Startseite Medizin Clinical Decision Support System in laboratory medicine
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Clinical Decision Support System in laboratory medicine

  • Emilio Flores ORCID logo EMAIL logo , Laura Martínez-Racaj , Ruth Torreblanca , Alvaro Blasco , Maite Lopez-Garrigós , Irene Gutiérrez und Maria Salinas ORCID logo
Veröffentlicht/Copyright: 5. Dezember 2023
Clinical Chemistry and Laboratory Medicine (CCLM)
Aus der Zeitschrift Clinical Chemistry and Laboratory Medicine (CCLM) Band 62 Heft 7

Abstract

Clinical Decision Support Systems (CDSS) have been implemented in almost all healthcare settings. Laboratory medicine (LM), is one of the most important structured health data stores, but efforts are still needed to clarify the use and scope of these tools, especially in the laboratory setting. The aim is to clarify CDSS concept in LM, in the last decade. There is no consensus on the definition of CDSS in LM. A theoretical definition of CDSS in LM should capture the aim of driving significant improvements in LM mission, prevention, diagnosis, monitoring, and disease treatment. We identified the types, workflow and data sources of CDSS. The main applications of CDSS in LM were diagnostic support and clinical management, patient safety, workflow improvements, and cost containment. Laboratory professionals, with their expertise in quality improvement and quality assurance, have a chance to be leaders in CDSS.

Keywords: clinical decision support system; clinical decision support; laboratory medicine; clinical laboratory; laboratory test
Corresponding author: Emilio Flores, PhD, Clinical Laboratory, University Hospital Sant Joan d’Alacant, Crta. Nacional N-332 s/n, 03550, San Juan de Alicante, Spain; and Clinical Medicine Department, Universidad Miguel Hernandez, Crta. Nacional N-332 s/n, 03550, San Juan de Alicante, Spain, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: Not applicable.

References

1. Berner, ES. Clinical decision support systems: theory and practice [Internet], 1st ed. New York, NY: Springer; 1999:61–74 pp. https://link.springer.com/chapter/10.1007/978-1-4757-3903-9_3 [cited 30 May 2023].10.1007/978-1-4757-3903-9_3Suche in Google Scholar

2. Cresswell, K, Callaghan, M, Khan, S, Sheikh, Z, Mozaffar, H, Sheikh, A. Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: a systematic review. Health Inf J 2020;26:2138–47. https://doi.org/10.1177/1460458219900452.Suche in Google Scholar PubMed

3. 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.Suche in Google Scholar PubMed

4. Salinas, M, López-Garrigós, M, Flores, E, Lugo, J, Leiva-Salinas, C, Massa-Dominguez, B, et al.. Laboratory computer-based interventions for better adherence to guidelines in the diagnosis and monitoring of type 2 diabetes. Diabetes Ther 2019;10:995–1003. https://doi.org/10.1007/s13300-019-0600-z.Suche in Google Scholar PubMed PubMed Central

5. Radišić Biljak, V, Honović, L, Matica, J, Krešić, B, Šimić Vojak, S, joint working group of Croatian Society of Medical Biochemistry and Laboratory Medicine and Croatian Chamber of Medical Biochemists for Laboratory Diagnostics in Chronic Kidney Disease. How well do Croatian laboratories adhere to national recommendations for laboratory diagnostics of chronic kidney disease (CKD)? Clin Chem Lab Med 2020;58:202–12. https://doi.org/10.1515/cclm-2019-0486.Suche in Google Scholar PubMed

6. 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.Suche in Google Scholar PubMed

7. Huang, M, Han, H, Wang, H, Li, L, Zhang, Y, Bhatti, UA. A clinical decision support framework for heterogeneous data sources. IEEE J Biomed Health Inf 2018;22:1824–33. https://doi.org/10.1109/jbhi.2018.2846626.Suche in Google Scholar PubMed

8. Osheroff, J, Teich, JM, Levick, D, Saldana, L, Velasco, F, Sittig, DF, et al.. Improving outcomes with clinical decision support: an implementer’s guide [Internet], 2nd ed. Chicago: CRC Press; 2012:323 p. https://www.routledge.com/Improving-Outcomes-with-Clinical-Decision-Support-An-Implementers-Guide/Osheroff-Teich-Levick-Saldana-Velasco-Sittig-Rogers-Jenders/p/book/9780984457731 [cited 26 Jun 2023].Suche in Google Scholar

