Skip to main content
SpringerLink
Log in

EHR Data: Enabling Clinical Surveillance and Alerting

  • Chapter
  • First Online:
Nursing Informatics

Part of the book series: Health Informatics ((HI))

Abstract

This chapter describes clinical needs, foundation for clinical alerting and surveillance, It started with historical perspective and outlining public health needs. Emergency services provide rapid response for deteriorating patients that use number of assessments tools. Emerging Clinical Decision Support Systems moved to more advanced systems that required better evaluation and adoption. This chapter provides foundation for understanding clinical impact. At the end of the chapter, there is brief case study of Clinical Control Tower.

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

Access this chapter

Log in via an institution
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 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 89.99
Price excludes VAT (USA)
  • Durable hardcover 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. Lee, L.M., Principles & practice of public health surveillance. 2010.

    Book  Google Scholar 

  2. Smith PF, et al. "Blueprint version 2.0": updating public health surveillance for the 21st century. J Public Health Manag Pract. 2013;19(3):231–9.

    Article  PubMed  Google Scholar 

  3. Dieleman JL, et al. US Spending on personal health care and public health, 1996-2013. JAMA. 2016;316(24):2627–46.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Beitler JR, et al. Reduction in hospital-wide mortality after implementation of a rapid response team: a long-term cohort study. Crit Care. 2011;15(6):R269.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hillman KM, et al. Duration of life-threatening antecedents prior to intensive care admission. Intensive Care Med. 2002;28(11):1629–34.

    Article  PubMed  Google Scholar 

  6. Barwise A, et al. Delayed rapid response team activation is associated with increased hospital mortality, morbidity, and length of stay in a tertiary care institution*. Crit Care Med. 2016;44(1):54–63.

    Article  PubMed  Google Scholar 

  7. Lyons PG, Edelson DP, Churpek MM. Rapid response systems. Resuscitation. 2018;128:191–7.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Berwick DM, et al. The 100 000 lives campaign setting a goal and a deadline for improving health care quality. JAMA. 2006;295(3):324–7.

    Article  CAS  PubMed  Google Scholar 

  9. Gunning K, Rowan K. ABC of intensive care: outcome data and scoring systems. BMJ. 1999;319(7204):241–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Carayon P. Sociotechnical systems approach to healthcare quality and patient safety. Work (Reading, Mass). 2012;41 Suppl 1(0 1):3850–4.

    Google Scholar 

  11. Smith ME, et al. Early warning system scores for clinical deterioration in hospitalized patients: a systematic review. Ann Am Thorac Soc. 2014;11(9):1454–65.

    Article  PubMed  Google Scholar 

  12. Downey CL, et al. Strengths and limitations of early warning scores: a systematic review and narrative synthesis. Int J Nurs Stud. 2017;76:106–19.

    Article  CAS  PubMed  Google Scholar 

  13. DeVita MA, et al. Use of medical emergency team responses to reduce hospital cardiopulmonary arrests. Qual Saf Health Care. 2004;13(4):251–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Moldenhauer K, et al. Clinical triggers: an alternative to a rapid response team. Jt Comm J Qual Patient Saf. 2009;35(3):164–74.

    PubMed  Google Scholar 

  15. Hillman K, et al. Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial. Lancet. 2005;365(9477):2091–7.

    Article  PubMed  Google Scholar 

  16. Bleyer AJ, et al. Longitudinal analysis of one million vital signs in patients in an academic medical center. (1873–1570 (Electronic)).

    Google Scholar 

  17. Bellomo R, et al. A controlled trial of electronic automated advisory vital signs monitoring in general hospital wards. Crit Care Med. 2012;40(8):2349–61.

    Article  PubMed  Google Scholar 

  18. Downey CL, et al. The impact of continuous versus intermittent vital signs monitoring in hospitals: a systematic review and narrative synthesis. (1873-491X (Electronic)).

    Google Scholar 

  19. Cardona-Morrell M, et al. Effectiveness of continuous or intermittent vital signs monitoring in preventing adverse events on general wards: a systematic review and meta-analysis. (1742–1241 (Electronic)).

