Download PDF
Am J Perinatol 2024; 41(S 01): e1982-e1988
DOI: 10.1055/a-2096-2168
Original Article

National Needs Assessment of Utilization of Common Newborn Clinical Decision Support Tools

Kristyn Beam
1   Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
,
Cindy Wang
2   Department of Statistics, Harvard University, Cambridge, Massachusetts
,
Andrew Beam 
3   Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
4   Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
,
Reese Clark 
5   The Pediatrix Center for Research, Education, Quality and Safety, Sunrise, Florida
,
Veeral Tolia
5   The Pediatrix Center for Research, Education, Quality and Safety, Sunrise, Florida
6   Department of Pediatrics, Baylor University Medical Center, Dallas, Texas
,
Kaashif Ahmad 
5   The Pediatrix Center for Research, Education, Quality and Safety, Sunrise, Florida
7   Department of Pediatrics, The Woman's Hospital of Texas, Houston, Texas
› Author Affiliations Funding Dr. Andrew Beam's work on this project was partially supported by funding from the National Heart, Lung, and Blood Institute (grant no.: K01 HL141771).
› Further Information
Also available at
    Permissions and Reprints

Abstract

Objective Clinical decision support tools (CDSTs) are common in neonatology, but utilization is rarely examined. We examined the utilization of four CDSTs in newborn care.

Study Design A 72-field needs assessment was developed. It was distributed to listservs encompassing trainees, nurse practitioners, hospitalists, and attendings. At the conclusion of data collection, responses were downloaded and analyzed.

Results We received 339 fully completed questionnaires. BiliTool and the Early-Onset Sepsis (EOS) tool were used by > 90% of respondents, the Bronchopulmonary Dysplasia tool by 39%, and the Extremely Preterm Birth tool by 72%. Common reasons CDSTs did not impact clinical care included lack of electronic health record integration, lack of confidence in prediction accuracy, and unhelpful predictions.

Conclusion From a national sample of neonatal care providers, there is frequent but variable use of four CDSTs. Understanding the factors that contribute to tool utility is vital prior to development and implementation.

Key Points

  • Clinical decision support tools are common in medicine.

  • There is a varied use of neonatal CDST.

  • Understanding the use of CDST is vital for future development.

Keywords

neonatology - clinical decision support - machine learning - electronic health records - health informatics

Authors' Contributions

K.B. conceptualized the study, designed the data collection tool, collected and analyzed the data. She drafted the initial manuscript and reviewed and revised the manuscript.


A.B., V.T., and K.A. conceptualized the study, revised, and reviewed the manuscript for important intellectual content.


C.W. designed the data collection tool, collected data, and revised the manuscript for important intellectual content.


R.C. reviewed and revised the manuscript for important intellectual content.


All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.