9. Campbell, JR. The five rights of clinical decision support: CDS tools helpful for meeting meaningful use. J AHIMA 2013;84:42–7.Suche in Google Scholar

10. Flores, E, Salinas, JM, Blasco, Á, López-Garrigós, M, Torreblanca, R, Carbonell, R, et al.. Clinical decision support systems: a step forward in establishing the clinical laboratory as a decision maker hub. Comput Struct Biotechnol J 2023;22:27–31. https://doi.org/10.1016/j.csbj.2023.08.006.Suche in Google Scholar PubMed PubMed Central

11. Plebani, M, Aita, A, Padoan, A, Sciacovelli, L. Decision support and patient safety. Clin Lab Med 2019;39:231–44. https://doi.org/10.1016/j.cll.2019.01.003.Suche in Google Scholar PubMed

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

13. Peleg, M. Guidelines and workflow models. In: Clinical decision support. MA, USA: Academic Press; 2007:281–306 pp.10.1016/B978-012369377-8/50014-3Suche in Google Scholar

14. Wasylewicz, ATM, Scheepers-Hoeks, AMJW. Clinical Decision Support Systems. In: Kubben, P, Dumontier, M, Dekker, A, editors. Fundamentals of Clinical Data Science. Cham (CH): Springer Open; 2019:153–69 pp.10.1007/978-3-319-99713-1_11Suche in Google Scholar PubMed

15. Sutton, RT, Pincock, D, Baumgart, DC, Sadowski, DC, Fedorak, RN, Kroeker, KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 2020;3:17. https://doi.org/10.1038/s41746-020-0221-y.Suche in Google Scholar PubMed PubMed Central

16. Marco-Ruiz, L, Moner, D, Maldonado, JA, Kolstrup, N, Bellika, JG. Archetype-based data warehouse environment to enable the reuse of electronic health record data. Int J Med Inf 2015;84:702–14. https://doi.org/10.1016/j.ijmedinf.2015.05.016.Suche in Google Scholar PubMed

17. Punchoo, R, Bhoora, S, Pillay, N. Applications of machine learning in the chemical pathology laboratory. J Clin Pathol 2021;74:435–42. https://doi.org/10.1136/jclinpath-2021-207393.Suche in Google Scholar PubMed

18. Kurstjens, S, van der Horst, A, Herpers, R, Geerits, MWL, Kluiters-De Hingh, YCM, Göttgens, EL, et al.. Rapid identification of SARS-CoV-2-infected patients at the emergency department using routine testing. Clin Chem Lab Med 2020;58:1587–93. https://doi.org/10.1515/cclm-2020-0593.Suche in Google Scholar PubMed

19. Worachartcheewan, A, Shoombuatong, W, Pidetcha, P, Nopnithipat, W, Prachayasittikul, V, Nantasenamat, C. Predicting metabolic syndrome using the random forest method. Sci World J 2015;2015:581501. https://doi.org/10.1155/2015/581501.Suche in Google Scholar PubMed PubMed Central

20. Su, M, Guo, J, Chen, H, Huang, J. Developing a machine learning prediction algorithm for early differentiation of urosepsis from urinary tract infection. Clin Chem Lab Med 2022;61:521–9. https://doi.org/10.1515/cclm-2022-1006.Suche in Google Scholar PubMed

21. Canovas-Segura, B, Campos, M, Morales, A, Juarez, JM, Palacios, F. Clinical decision support using antimicrobial susceptibility test results. Adv Artif Intell, CAEPIA 2016;9868:251–60.10.1007/978-3-319-44636-3_23Suche in Google Scholar

22. Wang, F, Mellett, J, Bauer, KA, Prier, B. Pharmacist-driven initiative for management of Staphylococcus aureus bacteremia using a clinical decision support system; 2018. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047734134&doi=10.2146%2Fajhp170087&partnerID=40&md5=2d4fa56c7e526b09f6cec18f6bbfb461.Suche in Google Scholar

