Brief report: Improving communication of diagnostic uncertainty to families of hospitalized children.

OBJECTIVES: Diagnostic uncertainty is not reliably communicated to patients and caregivers. This study aims to identify barriers and facilitators to effective communication of diagnostic uncertainty, including development of potential tools and strategies for improvement, as perceived by healthcare professionals and caregivers. METHODS: We completed structured interviews with providers and caregivers of hospitalized children with uncertain diagnoses (UD). The interview guides addressed barriers to communication, key components for communication of uncertainty, and qualities of effective communication. The interviews concluded with respondents prioritizing potential interventions to improve communication of uncertainty. Interviews were audio recorded, transcribed, and independently analyzed by two team members to identify common themes. RESULTS: Ten provider and five caregiver interviews were conducted. Common barriers to communication of uncertainty included time constraints, language barriers, and lack of clear definition of UD. Caregiver suggestions for improvement included sharing expectations of the diagnostic process and use of both written and visual communication tools. Interview respondents favored interventions of a sign summarizing the key components of diagnostic uncertainty for display in patient rooms and a structured diagnostic pause during daily rounds. CONCLUSIONS: We identified several potential interventions that may enhance communication of diagnostic uncertainty and better engage patients and caregivers in the diagnostic process.

Developing a machine learning model to detect diagnostic uncertainty in clinical documentation.

BACKGROUND AND OBJECTIVE: Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to identify distinct linguistic patterns associated with diagnostic uncertainty in clinical documentation. DESIGN, SETTING AND PARTICIPANTS: This case-control study compares the clinical documentation of hospitalized children who received a novel uncertain diagnosis (UD) diagnosis label during their admission to a set of matched controls. Linguistic analyses identified potential linguistic indicators (i.e., words or phrases) of diagnostic uncertainty that were then manually reviewed by a linguist and clinical experts to identify those most relevant to diagnostic uncertainty. A natural language processing program categorized medical terminology into semantic types (i.e., sign or symptom), from which we identified a subset of these semantic types that both categorized reliably and were relevant to diagnostic uncertainty. Finally, a competitive machine learning modeling strategy utilizing the linguistic indicators and semantic types compared different predictive models for identifying diagnostic uncertainty. RESULTS: Our cohort included 242 UD-labeled patients and 932 matched controls with a combination of 3070 clinical notes. The best-performing model was a random forest, utilizing a combination of linguistic indicators and semantic types, yielding a sensitivity of 89.4% and a positive predictive value of 96.7%. CONCLUSION: Expert labeling, natural language processing, and machine learning methods combined with human validation resulted in highly predictive models to detect diagnostic uncertainty in clinical documentation and represent a promising approach to detecting, studying, and ultimately mitigating diagnostic uncertainty in clinical practice.

Diagnosis Code and Health Care Utilization Patterns Associated With Diagnostic Uncertainty.

BACKGROUND AND OBJECTIVES: Diagnostic uncertainty is challenging to identify and study in clinical practice. This study compares differences in diagnosis code and health care utilization between a unique cohort of hospitalized children with uncertain diagnoses (UD) and matched controls. PATIENTS AND METHODS: This case-control study was conducted at Cincinnati Children's Hospital Medical Center. Cases were defined as patients admitted to the pediatric hospital medicine service and having UDs during their hospitalization. Control patients were matched on age strata, biological sex, and time of year. Outcomes included type of diagnosis codes used (ie, disease- or nondisease-based) and change in code from admission to discharge. Differences in diagnosis codes were evaluated using conditional logistic regression. Health care utilization outcomes included hospital length of stay (LOS), hospital transfer, consulting service utilization, rapid response team activations, escalation to intensive care, and 30-day health care reutilization. Differences in health care utilization were assessed using bivariate statistics. RESULTS: Our final cohort included 240 UD cases and 911 matched controls. Compared with matched controls, UD cases were 8 times more likely to receive a nondisease-based diagnosis code (odds ratio [OR], 8.0; 95% confidence interval [CI], 5.7-11.2) and 2.5 times more likely to have a change in their primary International Classification of Disease, 10th revision, diagnosis code between admission and discharge (OR, 2.5; 95% CI, 1.9-3.4). UD cases had a longer average LOS and higher transfer rates to our main hospital campus, consulting service use, and 30-day readmission rates. CONCLUSIONS: Hospitalized children with UDs have meaningfully different patterns of diagnosis code use and increased health care utilization compared with matched controls.

Diagnostic Error in Pediatrics: A Narrative Review.

A priority topic for patient safety research is diagnostic errors. However, despite the significant growth in awareness of their unacceptably high incidence and associated harm, a relative paucity of large, high-quality studies of diagnostic error in pediatrics exists. In this narrative review, we present what is known about the incidence and epidemiology of diagnostic error in pediatrics as well as the established research methods for identifying, evaluating, and reducing diagnostic errors, including their strengths and weaknesses. Additionally, we highlight that pediatric diagnostic error remains an area in need of both innovative research and quality improvement efforts to apply learnings from a rapidly growing evidence base. We propose several key research questions aimed at addressing persistent gaps in the pediatric diagnostic error literature that focus on the foundational knowledge needed to inform effective interventions to reduce the incidence of diagnostic errors and their associated harm. Additional research is needed to better establish the epidemiology of diagnostic error in pediatrics, including identifying high-risk clinical scenarios, patient populations, and groups of diagnoses. A critical need exists for validated measures of both diagnostic errors and diagnostic processes that can be adapted for different clinical settings and standardized for use across varying institutions. Pediatric researchers will need to work collaboratively on large-scale, high-quality studies to accomplish the ultimate goal of reducing diagnostic errors and their associated harm in children by addressing these fundamental gaps in knowledge.

