Skip to main content
  • 520 Accesses

Abstract

Decision-making is a process through which an alternative is selected among others relied on gathered information. In order to select this alternative, it should have a reasonable priority based on its weight. Within the context of everyday practice in healthcare, clinical decision-making holds a fundamental place. Decision-making occurs numerous times throughout the management of patients (across the disease trajectory) and consists of a complex task which often involves diverse disciplines (i.e., decision-making within multidisciplinary team meetings). The complexity of the task can be further reinforced in patients with multimorbidity. In any clinical context, the task of decision-making carries with it the burden of responsibility for a patient’s health and well-being. Echoing these challenging circumstances in healthcare, computerized clinical decision support systems (CDSSs) and medical diagnostic decision support systems (MDSS) have been developed to augment clinicians in their complex decision-making processes. These decision support systems represent a paradigm shift in healthcare today and their contribution to effective clinical decision-making is invaluable, however not without challenges. The scope of this chapter includes a review on the utilization of technological decision support systems within healthcare (and within specific diseases), an analysis of the limitations presented by these technologies, and finally present the features that the design of efficient, effective, and safe CDSS should incorporate.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bate L, Hutchinson A, Underhill J, Maskrey N. How clinical decisions are made. Br J Clin Pharmacol. 2012;74(4):614–20. https://doi.org/10.1111/j.1365-2125.2012.04366.x.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ofstad EH, Frich JC, Schei E, Frankel RM, Gulbrandsen P. What is a medical decision? A taxonomy based on physician statements in hospital encounters: a qualitative study. BMJ Open. 2016;6(2):e010098. https://doi.org/10.1136/bmjopen-2015-010098.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Thompson C, Stapley S. Do educational interventions improve nurses’ clinical decision making and judgement? A systematic review. Int J Nurs Stud. 2011;48:881–93. https://doi.org/10.1016/j.ijnurstu.2010.12.005.

    Article  PubMed  Google Scholar 

  4. Tiffen J, Corbridge SJ, Slimmer L. Enhancing clinical decision making: development of a contiguous definition and conceptual framework. J Prof Nurs. 2014;30(5):399–405. https://doi.org/10.1016/j.profnurs.2014.01.006.

    Article  PubMed  Google Scholar 

  5. Shortliffe EH, Davis R, Axline SG, et al. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res. 1975;8(4):303–20.

    Article  CAS  Google Scholar 

  6. Lamott K. Using computers in planning and programming. Hospitals. 1967;41:127.

    CAS  PubMed  Google Scholar 

  7. Lindberg D. Collection, evaluation, and transmission of hospital laboratory data. Methods Inf Med. 1967;6:97.

    Article  CAS  Google Scholar 

  8. Long JM, Flanigan WJ, Hara M, Levy GC. Planning and managing new and complex medical and surgical procedures. JAMA. 1966;196:161.

    Article  Google Scholar 

  9. Hagiwara MA, Sjöqvist BA, Lundberg L, Suserud B-O, Henricson M, Jonsson A. Decision support system in prehospital care: a randomized controlled simulation study. Am J Emerg Med. 2013;31(1):145–53. https://doi.org/10.1016/j.ajem.2012.06.030.

    Article  PubMed  Google Scholar 

  10. Teich JM, Osheroff JA, Pifer EA, Sittig DF, Jenders RA. Clinical decision support in electronic prescribing: recommendations and an action plan. J Am Med Inform Assoc. 2005;12(4):365–76.

    Article  Google Scholar 

  11. Zaraté P, Liu S. A new trend for knowledge-based decision support systems design. Int J Inf Decis Sci. 2016;8(3):305–24.

    Google Scholar 

  12. Chung K, Boutaba R, Hariri S. Knowledge based decision support system. Inf Technol Manag. 2016;17:1–3. https://doi.org/10.1007/s10799-015-0251-3.

    Article  Google Scholar 

  13. Yang H, Li W, Liu K, Zhang J. Knowledge-based clinical pathway for medical quality improvement. Inf Syst Front. 2011;14(1):105–17.

