Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

Abstract

Objectives The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

Methods We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework.

Results Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care.

Conclusion In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

Keywords

data science - machine learning - nursing - prediction - implementation - deployment - pilot

Protection of Human and Animal Subjects

This research does not involve human subjects.

Publication History

Received: 29 November 2022

Accepted: 03 May 2023

Accepted Manuscript online:
07 May 2023

Article published online:
02 August 2023

• References

• 1 Subrahmanya SVG, Shetty DK, Patil V. et al. The role of data science in healthcare advancements: applications, benefits, and future prospects. Ir J Med Sci 2022; 191 (04) 1473-1483
• 2 Chi W, Andreyeva E, Zhang Y, Kaushal R, Haynes K. Neighborhood-level social determinants of health improve prediction of preventable hospitalization and emergency department visits beyond claims history. Popul Health Manag 2021; 24 (06) 701-709
• 3 Baig MM, GholamHosseini H, Gutierrez J, Ullah E, Lindén M. Early detection of prediabetes and T2DM using wearable sensors and internet-of-things-based monitoring applications. Appl Clin Inform 2021; 12 (01) 1-9
• 4 Zhang X, Pérez-Stable EJ, Bourne PE. et al. Big data science: opportunities and challenges to address minority health and health disparities in the 21st century. Ethn Dis 2017; 27 (02) 95-106
• 5 Hayes CJ, Cucciare MA, Martin BC. et al. Using data science to improve outcomes for persons with opioid use disorder. Subst Abus 2022; 43 (01) 956-963
• 6 Stingone JA, Triantafillou S, Larsen A, Kitt JP, Shaw GM, Marsillach J. Interdisciplinary data science to advance environmental health research and improve birth outcomes. Environ Res 2021; 197: 111019
• 7 Lee TC, Shah NU, Haack A, Baxter SL. Clinical implementation of predictive models embedded within electronic health record systems: a systematic review. Informatics (MDPI) 2020; 7 (03) 25
• 8 Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial intelligence and the implementation challenge. J Med Internet Res 2019; 21 (07) e13659
• 9 Yang C, Kors JA, Ioannou S. et al. Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review. J Am Med Inform Assoc 2022; 29 (05) 983-989
• 10 Schwartz JM, Moy AJ, Rossetti SC, Elhadad N, Cato KD. Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: a scoping review. J Am Med Inform Assoc 2021; 28 (03) 653-663
• 11 Moorman LP. Principles for real-world implementation of bedside predictive analytics monitoring. Appl Clin Inform 2021; 12 (04) 888-896
• 12 Osterman CK, Sanoff HK, Wood WA, Fasold M, Lafata JE. Predictive modeling for adverse events and risk stratification programs for people receiving cancer treatment. JCO Oncol Pract 2022; 18 (02) 127-136
• 13 Topaz M, Pruinelli L. Big data and nursing: implications for the future. Stud Health Technol Inform 2017; 232: 165-171
• 14 Center for Nursing Informatics. Data Science Workgroup Paper. 2019. Accessed June 22, 2023 at: https://nursing.umn.edu/centers/center-nursing-informatics
• 15 Schultz MA, Walden RL, Cato K. et al. Data science methods for nursing-relevant patient outcomes and clinical processes: the 2019 literature year in review. Comput Inform Nurs 2021; 39 (11) 654-667
• 16 Douthit BJ, Walden RL, Cato K. et al. Data science trends relevant to nursing practice: a rapid review of the 2020 literature. Appl Clin Inform 2022; 13 (01) 161-179
• 17 Paré G, Kitsiou S. Methods for literature reviews. Handbook of eHealth Evaluation: An Evidence-based Approach. In: Lau F, Kuziemsky C. eds. Victoria, C: University of Victoria; 2017: 157-179
• 18 Pedregosa F, Varoquaux G, Gramfort A. et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011; 12: 2825-2830
• 19 Heslop L, Lu S, Xu X. Nursing-sensitive indicators: a concept analysis. J Adv Nurs 2014; 70 (11) 2469-2482
• 20 Covidence systematic review software. 2022. Accessed May 19, 2023 at: www.covidence.org
• 21 Vasey B, Nagendran M, Campbell B. et al; DECIDE-AI expert group. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 2022; 377: e070904
• 22 Altieri Dunn SC, Bellon JE, Bilderback A. et al. SafeNET: initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care. PLoS One 2021; 16 (02) e0246669
• 23 Fenn A, Davis C, Buckland DM. et al. Development and validation of machine learning models to predict admission from emergency department to inpatient and intensive care units. Ann Emerg Med 2021; 78 (02) 290-302
• 24 Han P, Lee SH, Noro K. et al. Improving early identification of significant weight loss using clinical decision support system in lung cancer radiation therapy. JCO Clin Cancer Inform 2021; 5: 944-952
• 25 Jauk S, Kramer D, Avian A, Berghold A, Leodolter W, Schulz S. Technology acceptance of a machine learning algorithm predicting delirium in a clinical setting: a mixed-methods study. J Med Syst 2021; 45 (04) 48
• 26 Ng R, Tan KB. Implementing an individual-centric discharge process across singapore public hospitals. Int J Environ Res Public Health 2021; 18 (16) 8700
• 27 Strömblad CT, Baxter-King RG, Meisami A. et al. Effect of a predictive model on planned surgical duration accuracy, patient wait time, and use of presurgical resources: a randomized clinical trial. JAMA Surg 2021; 156 (04) 315-321
• 28 Møller JK, Sørensen M, Hardahl C. Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: a retrospective cohort study. PLoS One 2021; 16 (03) e0248636
• 29 Murphree DH, Wilson PM, Asai SW. et al. Improving the delivery of palliative care through predictive modeling and healthcare informatics. J Am Med Inform Assoc 2021; 28 (06) 1065-1073
• 30 Wu CX, Suresh E, Phng FWL. et al. Effect of a real-time risk score on 30-day readmission reduction in Singapore. Appl Clin Inform 2021; 12 (02) 372-382
• 31 Bertsimas D, Boussioux L, Cory-Wright R. et al. From predictions to prescriptions: a data-driven response to COVID-19. Health Care Manage Sci 2021; 24 (02) 253-272
• 32 Wu CT, Li GH, Huang CT. et al. Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study. JMIR Mhealth Uhealth 2021; 9 (05) e22591
• 33 Gallagher RM, Rowell PA. Claiming the future of nursing through nursing-sensitive quality indicators. Nurs Adm Q 2003; 27 (04) 273-284
• 34 Isis Montalvo M. The national database of nursing quality indicators (TM)(NDNQI). Online J Issues Nurs 2007; 12 (03)
• 35 American Nurses Association. Guidelines for data collection on the American Nurses Association's national quality forum endorsed measures: nursing care hours per patient day; skill-mix; falls; falls with injury. 2010
• 36 Monsen KA, Austin RR, Jones RC, Brink D, Mathiason MA, Eder M. Incorporating a whole-person perspective in consumer-generated data: social determinants, resilience, and hidden patterns. Comput Inform Nurs 2021; 39 (08) 402-410
• 37 Shang Z. A concept analysis on the use of artificial intelligence in nursing. Cureus 2021; 13 (05) e14857
• 38 Watson J, Hutyra CA, Clancy SM. et al. Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?. JAMIA Open 2020; 3 (02) 167-172
• 39 Stiglic G, Kocbek P, Fijacko N, Zitnik M, Verbert K, Cilar L. Interpretability of machine learning-based prediction models in healthcare. Wiley Interdiscip Rev Data Min Knowl Discov 2020; 10 (05) e1379