significant opportunities to improve coordination of care through multifaceted and fully integrated informatics solutions.

The role of emotion in clinical decision making: an integrative literature review

BACKGROUND

Traditionally, clinical decision making has been perceived as a purely rational and cognitive process. Recently, a number of authors have linked emotional intelligence (EI) to clinical decision making (CDM) and calls have been made for an increased focus on EI skills for clinicians. The objective of this integrative literature review was to identify and synthesise the empirical evidence for a role of emotion in CDM.

METHODS

A systematic search of the bibliographic databases PubMed, PsychINFO, and CINAHL (EBSCO) was conducted to identify empirical studies of clinician populations. Search terms were focused to identify studies reporting clinician emotion OR clinician emotional intelligence OR emotional competence AND clinical decision making OR clinical reasoning.

RESULTS

Twenty three papers were retained for synthesis. These represented empirical work from qualitative, quantitative, and mixed-methods approaches and comprised work with a focus on experienced emotion and on skills associated with emotional intelligence. The studies examined nurses (10), physicians (7), occupational therapists (1), physiotherapists (1), mixed clinician samples (3), and unspecified infectious disease experts (1). We identified two main themes in the context of clinical decision making: the subjective experience of emotion; and, the application of emotion and cognition in CDM. Sub-themes under the subjective experience of emotion were: emotional response to contextual pressures; emotional responses to others; and, intentional exclusion of emotion from CDM. Under the application of emotion and cognition in CDM, sub-themes were: compassionate emotional labour - responsiveness to patient emotion within CDM; interdisciplinary tension regarding the significance and meaning of emotion in CDM; and, emotion and moral judgement.

CONCLUSIONS

Clinicians' experienced emotions can and do affect clinical decision making, although acknowledgement of that is far from universal. Importantly, this occurs in the in the absence of a clear theoretical framework and educational preparation may not reflect the importance of emotional competence to effective CDM.

Practical tips for bedside teaching of infectious diseases practice to junior doctors

This article was migrated. The article was marked as recommended. Infectious diseases, being a cognitive specialty, is commonly perceived to be difficult to teach at the bedside. Most junior doctors have had variable amounts of exposure to infectious diseases as medical students, primarily in the form of lectures as part of a microbiology curriculum. Teaching principles of infectious diseases practice to junior doctors is essential to develop good antibiotic prescribing skills. We suggest a practical guide with 3 x 3 steps to teach infectious disease practice at the bedside. We have attempted to outline the anticipated problems with the instruction of Infectious disease practice in the clinical setting and then have proceeded to adopt an expanded version of the clinical micro-skills model to address those gaps.

Dealing with uncertainty when using a surveillance system

INTRODUCTION

Epidemiologists manage outbreak identification and confirmation by means of a "situation diagnosis", which involves validating (or invalidating) an alarm (signal identified as abnormal) as an alert (a real, characterized outbreak) and proposing the first countermeasures. This work investigates how uncertainty is materialized during this stage, and how experts develop strategies to address this uncertainty with the help of an early warning system.

METHODS

We built an experiment using a simulation platform with a scenario involving both a natural and an intentional outbreak. Observations of expert activities were recorded and formalised using a specific task analysis method. These formatted data were then categorized by applying RAWFS (Reduction- Assumption - Weighing - Forestalling- Suppression) heuristics.

RESULTS

We quantified uncertainty and the mechanisms involved. During the situation diagnosis, two sorts of uncertainty were characterized: practice-imposed uncertainty and situation-imposed uncertainty. We did not find either weighing pros and cons or suppression strategies in this area of expertise, but highlight the predominance of coping strategies that relied on reduction (66,4%) and assumption-based reasoning. We observed a predominance of the phone (89%) to cope with uncertainty and among electronic tools, the surveillance system plays a major role (69% of cases) and is mainly used in reduction strategies. We detail tools and systems used to support experts in their coping strategy.

CONCLUSION

We confirmed that a surveillance system must include different features that provide relevant information to help users reduce uncertainty and thus support their decision making. In that perspective, the flow diagram and proposal presented in this study can help prioritize the necessary changes to surveillance system design.

Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition

Complex clinical decision-making could be facilitated by using population health data to inform clinicians. In two previous studies, we interviewed 16 infectious disease experts to understand complex clinical reasoning. For this study, we focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. We found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians' cognition to deal with complex problems. These cognitive strategies could be supported by population health data, and all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records. Our findings provide directions for task allocation and design of decision-support applications for health care industry development of Big data based decision-support systems.

Copy Number Variation of Mitochondrial DNA Genes in Pneumocystis jirovecii According to the Fungal Load in BAL Specimens

Pneumocystis jirovecii is an unculturable fungus and the causative agent of Pneumocystis pneumonia, a life-threatening opportunistic infection. Although molecular diagnosis is often based on the detection of mtLSU rRNA mitochondrial gene, the number of copies of mitochondrial genes had not been investigated. We developed and optimized six real-time PCR assays in order to determine the copy number of four mitochondrial genes (mtSSU rRNA, mtLSU rRNA, NAD1, and CYTB) in comparison to nuclear genome (DHPS and HSP70) and tested 84 bronchoalveolar fluids of patients at different stages of the infection. Unexpectedly, we found that copy number of mitochondrial genes varied from gene to gene with mtSSU rRNA gene being more represented (37 copies) than NAD1 (23 copies), mtLSU rRNA (15 copies) and CYTB (6 copies) genes compared to nuclear genome. Hierarchical clustering analysis (HCA) allowed us to define five major clusters, significantly associated with fungal load (p = 0.029), in which copy number of mitochondrial genes was significantly different among them. More importantly, copy number of mtLSU rRNA, NAD1, and CYTB but not mtSSU rRNA differed according to P. jirovecii physiological state with a decreased number of copies when the fungal load is low. This suggests the existence of a mixture of various subspecies of mtDNA that can harbor different amplification rates. Overall, we revealed here an unexpected variability of P. jirovecii mtDNA copy number that fluctuates according to P. jirovecii’s physiological state, except for mtSSU that is the most stable and the most present mitochondrial gene.

Supporting novice clinicians cognitive strategies: System design perspective

Infections occur among all clinical domains. The changing nature of microbes, viruses and infections poses a great threat to the overall well-being in medicine. Clinicians in the infectious disease (ID) domain deal with diagnostic as well as treatment uncertainty in their everyday practice. Our current health information technology (HIT) systems do not consider the level of clinician expertise into the system design process. Thus, information is presented to both novice and expert ID clinicians in identical ways. The purpose of this study was to identify the cognitive strategies novice ID clinicians use in managing complex cases to make better recommendations for system design. In the process, we interviewed 14 ID experts and asked them to give us a detailed description of how novice clinicians would have dealt with complex cases. From the interview transcripts, we identified four major themes that expert clinicians suggested about novices' cognitive strategies including: A) dealing with uncertainty, B) lack of higher macrocognition, C) oversimplification of problems through heuristics and D) dealing with peer pressure. Current and future innovative decision support tools embedded in the electronic health record that can match these cognitive strategies may hold the key to cognitively supporting novice clinicians. The results of this study may open up avenues for future research and suggest design directions for better healthcare systems.