CoChat
Sign Up
Cited by 49Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareElectronic Health Records Systems

Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders

Open access
Cato, Kenrick D., McGrow, Kathleen, Rossetti, Sarah Collins·2020·Nursing Management
DOI 10.1097/01.numa.0000719396.83518.d6PMC PMC8018525
Open this paper in CoChat — free account

Abstract

In Brief Recently, there's been a dramatic rise in artificial intelligence (AI) research in healthcare. This article explains the key concepts of AI and clinical decision support with examples of real-world applications in clinical and operational scenarios.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1097/01.numa.0000719396.83518.d6

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 21

  1. [1]Simon HA. Artificial intelligence: an empirical science. Artif Intell. 1995; 77(1): 95– 127.
  2. [2]Fogel AL, Kvedar JC. PERSPECTIVE Artificial intelligence powers digital medicine. NPJ Digit Med. 2018; 1: 5. 31304291 PMC6548340
  3. [3]Graves JR, Corcoran S. The study of nursing informatics. Image J Nurs Scholarsh. 1989; 21(4): 227– 231. 2807330
  4. [5]Matney SA, Staggers N, Clark L. Nurses’ wisdom in action in the emergency department. Glob Qual Nurs Res. 2016; 3: 2333393616650081. PMC5342640 28462339
  5. [7]Ackoff RL. From data to wisdom. J Appl Syst Anal. 1989; 16(1): 3– 9.
  6. [9]Targowski A. From data to wisdom. Dialogue Universalism. 2005; 15(5): 55– 71.
  7. [10]Matney S, Brewster PJ, Sward KA, Cloyes KG, Staggers N. Philosophical approaches to the nursing informatics data-information- knowledge-wisdom framework. Adv Nurs Sci. 2011; 34(1): 6– 18. 21150551
  8. [11]Shukla Shubhendu S, Vijay J. Applicability of artificial intelligence in different fields of life. Int J Scientific Engineering Res. 2013; 1(1): 28– 35.
  9. [14]Teich JM, Osheroff JA, Pifer EA, Sittig DF. Clinical decision support in electronic prescribing: recommendations and an action plan. J Am Med Inform Assoc. 2005; 12(4): 365– 376. 15802474 PMC1174880
  10. [16]Whalen K, Bavuso K, Bouyer-Ferullo S, Analysis of nursing clinical decision support requests and strategic plan in a large academic health system. Appl Clin Inform. 2016; 7(2): 227– 237. 27437036 PMC4941835
  11. [17]Paul M, Andreassen S, Nielsen AD, Prediction of bacteremia using TREAT, a computerized decision-support system. Clin Infect Dis. 2006; 42(9): 1274– 1282. 16586387
  12. [18]Harrison AM, Herasevich V, Gajic O. Automated sepsis detection, alert, and clinical decision support: act on it or silence the alarm? Crit Care Med. 2015; 43(8): 1776– 1777. 26181117
  13. [19]Nguyen SQ, Mwakalindile E, Booth JS, Automated electronic medical record sepsis detection in the emergency department. PeerJ. 2014; 2014(1): e343. PMC3994640 24765577
  14. [20]Semler MW, Weavind L, Hooper MH, An electronic tool for the evaluation and treatment of sepsis in the ICU: a randomized controlled trial. Crit Care Med. 2015; 43(8): 1595– 1602. 25867906 PMC4506222
  15. [21]Buck KM. Developing an early sepsis alert program. J Nurs Care Qual. 2014; 29(2): 124– 132. 24048073
  16. [22]Henry KE, Hager DN, Pronovost PJ, Saria S. A targeted real-time early warning score (TREWScore) for septic shock. Sci Transl Med. 2015; 7(299): 299ra122–299ra122. 26246167
  17. [23]Rossetti SC, Knaplund C, Albers D, Leveraging clinical expertise as a feature--not an outcome--of predictive models: evaluation of an early warning system use case. AMIA Annu Symp Proc. 2020; 2019: 323– 332. 32308825 PMC7153052
  18. [24]Teng AK, Wilcox AB. A review of predictive analytics solutions for sepsis patients. Appl Clin Inform. 2020; 11(3): 387– 398. PMC7253313 32462640
  19. [25]Lenert MC, Matheny ME, Walsh CG. Prognostic models will be victims of their own success, unless... J Am Med Inform Assoc. 2019; 26(12): 1645– 1650. 31504588 PMC6857506
  20. [26]Blum JM. Beware of the magic eight ball in medicine. Crit Care Med. 2019; 47(11): 1650– 1651. 31609258
  21. [28]Kwon JY, Karim ME, Topaz M, Currie LM. Nurses “seeing forest for the trees” in the age of machine learning: using nursing knowledge to improve relevance and performance. Comput Inform Nurs. 2019; 37(4): 203– 212. 30688670

Cited by

Sorted by citations

Authors
Cato, K. D., McGrow, K., & Rossetti, S. C.
Affiliations
Microsoft (United States) (US)
ORCID
Venue
Nursing Management
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2020-11
Volume
51
Issue
11
Pages
24-30
ISSN
0744-6314 (ISSN-L)
DOI
10.1097/01.numa.0000719396.83518.d6
PMC
PMC8018525
OpenAlex
W3093698991
Cited by
49
References
3
Citations by year
2026: 14 · 2025: 13 · 2024: 9 · 2023: 5 · 2022: 6 · 2021: 2
FWCI
0.96
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Nursing Management (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by)
Funders
National Institute of Nursing Research (R01NR016941)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
artificial intelligence in healthcare, clinical decision support, clinical data
MeSH terms
Artificial Intelligence, Humans, Nurse Administrators, Nursing Informatics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review