Advancing Diagnostic Safety Research: Results of a Systematic Research Priority Setting Exercise

Partnership as a Pathway to Diagnostic Excellence: The Challenges and Successes of Implementing the Safer Dx Learning Lab

BACKGROUND

Learning health system (LHS) approaches could potentially help health care organizations (HCOs) identify and address diagnostic errors. However, few such programs exist, and their implementation is poorly understood.

METHODS

The authors conducted a qualitative evaluation of the Safer Dx Learning Lab, a partnership between a health system and a research team, to identify and learn from diagnostic errors and improve diagnostic safety at an organizational level. The research team conducted virtual interviews to solicit participant feedback regarding experiences with the lab, focusing specifically on implementation and sustainment issues.

RESULTS

Interviews of 25 members associated with the lab identified the following successes: learning and professional growth, improved workflow related to streamlining the process of reporting error cases, and a psychologically safe culture for identifying and reporting diagnostic errors. However, multiple barriers also emerged: competing priorities between clinical responsibilities and research, time-management issues related to a lack of protected time, and inadequate guidance to disseminate findings. Lessons learned included understanding the importance of obtaining buy-in from leadership and interested stakeholders, creating a psychologically safe environment for reporting cases, and the need for more protected time for clinicians to review and learn from cases.

CONCLUSION

Findings suggest that a learning health systems approach using partnerships between researchers and a health system affected organizational culture by prioritizing learning from diagnostic errors and encouraging clinicians to be more open to reporting. The study findings can help organizations overcome barriers to engage clinicians and inform future implementation and sustainment of similar initiatives.

Trends of diagnostic adverse events in hospital deaths: longitudinal analyses of four retrospective record review studies

OBJECTIVES

To investigate longitudinal trends in the incidence, preventability, and causes of DAEs (diagnostic adverse events) between 2008 and 2019 and compare DAEs to other AE (adverse event) types.

METHODS

This study investigated longitudinal trends of DAEs using combined data from four large Dutch AE record review studies. The original four AE studies included 100-150 randomly selected records of deceased patients from around 20 hospitals in each study, resulting in a total of 10,943 patient records. Nurse reviewers indicated cases with potential AEs using a list of triggers. Subsequently, experienced physician reviewers systematically judged the occurrence of AEs, the clinical process in which these AEs occurred, and the preventability and causes.

RESULTS

The incidences of DAEs, potentially preventable DAEs and potentially preventable DAE-related deaths initially declined between 2008 and 2012 (2.3 vs. 1.2; OR=0.52, 95 % CI: 0.32 to 0.83), after which they stabilized up to 2019. These trends were largely the same for other AE types, although compared to DAEs, the incidence of other AE types increased between 2016 (DAE: 1.0, other AE types: 8.5) and 2019 (DAE: 0.8, other AE types: 13.0; rate ratio=1.88, 95 % CI: 1.12 to 2.13). Furthermore, DAEs were more preventable (p<0.001) and were associated with more potentially preventable deaths (p=0.016) than other AE types. In addition, DAEs had more and different underlying causes than other AE types (p<0.001). The DAE causes remained stable over time, except for patient-related factors, which increased between 2016 and 2019 (29.5 and 58.6 % respectively, OR=3.40, 95 % CI: 1.20 to 9.66).

CONCLUSIONS

After initial improvements of DAE incidences in 2012, no further improvement was observed in Dutch hospitals in the last decade. Similar trends were observed for other AEs. The high rate of preventability of DAEs suggest a high potential for improvement, that should be further investigated.

What is diagnostic safety? A review of safety science paradigms and rethinking paths to improving diagnosis

Diagnostic errors in health care are a global threat to patient safety. Researchers have traditionally focused diagnostic safety efforts on identifying errors and their causes with the goal of reducing diagnostic error rates. More recently, complementary approaches to diagnostic errors have focused on improving diagnostic performance drawn from the safety sciences. These approaches have been called Safety-II and Safety-III, which apply resilience engineering and system safety principles, respectively. This review explores the safety science paradigms and their implications for analyzing diagnostic errors, highlighting their distinct yet complementary perspectives. The integration of Safety-I, Safety-II, and Safety-III paradigms presents a promising pathway for improving diagnosis. Diagnostic researchers not yet familiar with the various approaches and potential paradigm shift in diagnostic safety research may use this review as a starting point for considering Safety-I, Safety-II, and Safety-III in their efforts to both reduce diagnostic errors and improve diagnostic performance.

