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Learn how AI can help HCOs to facilitate medication reconciliation, reduce ADR-related admissions, and anticipate and avoid ADRs. Discover how AI can help to support continuous monitoring of patient health and behaviors, identify individuals at the greatest risk of ADRs, and accurately predict near-term ADRs.

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

Rx Claims

Data extracted from health insurance pharmacy claims with details about each medication and its type, fill dates, days supply, pharmacy location, and prescribing clinician.

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.

Health Risk Assessments

Self-reported data from health questionnaires that assess a person’s individual medical history, health risks, lifestyle, health behaviors, and quality of life.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Cramer Tillerson
76-Year-Old Male
Risk of serious fall-related injury in the next 12 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+24% | Change in Anticholinergic Cognitive Burden (ACB) Scale
1 to 5
+21% | Increase in # of Unique Prescribers
5 to 7
+19% | Frailty Percentile
+14% | Level of Reported Medication Side Effects

Why It Matters

Every year, adverse drug reactions (ADRs) – unintended, harmful events attributed to intended medicine use – result in more than 750,000 inpatient injuries or deaths, affect nearly two million hospital stays, and directly result in over one million ED visits and 125,000 hospitalizations.¹𝄒² Older adults are more vulnerable to ADRs due to aging-related kidney and liver changes that create increased sensitivity and exposure to pharmaceuticals. They experience ADRs twice as frequently as their younger counterparts, and are four times as likely to be hospitalized.³ ADRs are estimated to cause at least 10% of all admissions in older adults, and between 10–39% of hospitalized older adults will experience an ADR.⁴𝄒⁵ They are also more likely to die from ADRs; a recent study found that fatal outcomes were reported approximately three times more often for older adults.⁶

One reason for increased risk of ADRs is the use of Potentially Inappropriate Medications (PIMs), particularly for older adults who may be taking multiple medications. Polypharmacy, commonly defined as regular use of five or more medications, and the prevalence of PIMs are strongly associated with increased risk of ADRs in older adults. Alarmingly, the prevalence of PIMs ranges from 20–60% of all older adults depending on healthcare setting and criteria used to define inappropriate prescribing.⁷ PIM use is associated with a 10–30% increased risk of hospitalization, and older adults with polypharmacy are roughly 80% more likely to be hospitalized within a year relative to equivalent patients without polypharmacy.⁷𝄒⁸ 

PIMs and polypharmacy can result in considerable cognitive impairment consistent with dementia and may lead to misdiagnosis and further prescriptions, potentially adding to an already-elevated ADR risk. Despite this, opportunities for medication reconciliation and deprescribing are frequently missed. A recent study found that 66% of hospitalized older patients had at least one PIM prescribed at discharge, 49% continued a previously prescribed PIM, 31% were prescribed a new PIM during hospitalization, and ultimately 36% visited the ED, were rehospitalized, or died within 30 days of discharge.⁹ 

AI Presents an Opportunity

Up to two-thirds of ADRs in hospitalized and multi-morbid older adults are considered preventable, and AI-based models are ideal to help Healthcare organizations (HCOs) identify, anticipate, and avoid these adverse outcomes.⁷ Predictive analytics enable HCOs to integrate patient-specific data (e.g., conditions, comorbidities, physiologic vulnerabilities, and medications) with drug burden indices, support continuous monitoring of health and behaviors, and predict individuals at the greatest risk of ADRs. This insight provides care teams with the ability to proactively initiate tailored interventions. For example, interventions designed around deprescribing and reconciling medication use have been shown to reduce ADR risk.⁷𝄒¹⁰ 


Did You Know…

  • 750,000 inpatient injuries or deaths are attributed to ADRs annually¹ 
  • 2x increased incidence of ADRs in older adults³
  • 3x older adults are three times as likely to die of ADRs³
  • 20-60% is the prevalence of PIM usage among older adults⁷
Citations & Footnotes

1. Sarah P., Slight, et al. “The national cost of adverse drug events resulting from inappropriate medication-related alert overrides in the United States.” Journal of the American Medical Informatics Association, Volume 25, Issue 9, Sep. 2018, pp. 1183–1188, https://doi.org/10.1093/jamia/ocy066

2. “Adverse Drug Events.” Department of Health and Human Services: Healthcare Quality, 2 Feb. 2020, https://health.gov/our-work/health-care-quality/adverse-drug-events. Accessed 23 Jun. 2021.  

3. Beijer, H J M, and C J de Blaey. “Hospitalisations caused by adverse drug reactions (ADR): a meta-analysis of observational studies.” Pharmacy World & Science, vol. 24, no. 2, 24 Apr. 2002, pp. 46-54. doi:10.1023/a:1015570104121. 

4. Jennings, Emma, et al. “Detection and Prevention of Adverse Drug Reactions in Multi-Morbid Older Patients.” Age and Ageing, vol. 48, no. 1, 12 Sep. 2018, pp. 10–13, academic.oup.com/ageing/article/48/1/10/5123812, 10.1093/ageing/afy157. Accessed 23 Jun 2021.

5. Parameswaran, Nair N, et al. “Hospitalization in older patients due to adverse drug reactions -the need for a prediction tool.” Clin Interv Aging. 2016;11:497-505. 2 May 2016. doi:10.2147/CIA.S99097. 

6. Dubrall, Diana et al. “Adverse drug reactions in older adults: a retrospective comparative analysis of spontaneous reports to the German Federal Institute for Drugs and Medical Devices.” BMC pharmacology & toxicology vol. 21, no. 25, 23 Mar. 2020, doi:10.1186/s40360-020-0392-9. 

7. Weir, Daniala L., et al. “Both New and Chronic Potentially Inappropriate Medications Continued at Hospital Discharge Are Associated With Increased Risk of Adverse Events.” Journal of the American Geriatrics Society, vol. 68, no. 6, 31 Mar. 2020, pp. 1184–1192, pubmed.ncbi.nlm.nih.gov/32232988/, 10.1111/jgs.16413. Accessed 23 Jun 2021.

8. Finkelstein, Joseph et al. “Pharmacogenetic polymorphism as an independent risk factor for frequent hospitalizations in older adults with polypharmacy: a pilot study.” Pharmacogenomics and personalized medicine vol. 9, 14 Oct. 2016, pp. 107-116. doi:10.2147/PGPM.S117014. 

9. Fick, Donna M. “Less Really Is More in Inappropriate Medication Use in Older Adults: How Can We Improve Prescribing and Deprescribing in Older Adults?” Journal of the American Geriatrics Society, vol. 68, no. 6, 4 May 2020, pp. 1175–1176, onlinelibrary.wiley.com/doi/full/10.1111/jgs.16485, 10.1111/jgs.16485. Accessed 23 Jun 2021.

10. Gray, Shelly L., et al. “Meta-Analysis of Interventions to Reduce Adverse Drug Reactions in Older Adults.” Journal of the American Geriatrics Society, vol. 66, no. 2, 19 Dec. 2017, pp. 282–288, pubmed.ncbi.nlm.nih.gov/29265170/, 10.1111/jgs.15195. Accessed 23 Jun 2021.

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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