Health systems invest heavily to improve patient care through faster decisions - whether catching a deteriorating patient before a crisis, engaging high-risk members proactively, or aligning unit staffing with actual real-time census. However, this investment rests on a flawed assumption: that data is fully up to date when a clinician or algorithm evaluates it. In reality, it rarely is.
Across the healthcare life cycle, actions occur too late or rely on outdated clinical pictures. The core issue arises upstream of analytics, rooted in how data is structured and accessed.
While intelligence tools - dashboards, risk algorithms, and chart-summarizing copilots - reside in central interfaces, the underlying data remains fragmented across disparate, legacy systems. This data aggregates into a unified view only after entering a central warehouse, which typically updates on nightly or hourly schedules. As a result, even if primary sources record events instantly, the consolidated snapshot presented to users and AI models is routinely hours behind reality.
The true cost of data delays
Data delays impose financial, operational, and clinical costs across three critical areas.
Erosion of AI Investments
Health systems invest heavily in data AI initiatives - including early warning models for patient deterioration, ambient documentation scribes, chart-summarizing copilots, and automated referral triage agents. While compelling in demonstrations, these models rely in production on whatever data pipelines deliver. Consequently, risk algorithms evaluate stale lab results hours behind real time, and vector indices drift out of alignment with primary records. Despite AI outputs appearing valid on real-time dashboards, they operate on inaccurate inputs. Building AI on consolidated, live data ensures reliability; attaching it to fragmented legacy systems leaves infrastructure brittle and expensive to run, delays deployment, and undermines clinical trust in AI outputs.
Loss of Organizational Trust
Discrepancies arising when separate teams extract conflicting metrics from redundant data copies erode confidence in business intelligence reporting. Once burned, clinical and financial leaders revert to subjective intuition and informal communications, creating a loss of trust that requires substantial time to rebuild. Attempting to resolve read latency by provisioning additional data replicas compounds the issue by increasing overhead, synchronization requirements, security surface area, and data drift.
Operational and Clinical Risk
Data pipeline latencies have expanded beyond back-office reporting directly into mission-critical hospital operations. Delayed dashboard analytics represent a manageable inconvenience. However, autonomous agents and clinical models executing decisions on stale, fragmented data introduce direct liability - with failures impacting patient care within the same operational shift. As automated decision-making scales, health systems increasingly require unified, real-time data aligned with operational workflows.
Root causes of system latency
Data delays stem from structural history. As healthcare organizations expanded through mergers and acquisitions, they accumulated disconnected software stacks and specialized departmental tools. This required disparate data streams to funnel through a centralized warehouse before integration could occur.
Although 90 percent of hospitals adopted a unified EHR vendor across inpatient and outpatient settings by 2024 to consolidate core records, fragmentation persisted at the perimeter. At many points in the healthcare life cycle, high-stakes decisions depend heavily on external feeds, yet ONC survey findings show that ingesting outside health data remains the least effective capability for hospitals, with only around 40 percent performing it routinely.
External data latency remains a challenge, even for Epic
With coverage across roughly 40% of US inpatient beds, Epic clearly illustrates the division between operational and analytical systems. Inside its platform, these two functions are intentionally segmented. Chronicles, the core transactional engine, processes queries in under a second to power real-time clinical tools like the Deterioration Index, which updates every 15 minutes. In contrast, analytical and reporting systems - Clarity for deep SQL queries and Caboodle for enterprise-wide analytics - have historically relied on nightly data warehouse refreshes. While patient data is recorded instantly at the bedside, delays emerge as records transition from transactional storage to the analytics layer - a pipeline bottleneck rather than a fundamental limitation of the underlying tech.
Epic is actively addressing this lag. Following its 2023 user conference, the organization started transitioning its analytics architecture to a cloud lakehouse model, reducing ingestion latency from nightly batches to hourly updates - a change currently being adopted by health systems. However, this modernization primarily benefits Epic-native data. While cloud analytics can unify inpatient and outpatient records across Epic sites, external data sources remain isolated. Third-party monitoring tools, connected medical devices, outside laboratory feeds, health information exchanges (HIEs), and claims feeds continue to sit outside this pipeline. As a result, whenever a clinical or operational decision requires joining Epic data with external sources, workflows are once again constrained by the latency of the analytical layer.
The Constraints of Speed Alone
Relying solely on speed can be misleading, as evidence clearly indicates. In a 2024 study spanning seven Yale New Haven hospitals, researchers evaluated six deterioration scoring tools across more than 360,000 ward stays. Systems that recalculated data continuously offered no innate edge. Lead time for identifying patient decompensation varied drastically - ranging from roughly eleven hours using the top-performing model to merely one hour with Epic’s system. In fact, a basic vitals checklist devoid of machine learning surpassed both proprietary AI models.
These findings do not negate the value of real-time information; rather, they clarify its proper role. Across healthcare and life sciences, data currency and model accuracy represent distinct attributes, neither of which delivers real value in isolation. Streaming live inputs into an inferior model simply yields rapid errors. Conversely, applying a sophisticated model to delayed or fragmented data introduces different risks - potentially forcing it to resolve conflicting records without context and adopt the wrong conclusion. To improve patient outcomes, health systems need high-performing models paired with data that is current, comprehensive, and fully consolidated.
Transitioning from a Data Platform to an Intelligence Layer
Combining these two requirements naturally shapes the architecture. Effective intelligence depends on data that is both fresh and complete. Therefore, the reasoning engine and the underlying data cannot reside in separate systems linked by a replication pipeline. The LLM, dashboards, retrieval layer, and decision logic must all access the same live dataset; otherwise, they risk reasoning over conflicting versions of the same patient profile.
