Physicians Report Benefit from Guided Critical Care Algorithms During Inpatient Rapid Responses

Abstract

Objective

Patient decompensation necessitating rapid response team (RRT) care in the hospital setting involves complex medical decision making, strong leadership skills, and precise communication where every second matters. However, RRT outcomes can vary based on leader training, knowledge, and experience. We designed five digital, condition-specific, guided algorithms to improve RRT care and compared user survey data among three physician cohorts across the clinical training spectrum to assess the practicality of real-world usage in a small feasibility study.

Methods

Guided algorithms to common RRT scenarios, including tachycardia, bradycardia, hypotension, hypoxia, and altered mental status, were used by 157 physicians at our institution across three Internal Medicine user cohorts (1: end-of-year PGY-2-5 residents, 2: new PGY-2 residents, and 3: attending hospitalists) from April to December 2024. Survey data from 28 respondents were compared across cohorts using Kruskal–Wallis and Dunn statistical analyses.

Results

Survey responses demonstrated consistently high scores across cohorts regarding improvement in patient care, improved RRT leader experience, improved confidence, reduced stress/cognitive load, potential for standardization of care, and likelihood of recommendation to a colleague. Interestingly, new PGY-2 residents rated ease of navigation at 7/10 compared to 10/10 by attending hospitalists (p = 0.016).

Conclusion

Digital, guided RRT algorithms are a practical and effective tool for enhancing physician care delivery during inpatient rapid response events across all levels of training. High survey scores across cohorts warrant consideration for broader implementation. Variation in ease of navigation scores highlights the importance of tailoring information flow and usability features to less experienced users. Overall, these algorithms show promise as valuable adjuncts during acute care delivery in high-stakes clinical settings.

Keywords

clinical decision support - rapid response - critical care - iOS - android - mobile app

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed by Brigham and Women's Hospital's Institutional Review Board.

Data Availability Statement

The experimental data and the simulation results that support the findings of this study are available upon request.

Publication History

Received: 19 May 2025

Accepted: 28 October 2025

Article published online:
20 November 2025

• References

• 1 Stefan MS, Belforti RK, Langlois G, Rothberg MB. A simulation-based program to train medical residents to lead and perform advanced cardiovascular life support. Hosp Pract 2011; 39 (04) 63-69
• 2 Hippe DS, Umoren RA, McGee A, Bucher SL, Bresnahan BW. A targeted systematic review of cost analyses for implementation of simulation-based education in healthcare. SAGE Open Med 2020; 8: 2050312120913451
• 3 Rolston DM, Li T, Owens C. et al. Mechanical, team-focused, video-reviewed cardiopulmonary resuscitation improves return of spontaneous circulation after emergency department implementation. J Am Heart Assoc 2020; 9 (06) e014420
• 4 Low D, Clark N, Soar J. et al. A randomised control trial to determine if use of the iResus application on a smart phone improves the performance of an advanced life support provider in a simulated medical emergency. Anaesthesia 2011; 66 (04) 255-262
• 5 Fitzgerald M, Cameron P, Mackenzie C. et al. Trauma resuscitation errors and computer-assisted decision support. Arch Surg 2011; 146 (02) 218-225
• 6 Hsu JM. Digital health technology and trauma: development of an app to standardize care. ANZ J Surg 2015; 85 (04) 235-239
• 7 Roitsch CM, Patricia KE, Hagan JL, Arnold JL, Sundgren NC. Tablet-based decision support tool improves performance of neonatal resuscitation: a randomized trial in simulation. Simul Healthc 2020; 15 (04) 243-250
• 8 Corazza F, Fiorese E, Arpone M. et al. The impact of cognitive aids on resuscitation performance in in-hospital cardiac arrest scenarios: a systematic review and meta-analysis. Intern Emerg Med 2022; 17 (07) 2143-2158
• 9 Siebert JN, Ehrler F, Combescure C. et al. A Mobile device app to reduce time to drug delivery and medication errors during simulated pediatric cardiopulmonary resuscitation: a randomized controlled trial. J Med Internet Res 2017; 19 (02) e31
• 10 Siebert JN, Ehrler F, Combescure C. et al; PedAMINES Trial Group. A mobile device application to reduce medication errors and time to drug delivery during simulated paediatric cardiopulmonary resuscitation: a multicentre, randomised, controlled, crossover trial. Lancet Child Adolesc Health 2019; 3 (05) 303-311
• 11 Grundgeiger T, Hahn F, Wurmb T, Meybohm P, Happel O. The use of a cognitive aid app supports guideline-conforming cardiopulmonary resuscitations: a randomized study in a high-fidelity simulation. Resusc Plus 2021; 7: 100152
• 12 Crabb DB, Hurwitz JE, Reed AC. et al. Innovation in resuscitation: a novel clinical decision display system for advanced cardiac life support. Am J Emerg Med 2021; 43: 217-223
• 13 Chu AL, Ziperstein JC, Niccum BA, Joice MG, Isselbacher EM, Conley J. STAT: mobile app helps clinicians manage inpatient emergencies. Healthcare (Amst) 2021; 9 (04) 100590
• 14 Nelson McMillan K, Rosen MA, Shilkofski NA, Bradshaw JH, Saliski M, Hunt EA. Cognitive aids do not prompt initiation of cardiopulmonary resuscitation in simulated pediatric cardiopulmonary arrests. Simul Healthc 2018; 13 (01) 41-46
• 15 Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some unintended consequences of clinical decision support systems. AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2007;26–30
• 16 Stone EG. Unintended adverse consequences of a clinical decision support system: two cases. J Am Med Inform Assoc 2018; 25 (05) 564-567
• 17 Wright A, Hickman TTT, McEvoy D. et al. Analysis of clinical decision support system malfunctions: a case series and survey. J Am Med Inform Assoc 2016; 23 (06) 1068-1076
• 18 Senter-Zapata M, Neel DV, Colocci I. et al. An advanced cardiac life support application improves performance during simulated cardiac arrest. Appl Clin Inform 2024; 15 (04) 798-807
• 19 Clay-Williams R, Colligan L. Back to basics: checklists in aviation and healthcare. BMJ Qual Saf 2015; 24 (07) 428-431
• 20 Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc 2007; 14 (02) 141-145
• 21 Solomon RS, Corwin GS, Barclay DC, Quddusi SF, Dannenberg MD. Effectiveness of rapid response teams on rates of in-hospital cardiopulmonary arrest and mortality: a systematic review and meta-analysis. J Hosp Med 2016; 11 (06) 438-445
• 22 Factora F, Maheshwari K, Khanna S. et al. Effect of a rapid response team on the incidence of in-hospital mortality. Anesth Analg 2022; 135 (03) 595-604
• 23 Penketh J, Nolan JP. In-hospital cardiac arrest: the state of the art. Crit Care 2022; 26 (01) 376
• 24 Korach ZT, Cato KD, Collins SA. et al. Unsupervised machine learning of topics documented by nurses about hospitalized patients prior to a rapid-response event. Appl Clin Inform 2019; 10 (05) 952-963