How do Clinicians Use Electronic Health Records for Respiratory Support Decisions? A Qualitative Study in Critical Care

Abstract

Objectives Selecting appropriate respiratory support in critical care is complex, and some decisions require information that may be unknown when the treatment assignment is necessary. Digital technologies such as electronic health records (EHR) are essential components in critical care medicine to support respiratory support care delivery and management of patients with respiratory failure. However, there are limited studies on EHR use that enable clinical decisions related to respiratory support. The objective of this study is to understand how clinicians use EHRs for their decision-making related to respiratory support in intensive care units (ICUs).

Methods Using a socio-technical systems approach, we conducted nine observations with nine different care teams for 35 hours at two ICUs within a large academic hospital system. We created a journey map to illustrate clinicians' respiratory support decision-making processes. We identified barriers related to decision-making processes within the ICU socio-technical work context and characterized them based on macro-cognitive functions to derive themes that can capture the decision-making patterns associated with EHR use.

Results Our analysis identified three overarching themes that represent clinicians' use of EHR for their respiratory support decisions: (1) fragmented information and tasks for individual sensemaking; (2) EHR workarounds for collaborative decision-making; and (3) interruptive order entry and order execution. These three themes represent three major sequential stages (i.e., before, during, and after morning rounds) related to clinicians' respiratory support decision-making processes, and their interaction with EHR significantly varies between stages.

Conclusion Our findings reflected different EHR use patterns before, during, and after morning rounds for decision-making related to respiratory support. These findings indicated potential opportunities for diagnostic clinical decision support (CDS) to facilitate respiratory support decisions.

Keywords

electronic health record - critical care - respiratory support - clinical decision support - qualitative study

Protection of Human and Animal Subjects

This study was approved by the University of Arizona Institutional Review Board (Protocol #: 2011215104A001). All methods were performed in accordance with the relevant guidelines and regulations. All informed consent forms were distributed and received via REDCap.