From stable teamwork to dynamic teaming in the ambulatory care diagnostic process

Dynamic teaming is required whenever people must coordinate with one another in a fluid context, particularly when the fundamental structures of a team, such as membership, priorities, tasks, modes of communication, and location are in near-constant flux. This is certainly the case in the contemporary ambulatory care diagnostic process, where circumstances and conditions require a shifting cast of individuals to coordinate dynamically to ensure patient safety. This article offers an updated perspective on dynamic teaming commonly required during the ambulatory diagnostic process. Drawing upon team science, it clarifies the characteristics of dynamic diagnostic teams, identifies common risk points in the teaming process and the practical implications of these risks, considers the role of providers and patients in averting adverse outcomes, and provides a case example of the challenges of dynamic teaming during the diagnostic process. Based on this, future research needs are offered as well as clinical practice recommendations related to team characteristics and breakdowns, team member knowledge/cognitions, teaming dynamics, and the patient as a team member.

Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study

BACKGROUND

Diagnostic errors are an underappreciated cause of preventable mortality in hospitals and pose a risk for severe patient harm and increase hospital length of stay.

OBJECTIVE

This study aims to explore the potential of machine learning and natural language processing techniques in improving diagnostic safety surveillance. We conducted a rigorous evaluation of the feasibility and potential to use electronic health records clinical notes and existing case review data.

METHODS

Safety Learning System case review data from 1 large health system composed of 10 hospitals in the mid-Atlantic region of the United States from February 2016 to September 2021 were analyzed. The case review outcome included opportunities for improvement including diagnostic opportunities for improvement. To supplement case review data, electronic health record clinical notes were extracted and analyzed. A simple logistic regression model along with 3 forms of logistic regression models (ie, Least Absolute Shrinkage and Selection Operator, Ridge, and Elastic Net) with regularization functions was trained on this data to compare classification performances in classifying patients who experienced diagnostic errors during hospitalization. Further, statistical tests were conducted to find significant differences between female and male patients who experienced diagnostic errors.

RESULTS

In total, 126 (7.4%) patients (of 1704) had been identified by case reviewers as having experienced at least 1 diagnostic error. Patients who had experienced diagnostic error were grouped by sex: 59 (7.1%) of the 830 women and 67 (7.7%) of the 874 men. Among the patients who experienced a diagnostic error, female patients were older (median 72, IQR 66-80 vs median 67, IQR 57-76; P=.02), had higher rates of being admitted through general or internal medicine (69.5% vs 47.8%; P=.01), lower rates of cardiovascular-related admitted diagnosis (11.9% vs 28.4%; P=.02), and lower rates of being admitted through neurology department (2.3% vs 13.4%; P=.04). The Ridge model achieved the highest area under the receiver operating characteristic curve (0.885), specificity (0.797), positive predictive value (PPV; 0.24), and F1-score (0.369) in classifying patients who were at higher risk of diagnostic errors among hospitalized patients.

CONCLUSIONS

Our findings demonstrate that natural language processing can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review and therefore reducing the case review burden.

Breast imaging in focus: A bibliometric overview of visual quality, modality innovations, and diagnostic performance

INTRODUCTION

Breast imaging plays a crucial role in the early detection and management of breast cancer, with visual quality, modality innovation and diagnostic performance being key factors in achieving accurate diagnoses and optimal patient outcomes. This paper presents a comprehensive bibliometric analysis of the literature on the three above elements focusing on breast imaging, aiming to uncover publication trends, identify influential works and authors, and highlight future research directions.

METHODS

We employed a methodical bibliometric approach, making use of Scopus and Web of Science (WoS) databases for gathering literatures. We planned our search strategy, concentrating on terms linked to "breast imaging," "image quality," and "diagnostic accuracy" to ensure a systematic examination of the subject. The enhanced search functions in these databases enabled us to narrow down and improve our findings, choosing only the articles, conference papers, and book sections that are most relevant. After conducting a thorough screening process to remove duplicates and evaluate significance, we utilized ScientoPy and VOSviewer software for an in-depth bibliometric analysis. This helped to explore trends in publications, patterns of citations, and thematic groups, giving us a better understanding of how the field has changed and where it currently stands. Our approach prioritized assessing methodological quality and bias in the studies we included, guaranteeing the reliability of our findings.

RESULTS

We reviewed 2984 relevant publications, revealing a consistent annual growth rate of 2.8% in breast imaging research, with the United States and Europe leading in contributions. The study found that advancements in radiological technologies and international collaboration are driving forces behind the field's expansion. Key subject areas such as 'Radiology, Nuclear Medicine, and Medical Imaging' dominated, underscoring their impact on diagnostic quality. Notable authors and institutions have been identified for their influential research, characterized by high citation metrics and significant scholarly impact.

CONCLUSION

The study shows a continuous increase in research on breast imaging, considered by new technologies and teamwork defining the present time. The assessment highlights a key move towards utilizing digital imaging methods and computational analysis, affecting the improvement of future diagnostic procedures and patients' results. The study highlights the importance of continued international collaborations to tackle the new barriers in breast imaging and make the most of technological progress.

IMPLICATIONS FOR PRACTICE

This study shows a focus on using interdisciplinary methods and cutting-edge technology in breast imaging to help healthcare professionals improve their performance and accuracy in diagnosis. Recognizing vital research and emerging trends should guide clinical guidelines, radiology training, and patient care plans to encourage the use of effective techniques and stimulate innovation in diagnostic approaches.

Exploring Clinical Lessons Learned by Experienced Hospitalists from Diagnostic Errors and Successes

BACKGROUND

Diagnostic errors cause significant patient harm. The clinician's ultimate goal is to achieve diagnostic excellence in order to serve patients safely. This can be accomplished by learning from both errors and successes in patient care. However, the extent to which clinicians grow and navigate diagnostic errors and successes in patient care is poorly understood. Clinically experienced hospitalists, who have cared for numerous acutely ill patients, should have great insights from their successes and mistakes to inform others striving for excellence in patient care.

OBJECTIVE

To identify and characterize clinical lessons learned by experienced hospitalists from diagnostic errors and successes.

DESIGN

A semi-structured interview guide was used to collect qualitative data from hospitalists at five independently administered hospitals in the Mid-Atlantic area from February to June 2022.

PARTICIPANTS

12 academic and 12 community-based hospitalists with ≥ 5 years of clinical experience.

APPROACH

A constructivist qualitative approach was used and "reflexive thematic analysis" of interview transcripts was conducted to identify themes and patterns of meaning across the dataset.

RESULTS

Five themes were generated from the data based on clinical lessons learned by hospitalists from diagnostic errors and successes. The ideas included appreciating excellence in clinical reasoning as a core skill, connecting with patients and other members of the health care team to be able to tap into their insights, reflecting on the diagnostic process, committing to growth, and prioritizing self-care.

CONCLUSIONS

The study identifies key lessons learned from the errors and successes encountered in patient care by clinically experienced hospitalists. These findings may prove helpful for individuals and groups that are authentically committed to moving along the continuum from diagnostic competence towards excellence.

Refining a Framework to Enhance Communication in the Emergency Department During the Diagnostic Process: An eDelphi Approach

BACKGROUND

Emergency departments (EDs) are susceptible to diagnostic error. Suboptimal communication between the patient and the interdisciplinary care team increases risk to diagnostic safety. The role of communication remains underrepresented in existing diagnostic decision-making conceptual models.