    Article  Google Scholar 

  14. Lee J, Jang J, Shim B, Kim ST, Kim HY, Song S, Kim J, Cho I, Kim YA. Workflow based clinical decision support system through integration of clinical workflow and knowledge processing. Int J Innov Comput Inf Contr. 2012;8(7):5251–64.

    Google Scholar 

  15. Kong G, Xu DL, Body R, Yang JB, Mackway-Jones K, Carley S. A belief rule-based decision support systems for clinical risk assessment. Eur J Oper Res. 2012;219:564–73.

    Article  Google Scholar 

  16. Dagliati A, Tibollo V, Sacchi L, Malovini A, Limongelli I, Gabetta M, Napolitano C, Mazzanti A, De Cata P, Chiovato L, Priori S, Bellazzi R. Big data as a driver for clinical decision support systems: a learning health systems perspective. Front Digit Humanit. 2018;5:8. https://doi.org/10.3389/fdigh.2018.00008.

    Article  Google Scholar 

  17. Lu J, Liu A, Song Y, et al. Data-driven decision support under concept drift in streamed big data. Complex Intell Syst. 2020;6:157–63. https://doi.org/10.1007/s40747-019-00124-4.

    Article  Google Scholar 

  18. Jiang X, Wells A, Brufsky A, Neapolitan R. A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis. PLoS One. 2019;14(3):e0213292. https://doi.org/10.1371/journal.pone.0213292.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Nandra R, Parry M, Forsberg J, Grimer R. Can a Bayesian belief network be used to estimate 1-year survival in patients with bone sarcomas. Clin Orthop Relat Res. 2017;475(6):1681–9. https://doi.org/10.1007/s11999-017-5346-1.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Magrabi F, Ammenwerth E, Hypponen H, de Keizer N, Nykanen P, Rigby M, et al. Improving evaluation to address the unintended consequences of health information technology: a position paper from the Working Group on Technology Assessment & Quality Development. Yearb Med Inform. 2016;1:61–9.

    Google Scholar 

  21. Lehmann CU, Seroussi B, Jaulent MC. Troubled waters: navigating unintended consequences of health information technology. Yearb Med Inform. 2016;1:5–6.

    Article  Google Scholar 

  22. Mamlin BW, Tierney WM. The promise of information and communication technology in healthcare: extracting value from the chaos. Am J Med Sci. 2016;351(1):59–68.

    Article  Google Scholar 

  23. Wasylewicz ATM, Scheepers-Hoeks AMJW. Clinical decision support systems. In: Kubben P, Dumontier M, Dekker A, editors. Fundamentals of clinical data science. Cham: Springer; 2019.

    Google Scholar 

  24. Fraccaro P, Casteleiro MA, Ainsworth J, Buchan I. Adoption of clinical decision support in multimorbidity: a systematic review. JMIR Med Inform. 2015;3:1. https://doi.org/10.2196/medinform.3503.

    Article  Google Scholar 

  25. Møller NH, Bjørn P. Layers in sorting practices: sorting out patients with potential cancer. Comput Support Cooperat Work. 2011;20(3):123–53. https://doi.org/10.1007/s10606-011-9133-3.

    Article  Google Scholar 

  26. Nibbelink CW, Brewer BB. Decision-making in nursing practice: an integrative literature review. J Clin Nurs. 2018;27(5–6):917–28. https://doi.org/10.1111/jocn.14151.

    Article  PubMed  PubMed Central  Google Scholar 

  27. de Bock BA, Willems DL, Weinstein HC. Complexity perspectives on clinical decision making in an intensive care unit. J Eval Clin Pract. 2018;24:308–13. https://doi.org/10.1111/jep.12794.

    Article  PubMed  Google Scholar 

  28. Garg AX, Adhikari NKJ, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223–38.

    Article  CAS  Google Scholar 

  29. Kihlgren A, Svensson F, Lövbrand C, et al. A decision support system (DSS) for municipal nurses encountering health deterioration among older people. BMC Nurs. 2016;15:63. https://doi.org/10.1186/s12912-016-0184-0.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Vicente V. The use of a prehospital decision system in the emergency medical service – the acute emergency chain for geriatric patients. Thesis. Stockholm: Karolinska Institutet; 2013. ISBN 978-91-7549-021-2.