Both Snowflake and Databricks have encountered this challenge directly. Snowflake introduced Hybrid Tables within Unistore to add transactional capabilities to an analytics engine, later acquiring Crunchy Data in June 2025 to launch Snowflake Postgres. Positioned officially as a complement to Unistore, analysts largely viewed this as an acknowledgment that a dedicated transactional database was necessary. Databricks took a similar path with Neon by introducing Lakebase, a managed Postgres service operating alongside the lakehouse and synchronized via reverse ETL.
Both approaches arrive at the same architecture: an analytical store and a separate transactional database connected by continuous data movement. While faster than legacy batch loads, this structure still relies on two storage layers joined by a synchronization pipeline. Under high-frequency append streams - such as second-by-second patient telemetry or dynamic joins across rapidly updating table states - the friction becomes clear. A query encountering a temporal join error might report an occupied bed as vacant, or vice versa, making it harder to improve patient flow and avoid unnecessary ED delays.
SingleStore was engineered as a single, unified engine from inception. Transactional operations (such as patient care events) and analytical workloads (such as unit-wide patient-to-nurse ratios) execute against a single shared data copy. Writes become immediately visible to analytical queries without requiring reconciliation across secondary stores or read replicas. This unified engine handles high-frequency writes alongside intensive analytical reads in real time, eliminating batch windows and sync pipelines. Vector search, full-text queries, and relational joins execute within the same engine over current records, allowing AI agents and copilots to evaluate the live state of a facility directly.
A true intelligence layer requires this single shared data foundation. While a standard data platform primarily ingests and stores records, an intelligence layer actively reasons over live data at the decision point - frequently via automated software workflows. SingleStore enables healthcare and life sciences teams to run models and decision engines to operate on unified, real-time data within existing enterprise governance frameworks while augmenting existing warehouse and lakehouse investments.
Rather than replacing established warehouses or lakehouses, SingleStore integrates upstream as a complementary real-time layer:
- The lakehouse or warehouse continues managing deep historical storage, complex batch transformations, and large-scale ML model training.
- SingleStore functions as the real-time front layer, handling high-volume transactions, operational updates, concurrent analytics, native semantic search for AI/ML, and low-latency joins across systems.
For healthcare and life sciences organizations, this dual-layer approach maintains existing security and compliance controls while supporting modern real-time operational demands.
Essential Questions for Data Architects
Any valid architecture must withstand the primary questions a data architect will invariably ask.
1. How does this differ from a traditional operational data store (ODS)?
While an ODS handles transactional workloads, this architecture simultaneously supports live transactional intake and fast analytical queries on a single data copy. Heavy transformations, historical archiving, and machine learning model training remain in the lakehouse. By letting each system focus on its core strength, you eliminate the operational complexity of reconciling duplicate datasets.
2. Does streaming directly to a live engine risk introducing unvalidated data into production?
Only if upstream validation steps are removed - which they should not be. Removing the overnight staging window simply shifts schema enforcement, validation, deduplication, and error filtering directly into the streaming pipeline. In fact, in-flight validation becomes even more vital without a nightly batch window to manually identify and correct issues. In practice, teams execute these checks in real time between the EHR and SingleStore.
3. Why not rely on hybrid tables from an existing vendor?
In some cases, existing options like Unistore, Lakebase, or PostgreSQL with pgvector may satisfy specific workload requirements. However, architectures should be evaluated against your exact production conditions using three key benchmarks:
- Concurrent Workload Performance: Can the system sustain high write throughput while handling intensive concurrent analytical queries? Systems optimized primarily for reads or writes often degrade under the pressure of hundreds of simultaneous hospital users.
- Peak Concurrency Latency: How do response times hold up during high-demand operational periods, such as a Monday morning peak shift?
- Unified Query Execution: Can relational filters, full-text search, and vector search resolve natively within a single SQL query, or must results be joined across application layers?
If your current infrastructure passes all three benchmarks for your operational scale, sticking with it is the correct choice.
The Core Dilemma
At its core, healthcare systems are placing a major bet: that substantial investments in deterioration algorithms, referral-triage agents, and care-management workflows will enable faster, more accurate decision-making. However, this strategy yields returns only if the intelligence evaluates real-time patient conditions - this minute, this hour, this shift. Regrettably, most modern data architectures fall short of fulfilling that promise.
Directly at the bedside, these data lags carry tangible consequences:
- Clinical Risk: Identifying patient deterioration an hour late means missing a critical window for intervention.
- Operational Bottlenecks: A bed incorrectly flagged as occupied prevents timely admissions, increases emergency department wait times, and defers revenue recognition until system data updates.
The next article in this series follows these delays through the healthcare life cycle, focusing on where they have the greatest operational and clinical impact at the bedside.
About the author
Tim Clendaniel, PT, DPT, is a Solutions Engineer at SingleStore specializing in Healthcare and Life Sciences. A physical therapist and former clinic director, he has used these systems in clinical roles across Level 1 trauma hospitals, the University of Washington Medical Center, and outpatient clinics, from both clinician and director perspectives. For the past four years he has been a Senior Solutions Engineer working with some of the largest healthcare and life sciences organizations in the US, building solutions spanning most parts of a health system: patient care, provider credentialing, revenue cycle management, regulatory, insurance authorization, and clinical trials.