Declaring Uncertainty: Using Quality Improvement Methods to Change the Conversation of Diagnosis.

BACKGROUND: Diagnostic uncertainty may be a sign that a patient's working diagnosis is incorrect, but literature on proactively identifying diagnostic uncertainty is lacking. Using quality improvement methodologies, we aimed to create a process for identifying patients with uncertain diagnoses (UDs) on a pediatric inpatient unit and communicating about them with the interdisciplinary health care team. METHODS: Plan-do-study-act cycles were focused on interdisciplinary communication, structured handoffs, and integration of diagnostic uncertainty into the electronic medical record. Our definition of UD was as follows: "you wouldn't be surprised if the patient had a different diagnosis that required a change in management." The primary measure, which was tracked on an annotated run chart, was percentage agreement between the charge nurse and primary clinician regarding which patients had a UD. Secondary measures included the percentage of patient days during which patients had UDs. Data were collected 3 times daily by text message polls. RESULTS: Over 13 months, the percentage agreement between the charge nurse and primary clinician about which patients had UDs increased from a baseline of 19% to a median of 84%. On average, patients had UDs during 11% of patient days. CONCLUSIONS: We created a novel and effective process to improve shared recognition of patients with diagnostic uncertainty among the interdisciplinary health care team, which is an important first step in improving care for these patients.

Uncertain diagnoses in a children's hospital: patient characteristics and outcomes.

BACKGROUND: A quality improvement initiative at our institution resulted in a new process for prospectively identifying pediatric hospital medicine (PHM) patients with uncertain diagnoses (UD). This study describes the clinical characteristics and healthcare utilization patterns of patients with UD. METHODS: This single center cross-sectional study included all PHM patients identified with UD during their admission. A structured chart review was used to abstract patient demographics, primary symptoms, discharge diagnoses, and healthcare utilization patterns, including consult service use, length of stay (LOS), escalation in care, and 30-day healthcare reutilization. Appropriate descriptive statistics were used for categorical and continuous variables. RESULTS: This study includes 200 PHM patients identified with UD. Gastrointestinal symptoms were the primary finding in 45% of patients with UD. Consult service use was highly variable, with a range of 0-8 consult services for individual patients. The median LOS was 1.6 days and only 5% required a rapid response team evaluation. As for reutilization, 7% of patients were readmitted within 30 days. CONCLUSIONS: This descriptive study highlights the heterogeneity of patients with uncertain diagnoses. Ongoing work is needed to further understand the impact of UD and to optimize the care of these patients.

Increasing Physician Reporting of Diagnostic Learning Opportunities.

BACKGROUND: An estimated 10% of Americans experience a diagnostic error annually, yet little is known about pediatric diagnostic errors. Physician reporting is a promising method for identifying diagnostic errors. However, our pediatric hospital medicine (PHM) division had only 1 diagnostic-related safety report in the preceding 4 years. We aimed to improve attending physician reporting of suspected diagnostic errors from 0 to 2 per 100 PHM patient admissions within 6 months. METHODS: Our improvement team used the Model for Improvement, targeting the PHM service. To promote a safe reporting culture, we used the term diagnostic learning opportunity (DLO) rather than diagnostic error, defined as a "potential opportunity to make a better or more timely diagnosis." We developed an electronic reporting form and encouraged its use through reminders, scheduled reflection time, and monthly progress reports. The outcome measure, the number of DLO reports per 100 patient admissions, was tracked on an annotated control chart to assess the effect of our interventions over time. We evaluated DLOs using a formal 2-reviewer process. RESULTS: Over the course of 13 weeks, there was an increase in the number of reports filed from 0 to 1.6 per 100 patient admissions, which met special cause variation, and was subsequently sustained. Most events (66%) were true diagnostic errors and were found to be multifactorial after formal review. CONCLUSIONS: We used quality improvement methodology, focusing on psychological safety, to increase physician reporting of DLOs. This growing data set has generated nuanced learnings that will guide future improvement work.

Prevalence of baby bottle versus breastfeeding graphics on products in national chain stores.

OBJECTIVE: This study surveyed the prevalence of bottle versus breastfeeding graphic images on products marketed for pregnant mothers and young children available for purchase in national chain stores. STUDY DESIGN AND METHODS: This was a product survey/content analysis. Eighteen national chain stores located in a 10-mile radius of Charlottesville, VA were visited. In total, 2,670 individual items in 11 categories of baby shower and baby gift merchandise (shower invitations, greeting cards, gift wrap, shower decorations, baby dolls, baby books, infant clothing, bibs, nursery decorations, baby blankets, and disposable diapers) were assessed. The main outcome measures were prevalences of baby bottle and breastfeeding graphic images. RESULTS: Baby bottle images were found on products in eight of the 11 categories of items surveyed. Thirty-five percent of baby dolls were marketed with a baby bottle. The prevalence of bottle images on items in all other categories, however, was low. Of the 2,670 items surveyed, none contained a breastfeeding image. CONCLUSIONS: The low prevalence of baby bottle images on commonly purchased baby gift and baby shower items is encouraging. However, the absence of breastfeeding images and the relatively high prevalence of baby dolls marketed with a baby bottle demonstrate that breastfeeding is not portrayed as the physiologic norm on these products. Product designers should explore ways to promote breastfeeding, consumers should make informed choices in product selection, and advocacy groups should promote guidelines for these products.