    Google Scholar 

  31. Lopez KD, Gephart SM, Raszewski R, Sousa V, Shehorn LE, Abraham I. Integrative review of clinical decision support for registered nurses in acute care settings. J Am Med Inform Assoc. 2017;24(2):441–50. https://doi.org/10.1093/jamia/ocw084.

    Article  Google Scholar 

  32. Oudshoorn N. Telecare technologies and the transformation of health care. Basingstoke: Palgrave McMillian; 2016.

    Google Scholar 

  33. Velickovski F, Ceccaroni L, Roca J, et al. Clinical Decision Support Systems (CDSS) for preventive management of COPD patients. J Transl Med. 2014;12(Suppl 2):S9. https://doi.org/10.1186/1479-5876-12-S2-S9.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Capobianco E. Data-driven clinical decision processes: it’s time. J Transl Med. 2019;17(1):44. https://doi.org/10.1186/s12967-019-1795-5.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Jaspers MW, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Inform Assoc. 2011;18(3):327–34. https://doi.org/10.1136/amiajnl-2011-000094.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, Samsa G, Hasselblad V, Williams JW, Musty MD, Wing L, Kendrick AS, Sanders GD, Lobach D. Effect of clinical decision-support systems: a systematic review. Ann Intern Med. 2012;157(1):29–43. https://doi.org/10.7326/0003-4819-157-1-201207030-0045.

    Article  PubMed  Google Scholar 

  37. Groenhof TKJ, Asselbergs FW, Groenwold RHH, et al. The effect of computerized decision support systems on cardiovascular risk factors: a systematic review and meta-analysis. BMC Med Inform Decis Mak. 2019;19:108. https://doi.org/10.1186/s12911-019-0824-x.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Melton BL. Systematic review of medical informatics-supported medication decision making. Biomed Inform Insights. 2017;9:1178222617697975. https://doi.org/10.1177/1178222617697975.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Blake JN, Kerr DV, Gammack GJ. Streamlining patient consultations for sleep disorders with a knowledge-based CDSS. Inf Syst. 2016;56:109–19.

    Article  Google Scholar 

  40. Goodman KW. Ethical and legal issues in decision support. In: Berner ES, editor. Clinical decision support systems. Health informatics. New York, NY: Springer; 2007.

    Google Scholar 

  41. Phillips W. Ethical controversies about proper health informatics practices. Mo Med. 2015;112(1):53–7.

    PubMed  PubMed Central  Google Scholar 

  42. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Moja L, Kwag KH, Lytras T, et al. Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. Am J Public Health. 2014;104(12):e12–22. https://doi.org/10.2105/AJPH.2014.302164.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Roshanov PS, Fernandes N, Wilczynski JM, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013;346:f657.

    Article  Google Scholar 

  45. Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;163(12):1409–16.

    Article  Google Scholar 

  46. Miller K, Mosby D, Capan M, et al. Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support. J Am Med Inform Assoc. 2018;25(5):585–92. https://doi.org/10.1093/jamia/ocx118.

    Article  PubMed  Google Scholar 

  47. Khalifa M, Zabani I. Improving utilization of clinical decision support systems by reducing alert fatigue: strategies and recommendations. Stud Health Technol Inform. 2016;226:51–4.

    PubMed  Google Scholar 

  48. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765.

    Article  Google Scholar 

  49. Zikos D, DeLellis N. CDSS-RM: a clinical decision support system reference model. BMC Med Res Methodol. 2018;18:137. https://doi.org/10.1186/s12874-018-0587-6.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bilici E, Despotou G, Arvanitis TN. The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: a review. Digit Health. 2018;4:2055207618804927.

    PubMed  PubMed Central  Google Scholar 

  51. Vassilaki M, Aakre JA, Cha RH. Multimorbidity and risk of mild cognitive impairment. J Am Geriatr Soc. 2015;63(9):1783–90.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andreas Charalambous .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Charalambous, A. (2020). Supporting Decision-Making Through Technology. In: Charalambous, A. (eds) Developing and Utilizing Digital Technology in Healthcare for Assessment and Monitoring. Springer, Cham. https://doi.org/10.1007/978-3-030-60697-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60697-8_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60696-1

  • Online ISBN: 978-3-030-60697-8

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics