Predict Delirium

Delirium is an acute, fluctuating syndrome of disturbed attention, awareness, and cognition that is estimated to complicate hospital stays for 20–30% of adults aged 65 and older (1). Patients with delirium are at increased risk for poor functional status, institutionalization, increased length of stay (LOS), and increased risk of mortality (1). A study of delirium’s impact on mortality showed 41.6% of patients with delirium died within the 12 months following discharge, a more than twofold increase in risk, and the effect was particularly strong among patients without dementia (2).

Size of the Problem

• 2.5x is the average increase in healthcare costs per patient due to delirium (8).
• More than 50% of delirium cases are unrecognized and undiagnosed (8).
• 20–30% of older adult hospital stays are complicated by delirium (1).

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Delirium also imposes significant financial strain on the healthcare system. Annual costs attributable to delirium are estimated to range from $16,303 to $64,421 per patient, and the total cost of delirium in older adults is estimated to range from $143 billion to $152 billion nationally (3).Research has consistently demonstrated that in most cases, delirium is not detected in hospital settings and often persists after discharge (2). Identification of delirium requires bedside cognitive assessments and validated diagnostic methods, but screening is inconsistent and measures are not routinely documented (4). Fortunately, validated algorithms have demonstrated high specificity and high positive predictive values for both detecting and predicting delirium (4,5). Thus, predictive analytics leveraging these algorithms are perfectly positioned to facilitate delirium prevention and recognition.

AI enables providers to accurately identify delirious inpatients, predict patients at high risk for delirium, and initiate intervention efforts. Approximately 30–40% of all delirium episodes are considered to be preventable, and the severity of episodes can be reduced through targeted interventions (6). Preventive interventions that address modifiable risk factors, such as ensuring proper sleep patterns, adequate nutrition, and frequent reorientation, have all been proven to reduce the incidence of delirium regardless of the care environment (7). Management interventions have also been proven to reduce falls by up to 60%, lower LOS by up to two days, and save approximately $9,000 per patient in healthcare costs annually (8,9).

• Electronic Health Records: EHR data with comprehensive patient histories of vital signs and symptoms, problem lists and chief complaints, tests results, diagnoses and procedures, and prescriptions.
• Unstructured Clinical Notes: Data extracted from EHR clinical notes for conditions being diagnosed, monitored, or treated about important clinical concepts related to symptoms, test results, diagnoses and treatments.
• Medical Claims: Data extracted from health insurance medical claims with details about dates and place of service, diagnosis codes, key procedures, use of medical equipment, and provider specialties.