Publication History

Received: 03 May 2024

Accepted: 09 January 2025

Article published online:
17 February 2025

• References

• 1 Ventilator management strategies for adults with acute respiratory distress syndrome - UpToDate. Accessed December 23, 2022 at: https://www.uptodate.com/contents/ventilator-management-strategies-for-adults-with-acute-respiratory-distress-syndrome#H1120039629
• 2 Chakrabarti B, Calverley PMA. Management of acute ventilatory failure. Postgrad Med J 2006; 82 (969) 438-445
• 3 Plant PK, Owen JL, Elliott MW. Early use of non-invasive ventilation for acute exacerbations of chronic obstructive pulmonary disease on general respiratory wards: a multicentre randomised controlled trial. Lancet 2000; 355 (9219): 1931-1935
• 4 Adda M, Coquet I, Darmon M, Thiery G, Schlemmer B, Azoulay E. Predictors of noninvasive ventilation failure in patients with hematologic malignancy and acute respiratory failure. Crit Care Med 2008; 36 (10) 2766-2772
• 5 Ozyilmaz E, Ugurlu AO, Nava S. Timing of noninvasive ventilation failure: causes, risk factors, and potential remedies. BMC Pulm Med 2014; 14 (01) 19
• 6 Peñuelas Ó, Esteban A. Noninvasive ventilation for acute respiratory failure: the next step is to know when to stop. Eur Respir J 2018; 52 (02) 1183-1185
• 7 Dilken O, Erdogan E, Dikmen Y. Noninvasive ventilation. Challenges and Pitfalls. 2018; 6 (01) 100-108
• 8 Essay P, Mosier JM, Nayebi A, Fisher JM, Subbian V. Predicting failure of noninvasive respiratory support using deep recurrent learning. Respir Care 2023; 68 (04) 488-496
• 9 Health Information Technology for Economic and Clinical Health (HITECH) Audits | CMS. Accessed December 24, 2022 at: . https://www.cms.gov/medicare/compliance-and-audits/part-a-cost-report-audit-and-reimbursement/hitech-audits
• 10 Menachemi N, Collum TH. Benefits and drawbacks of electronic health record systems. Risk Manag Healthc Policy 2011; 4: 47-55
• 11 Henricks WH. “Meaningful use” of electronic health records and its relevance to laboratories and pathologists. J Pathol Inform 2011; 2 (01) 7
• 12 Sanchez-Pinto LN, Luo Y, Churpek MM. Big data and data science in critical care. Chest 2018; 154 (05) 1239-1248
• 13 Jalilian L, Khairat S. The next-generation electronic health record in the ICU: a focus on user-technology interface to optimize patient safety and quality. Perspect Health Inf Manag 2022; 19 (01) 1g
• 14 Williams CN, Bratton SL, Hirshberg EL. Computerized decision support in adult and pediatric critical care. World J Crit Care Med 2013; 2 (04) 21-28
• 15 Herasevich V, Tsapenko M, Kojicic M. et al. Limiting ventilator-induced lung injury through individual electronic medical record surveillance. Crit Care Med 2011; 39 (01) 34-39
• 16 Eslami S, Abu-Hanna A, Schultz MJ, de Jonge E, de Keizer NF. Evaluation of consulting and critiquing decision support systems: effect on adherence to a lower tidal volume mechanical ventilation strategy. J Crit Care 2012; 27 (04) 425.e1-425.e8
• 17 Karbing DS, Allerød C, Thorgaard P. et al. Prospective evaluation of a decision support system for setting inspired oxygen in intensive care patients. J Crit Care 2010; 25 (03) 367-374
• 18 Karbing DS, Allerød C, Thomsen LP. et al. Retrospective evaluation of a decision support system for controlled mechanical ventilation. Med Biol Eng Comput 2012; 50 (01) 43-51
• 19 Bouadma L, Lellouche F, Cabello B. et al. Computer-driven management of prolonged mechanical ventilation and weaning: a pilot study. Intensive Care Med 2005; 31 (10) 1446-1450
• 20 Essay P, Fisher JM, Mosier JM, Subbian V. Validation of an electronic phenotyping algorithm for patients with acute respiratory failure. Crit Care Explor 2022; 4 (03) e0645
• 21 Essay P, Mosier J, Subbian V. Rule-based cohort definitions for acute respiratory failure: electronic phenotyping algorithm. JMIR Med Inform 2020; 8 (04) e18402
• 22 Singh H, Spitzmueller C, Petersen NJ, Sawhney MK, Sittig DF. Information overload and missed test results in electronic health record-based settings. JAMA Intern Med 2013; 173 (08) 702-704
• 23 Muhiyaddin R, Abd-Alrazaq AA, Househ M, Alam T, Shah Z. The impact of clinical decision support systems (CDSS) on physicians: a scoping review. Stud Health Technol Inform 2020; 272: 470-473
• 24 Carayon P, Schoofs Hundt A, Karsh B-TT. et al. Work system design for patient safety: the SEIPS model. Qual Saf Health Care 2006; 15 (Suppl. 01) i50-i58
• 25 Holden RJ, Carayon P, Gurses AP. et al. SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients. Ergonomics 2013; 56 (11) 1669-1686
• 26 Carayon P, Wooldridge A, Hoonakker P, Hundt AS, Kelly MM. SEIPS 3.0: Human-centered design of the patient journey for patient safety. Appl Ergon 2020; 84: 103033
• 27 Holden RJ, Carayon P. SEIPS 101 and seven simple SEIPS tools. BMJ Qual Saf 2021; 30 (11) 901-910
• 28 Kiger ME, Varpio L. Thematic analysis of qualitative data: AMEE guide no. 131. Med Teach 2020; 42 (08) 846-854
• 29 Anfara VA, Brown KM, Mangione TL. Qualitative analysis on stage: making the research process more public. Educ Res 2002; 31 (07) 28-38
• 30 Patton MQ. Enhancing the quality and credibility of qualitative analysis. Health Serv Res 1999; 34 (5 Pt 2): 1189-1208
• 31 Wells BJ, Chagin KM, Nowacki AS, Kattan MW. Strategies for handling missing data in electronic health record derived data. EGEMS (Wash DC) 2013; 1 (03) 1035
• 32 Niazkhani Z, Pirnejad H, Berg M, Aarts J. The impact of computerized provider order entry systems on inpatient clinical workflow: a literature review. J Am Med Inform Assoc 2009; 16 (04) 539-549
• 33 Assis-Hassid S, Grosz BJ, Zimlichman E, Rozenblum R, Bates DW. Assessing EHR use during hospital morning rounds: a multi-faceted study. PLoS One 2019; 14 (02) e0212816
• 34 Colicchio TK, Dissanayake PI, Cimino JJ. Formal representation of patients' care context data: the path to improving the electronic health record. J Am Med Inform Assoc 2020; 27 (11) 1648-1657
• 35 Colicchio TK, Liang WH, Dissanayake PI, Do Rosario CV, Cimino JJ. Physicians' perceptions about a semantically integrated display for chart review: a multi-specialty survey. Int J Med Inform 2022; 163 (May): 104788
• 36 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
• 37 Osheroff JA, Teich JM, Middleton B. JAMIA perspectives on informatics a roadmap for national action on clinical decision support. III J Am Med Inf Assoc 2007; 14 (02) 141-145
• 38 Yang Y, Rivera AJ. An observational study of hands-free communication devices mediated interruption dynamics in a nursing work system. Health Policy Technol 2015; 4 (04) 378-386