Publication History

Received: 10 February 2023

Accepted: 18 May 2023

Accepted Manuscript online:
19 May 2023

Article published online:
19 June 2023

© 2023. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 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 (01) 17
    CrossrefPubMedSearch in Google Scholar
  • 2 Sittig DF, Wright A, Osheroff JA. et al. Grand challenges in clinical decision support. J Biomed Inform 2008; 41 (02) 387-392
    CrossrefPubMedSearch in Google Scholar
  • 3 Escobar GJ, Puopolo KM, Wi S. et al. Stratification of risk of early-onset sepsis in newborns ≥ 34 weeks' gestation. Pediatrics 2014; 133 (01) 30-36
    CrossrefPubMedSearch in Google Scholar
  • 4 Kuzniewicz MW, Puopolo KM, Fischer A. et al. A quantitative, risk-based approach to the management of neonatal early-onset sepsis. JAMA Pediatr 2017; 171 (04) 365-371
    CrossrefPubMedSearch in Google Scholar
  • 5 Puopolo KM, Draper D, Wi S. et al. Estimating the probability of neonatal early-onset infection on the basis of maternal risk factors. Pediatrics 2011; 128 (05) e1155-e1163
    CrossrefPubMedSearch in Google Scholar
  • 6 Bhutani VK, Johnson L, Sivieri EM. Predictive ability of a predischarge hour-specific serum bilirubin for subsequent significant hyperbilirubinemia in healthy term and near-term newborns. Pediatrics 1999; 103 (01) 6-14
    CrossrefPubMedSearch in Google Scholar
  • 7 Jensen EA, Dysart K, Gantz MG. et al. The diagnosis of bronchopulmonary dysplasia in very preterm infants. An evidence-based approach. Am J Respir Crit Care Med 2019; 200 (06) 751-759
    CrossrefPubMedSearch in Google Scholar
  • 8 Greenberg RG, McDonald SA, Laughon MM. et al; Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network. Online clinical tool to estimate risk of bronchopulmonary dysplasia in extremely preterm infants. Arch Dis Child Fetal Neonatal Ed 2022; (e-pub ahead of print).
    CrossrefPubMedSearch in Google Scholar
  • 9 Rysavy MA, Horbar JD, Bell EF. et al; Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network and Vermont Oxford Network. Assessment of an updated neonatal research network extremely preterm birth outcome model in the Vermont Oxford Network. JAMA Pediatr 2020; 174 (05) e196294
    CrossrefPubMedSearch in Google Scholar
  • 10 Stoll BJ, Hansen NI, Bell EF. et al; Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network. Neonatal outcomes of extremely preterm infants from the NICHD Neonatal Research Network. Pediatrics 2010; 126 (03) 443-456
    CrossrefPubMedSearch in Google Scholar
  • 11 Beam AL, Kohane IS. Big data and machine learning in health care. JAMA 2018; 319 (13) 1317-1318
    CrossrefPubMedSearch in Google Scholar
  • 12 Laughon MM, Langer JC, Bose CL. et al; Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network. Prediction of bronchopulmonary dysplasia by postnatal age in extremely premature infants. Am J Respir Crit Care Med 2011; 183 (12) 1715-1722
    CrossrefPubMedSearch in Google Scholar
  • 13 Cummings JJ, Pramanik AK. COMMITTEE ON FETUS AND NEWBORN. Postnatal Corticosteroids to Prevent or Treat Chronic Lung Disease Following Preterm Birth. Pediatrics 2022; 149 (06) e2022057530
    CrossrefPubMedSearch in Google Scholar
  • 14 Cuna A, Liu C, Govindarajan S, Queen M, Dai H, Truog WE. Usefulness of an online risk estimator for bronchopulmonary dysplasia in predicting corticosteroid treatment in infants born preterm. J Pediatr 2018; 197: 23-28.e2
    CrossrefPubMedSearch in Google Scholar
  • 15 Stipelman CH, Smith ER, Diaz-Ochu M. et al. Early-onset sepsis risk calculator integration into an electronic health record in the nursery. Pediatrics 2019; 144 (02) e20183464
    CrossrefPubMedSearch in Google Scholar
  • 16 Dhudasia MB, Mukhopadhyay S, Puopolo KM. Implementation of the sepsis risk calculator at an academic birth hospital. Hosp Pediatr 2018; 8 (05) 243-250
    CrossrefPubMedSearch in Google Scholar
  • 17 Lou SS, Lew D, Harford DR. et al. Temporal associations between EHR-derived workload, burnout, and errors: a prospective cohort study. J Gen Intern Med 2022; 37 (09) 2165-2172
    CrossrefPubMedSearch in Google Scholar
  • 18 Andrews B, Myers P, Lagatta J, Meadow W. A comparison of prenatal and postnatal models to predict outcomes at the border of viability. J Pediatr 2016; 173: 96-100
    CrossrefPubMedSearch in Google Scholar
  • 19 Finlayson SG, Subbaswamy A, Singh K. et al. The clinician and dataset shift in artificial intelligence. N Engl J Med 2021; 385 (03) 283-286
    CrossrefPubMedSearch in Google Scholar
  • 20 Luo G, Stone BL, Johnson MD. et al. Automating construction of machine learning models with clinical big data: proposal rationale and methods. JMIR Res Protoc 2017; 6 (08) e175
    CrossrefPubMedSearch in Google Scholar
  • 21 Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health 2021; 3 (11) e745-e750
    CrossrefPubMedSearch in Google Scholar