METHODS

The authors used eDelphi methodology, whereby data are collected electronically, to achieve consensus among an expert panel of 18 clinicians, patients, family members, and other participants on a refined ED-based diagnostic decision-making framework that integrates several potential opportunities for communication to enhance diagnostic quality. This study examined the entire diagnostic process in the ED, from prehospital to discharge or transfer to inpatient care, and identified where communication breakdowns could occur. After four iterative rounds of the eDelphi process, including a final validation round by all participants, the project's a priori consensus threshold of 80% agreement was reached.

RESULTS

The authors developed a final framework that positions communication more prominently in the diagnostic process in the ED and enhances the original National Academies of Sciences, Engineering, and Medicine (NASEM) and ED-adapted NASEM frameworks. Specific points in the ED journey were identified where more attention to communication might be helpful. Two specific types of communication-information exchange and shared understanding-were identified as high priority for optimal outcomes. Ideas for communication-focused interventions to prevent diagnostic error in the ED fell into three categories: patient-facing, clinician-facing, and system-facing interventions.

CONCLUSION

This project's refinement of the NASEM framework adapted to the ED can be used to develop communications-focused interventions to reduce diagnostic error in this highly complex and error-prone setting.

The PRIDx framework to engage payers in reducing diagnostic errors in healthcare

OBJECTIVES

No framework currently exists to guide how payers and providers can collaboratively develop and implement incentives to improve diagnostic safety. We conducted a literature review and interviews with subject matter experts to develop a multi-component 'Payer Relationships for Improving Diagnoses (PRIDx)' framework, that could be used to engage payers in diagnostic safety efforts.

CONTENT

The PRIDx framework, 1) conceptualizes diagnostic safety links to care provision, 2) illustrates ways to promote payer and provider engagement in the design and adoption of accountability mechanisms, and 3) explicates the use of data analytics. Certain approaches suggested by PRIDx were refined by subject matter expert interviewee perspectives.

SUMMARY

The PRIDx framework can catalyze public and private payers to take specific actions to improve diagnostic safety.

OUTLOOK

Implementation of the PRIDx framework requires new types of partnerships, including external support from public and private payer organizations, and requires creation of strong provider incentives without undermining providers' sense of professionalism and autonomy. PRIDx could help facilitate collaborative payer-provider approaches to improve diagnostic safety and generate research concepts, policy ideas, and potential innovations for engaging payers in diagnostic safety improvement activities.

Behavioral sciences applied to acute care teams: a research agenda for the years ahead by a European research network

BACKGROUND

Multi-disciplinary behavioral research on acute care teams has focused on understanding how teams work and on identifying behaviors characteristic of efficient and effective team performance. We aimed to define important knowledge gaps and establish a research agenda for the years ahead of prioritized research questions in this field of applied health research.

METHODS

In the first step, high-priority research questions were generated by a small highly specialized group of 29 experts in the field, recruited from the multinational and multidisciplinary "Behavioral Sciences applied to Acute care teams and Surgery (BSAS)" research network - a cross-European, interdisciplinary network of researchers from social sciences as well as from the medical field committed to understanding the role of behavioral sciences in the context of acute care teams. A consolidated list of 59 research questions was established. In the second step, 19 experts attending the 2020 BSAS annual conference quantitatively rated the importance of each research question based on four criteria - usefulness, answerability, effectiveness, and translation into practice. In the third step, during half a day of the BSAS conference, the same group of 19 experts discussed the prioritization of the research questions in three online focus group meetings and established recommendations.

RESULTS

Research priorities identified were categorized into six topics: (1) interventions to improve team process; (2) dealing with and implementing new technologies; (3) understanding and measuring team processes; (4) organizational aspects impacting teamwork; (5) training and health professions education; and (6) organizational and patient safety culture in the healthcare domain. Experts rated the first three topics as particularly relevant in terms of research priorities; the focus groups identified specific research needs within each topic.

CONCLUSIONS

Based on research priorities within the BSAS community and the broader field of applied health sciences identified through this work, we advocate for the prioritization for funding in these areas.

