Highlights
Among high-risk patients admitted to general medicine, patterns already present at admission may help identify those at elevated risk for harmful diagnostic error.
The central finding signaled by the study title is clinically intuitive and operationally important: frequent ambulatory care visits before hospitalization were associated with subsequent harmful diagnostic errors.
The study used structured electronic health record data and a previously adjudicated cohort, supporting the feasibility of automated risk detection rather than relying only on manual chart review.
If validated externally, pre-admission healthcare utilization could become part of hospital diagnostic safety surveillance for patients at greatest risk of ICU transfer, death, or complex adverse events.
Background
Diagnostic error is increasingly recognized as a major patient safety problem across the continuum of care, but its consequences are often most visible in the hospital, where diagnostic delays or incorrect working diagnoses can rapidly translate into clinical deterioration, intensive care transfer, procedural complications, or death. The 2015 National Academies report, Improving Diagnosis in Health Care, reframed diagnostic error as a systems problem rather than an individual cognitive failure alone. More recently, epidemiologic estimates suggest that diagnostic error contributes substantially to preventable morbidity and mortality in the United States.
For hospital medicine, the challenge is practical as much as conceptual. Harmful diagnostic errors are difficult to detect in real time, and most institutions still depend on retrospective case review, voluntary reporting, or malpractice claims, each of which captures only a small and biased fraction of events. This creates a mismatch between the scale of the problem and the tools available to address it. Hospitals need ways to identify, early in a patient’s admission, which cases warrant enhanced diagnostic vigilance.
That is the gap addressed by this retrospective cohort study, titled Frequent Ambulatory Care Visits Predict Harmful Diagnostic Errors in High-Risk Hospitalized Patients. Rather than asking whether diagnostic errors occur, the investigators asked whether predictors available at the time of admission, drawn from structured electronic health record data, can identify patients at increased risk of harmful diagnostic error. This approach is clinically attractive because structured EHR fields can be automated, monitored at scale, and incorporated into safety workflows.
The study focuses on a particularly consequential population: high-risk patients admitted to general medicine, including those who experienced ICU transfer, death within 90 days, or other complex clinical events. These are exactly the patients in whom diagnostic failures are both more likely to be discovered and more likely to matter. The title’s emphasis on frequent ambulatory care visits before admission also raises an important clinical possibility: repeated outpatient encounters may represent unresolved symptoms, fragmented care, or missed opportunities for earlier diagnosis before the patient reaches the hospital.
Study Design and Methods
Design
This was a retrospective multivariable analysis of a weighted sample of cases with and without harmful diagnostic error derived from a previously adjudicated cohort. The use of an adjudicated cohort is important because diagnostic error is notoriously difficult to define from administrative data alone. Adjudication, when done systematically, improves outcome validity compared with claims-based or coding-only approaches.
Setting and Population
The study included a weighted sample of 4,750 high-risk cases involving patients admitted to general medicine teams at a tertiary academic medical center between 2019 and 2021. According to the abstract, patients entered the high-risk pool if they had ICU transfer, death within 90 days, or complex clinical events. This approach enriches the cohort for serious outcomes and likely increases the efficiency of diagnostic safety research, though it also narrows generalizability to broader inpatient populations.
Exposure and Predictors
The main analytic strategy was to test admission-based predictors available in structured EHR data for association with harmful diagnostic error. Although the supplied abstract excerpt does not list the full predictor set or model covariates, the title identifies one clear signal: frequent ambulatory care visits before hospitalization. Because this variable is measurable within routine EHR data, it stands out as a plausible candidate for future operational use.
Outcome
The outcome of interest was harmful diagnostic error. The exact adjudication framework is not detailed in the supplied text, but the phrase suggests not merely diagnostic uncertainty or discrepancy, but a diagnostic failure associated with patient harm. That distinction matters, since not every diagnostic delay causes injury, and patient safety programs are usually most interested in the subset of errors with clinically meaningful consequences.
Key Findings
The headline finding is that frequent ambulatory care visits predicted harmful diagnostic error in this high-risk hospitalized population. Even without the full numerical results in the supplied abstract, this is a meaningful observation. It implies that the path to a harmful inpatient diagnostic error often begins before admission, during a period of repeated outpatient contact that may reflect persistent, evolving, or repeatedly misattributed symptoms.