23. Rudolf, J, Baron, J, Dighe, A. Order indication solicitation to assess clinical laboratory test utilization: D-dimer order patterns as an illustrative case. J Pathol Inf 2019;10:36. https://doi.org/10.4103/jpi.jpi_46_19.Suche in Google Scholar PubMed PubMed Central

24. Comito, C, Forestiero, A, Papuzzo, G. A clinical decision support framework for automatic disease diagnoses. In: 2019 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). New York: ACM Digital Library; 2019:933–6 pp.10.1145/3341161.3343509Suche in Google Scholar

25. Saegerman, C, Gilbert, A, Donneau, AF, Gangolf, M, Diep, AN, Meex, C, et al.. Clinical decision support tool for diagnosis of COVID-19 in hospitals. PLoS One 2021;16:e0247773. https://doi.org/10.1371/journal.pone.0247773.Suche in Google Scholar PubMed PubMed Central

26. Demirci, F, Akan, P, Kume, T, Sisman, AR, Erbayraktar, Z, Sevinc, S. Artificial neural network approach in laboratory test reporting. Am J Clin Pathol 2016;146:227–37. https://doi.org/10.1093/ajcp/aqw104.Suche in Google Scholar PubMed

27. Baron, JM, Mermel, CH, Lewandrowski, KB, Dighe, AS. Detection of preanalytic laboratory testing errors using a statistically guided protocol. Am J Clin Pathol 2012;138:406–13. https://doi.org/10.1309/ajcpqirib3ct1ejv.Suche in Google Scholar

28. Bellodi, E, Vagnoni, E, Bonvento, B, Lamma, E. Economic and organizational impact of a clinical decision support system on laboratory test ordering; 2017. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038951721&doi=10.1186%2Fs12911-017-0574-6&partnerID=40&md5=f1cd860efb6e363a96dea3f60ed43f70.Suche in Google Scholar

29. Piessens, V, Delvaux, N, Heytens, S, Aertgeerts, B, De Sutter, A. Downstream activities after laboratory testing in primary care: an exploratory outcome of the ELMO cluster randomised trial (Electronic laboratory medicine ordering with evidence-based order sets in primary care). BMJ Open 2022;12:e059261. https://doi.org/10.1136/bmjopen-2021-059261.Suche in Google Scholar PubMed PubMed Central

30. Procop, GW, Keating, C, Stagno, P, Kottke-Marchant, K, Partin, M, Tuttle, R, et al.. Reducing duplicate testing a comparison of two clinical decision support tools. Am J Clin Pathol 2015;143:623–6. https://doi.org/10.1309/ajcpjoj3hkebd3tu.Suche in Google Scholar PubMed

31. Procop, GW, Yerian, LM, Wyllie, R, Harrison, AM, Kottke-Marchant, K. Duplicate laboratory test reduction using a clinical decision support tool. Am J Clin Pathol 2014;141:718–23. https://doi.org/10.1309/ajcpowhoizbz3frw.Suche in Google Scholar PubMed

32. Delvaux, N, Piessens, V, Burghgraeve, TD, Mamouris, P, Vaes, B, Stichele, RV, et al.. Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial. Implement Sci 2020;15:100. https://doi.org/10.1186/s13012-020-01059-y.Suche in Google Scholar PubMed PubMed Central

33. Delvaux, N, De Sutter, A, de Velde, S, Ramaekers, D, Fieuws, S, Aertgeerts, B. Electronic laboratory medicine ordering with evidence-based order sets in primary care (ELMO study): protocol for a cluster randomised trial. Implement Sci 2017;12:147. https://doi.org/10.1186/s13012-017-0685-6.Suche in Google Scholar PubMed PubMed Central

34. Strockbine, VL, Gehrie, EA, Zhou, QP, Guzzetta, CE. Reducing unnecessary phlebotomy testing using a clinical decision support system. J Healthc Qual 2020;42:98–105. https://doi.org/10.1097/jhq.0000000000000245.Suche in Google Scholar PubMed

35. Baron, JM, Huang, R, McEvoy, D, Dighe, AS. Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts. JAMIA Open 2021;4:1–9. https://doi.org/10.1093/jamiaopen/ooab006.Suche in Google Scholar PubMed PubMed Central