The Nature, Causes, and Clinical Impact of Errors in the Clinical Laboratory Testing Process Leading to Diagnostic Error: A Voluntary Incident Report Analysis

OBJECTIVES

Diagnostic errors, that is, missed, delayed, or wrong diagnoses, are a common type of medical errors and preventable iatrogenic harm. Errors in the laboratory testing process can lead to diagnostic errors. This retrospective analysis of voluntary incident reports aimed to investigate the nature, causes, and clinical impact of errors, including diagnostic errors, in the clinical laboratory testing process.

METHODS

We used a sample of 600 voluntary incident reports concerning diagnostic testing selected from all incident reports filed at the University Medical Center Utrecht in 2017-2018. From these incident reports, we included all reports concerning the clinical laboratory testing process. For these incidents, we determined the following: nature: in which phase of the testing process the error occurred; cause: human, technical, organizational; and clinical impact: the type and severity of the harm to the patient, including diagnostic error.

RESULTS

Three hundred twenty-seven reports were included in the analysis. In 77.1%, the error occurred in the preanalytical phase, 13.5% in the analytical phase and 8.0% in the postanalytical phase (1.5% undetermined). Human factors were the most frequent cause (58.7%). Severe clinical impact occurred relatively more often in the analytical and postanalytical phase, 32% and 28%, respectively, compared with the preanalytical phase (40%). In 195 cases (60%), there was a potential diagnostic error as consequence, mainly a potential delay in the diagnostic process (50.5%).

CONCLUSIONS

Errors in the laboratory testing process often lead to potential diagnostic errors. Although prone to incomplete information on causes and clinical impact, voluntary incident reports are a valuable source for research on diagnostic error related to errors in the clinical laboratory testing process.

Diagnostic error experiences of patients and families with limited English-language health literacy or disadvantaged socioeconomic position in a cross-sectional US population-based survey

BACKGROUND

Language barrier, reduced self-advocacy, lower health literacy or biased care may hinder the diagnostic process. Data on how patients/families with limited English-language health literacy (LEHL) or disadvantaged socioeconomic position (dSEP) experience diagnostic errors are sparse.

METHOD

We compared patient-reported diagnostic errors, contributing factors and impacts between respondents with LEHL or dSEP and their counterparts in the 2017 Institute for Healthcare Improvement US population-based survey, using contingency analysis and multivariable logistic regression models for the analyses.

RESULTS

596 respondents reported a diagnostic error; among these, 381 reported LEHL or dSEP. After adjusting for sex, race/ethnicity and physical health, individuals with LEHL/dSEP were more likely than their counterparts to report unique contributing factors: "(No) qualified translator or healthcare provider that spoke (the patient's) language" (OR and 95% CI 4.4 (1.3 to 14.9)); "not understanding the follow-up plan" (1.9 (1.1 to 3.1)); "too many providers… but no clear leader" (1.8 (1.2 to 2.7)); "not able to keep follow-up appointments" (1.9 (1.1 to 3.2)); "not being able to pay for necessary medical care" (2.5 (1.4 to 4.4)) and "out-of-date or incorrect medical records" (2.6 (1.4 to 4.8)). Participants with LEHL/dSEP were more likely to report long-term emotional, financial and relational impacts, compared with their counterparts. Subgroup analysis (LEHL-only and dSEP-only participants) showed similar results.

CONCLUSIONS

Individuals with LEHL or dSEP identified unique and actionable contributing factors to diagnostic errors. Interpreter access should be viewed as a diagnostic safety imperative, social determinants affecting care access/affordability should be routinely addressed as part of the diagnostic process and patients/families should be encouraged to access and update their medical records. The frequent and disproportionate long-term impacts from self-reported diagnostic error among LEHL/dSEP patients/families raises urgency for greater prevention and supportive efforts.

Towards diagnostic excellence on academic ward teams: building a conceptual model of team dynamics in the diagnostic process

OBJECTIVES

Achieving diagnostic excellence on medical wards requires teamwork and effective team dynamics. However, the study of ward team dynamics in teaching hospitals is relatively underdeveloped. We aim to enhance understanding of how ward team members interact in the diagnostic process and of the underlying behavioral, psychological, and cognitive mechanisms driving team interactions.

