Mapping the hospital data landscape beyond the EHR

Owner: Operations and nursing informatics. Refresh: near real time for ADT, which is why ADT is the most valuable operational stream you have.

A sample ADT message (an A01 admit event) looks like this:

The PV1 segment tells you the patient is inpatient (I) in unit 4WEST, room 412. Downstream dashboards parse thousands of these per day to compute live census.

2. financial data (the data, not the ratios)

We care here about the datasets, not accounting metrics.

• Cost accounting / decision support systems. Products like Strata (Syntellis) or the EHR's own module allocate costs down to the encounter or procedure level. This is where "what did this case cost" is answered, using either ratio-of-cost-to-charges (RCC) or activity-based costing.
• Charge Description Master (CDM or "chargemaster"). The master list of every billable item and its charge. A single hospital chargemaster can have 20,000 to 50,000 line items (widely cited estimate; varies by facility).
• Claims and remittance data. The 837 (claim submitted to payer) and 835 (remittance / payment) files in the X12 EDI standard. These tell you what was billed and what was actually paid.

Owner: Finance and revenue cycle. Quality risk: the chargemaster drifts constantly as codes change; stale entries cause claim denials.

3. Patient-Experience Data

• HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems). The standardized US patient-experience survey, mandated by CMS (Centers for Medicare and Medicaid Services). Results are publicly reported and tied to reimbursement. Surveys are typically administered by a third-party vendor (for example Press Ganey or NRC Health), so this data lives outside your walls.
• Real-time feedback and complaint logs. Often a separate system from the mandated survey.

Owner: Patient experience office, often reporting to quality. Quirk: HCAHPS response rates are low (national response rates have often run in the roughly 20 to 30 percent range, an estimate that varies by year and mode), so sample sizes are small and lag by weeks. You can browse the public results yourself at Medicare Care Compare.

4. Clinical Registries

Registries are curated, condition-specific databases, usually maintained by trained abstractors who read charts and code cases to strict specifications. They are higher quality than raw EHR data but slower and more expensive to produce.

• Cancer registry. In the US, hospitals with accredited cancer programs report to a state central registry and often to the National Cancer Database. Data is abstracted by Certified Tumor Registrars, coded to NAACCR standards.
• Trauma registry, STS (Society of Thoracic Surgeons) database for cardiac surgery, GWTG (Get With The Guidelines) for stroke and cardiac care, and disease-specific registries.

Owner: Registry staff inside quality or specific service lines. Refresh: monthly to annual. Value: these are your gold-standard outcomes data, but never mistake them for real time.

Europe: a different shape

In much of Europe the registry culture is stronger and more centralized. The Scandinavian countries run national quality registries that cover most of the population for many conditions. The UK has national audits like the National Joint Registry. National patient-experience surveys (such as the NHS Adult Inpatient Survey in England) play a role similar to HCAHPS. The lesson: outside the US, more of your best data may sit in national systems rather than inside a single hospital.

Who owns what: the governance reality

The single hardest fact in hospital data is that no one person owns it all.

| Domain | Typical system owner | Data stewardData stewardA business-side owner responsible for the quality, consistency and appropriate use of data in their domain.Voir la définition complète → |

|---|---|---|

| ADT / census | IT + nursing informatics | Bed management |

| Cost accounting | Finance | Decision supportDecision supportTechnologies and processes that turn raw data into actionable insights via reporting, dashboards and analysis, so teams can decide based on facts rather than intuition.Voir la définition complète → analyst |

| Chargemaster | Revenue cycle | CDM coordinator |

| HCAHPS | Patient experience | Survey vendor + PX team |

| Cancer registry | Quality / oncology | Certified Tumor Registrar |

This fragmentation is why hospitals stand up enterprise data warehouses (EDWs) and now cloud lakehouses to bring these sources together. The integration standard shifting the field is FHIR (Fast Healthcare Interoperability Resources), a modern APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète →-based standard that increasingly complements older HL7 v2 feeds. US regulation (the ONC information-blocking rules under the 21st Century Cures Act) has pushed adoption hard.

A worked example: joining cost to outcome

Here is the analytics payoff of understanding the landscape. Suppose you want the cost per case for hip replacements and their readmission rate.

You need three sources:

1. ADT + EHR: identify the encounters (by procedure code) and any 30-day readmissions.

2. Cost accounting: pull the fully allocated cost per encounter.

3. Registry (optional): confirm complications.

Say you have 200 hip replacement cases in a quarter. Cost accounting reports a total allocated cost of 2,600,000 US dollars (illustrative figure).

Cost per case = 2,600,000 / 200 = 13,000 dollars.

Now the readmission rate. ADT shows 14 of those patients were readmitted within 30 days.

Readmission rate = 14 / 200 = 7 percent.

Neither number lives in one system. The 13,000 came from finance's cost tool, the 7 percent came from ADT joined to itself across encounters. The join key is the patient identifier, which is exactly where data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → bites.

The data-quality metrics that matter here

• Patient matching / duplicate rate. How often the same real person exists under two medical record numbers. Duplicate rates in the low single-digit percentages are common and estimates suggest they can climb higher without active management. Duplicates corrupt every longitudinal metric (like readmissions).
• Feed timeliness. Is the ADF feed lagging? A stalled HL7 interface silently freezes your census dashboard.
• Completeness of coded fields. What percent of encounters have a valid discharge disposition, race/ethnicity, or diagnosis code.
• Reconciliation gaps. Does encounter count in the EDWEDWA central repository that consolidates data from many source systems into a structured, query-optimized store designed for analytics, reporting, and business intelligence.Voir la définition complète → match the source EHR each night?

These are the governance metrics a hospital data team reports on, and they are what separate a trustworthy dashboard from a dangerous one.

Key Takeaways

• The EHR is one source among 100+. Operational (ADT, scheduling), financial (cost accounting, chargemaster, 837/835 claims), experience (HCAHPS), and registry data all live in separate systems with separate owners.
• ADT is your highest-value operational feed because it is near real time and drives census, length of stay, and readmission logic.
• Registries are gold-standard but slow. Use them for outcomes, never for real-time operations. Europe leans more on centralized national registries than the US does.
• Every cross-domain analytic is an integration problem whose weakest link is patient matching. Track your duplicate rate, feed timeliness, and completeness as first-class quality metrics.
• No single owner exists. Effective hospital analytics depends on a data warehousedata warehouseA central repository that consolidates data from many source systems into a structured, query-optimized store designed for analytics, reporting, and business intelligence.Voir la définition complète → or lakehouse, FHIR-based integration, and clear stewardship for each source.