Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond

Overview

Journal Crit Care Clin

Specialty Critical Care

Date 2021 Nov 19

PMID 34794627

Citations 3

Authors

Robert El-Kareh

Dean F Sittig

Affiliations

Soon will be listed here.

Abstract

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.

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References

Nibbelink C, Young J, Carrington J, Brewer B . Informatics Solutions for Application of Decision-Making Skills. Crit Care Nurs Clin North Am. 2018; 30(2):237-246. PMC: 5941940. DOI: 10.1016/j.cnc.2018.02.006. View

King A, Cooper G, Clermont G, Hochheiser H, Hauskrecht M, Sittig D . Using machine learning to selectively highlight patient information. J Biomed Inform. 2019; 100:103327. PMC: 6932869. DOI: 10.1016/j.jbi.2019.103327. View

Downey C, Tahir W, Randell R, Brown J, Jayne D . Strengths and limitations of early warning scores: A systematic review and narrative synthesis. Int J Nurs Stud. 2017; 76:106-119. DOI: 10.1016/j.ijnurstu.2017.09.003. View

Bone R, Balk R, Cerra F, Dellinger R, Fein A, Knaus W . Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992; 101(6):1644-55. DOI: 10.1378/chest.101.6.1644. View

Wright A, Sittig D, Ash J, Feblowitz J, Meltzer S, McMullen C . Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems. J Am Med Inform Assoc. 2011; 18(3):232-42. PMC: 3078666. DOI: 10.1136/amiajnl-2011-000113. View

Pickering B, Gajic O, Ahmed A, Herasevich V, Keegan M . Data utilization for medical decision making at the time of patient admission to ICU. Crit Care Med. 2013; 41(6):1502-10. DOI: 10.1097/CCM.0b013e318287f0c0. View

Shime N, Kawasaki T, Nakagawa S . Proposal of a New Pediatric Sequential Organ Failure Assessment Score for Possible Validation. Pediatr Crit Care Med. 2017; 18(1):98-99. DOI: 10.1097/PCC.0000000000001009. View

Calzoni L, Clermont G, Cooper G, Visweswaran S, Hochheiser H . Graphical Presentations of Clinical Data in a Learning Electronic Medical Record. Appl Clin Inform. 2020; 11(4):680-691. PMC: 7560537. DOI: 10.1055/s-0040-1709707. View

Hussey P, Timbie J, Burgette L, Wenger N, Nyweide D, Kahn K . Appropriateness of advanced diagnostic imaging ordering before and after implementation of clinical decision support systems. JAMA. 2015; 313(21):2181-2. DOI: 10.1001/jama.2015.5089. View

10.

Santhosh L, Lyons P, Rojas J, Ciesielski T, Beach S, Farnan J . Characterising ICU-ward handoffs at three academic medical centres: process and perceptions. BMJ Qual Saf. 2019; 28(8):627-634. DOI: 10.1136/bmjqs-2018-008328. View

11.

Subbe C, Kruger M, Rutherford P, Gemmel L . Validation of a modified Early Warning Score in medical admissions. QJM. 2001; 94(10):521-6. DOI: 10.1093/qjmed/94.10.521. View

12.

Liu S, See K, Ngiam K, Celi L, Sun X, Feng M . Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review. J Med Internet Res. 2020; 22(7):e18477. PMC: 7400046. DOI: 10.2196/18477. View

13.

Barwise A, Garcia-Arguello L, Dong Y, Hulyalkar M, Vukoja M, Schultz M . Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN): evolution of a content management system for point-of-care clinical decision support. BMC Med Inform Decis Mak. 2016; 16(1):127. PMC: 5048402. DOI: 10.1186/s12911-016-0367-3. View

14.

Pirracchio R, Cohen M, Malenica I, Cohen J, Chambaz A, Cannesson M . Big data and targeted machine learning in action to assist medical decision in the ICU. Anaesth Crit Care Pain Med. 2018; 38(4):377-384. DOI: 10.1016/j.accpm.2018.09.008. View

15.

Letourneau-Guillon L, Camirand D, Guilbert F, Forghani R . Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics. Neuroimaging Clin N Am. 2020; 30(4):e1-e15. DOI: 10.1016/j.nic.2020.08.008. View

16.

Waller R, Wright M, Segall N, Nesbitt P, Reese T, Borbolla D . Novel displays of patient information in critical care settings: a systematic review. J Am Med Inform Assoc. 2019; 26(5):479-489. PMC: 6657276. DOI: 10.1093/jamia/ocy193. View

17.

Bassily-Marcus A . Early detection of deteriorating patients: leveraging clinical informatics to improve outcome*. Crit Care Med. 2014; 42(4):976-8. DOI: 10.1097/CCM.0000000000000093. View

18.

Sanchez-Pinto L, Khemani R . Development of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Electronic Health Record Data. Pediatr Crit Care Med. 2016; 17(6):508-15. DOI: 10.1097/PCC.0000000000000750. View

19.

Cifra C, Sittig D, Singh H . Bridging the feedback gap: a sociotechnical approach to informing clinicians of patients' subsequent clinical course and outcomes. BMJ Qual Saf. 2021; 30(7):591-597. PMC: 8237185. DOI: 10.1136/bmjqs-2020-012464. View

20.

Yu K, Beam A, Kohane I . Artificial intelligence in healthcare. Nat Biomed Eng. 2019; 2(10):719-731. DOI: 10.1038/s41551-018-0305-z. View