METHODS

We used mixed-methods to develop and refine a conceptual model of how ward team dynamics in an academic medical center influence the diagnostic process. First, we systematically searched existing literature for conceptual models and empirical studies of team dynamics. Then, we conducted field observations with thematic analysis to refine our model.

RESULTS

We present a conceptual model of how medical ward team dynamics influence the diagnostic process, which serves as a roadmap for future research and interventions in this area. We identified three underexplored areas of team dynamics that are relevant to diagnostic excellence and that merit future investigation (1): ward team structures (e.g., team roles, responsibilities) (2); contextual factors (e.g., time constraints, location of team members, culture, diversity); and (3) emergent states (shared mental models, psychological safety, team trust, and team emotions).

CONCLUSIONS

Optimizing the diagnostic process to achieve diagnostic excellence is likely to depend on addressing all of the potential barriers and facilitators to ward team dynamics presented in our model.

Impact of diagnostic checklists on the interpretation of normal and abnormal electrocardiograms

OBJECTIVES

Checklists that aim to support clinicians' diagnostic reasoning processes are often recommended to prevent diagnostic errors. Evidence on checklist effectiveness is mixed and seems to depend on checklist type, case difficulty, and participants' expertise. Existing studies primarily use abnormal cases, leaving it unclear how the diagnosis of normal cases is affected by checklist use. We investigated how content-specific and debiasing checklists impacted performance for normal and abnormal cases in electrocardiogram (ECG) diagnosis.

METHODS

In this randomized experiment, 42 first year general practice residents interpreted normal, simple abnormal, and complex abnormal ECGs without a checklist. One week later, they were randomly assigned to diagnose the ECGs again with either a debiasing or content-specific checklist. We measured residents' diagnostic accuracy, confidence, patient management, and time taken to diagnose. Additionally, confidence-accuracy calibration was assessed.

RESULTS

Accuracy, confidence, and patient management were not significantly affected by checklist use. Time to diagnose decreased with a checklist (M=147s (77)) compared to without a checklist (M=189s (80), Z=-3.10, p=0.002). Additionally, residents' calibration improved when using a checklist (phase 1: R2=0.14, phase 2: R2=0.40).

CONCLUSIONS

In both normal and abnormal cases, checklist use improved confidence-accuracy calibration, though accuracy and confidence were not significantly affected. Time to diagnose was reduced. Future research should evaluate this effect in more experienced GPs. Checklists appear promising for reducing overconfidence without negatively impacting normal or simple ECGs. Reducing overconfidence has the potential to improve diagnostic performance in the long term.

Patient generated research priorities to improve diagnostic safety: A systematic prioritization exercise

BACKGROUND

Most people experience a diagnostic error at least once in their lifetime. Patients' experiences with their diagnosis could provide important insights when setting research priorities to reduce diagnostic error.

OBJECTIVE

Our objective was to engage patients in research agenda setting for improving diagnosis.

PATIENT INVOLVEMENT

Patients were involved in generating, discussing, prioritizing, and ranking of research questions for diagnostic error reduction.

METHODS

We used the prioritization methodology based on the Child Health and Nutrition Research Initiative (CHNRI). We first solicited research questions important for diagnostic error reduction from a large group of patients. Thirty questions were initially prioritized at an in-person meeting with 8 patients who were supported by 4 researchers. The resulting list was further prioritized by patients who scored questions on five predefined criteria. We then applied previously determined weights to these prioritization criteria to adjust the final prioritization score for each question, resulting in 10 highest priority research questions.

RESULTS

Forty-one patients submitted 171 research questions. After prioritization, the highest priority topics included better care coordination across the diagnostic continuum and improving care transitions, improved identification and measurement of diagnostic errors and attention for implicit bias towards patients who are vulnerable to diagnostic errors.