    Google Scholar 

  20. Evans RS. Electronic health records: then, now, and in the future. Yearb Med Inform. 2016;Suppl 1(Suppl 1):S48–61.

    CAS  PubMed  Google Scholar 

  21. Rhee C, et al. Incidence and trends of sepsis in us hospitals using clinical vs claims data, 2009-2014. JAMA. 2017;318(13):1241–9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Seymour CW, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med. 2017;376(23):2235–44.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Levy MM, et al. The surviving sepsis campaign: results of an international guideline-based performance improvement program targeting severe sepsis. Crit Care Med. 2010;38(2):367–74.

    Article  PubMed  Google Scholar 

  24. Seymour CW, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321(20):2003–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Seymour CW, et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):762–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Singer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Alberto L, et al. Screening for sepsis in general hospitalized patients: a systematic review. J Hosp Infect. 2017;96(4):305–15.

    Article  CAS  PubMed  Google Scholar 

  28. Hooper MH, et al. Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit*. Crit Care Med. 2012;40(7):2096–101.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Villegas N, Moore LJ. Sepsis screening: current evidence and available tools. Surg Infect. 2018;19(2):126–30.

    Article  Google Scholar 

  30. Manaktala S, Claypool SR. Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality. J Am Med Inform Assoc. 2017;24(1):88–95.

    Article  PubMed  Google Scholar 

  31. Thiel SW, et al. Early prediction of septic shock in hospitalized patients. J Hosp Med. 2010;5(1):19–25.

    Article  PubMed  Google Scholar 

  32. Ginestra JC, et al. Clinician perception of a machine learning-based early warning system designed to predict severe sepsis and septic shock. Crit Care Med. 2019;47(11):1477–84.

    Article  PubMed  PubMed Central  Google Scholar 

  33. The Lancet Respiratory, M. Crying wolf: the growing fatigue around sepsis alerts. Lancet Respir Med. 2018;6(3):161.

    Article  Google Scholar 

  34. Shah NR, et al. Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc. 2006;13(1):5–11.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Amroze A, et al. Use of electronic health record access and audit logs to identify physician actions following noninterruptive alert opening: descriptive study. JMIR Med Inform. 2019;7(1):e12650.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Afshar M, et al. Patient outcomes and cost-effectiveness of a sepsis care quality improvement program in a health system. Crit Care Med. 2019;47(10):1371–9.

    Article  PubMed  Google Scholar 

  37. Lo HG, et al. Impact of non-interruptive medication laboratory monitoring alerts in ambulatory care. J Am Med Inform Assoc. 2009;16(1):66–71.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kreimeyer K, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform. 2017;73:14–29.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Marafino BJ, et al. Validation of prediction models for critical care outcomes using natural language processing of electronic health record data. JAMA Netw Open. 2018;1(8):e185097.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Senders JT, et al. Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg. 2018;109:476–486.e1.

    Article  PubMed  Google Scholar 

  42. Deist TM, et al. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys. 2018;45(7):3449–59.

    Article  PubMed  Google Scholar 

  43. Hernandez-Suarez DF, et al. Machine-learning-based in-hospital mortality prediction for transcatheter mitral valve repair in the United States. Cardiovasc Revasc Med. 2020;

    Google Scholar 

  44. Hernandez-Suarez DF, et al. Machine learning prediction models for in-hospital mortality after transcatheter aortic valve replacement. JACC Cardiovasc Interv. 2019;12(14):1328–38.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Suzuki S, et al. Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis. PLoS One. 2019;14(9):e0221911.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sousa FS, et al. Application of the intelligent techniques in transplantation databases: a review of articles published in 2009 and 2010. Transplant Proc. 2011;43(4):1340–2.

    Article  CAS  PubMed  Google Scholar 

  47. Menger V, et al. Machine learning approach to inpatient violence risk assessment using routinely collected clinical notes in electronic health records. JAMA Netw Open. 2019;2(7):e196709.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Shillan D, et al. Use of machine learning to analyse routinely collected intensive care unit data: a systematic review. Crit Care. 2019;23(1):284.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Linnen DT, et al. Statistical modeling and aggregate-weighted scoring systems in prediction of mortality and icu transfer: a systematic review. J Hosp Med. 2019;14(3):161–9.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Layeghian Javan S, Sepehri MM, Aghajani H. Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework. J Biomed Inform. 2018;88:70–89.

