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Most mid-sized and large Brazilian hospitals operate an in-house laboratory coupled to the clinical operation. This laboratory processes inpatient, emergency, and outpatient tests in intense flow, feeds clinical decisions in real time, and responds to demand that never stops. For hospital management, it is treated, almost always, as a cost center and support function: a department that needs to deliver reliable results on time, with analytical quality and controlled cost. That characterization isn't wrong. It is incomplete.
The hospital's in-house laboratory produces, every day, an asset whose value most institutions don't capture: structurable clinical data about the population the hospital serves. When that data stays confined to the LIS, feeding only the clinical decision of the moment, the laboratory operates as an island. And islands waste most of the value they produce.
The data island in practice
The typical flow of a test in the in-house hospital laboratory ends at the same place where most of its data remains: the result is processed, recorded in the LIS, made available to the physician who ordered it, and incorporated into the clinical decision of that encounter. Once that function is fulfilled, the data stops. It exists in the LIS, technically accessible, but isolated from practically every other use it could have.
When the patient is discharged and seeks a specialist outside the hospital, the laboratory history produced during the admission stays behind, accessible only upon chart request, and even then in a format that is rarely comparable to the data the external specialist will produce. When an insurer's beneficiary treated by the hospital has follow-up tests at another provider, neither the hospital nor the insurer can cross-reference the two datasets in structured form. When the hospital itself wants to feed population health analyses, risk management programs, or clinical research initiatives, the in-house laboratory data needs to be exported and handled in an artisanal way, project by project.
This isolation isn't the result of a deliberate decision. It is the natural consequence of a laboratory whose data infrastructure was designed for the immediate clinical function and not for the strategic use of the data it produces. The LIS does well what it was designed to do: manage the analytical operation. What's missing is the layer that transforms the output of that operation into a usable data asset beyond the clinical episode.
The opportunity cost that doesn't appear in the budget
The data island has a cost that doesn't appear in any line of the laboratory's budget, because it is an opportunity cost: the value of the uses the data could have and doesn't.
On the clinical axis, the inability to compare the admission's laboratory history with outpatient follow-up data compromises continuity of care. Research on inter-level care communication documents that failures in clinical information transfer between the hospital and post-discharge care are associated with readmissions, adverse events, and test duplication.¹ The laboratory that structures its data so that it can follow the patient out of the hospital contributes directly to reducing those outcomes, and reducing readmissions is, for the hospital, clinical and financial gain simultaneously.
On the management axis, structured laboratory data is the raw material of strategic indicators that sustain clinical governance. Serial biomarker analysis of an inpatient population allows identifying patterns, anticipating deterioration, and evaluating protocol effectiveness. A laboratory that operates as an island cannot feed those analyses without significant manual effort, and significant manual effort is, in practice, the reason those analyses frequently don't happen.
On the institutional relationship axis, there is a competitive cost. Hospitals negotiate with insurers based on quality and outcome data. A hospital whose laboratory produces structured and comparable data has a negotiation argument that a hospital with island-confined data does not. The ability to demonstrate, with structured data, the quality of care delivered is a differentiator in a market where value-based contracting is gaining ground.
What the RNDS changes in the equation
The publication of Ordinance GM No. 8,276 by the Ministry of Health, in October 2025, changed the context in which this discussion happens. The ordinance established the Laboratory Test Result Information Model (REL) within the National Health Data Network, requiring that laboratory results from across the national territory follow defined standards, with LOINC terminologies or the codification adopted in GAL, and be sent regularly to RNDS.²
For the in-house hospital laboratory, this means that producing structured and interoperable data has stopped being an optional strategic choice and become a regulatory obligation. The hospital that will comply with the REL model will need, by construction, to structure its in-house laboratory data in semantically standardized form. The question that arises is: once that structuring work will need to be done anyway to comply with RNDS, why limit the use of that structured data to the regulatory obligation alone?
The same data the hospital will need to structure to send to RNDS can, with the right infrastructure, feed post-discharge continuity of care, clinical governance indicators, population health analyses, and value-based negotiation with insurers. The regulatory obligation, in this framing, stops being merely a compliance cost and becomes the trigger for building an infrastructure that unlocks all the value the in-house laboratory produces and today wastes.
From cost center to data producer
The change in perspective this context requires is to stop seeing the in-house laboratory as a support function that only needs to deliver results, and start seeing it as the producer of a data asset that is strategic for the entire institution. This change doesn't require reconfiguring the laboratory's analytical operation. It requires adding the layer that structures and harmonizes the output of that operation, making it usable beyond the clinical episode in which it was generated.
Laboratory medicine has gone, over the past decades, through an identity transformation: from test executor to producer of clinically structured data that sustains decisions far beyond the individual diagnosis. The in-house hospital laboratory is, perhaps, the place where this transformation is most delayed, precisely because its immediate clinical function is so dominant that it overshadows the strategic value of the data it produces. The literature on the evolution of the laboratory role recognizes that the value of aggregated laboratory data extends far beyond the individual diagnostic episode, encompassing population surveillance, care process optimization, and research support.³
This is where OpenHealth Technologies operates. The platform automatically correlates multiple data streams with rigorously validated logical layers of laboratory tests, operating as the layer that structures and harmonizes the output of the in-house hospital laboratory, mapping it to LOINC and making it comparable and usable beyond the LIS. For hospitals, this means the structuring work that RNDS already requires comes to unlock, simultaneously, post-discharge continuity of care, clinical governance indicators, population analyses, and value-based negotiation with insurers. The in-house laboratory stops operating as an island and becomes the producer of a data asset that serves the entire institution.
Learn how your institution can transform the in-house laboratory from a data island into the producer of a strategic asset that serves the entire hospital operation.