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Learn how AI can help to promote early diagnosis of delirium in the inpatient setting, reduce the incidence of delirium following hospitalization, and address functional decline in delirious patients. Discover how AI can help to promote early identification of delirium, predict patients at increased risk of developing delirium, and predict increased risk of functional decline.

Automatically ingest data from dozens of health data sources including...

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.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Chen Trong
78-Year-Old Male
Risk of death in the next 12 months related to delirium
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+25% | Age
+19% | Stroke Severity Index
+12% | Medicare Disability Status
+11% | Rise in Charlson Comorbidity Index
7 to 10

What Is 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

Why It Matters

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 Presents an Opportunity

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 

Did You Know…

  • 2.5x is the average increase in healthcare costs per patient due to delirium⁸
  • More than 50% of delirium cases are unrecognized and undiagnosed⁸
  • 20–30% of older adult hospital stays are complicated by delirium¹
Citations & Footnotes

1. Siddiqi, Najma, et al. “Occurrence and outcome of delirium in medical in-patients: a systematic literature review.” Age and ageing, vol. 35, no. 4, 2006. Pp: 350-364. doi:10.1093/ageing/afl005.

2. McCusker, Jane et al. “Delirium predicts 12-month mortality.” Archives of internal medicine vol. 162, no. 4, 25 Feb. 2002, pp: 457-63. doi:10.1001/archinte.162.4.457

3. Leslie, Douglas L., and Inouye, Sharon K. “The importance of delirium: economic and societal costs.” Journal of the American Geriatrics Society, vol. 59, Nov. 2011, pp:241-243. doi:10.1111/j.1532-5415.2011.03671.x

4. Kim, Dae Hyun, et al. “Evaluation of Algorithms to Identify Delirium in Administrative Claims and Drug Utilization Database.” Pharmacoepidemiology and Drug Safety, vol. 26, no. 8, 9 May 2017, pp. 945–953, 10.1002/pds.4226.

5. Zhong, Xiaobo, et al. “Derivation and validation of a novel comorbidity-based delirium risk index to predict postoperative delirium using national administrative healthcare database.” Health Services Research, 6 Oct 2020, https://doi.org/10.1111/1475-6773.13565.

6. Ghaeli, Padideh, et al. “Preventive Intervention to Prevent Delirium in Patients Hospitalized in Intensive Care Unit.” Iranian Journal of Psychiatry, vol. 13, no. 2, 2018, pp. 142–147, www.ncbi.nlm.nih.gov/pmc/articles/PMC6037578/. 

7. Kalish, Virginia B., et al. “Delirium in older persons: evaluation and management.” American family physician vol. 90, no. 3, 1 Aug. 2014, pp: 150-8. 

8. Oh-Park, Mooyeon, et al. “Delirium Screening and Management in Inpatient Rehabilitation Facilities.” American Journal of Physical Medicine & Rehabilitation, vol. 97, no. 10, Oct. 2018, pp. 754–762, doi: 10.1097/PHM.0000000000000962.

9. Angle, Clay. “Standardizing Management of Adults with Delirium Hospitalized on Medical-Surgical Units.” The Permanente Journal, vol. 20, no. 4, 12 Sept. 2016, doi.org/10.7812/TPP/16-002, 10.7812/tpp/16-002.

ClosedLoop is an exciting and important partner in our strategy to develop timely predictive insights through operationalized AI solutions that will drive member engagement and better health outcomes.

Pat Wang
President & CEO, Healthfirst

Our partnership with ClosedLoop adds the depth of AI to more accurately identify those patients who are most likely to benefit from our primary care interventions as well to evaluate these interventions and make more relevant and timely modifications to improve their effectiveness.

David Klebonis
Chief Operating Officer, PBACO

We are extremely impressed with the predictive modeling capabilities the ClosedLoop platform has delivered. The ClosedLoop team has exceeded all defined goals and benchmarks set to date and we anticipate a substantial return on our investment as these predictions are operationally deployed.

Cheryl Lulias
President & Executive Director, MHN

We’re able to store and operationalize analytics directly from ClosedLoop. That’s driving real value—it’s accelerated the implementation of key insights into clinical workflows and it allows us to more easily account for all of the different factors that influence intervention decisions.

Christer Johnson
Chief Analytics Officer, Healthfirst

At SWHR, our patient-centered network is committed to innovative care models that are value-based, high-quality, and data-driven. Because of our partnership with ClosedLoop, we’re better able to identify and act on the most impactful opportunities for our physician partners to improve patient results and reduce unnecessary costs.

Dr. Jason Fish
Chief Medical Officer & SVP, Southwestern Health Resources

We compared the ClosedLoop predicted spend to the spend predicted by a leading rules-based risk prediction engine and found that 75% of the time ClosedLoop was closer, and in most cases significantly closer, to the actual spend.

Phillip Bruns
Chief Technology Officer, CareATC

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