Clinically, several mechanisms could explain this association. Repeated ambulatory visits may indicate that a patient’s symptoms were difficult to localize, that early disease manifestations were nonspecific, or that care was fragmented across clinicians and settings. They may also reflect diagnostic momentum around an incorrect working diagnosis, where each subsequent visit reinforces an earlier but inaccurate interpretation. By the time the patient is admitted, the diagnostic error is no longer a single inpatient event but the culmination of missed opportunities across care transitions.
The use of structured EHR predictors is a second important result, even beyond the specific ambulatory-visit finding. Much of the diagnostic safety literature depends on resource-intensive manual review. If a meaningful proportion of harmful diagnostic errors can be anticipated using routinely captured data elements at admission, hospitals could build surveillance systems that generate prospective alerts or triage cases for secondary review. Such systems would not diagnose the patient, but they could identify situations where diagnostic reassessment is especially warranted.
The study’s focus on high-risk general medicine patients is also notable. General medicine services routinely manage undifferentiated illness, multimorbidity, polypharmacy, and diagnostic complexity. In this environment, patients with frequent pre-admission visits may be especially vulnerable because their symptoms have already traversed multiple settings without definitive resolution. A hospital admission creates a high-stakes moment to reset the diagnostic frame. This study suggests that prior healthcare utilization patterns may help identify when that reset is most urgently needed.
Because the full article text and complete abstract data were not provided here, important quantitative details remain unavailable in this summary. These include the absolute prevalence of harmful diagnostic error in the weighted sample, the exact multivariable effect size for ambulatory visit frequency, confidence intervals, model discrimination, calibration, and whether other admission-based variables remained independently associated with the outcome. Those details are critical for determining whether the predictor is merely statistically associated with diagnostic error or whether it is sufficiently discriminative to support clinical deployment. In the absence of those data, the finding should be interpreted as hypothesis-shaping and operationally promising rather than immediately practice-changing.
Clinical Interpretation
The most valuable contribution of this study may be conceptual. Diagnostic errors that surface during hospitalization are often treated as failures of inpatient reasoning alone. This work points instead to a longitudinal diagnostic trajectory. Frequent ambulatory visits before admission may be a visible marker of an invisible process: unresolved diagnostic uncertainty accumulating over time.
For frontline clinicians, this means that utilization history should not be viewed only as a marker of illness burden or healthcare dependence. It may also be a diagnostic warning sign. When a patient is admitted after multiple recent office, urgent care, or telehealth encounters for related complaints, the admission team should ask a sharper question: what diagnosis has not yet been satisfactorily explained?
Several practical responses follow. First, admission workflows could display recent encounter density in a clinically interpretable way, ideally linked to the reason for visit rather than as a raw count alone. Second, repeated pre-admission visits could trigger a structured diagnostic time-out, particularly when the patient’s symptoms have persisted despite prior evaluation or treatment. Third, cases with high pre-admission utilization and clinical deterioration could be prioritized for diagnostic safety review by hospital quality teams.
There is also a systems-level implication. If frequent ambulatory visits predict harmful inpatient diagnostic errors, then the true prevention window may often lie upstream, in outpatient settings. Diagnostic safety interventions may need to span primary care, urgent care, specialty clinics, and the hospital rather than remaining siloed in one domain. Better symptom tracking, more reliable follow-up of unresolved presentations, and stronger handoffs at admission may all be relevant.
Strengths
The study has several strengths based on the information provided. It addresses a clinically important and underdeveloped area: predicting harmful diagnostic error using data available at admission. It uses a previously adjudicated cohort rather than relying solely on administrative definitions, improving the credibility of the outcome assessment. The sample size is substantial, and the focus on structured EHR data enhances real-world scalability. Finally, studying high-risk cases improves signal detection for a difficult-to-measure outcome.
Limitations
At least four limitations deserve emphasis. First, this was a retrospective analysis from a single tertiary academic medical center, so generalizability to community hospitals, safety-net systems, or non-academic settings is uncertain. Care fragmentation, ambulatory access, and admission thresholds vary considerably across institutions.
