This blog was updated on April 2, 2026
The article at a glance:
Analytic latency limits action. Data must be transformed into insights fast enough to inform decisions and workflows.
Timeliness drives cost and outcomes. Rapid, accurate analytics are essential to identify high-risk members and prevent avoidable, expensive hospitalizations.
Fresh data fuels effective prediction. Predictive models, like Truven’s AI-driven Risk of Hospitalization, work best for outreach when analytic lag is minimized, enabling proactive outreach within critical intervention windows.
When it comes to data-driven decision making, analytic latency can have a major impact on an organization’s ability to make fast decisions and prioritize member outreach correctly and efficiently. This will ultimately reduce organizations’ ability to create proactive strategies for improving population health, while also controlling costs. While it is critical for payers to address data latency in their processes, it is equally important to consider analytic latency in data-driven initiatives. It is not enough for the data to be available; it needs to be actionable.
Just 5% of patients account for 50% of total costs, so the need to identify at risk patients, their conditions, and their risk drivers is critical. Pairing the right models with the right cadence can make data-driven decisions more effective. The ability to reduce analytic latency and run analytics on demand (as you need them, i.e., nightly, weekly, etc.) can reduce time to insights that have wide impacts on strategies relying on those insights. Timeliness of insights impacts the ability to intervene for patients with gaps in care that may otherwise be resolved, reducing critical events and exacerbations of conditions down the line.
Lagging insights on care gaps may also reduce performance ratings, such as Star ratings for Medicare Advantage plans, HEDIS, and also impact value-based care arrangements. Timely insights allow payers and employers to identify newly diagnosed conditions as data becomes available for program enrollment and increases the ability to properly quantify patient risk for stratification and prioritization. Episodic costs may also be understated due to analytic lag. Where the ability to run analytics on demand may be most notable is in predictive analytics models often utilized by care teams to better focus their limited resources and ideally prevent healthcare complications that may have otherwise been prevented.
A person’s health status is dynamic, and advanced predictive models are sensitive to data variations, particularly models intended for outreach and prevention. To illustrate this, we will take a closer look at avoidable admissions.
U.S. healthcare spending on hospital care reached nearly $1.6 trillion in 2024, representing 31% of total healthcare spend by type of service. Inpatient care remains the most expensive site of care on a per episode basis, and avoidable admissions are widely recognized as one of the largest controllable cost drivers—particularly for members with chronic conditions such as asthma, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and diabetes.
So, what exactly are avoidable admissions?
Avoidable admissions refer to hospitalizations that may have been prevented through timely, high quality outpatient care. They are commonly measured using AHRQ’s Prevention Quality Indicators (PQIs), which assess potentially preventable inpatient stays for select conditions. Importantly, the estimated impact of avoidable admissions varies based on the methodology applied, the population analyzed, and the conditions included.
To better understand the financial implications, we evaluated avoidable admissions across four high prevalence chronic conditions using MarketScan’s Research Databases and AHRQ PQI criteria. Even modest improvements can be meaningful: a 10% reduction in avoidable admissions translates to more than $1.1M in annual savings for a 250,000 member health plan—approximately $0.37 PMPM.
Reducing avoidable admissions at scale depends on how quickly plans can identify and engage members who are truly at risk. Person-centric hospitalization models—such as Truven’s AI-driven Risk of Hospitalization (ROH) model—are designed to predict the risk of avoidable admissions for key chronic conditions. These models are most effective for outreach when analytic lag is minimized, enabling earlier, more targeted interventions that can improve outcomes while bending the cost curve.
Care managers need to prioritize patients for outreach and intervention, so having access to critical information as it becomes available is necessary. Models like the Risk of Hospitalization draw on several critical inputs to determine patient risk levels, making prompt updates to those inputs vital for producing accurate and actionable scores. This can include recent changes to disease history such as new diagnoses or variations in disease severity, prescription drug use to treat chronic conditions, utilization inputs such as visit counts, capture of specific procedures, patient cost, and visit recency. Prioritization will change as inputs change.
The clinical specificity of the Risk of Hospitalization model also helps inform care managers of the drivers associated with patient risk, allowing them to tailor their interventions to the patient.
Outreach alone, however, is not enough if care managers are unable to reach members within a critical time frame. Predictive analytics such as the Risk of Hospitalization model allow care managers to take a proactive rather than a reactive approach. A proactive approach aims to prevent hospitalization and readmission events in the first place rather than manage patients after the fact. With a predictive window of 6 months, reducing analytic latency is critical to the ROH model and models like it. Intervention strategies can also vary based on short- and long-term intervention strategies, so reducing analytic latency allows for better prioritization along with a more comprehensive approach.
Near real-time analytics are needed for workflows, value-based care, and population health initiatives. Use cases will vary, but the ability to transform data into predictive, prescriptive, and actionable analytics when you need them is a critical need for payers. While payers have made advancements to address data latency in their processes, analytic latency is another strategic imperative to focus on, directly affecting data driven decisions. The ability to run analytics when you need them improves payers’ ability to advance population health, close gaps in care, increase performance ratings, and will ultimately impact their bottom-line.
Learn more about running analytics on demand with Truven Flexible Analytics.