    Article  PubMed  Google Scholar 

  51. Joshi M, et al. Wearable sensors to improve detection of patient deterioration. Expert Rev Med Devices. 2019;16(2):145–54.

    Article  CAS  PubMed  Google Scholar 

  52. Albahri OS, et al. Systematic review of real-time remote health monitoring system in triage and priority-based sensor technology: taxonomy, open challenges, motivation and recommendations. J Med Syst. 2018;42(5):80.

    Article  CAS  PubMed  Google Scholar 

  53. Hackmann G, et al. Toward a two-tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–9.

    PubMed  PubMed Central  Google Scholar 

  54. Kollef MH, et al. A randomized trial of real-time automated clinical deterioration alerts sent to a rapid response team. J Hosp Med. 2014;9(7):424–9.

    Article  PubMed  Google Scholar 

  55. Vitaly Herasevich MDPDMS, Brian MDMS, Pickering W. Health information technology evaluation handbook: from meaningful use to meaningful outcome. Taylor & Francis; 2017.

    Google Scholar 

  56. Weiss CH, et al. Prompting physicians to address a daily checklist and process of care and clinical outcomes: a single-site study. Am J Respir Crit Care Med. 2011;184(6):680–6.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Kahn JM, et al. Impact of nurse-led remote screening and prompting for evidence-based practices in the ICU*. Crit Care Med. 2014;42(4):896–904.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mayo Clinic, Rochester, MN, USA

    Vitaly Herasevich, Kirill Lipatov & Brian W. Pickering

Authors
  1. Vitaly Herasevich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kirill Lipatov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brian W. Pickering
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vitaly Herasevich .

Editor information

Editors and Affiliations

  1. University of Applied Sciences (UAS) Osnabrück, Osnabrück, Niedersachsen, Germany

    Ursula H. Hübner

  2. The University of Texas at Arlington (UTA), Arlington, TX, USA

    Gabriela Mustata Wilson

  3. Healthcare Information and Management Systems Society (HIMSS), Chicago, IL, USA

    Toria Shaw Morawski

  4. The University of Texas at Arlington (UTA), Arlington, TX, USA

    Marion J. Ball

Appendix: Answers to Review Questions

Appendix: Answers to Review Questions

  1. 1.

    Rapid Response System in context of healthcare is:

    1. (a)

      Joint effort of City Mayor office, Sheriff, and public.

    2. (b)

      Extended Emergency Department.

    3. (c)

      Hospital activity to bring resources to deteriorated patient.

      Explanation: Rapid Response System (Team) is relatively now concept to healthcare. Those services established inside hospital and usually compound from Critical Care practitioners.

  2. 2.

    Reliable Clinical Alerts in clinical environment could be achieved by training Machine Learning tools on Big Data.

    1. (a)

      True.

    2. (b)

      False.

      Explanation: Big data could be used to train Machine Learning tools. However, performance in clinical environment depends on other factors that not used during Big Data training.

  3. 3.

    ICD codes are excellent sources of outcome diagnoses (“gold standard”) for training computerized alerts.

    1. (a)

      True.

    2. (b)

      False.

      Explanation: ICD codes are used for billing and reporting purposes. That is documented in peer-review literature that ICD codes should not be used for using as “gold standard” for validation of clinical alerting and prediction models.

  4. 4.

    Ideal clinical surveillance technologies should be carefully evaluated before implementation to practice in following domains (check all what applies):

    1. (a)

      Better health.

    2. (b)

      Profitability.

    3. (c)

      Better care.

    4. (d)

      Lower cost.

Explanation: The Centers for Medicare & Medicaid Services (CMS) established those three goals as part of Affordable Care Act. Profitability of hospital/health care system is not part of that. Ideal technologies should address those three goals and not focus on increasing profitability.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Herasevich, V., Lipatov, K., Pickering, B.W. (2022). EHR Data: Enabling Clinical Surveillance and Alerting. In: Hübner, U.H., Mustata Wilson, G., Morawski, T.S., Ball, M.J. (eds) Nursing Informatics . Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-91237-6_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-91237-6_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91236-9

  • Online ISBN: 978-3-030-91237-6

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation