Brazil is experiencing an unprecedented surge in the production of laboratory data.
However, most of this volume has yet to be converted into clinical intelligence, operational efficiency, or competitive advantage, mainly due to the lack of proper data harmonization.
More than 2.5 billion laboratory tests were performed in Brazil in 2024, of which 53.6% were conducted within the Unified Health System (SUS) and 47.6% in the private diagnostic medicine sector.
Given the vast variety of tests, each generating results with its own formats, analytical standards, and requirements, the increase in test volume makes data management and interpretation significantly more complex.
On average, laboratories, hospitals, and diagnostic centers perform hundreds of analyses per day. This not only increases the volume of information but also expands the diversity of variables that must be correlated, validated, and integrated across different systems.
As a result, observing and analyzing these data becomes an increasingly difficult task, requiring more robust technology, greater standardization, and ever more rigorous review processes to ensure accuracy, safety, and agility in delivering results.
When laboratory data are not comparable or interoperable, the risks of clinical errors, operational rework, and analytical failures increase.
Inconsistent results can lead to misinterpretations, delayed diagnoses, and compromised medical decisions, while the lack of integration results in information loss and slower processes.
In addition, it hinders quality control and trend identification, reducing efficiency and increasing the potential for errors throughout the diagnostic value chain.
Even in a simple hemoglobin test, which measures the protein responsible for transporting oxygen in the blood, it is already possible to observe how basic results can vary across different hospitals, laboratories, and diagnostic centers.
Differences in methods, equipment, or reference ranges can lead to incorrect diagnoses, impact therapeutic decisions, and distort population-level indicators.
These variations also impair longitudinal patient follow-up and hinder integration between institutions, generating inconsistencies, rework, and divergent interpretations of the same result.
And they are not exceptions, but rather the rule in the healthcare ecosystem—especially in multisystem environments, because each institution may adopt distinct practices, methods, and standards, including aspects such as:
- Units of measurement, which can vary significantly and, in some cases, reach up to 20 possible variations for the same analyte;
- Reference values, meaning the range considered “normal,” which varies according to age, sex, geographic location, population characteristics, and even equipment calibration;
- Assay methods, since different laboratory techniques may be used to measure the same biomarker;
- Incomplete, inconsistent, or incorrect metadata, that is, the information that describes and contextualizes laboratory results, which hinders standardization and clinical interpretation and does not affect only human reading: it also limits the use of data in analytics, artificial intelligence models, clinical research, and advanced interoperability initiatives.
The question, then, is: how can diagnostic centers and hospitals not only manage, but truly transform patient data into clinical and operational value, unlocking their full potential more efficiently?
Laboratory data harmonization goes beyond standardization: while standardization merely unifies technical formats, harmonization ensures clinical and semantic equivalence between results from different laboratories, methods, and instruments.
In other words, standardization organizes the form, but harmonization aligns the meaning, enabling data to be compared, integrated, and interpreted in a way that is truly consistent and useful for clinical practice and operational analysis.
This is even more critical because there is no globally enforced standard for these data, and even when well-known standards such as HL7, FHIR, or LOINC* are adopted, they alone do not eliminate the clinical and semantic heterogeneity of laboratory results.
Properly harmonizing laboratory data is an essential step for laboratories, hospitals, and diagnostic centers to reduce rework, minimize inconsistencies that affect clinical outcomes, strengthen the reliability of medical decisions, and accelerate the development of partnerships with other healthcare systems and institutions.
These practical effects enhance service quality and increase trust and loyalty among patients and referring physicians.
Harmonization is a far more complex challenge than simply “standardizing nomenclature.”
Laboratories, hospitals, and diagnostic centers deal daily with disparate sources, heterogeneous formats, variations in terminology, and different analytical methods - conditions under which basic standardization solutions often fail, leading to incorrect interpretations, rework, and loss of clinical and operational value.
It is precisely in this context that OpenHealth Technologies stands out. Its technology was designed to address real-world complexity, capable not only of standardizing but also of extracting, normalizing, and interpreting data in any format.
This makes it possible to truly tackle the advanced level of the challenge, delivering reliable and scalable harmonization for healthcare institutions of all sizes.
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In addition, the solution automatically correlates these inputs to validated logical layers of tests, ensuring consistency and clinical accuracy.
As a result, data are unified into a standardized, traceable, and clinically meaningful timeline, providing a clear and integrated view for patients and for the entire healthcare value chain.
Learn how data harmonization can transform clinical and operational management.