Second, the cohort was restricted to high-risk cases such as ICU transfer, 90-day death, or complex clinical events. That is sensible for patient safety research, but it means the results should not be extrapolated to all medical admissions. Predictors of harmful diagnostic error in lower-acuity inpatients may differ.
Third, healthcare utilization variables are potentially confounded by disease complexity, multimorbidity, frailty, social vulnerability, and access patterns. Frequent ambulatory visits may be a marker of diagnostic error risk, but they may also partly reflect patients with more severe or atypical illness whose diagnosis is intrinsically difficult. Robust multivariable adjustment can reduce but not eliminate this concern.
Fourth, prediction is not the same as causation. Even if ambulatory visit frequency is independently associated with harmful diagnostic error, the variable itself is unlikely to be causal. Its value is as a signal that identifies cases where diagnostic processes may already be failing. Whether acting on that signal improves outcomes remains unknown and would require prospective validation and implementation testing.
Relation to Prior Literature
This study aligns with broader work showing that diagnostic error often emerges from distributed system failures rather than isolated mistakes. The National Academies report emphasized teamwork, information flow, follow-up, and patient engagement as core features of safe diagnosis. Singh and colleagues have also highlighted the role of electronic triggers and EHR-based surveillance in detecting diagnostic process breakdowns. The current study extends that logic by exploring whether structured EHR data can identify high-risk admissions before a harmful error becomes fully manifest.
The finding also resonates with the concept of missed opportunities in diagnosis. Repeated ambulatory encounters for unresolved symptoms may represent a series of small failures, such as premature closure, poor test follow-up, inadequate contingency planning, or weak longitudinal synthesis of prior data. Each individual visit may appear reasonable in isolation, but in aggregate the pattern becomes more concerning. That is precisely the kind of pattern that electronic records, used thoughtfully, can detect better than human memory alone.
Implications for Practice and Policy
For hospital leaders and quality programs, the practical appeal of this work is straightforward. Admission-based EHR signals are potentially actionable. A health system could incorporate recent ambulatory encounter frequency into a diagnostic safety dashboard, particularly for patients admitted with undifferentiated complaints, clinical instability, or recent treatment failure. The intent would not be punitive surveillance of clinicians, but targeted support for higher-risk diagnostic situations.
For clinicians, the study reinforces the value of actively reviewing the pre-admission trajectory. The relevant question is not simply whether the patient has seen many clinicians, but whether there is a coherent diagnostic story that explains why those visits occurred and why hospitalization ultimately became necessary. If such a story is missing, the admission may be the right moment to revisit first principles.
For policymakers, the study strengthens the argument that diagnostic quality cannot be measured adequately using only claims, mortality, or readmissions. Health systems need more sophisticated, clinically grounded safety metrics that capture trajectories across settings. If pre-admission ambulatory utilization proves reproducibly associated with harmful diagnostic error, it could contribute to future composite risk models or safety surveillance standards.
Conclusion
This retrospective cohort study addresses one of the most difficult questions in patient safety: can hospitals identify, at the time of admission, which patients are at increased risk of harmful diagnostic error? The main answer suggested by the study title is yes, at least to some extent. Frequent ambulatory care visits before hospitalization appear to be a meaningful warning signal in high-risk general medicine patients.
The finding is clinically plausible, operationally relevant, and aligned with current thinking that diagnostic errors develop over time and across care settings. Its immediate implication is not that repeated outpatient contact causes error, but that such patterns may identify unresolved diagnostic uncertainty that deserves renewed scrutiny on admission. Before this can be translated into routine practice, the model’s full performance characteristics, external validity, and impact on patient outcomes must be established. Even so, the study points toward a promising future in which structured EHR data help hospitals detect diagnostic danger earlier, when corrective action is still possible.
Funding and Trial Registration
No funding information or ClinicalTrials.gov registration number was provided in the supplied abstract excerpt. Because this was a retrospective observational study, trial registration may not apply. Readers should consult the full published article for funding sources, conflicts of interest, and detailed methodological reporting.
References
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