DISCUSSION

We systematically identified the top-10 patient generated research priorities for diagnostic error reduction using transparent and objective methods. Patients prioritized different research questions than researchers and therefore complemented an agenda previously generated by researchers.

PRACTICAL VALUE

Research priorities identified by patients can be used by funders and researchers to conduct future research focused on reducing diagnostic errors.

FUNDING

This project was funded by the Gordon and Betty Moore Foundation.

Developing a Framework and Electronic Tool for Communicating Diagnostic Uncertainty in Primary Care: A Qualitative Study

IMPORTANCE

Communication of information has emerged as a critical component of diagnostic quality. Communication of diagnostic uncertainty represents a key but inadequately examined element of diagnosis.

OBJECTIVE

To identify key elements facilitating understanding and managing diagnostic uncertainty, examine optimal ways to convey uncertainty to patients, and develop and test a novel tool to communicate diagnostic uncertainty in actual clinical encounters.

DESIGN, SETTING, AND PARTICIPANTS

A 5-stage qualitative study was performed between July 2018 and April 2020, at an academic primary care clinic in Boston, Massachusetts, with a convenience sample of 24 primary care physicians (PCPs), 40 patients, and 5 informatics and quality/safety experts. First, a literature review and panel discussion with PCPs were conducted and 4 clinical vignettes of typical diagnostic uncertainty scenarios were developed. Second, these scenarios were tested during think-aloud simulated encounters with expert PCPs to iteratively draft a patient leaflet and a clinician guide. Third, the leaflet content was evaluated with 3 patient focus groups. Fourth, additional feedback was obtained from PCPs and informatics experts to iteratively redesign the leaflet content and workflow. Fifth, the refined leaflet was integrated into an electronic health record voice-enabled dictation template that was tested by 2 PCPs during 15 patient encounters for new diagnostic problems. Data were thematically analyzed using qualitative analysis software.

MAIN OUTCOMES AND MEASURES

Perceptions and testing of content, feasibility, usability, and satisfaction with a prototype tool for communicating diagnostic uncertainty to patients.

RESULTS

Overall, 69 participants were interviewed. A clinician guide and a diagnostic uncertainty communication tool were developed based on the PCP interviews and patient feedback. The optimal tool requirements included 6 key domains: most likely diagnosis, follow-up plan, test limitations, expected improvement, contact information, and space for patient input. Patient feedback on the leaflet was iteratively incorporated into 4 successive versions, culminating in a successfully piloted prototype tool as an end-of-visit voice recognition dictation template with high levels of patient satisfaction for 15 patients with whom the tool was tested.

CONCLUSIONS AND RELEVANCE

In this qualitative study, a diagnostic uncertainty communication tool was successfully designed and implemented during clinical encounters. The tool demonstrated good workflow integration and patient satisfaction.

Imagining the future of diagnostic performance feedback

Diagnostic performance is uniquely challenging to measure, and providing feedback on diagnostic performance to catalyze diagnostic recalibration remains the exception to the rule in healthcare. Diagnostic accuracy, timeliness, and explanation to the patient are essential dimensions of diagnostic performance that each intersect with a variety of technical, contextual, cultural, and policy barriers. Setting aside assumptions about current constraints, we explore the future of diagnostic performance feedback by describing the "minimum viable products" and the "ideal state" solutions that can be envisioned for each of several important barriers. Only through deliberate and iterative approaches to breaking down these barriers can we improve recalibration and continuously drive the healthcare ecosystem towards diagnostic excellence.

Role of primary care physician factors on diagnostic testing and referral decisions for symptoms of possible cancer: a systematic review

BACKGROUND

Missed opportunities for diagnosing cancer cause patients harm and have been attributed to suboptimal use of tests and referral pathways in primary care. Primary care physician (PCP) factors have been suggested to affect decisions to investigate cancer, but their influence is poorly understood.

OBJECTIVE

To synthesise evidence evaluating the influence of PCP factors on decisions to investigate symptoms of possible cancer.