36. Van de Velde, S, Heselmans, A, Delvaux, N, Brandt, L, Marco-Ruiz, L, Spitaels, D, et al.. A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implement Sci 2018;13:114. https://doi.org/10.1186/s13012-018-0790-1.Suche in Google Scholar PubMed PubMed Central

37. Lundberg, GD. Adding outcome as the 10th step in the brain-to-brain laboratory test loop. Am J Clin Pathol 2014;141:767–9. https://doi.org/10.1309/ajcp5ksxwti2dmcc.Suche in Google Scholar

38. van Walraven, C, Naylor, CD. Do we know what inappropriate laboratory utilization is? JAMA 1998;280:550. https://doi.org/10.1001/jama.280.6.550.Suche in Google Scholar PubMed

39. Kalra, J. Medical errors: impact on clinical laboratories and other critical areas. Clin Biochem 2004;37:1052–62. https://doi.org/10.1016/j.clinbiochem.2004.08.009.Suche in Google Scholar PubMed

40. Salinas, M. Laboratory medicine: from just testing to saving lives. Germany: Clinical Chemistry and Laboratory Medicine; 2023.10.1515/cclm-2023-0379Suche in Google Scholar PubMed

Received: 2023-11-01
Accepted: 2023-11-24
Published Online: 2023-12-05
Published in Print: 2024-06-25

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. LC-MS/MS random access automation – a game changer for the 24/7 clinical laboratory
  4. Reviews
  5. Neurofilament light protein as a biomarker for spinal muscular atrophy: a review and reference ranges
  6. Differential diagnosis of ascites: etiologies, ascitic fluid analysis, diagnostic algorithm
  7. Opinion Papers
  8. Clinical Decision Support System in laboratory medicine
  9. Blood over-testing: impact, ethical issues and mitigating actions
  10. General Clinical Chemistry and Laboratory Medicine
  11. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure for the quantification of zonisamide in human serum and plasma
  12. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure for the quantification of carbamazepine in human serum and plasma
  13. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of phenobarbital in human serum and plasma
  14. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of primidone in human serum and plasma
  15. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure for the quantification of carbamazepine-10,11-epoxide in human serum and plasma
  16. Should we depend on reference intervals from manufacturer package inserts? Comparing TSH and FT4 reference intervals from four manufacturers with results from modern indirect methods and the direct method
  17. Comparison of three chatbots as an assistant for problem-solving in clinical laboratory
  18. Evidence-based cutoffs for total and adjusted calcium: a major factor in detecting severe hypo- and hypercalcemia
  19. Minor head injury in anticoagulated patients: performance of biomarkers S100B, NSE, GFAP, UCH-L1 and Alinity TBI in the detection of intracranial injury. A prospective observational study
  20. A comparative evaluation of the analytical performances of premier resolution-high-performance liquid chromatography (PR-HPLC) with capillary zone electrophoresis (CZE) assays for the detection of hemoglobin variants and the quantitation of HbA0, A2, E, and F
  21. Get reliable laboratory findings – how to recognize the deceptive effects of angiotensin-converting enzyme inhibitor therapy in the laboratory diagnostics of sarcoidosis?
  22. Reference Values and Biological Variations
  23. Vitamin D and vitamin K status in postmenopausal women with normal and low bone mineral density
  24. Hematology and Coagulation
  25. An automatic analysis and quality assurance method for lymphocyte subset identification
  26. Cancer Diagnostics
  27. Machine learning-based delta check method for detecting misidentification errors in tumor marker tests
  28. Cardiovascular Diseases
  29. Analytical evaluation of the novel Mindray high sensitivity cardiac troponin I immunoassay on CL-1200i
  30. Infectious Diseases
  31. A reactive monocyte subset characterized by low expression of CD91 is expanded during sterile and septic inflammation
  32. Letters to the Editor
  33. Inadvertent omission of a specimen integrity comment – an overlooked post-analytical error
  34. Falsely elevated T3 due to interference of anti-T3 autoantibodies: a case report
  35. Validation of the Siemens Atellica cortisol immunoassay compared to liquid chromatography mass spectrometry in adrenal venous sampling for primary hyperaldosteronism
  36. Lessons learned from site-specific sampling and biological half-life of IGFII and IIE(68-88) peptide: a case study
  37. The added value of automated HPC count: detecting clinically important interferences on the flow cytometric CD34+ cell count
  38. Clinical pilot study on microfluidic automation of IGH-VJ library preparation for next generation sequencing
  39. Long-term effects of interventions applied to optimize the use of 25-OH vitamin D tests in primary health care
https://doi.org/10.1515/cclm-2023-1239
Schlagwörter für diesen Artikel
clinical decision support system; clinical decision support; laboratory medicine; clinical laboratory; laboratory test

