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.

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.

A Validated Method for Identifying Unplanned Pediatric Readmission.

OBJECTIVE: To validate the accuracy of pre-encounter hospital designation as a novel way to identify unplanned pediatric readmissions and describe the most common diagnoses for unplanned readmissions among children. STUDY DESIGN: We examined all hospital discharges from 2 tertiary care children's hospitals excluding deaths, normal newborn discharges, transfers to other institutions, and discharges to hospice. We performed blinded medical record review on 641 randomly selected readmissions to validate the pre-encounter planned/unplanned hospital designation. We identified the most common discharge diagnoses associated with subsequent 30-day unplanned readmissions. RESULTS: Among 166,994 discharges (hospital A: n = 55,383; hospital B: n = 111,611), the 30-day unplanned readmission rate was 10.3% (hospital A) and 8.7% (hospital B). The hospital designation of "unplanned" was correct in 98% (hospital A) and 96% (hospital B) of readmissions; the designation of "planned" was correct in 86% (hospital A) and 85% (hospital B) of readmissions. The most common discharge diagnoses for which unplanned 30-day readmissions occurred were oncologic conditions (up to 38%) and nonhypertensive congestive heart failure (about 25%), across both institutions. CONCLUSIONS: Unplanned readmission rates for pediatrics, using a validated, accurate, pre-encounter designation of "unplanned," are higher than previously estimated. For some pediatric conditions, unplanned readmission rates are as high as readmission rates reported for adult conditions. Anticipating unplanned readmissions for high-frequency diagnostic groups may help focus efforts to reduce the burden of readmission for families and facilities. Using timing of hospital registration in administrative records is an accurate, widely available, real-time way to distinguish unplanned vs planned pediatric readmissions.