METHODS

We searched MEDLINE, Embase, Scopus, CINAHL and PsycINFO between January 1990 and March 2021 for relevant citations. Studies examining the effect or perceptions and experiences of PCP factors on use of tests and referrals for symptomatic patients with any cancer were included. PCP factors comprised personal characteristics and attributes of physicians in clinical practice.

DATA EXTRACTION AND SYNTHESIS

Critical appraisal and data extraction were undertaken independently by two authors. Due to study heterogeneity, data could not be statistically pooled. We, therefore, performed a narrative synthesis.

RESULTS

29 studies were included. Most studies were conducted in European countries. A total of 11 PCP factors were identified comprising modifiable and non-modifiable factors. Clinical judgement of symptoms as suspicious or 'alarm' prompted more investigations than non-alarm symptoms. 'Gut feeling' predicted a subsequent cancer diagnosis and was perceived to facilitate decisions to investigate non-specific symptoms as PCP experience increased. Female PCPs investigated cancer more than male PCPs. The effect of PCP age and years of experience on testing and referral decisions was inconclusive.

CONCLUSIONS

PCP interpretation of symptoms as higher risk facilitated testing and referral decisions for possible cancer. However, in the absence of 'alarm' symptoms or 'gut feeling', PCPs may not investigate cancer. PCPs require strategies for identifying patients with non-alarm and non-specific symptoms who need testing or referral.

PROSPERO REGISTRATION NUMBER

CRD420191560515.

A Research Agenda for Diagnostic Excellence in Critical Care Medicine

Diagnosing critically ill patients in the intensive care unit is difficult. As a result, diagnostic errors in the intensive care unit are common and have been shown to cause harm. Research to improve diagnosis in critical care medicine has accelerated in past years. However, much work remains to fully elucidate the diagnostic process in critical care. To achieve diagnostic excellence, interdisciplinary research is needed, adopting a balanced strategy of continued biomedical discovery while addressing the complex care delivery systems underpinning the diagnosis of critical illness.

Filling a gap in safety metrics: development of a patient-centred framework to identify and categorise patient-reported breakdowns related to the diagnostic process in ambulatory care

BACKGROUND

Patients and families are important contributors to the diagnostic team, but their perspectives are not reflected in current diagnostic measures. Patients/families can identify some breakdowns in the diagnostic process beyond the clinician's view. We aimed to develop a framework with patients/families to help organisations identify and categorise patient-reported diagnostic process-related breakdowns (PRDBs) to inform organisational learning.

METHOD

A multi-stakeholder advisory group including patients, families, clinicians, and experts in diagnostic error, patient engagement and safety, and user-centred design, co-developed a framework for PRDBs in ambulatory care. We tested the framework using standard qualitative analysis methods with two physicians and one patient coder, analysing 2165 patient-reported ambulatory errors in two large surveys representing 25 425 US respondents. We tested intercoder reliability of breakdown categorisation using the Gwet's AC1 and Cohen's kappa statistic. We considered agreement coefficients 0.61-0.8=good agreement and 0.81-1.00=excellent agreement.

RESULTS

The framework describes 7 patient-reported breakdown categories (with 40 subcategories), 19 patient-identified contributing factors and 11 potential patient-reported impacts. Patients identified breakdowns in each step of the diagnostic process, including missing or inaccurate main concerns and symptoms; missing/outdated test results; and communication breakdowns such as not feeling heard or misalignment between patient and provider about symptoms, events, or their significance. The frequency of PRDBs was 6.4% in one dataset and 6.9% in the other. Intercoder reliability showed good-to-excellent reliability in each dataset: AC1 0.89 (95% CI 0.89 to 0.90) to 0.96 (95% CI 0.95 to 0.97); kappa 0.64 (95% CI 0.62, to 0.66) to 0.85 (95% CI 0.83 to 0.88).

CONCLUSIONS

The PRDB framework, developed in partnership with patients/families, can help organisations identify and reliably categorise PRDBs, including some that are invisible to clinicians; guide interventions to engage patients and families as diagnostic partners; and inform whole organisational learning.