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. LC-MS/MS random access automation – a game changer for the 24/7 clinical laboratory
  4. Reviews
  5. Neurofilament light protein as a biomarker for spinal muscular atrophy: a review and reference ranges
  6. Differential diagnosis of ascites: etiologies, ascitic fluid analysis, diagnostic algorithm
  7. Opinion Papers
  8. Clinical Decision Support System in laboratory medicine
  9. Blood over-testing: impact, ethical issues and mitigating actions
  10. General Clinical Chemistry and Laboratory Medicine
  11. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure for the quantification of zonisamide in human serum and plasma
  12. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure for the quantification of carbamazepine in human serum and plasma
  13. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of phenobarbital in human serum and plasma
  14. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of primidone in human serum and plasma
  15. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure for the quantification of carbamazepine-10,11-epoxide in human serum and plasma
  16. Should we depend on reference intervals from manufacturer package inserts? Comparing TSH and FT4 reference intervals from four manufacturers with results from modern indirect methods and the direct method
  17. Comparison of three chatbots as an assistant for problem-solving in clinical laboratory
  18. Evidence-based cutoffs for total and adjusted calcium: a major factor in detecting severe hypo- and hypercalcemia
  19. Minor head injury in anticoagulated patients: performance of biomarkers S100B, NSE, GFAP, UCH-L1 and Alinity TBI in the detection of intracranial injury. A prospective observational study
  20. A comparative evaluation of the analytical performances of premier resolution-high-performance liquid chromatography (PR-HPLC) with capillary zone electrophoresis (CZE) assays for the detection of hemoglobin variants and the quantitation of HbA0, A2, E, and F
  21. Get reliable laboratory findings – how to recognize the deceptive effects of angiotensin-converting enzyme inhibitor therapy in the laboratory diagnostics of sarcoidosis?
  22. Reference Values and Biological Variations
  23. Vitamin D and vitamin K status in postmenopausal women with normal and low bone mineral density
  24. Hematology and Coagulation
  25. An automatic analysis and quality assurance method for lymphocyte subset identification
  26. Cancer Diagnostics
  27. Machine learning-based delta check method for detecting misidentification errors in tumor marker tests
  28. Cardiovascular Diseases
  29. Analytical evaluation of the novel Mindray high sensitivity cardiac troponin I immunoassay on CL-1200i
  30. Infectious Diseases
  31. A reactive monocyte subset characterized by low expression of CD91 is expanded during sterile and septic inflammation
  32. Letters to the Editor
  33. Inadvertent omission of a specimen integrity comment – an overlooked post-analytical error
  34. Falsely elevated T3 due to interference of anti-T3 autoantibodies: a case report
  35. Validation of the Siemens Atellica cortisol immunoassay compared to liquid chromatography mass spectrometry in adrenal venous sampling for primary hyperaldosteronism
  36. Lessons learned from site-specific sampling and biological half-life of IGFII and IIE(68-88) peptide: a case study
  37. The added value of automated HPC count: detecting clinically important interferences on the flow cytometric CD34+ cell count
  38. Clinical pilot study on microfluidic automation of IGH-VJ library preparation for next generation sequencing
  39. Long-term effects of interventions applied to optimize the use of 25-OH vitamin D tests in primary health care
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2026 De Gruyter Brill
Heruntergeladen am 4.2.2026 von https://www.degruyterbrill.com/document/doi/10.1515/cclm-2023-1239/pdf
Button zum nach oben scrollen