Skip to content
Publicly Available Published by De Gruyter May 9, 2023

Laboratory Medicine: from just testing to saving lives

  • Maria Salinas ORCID logo EMAIL logo

The contribution of Laboratory Medicine to the global patient healthcare procedure is significant. Currently 70–80 % of clinical decisions are based on laboratory data, as compared to only 10–15 %, 40 years ago [1]. Only in recent times, some laboratory tests at certain cut-off points strongly affect the clinical decision-making process (e.g. HbA1c in Diabetes or Troponin in acute coronary syndrome).

The above changes that have occurred in recent decades, have boosted the involvement of laboratory testing in the global patient journey, and have positioned Laboratory Medicine as the speciality that most frequently impacts patient care. This has resulted in the so called “empowerment of laboratory tests” since many of them are the ones making the clinical decision [2], and hence should make the laboratory professionals reflect about the laboratory model and the new role we now have in the global patient healthcare procedure [3, 4].

In fact, there has been a parallel evolution of the Laboratory Medicine model. Simply “testing” has been the Mission of the Traditional Laboratory for many years. Currently, however, many laboratories have embraced the Leading Laboratory model, with an upgraded Mission, – the “prevention, diagnosis, monitoring and treatment of the disease”, given they no longer only intervene in the clinical decision, but often “lead or make” the decision. Besides, by means of computer-based interventions designed in agreement with clinicians, to correct laboratory testing under-request or to improve the action after receiving the results, the Leading Laboratory can diagnose occult disease, or achieve an optimal patient monitoring or treatment [5].

However, even when one has been working for years under this new Leading Laboratory model, with a strong commitment to generate meaningful impact outside the walls of the laboratory, one realizes there is still a continuous need to keep bringing new capabilities, new pieces to the overall clinical process, new innovation and changes to continue building this model further.

It is already obvious that the clinical laboratory is a decision maker hub [5]. To continue to empower this model and the new role of the laboratory professional, the time has come to introduce digital solutions [6] in Laboratory Medicine, such as Clinical Decision Support (CDS) [7]. CDS in real time, consults data in our laboratory information system or in the patient’s data base – as traditional laboratory computer-based interventions do – but further in other platforms such as the electronic health records or computerized physician order entry systems. CDS, also in milliseconds, organises structures and consolidates this information, or calculates overall scores, and finally acts according to designed and established interventions. Furthermore solutions based on Artificial Intelligence, or Machine Learning can process massive amounts of data [8] to detect recurring patterns, and provide information both in real-time and for the future, to drive clinical decision making, such as predictive analysis [9].

There are many different scenarios where CDS can be applied in daily clinical practice, such as the early recognition of sepsis, acute pancreatitis or hypercalcemia of malignancy in the emergency department [10] conditions that may benefit from an early diagnosis, and/or early aggressive interventions. Or in the prediction of 10-year cardiovascular risk, heart failure or chronic kidney disease diagnosis, in primary care [11]. The ultimate goal would be the real time application of scientific evidence or current guidelines, to get an earlier disease diagnosis and hence treatment, to improve patient mortality and morbidity and consequently overall Healthcare safety [6].

We, as laboratory professionals, have the knowledge and the tools and are already in this new era, positioned in the front line position of Healthcare. This is now the laboratory of the future based on leadership and the use of digital solutions. In fact, Laboratory Medicine has gone from just Testing to Saving Lives.


Corresponding author: Maria Salinas, Clinical Laboratory, Hospital de San Juan de Alicante, Carretera Alicante San Juan s/n, 03550 San Juan, Alicante, Spain, E-mail:

References

1. Young, DS. How does one interpret a marginally abnormal serum chemistry test? J Occup Med 1982;24:104–8.Search in Google Scholar

2. Olver, P, Bohn, MK, Adeli, K. Central role of Laboratory Medicine in public health and patient care. Clin Chem Lab Med 2023;61:666–73. https://doi.org/10.1515/cclm-2022-1075.Search in Google Scholar PubMed

3. Cadamuro, J. Disruption vs. evolution in Laboratory Medicine. Current challenges and possible strategies, making laboratories and the laboratory specialist profession fit for the future. Clin Chem Lab Med 2023;61:558–66. https://doi.org/10.1515/cclm-2022-0620.Search in Google Scholar PubMed

4. Lippi, G, Plebani, M. A modern and pragmatic definition of Laboratory Medicine. Clin Chem Lab Med 2020;58:1171. https://doi.org/10.1515/cclm-2020-0114.Search in Google Scholar PubMed

5. Salinas, M, López-Garrigós, M, Flores, E, Martín, E, Leiva-Salinas, C. The clinical laboratory: a decision maker hub. Clin Chem Lab Med 2021;59:1634–41. https://doi.org/10.1515/cclm-2021-0421.Search in Google Scholar PubMed

6. Hulsen, T, Friedecký, D, Renz, H, Melis, E, Vermeersch, P, Fernandez-Calle, P. From big data to better patient outcomes. Clin Chem Lab Med 2023;61:580–6. https://doi.org/10.1515/cclm-2022-1096.Search in Google Scholar PubMed

7. Hughes, AEO, Jackups, R. Clinical decision support for laboratory testing. Clin Chem 2022;68:402–12. https://doi.org/10.1093/clinchem/hvab201.Search in Google Scholar PubMed

8. Padoan, A, Plebani, M. Artificial intelligence: is it the right time for clinical laboratories? Clin Chem Lab Med 2022;60:1859–61. https://doi.org/10.1515/cclm-2022-1015.Search in Google Scholar PubMed

9. Kurstjens, S, De Bel, T, Van Der Horst, A, Kusters, R, Krabbe, J, Van Balveren, J. Automated prediction of low ferritin concentrations using a machine learning algorithm. Clin Chem Lab Med 2022;60:1921–8. https://doi.org/10.1515/cclm-2021-1194.Search in Google Scholar PubMed

10. Salinas, M, Flores, E, Lopez-Garrigós, M, Salinas, CL. Artificial intelligence: a step forward in the clinical laboratory, a decision maker hub. Clin Biochem 2022;105–106:23–4. https://doi.org/10.1016/j.clinbiochem.2022.05.005.Search in Google Scholar PubMed

11. Lange, F. The laboratory journey to become a decision engine: a roadmap for diagnostic transformation. Clin Chem Lab Med 2023;61:576–9. https://doi.org/10.1515/cclm-2022-0889.Search in Google Scholar PubMed

Published Online: 2023-05-09
Published in Print: 2023-09-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 8.5.2024 from https://www.degruyter.com/document/doi/10.1515/cclm-2023-0379/html
Scroll to top button