1
|
Erikson EJ, Edelman DA, Brewster FM, Marshall SD, Turner MC, Sarode VV, Brewster DJ. The use of checklists in the intensive care unit: a scoping review. Crit Care 2023; 27:468. [PMID: 38037056 PMCID: PMC10691022 DOI: 10.1186/s13054-023-04758-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 11/24/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Despite the extensive volume of research published on checklists in the intensive care unit (ICU), no review has been published on the broader role of checklists within the intensive care unit, their implementation and validation, and the recommended clinical context for their use. Accordingly, a scoping review was necessary to map the current literature and to guide future research on intensive care checklists. This review focuses on what checklists are currently used, how they are used, process of checklist development and implementation, and outcomes associated with checklist use. METHODS A systematic search of MEDLINE (Ovid), Embase, Scopus, and Google Scholar databases was conducted, followed by a grey literature search. The abstracts of the identified studies were screened. Full texts of relevant articles were reviewed, and the references of included studies were subsequently screened for additional relevant articles. Details of the study characteristics, study design, checklist intervention, and outcomes were extracted. RESULTS Our search yielded 2046 studies, of which 167 were selected for further analysis. Checklists identified in these studies were categorised into the following types: rounding checklists; delirium screening checklists; transfer and handover checklists; central line-associated bloodstream infection (CLABSI) prevention checklists; airway management checklists; and other. Of 72 significant clinical outcomes reported, 65 were positive, five were negative, and two were mixed. Of 122 significant process of care outcomes reported, 114 were positive and eight were negative. CONCLUSIONS Checklists are commonly used in the intensive care unit and appear in many clinical guidelines. Delirium screening checklists and rounding checklists are well implemented and validated in the literature. Clinical and process of care outcomes associated with checklist use are predominantly positive. Future research on checklists in the intensive care unit should focus on establishing clinical guidelines for checklist types and processes for ongoing modification and improvements using post-intervention data.
Collapse
Affiliation(s)
- Ethan J Erikson
- Intensive Care Unit, Cabrini Hospital, Malvern, Melbourne, Australia
| | - Daniel A Edelman
- Department of Critical Care, Alfred Health, Melbourne, Australia
| | - Fiona M Brewster
- Department of Anaesthesia, The Royal Women's Hospital, Parkville, Melbourne, Australia
| | - Stuart D Marshall
- Department of Critical Care, University of Melbourne, Melbourne, Australia
- Department of Anaesthesia, Peninsula Health, Melbourne, Australia
| | - Maryann C Turner
- Department of Critical Care, University of Melbourne, Melbourne, Australia
- Department of Anaesthesia, The Royal Children's Hospital, Melbourne, Australia
| | - Vineet V Sarode
- Intensive Care Unit, Cabrini Hospital, Malvern, Melbourne, Australia
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - David J Brewster
- Intensive Care Unit, Cabrini Hospital, Malvern, Melbourne, Australia.
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
| |
Collapse
|
2
|
Fernando M, Abell B, Tyack Z, Donovan T, McPhail SM, Naicker S. Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review. J Med Internet Res 2023; 25:e45163. [PMID: 37851492 PMCID: PMC10620641 DOI: 10.2196/45163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 08/18/2023] [Accepted: 09/14/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals. OBJECTIVE This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described. METHODS A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies. RESULTS A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework. CONCLUSIONS Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs.
Collapse
Affiliation(s)
- Manasha Fernando
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Bridget Abell
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Zephanie Tyack
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Thomasina Donovan
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Steven M McPhail
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
- Digital Health and Informatics Directorate, Metro South Health, Brisbane, Australia
| | - Sundresan Naicker
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| |
Collapse
|
3
|
Davis CL, Bjoring M, Hursh J, Smith S, Blevins C, Blackstone K, Nicholson E, Hoke T, Michel J, Noth I, Barros A, Enfield K. The Intensive Care Unit Bundle Board: A Novel Real-Time Data Visualization Tool to Improve Maintenance Care for Invasive Catheters. Appl Clin Inform 2023; 14:892-902. [PMID: 37666277 PMCID: PMC10651369 DOI: 10.1055/a-2165-5861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/14/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Critically ill patients are at greater risk of healthcare-associated infections (HAIs). The use of maintenance bundles helps to reduce this risk but also generates a rapid accumulation of complex data that is difficult to aggregate and subsequently act upon. OBJECTIVES We hypothesized that a digital display summarizing nursing documentation of invasive catheters (including central venous access devices, arterial catheters, and urinary catheters) would improve invasive device maintenance care and documentation. Our secondary objectives were to see if this summary would reduce the duration of problematic conditions, that is, characteristics associated with increased risk of infection. METHODS We developed and implemented a data visualization tool called the "Bundle Board" to display nursing observations on invasive devices. The intervention was studied in a 28-bed medical intensive care unit (MICU). The Bundle Board was piloted for 6 weeks in June 2022 and followed by a comparison phase, where one MICU had Bundle Board access and another MICU at the same center did not. We retrospectively applied tile color coding logic to prior nursing documentation from 2021 until the pilot phase to facilitate comparison pre- and post-Bundle Board release. RESULTS After adjusting for time, other quality improvement efforts, and nursing shift, multiple linear regression demonstrated a statistically significant improvement in the completion of catheter care and documentation during the pilot phase (p < 0.0001) and comparison phase (p = 0.002). The median duration of documented problematic conditions was significantly reduced during the pilot phase (p < 0.0001) and in the MICU with the Bundle Board (comparison phase, p = 0.027). CONCLUSION We successfully developed a data visualization tool that changed ICU provider behavior, resulting in increased completion and documentation of maintenance care and reduced duration of problematic conditions for invasive catheters in MICU patients.
Collapse
Affiliation(s)
- Claire Leilani Davis
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Margot Bjoring
- Department of Quality and Performance Improvement, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Jordyn Hursh
- Department of Nursing, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Samuel Smith
- Department of Nursing, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Cheri Blevins
- Department of Nursing, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Kris Blackstone
- Department of Nursing, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Evie Nicholson
- Department of Quality and Performance Improvement, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Tracey Hoke
- Department of Quality and Performance Improvement, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Jonathan Michel
- Department of Quality and Performance Improvement, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Imre Noth
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Andrew Barros
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, United States
| | - Kyle Enfield
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia Health System, Charlottesville, Virginia, United States
| |
Collapse
|
4
|
Abstract
Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment patterns. By applying artificial intelligence to these novel data sources, implementation strategies can be tailored to individual patients, individual clinicians, and individual situations, revealing when evidence-based practices are missed and facilitating context-sensitive clinical decision support. To achieve these goals, technology developers should work closely with clinicians to create unbiased applications that are integrated into the clinical workflow.
Collapse
Affiliation(s)
- Andrew J King
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3500 Terrace Street, Suite 600, Pittsburgh, PA 15261, USA
| | - Jeremy M Kahn
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3500 Terrace Street, Suite 600, Pittsburgh, PA 15261, USA; Department of Health Policy and Management, University of Pittsburgh School of Public Health, 130 De Soto Street, Pittsburgh, PA 15261, USA.
| |
Collapse
|
5
|
King AJ, Angus DC, Cooper GF, Mowery DL, Seaman JB, Potter KM, Bukowski LA, Al-Khafaji A, Gunn SR, Kahn JM. A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds. J Biomed Inform 2023; 146:104483. [PMID: 37657712 PMCID: PMC10591951 DOI: 10.1016/j.jbi.2023.104483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/21/2023] [Accepted: 08/29/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVE To evaluate the technical feasibility and potential value of a digital assistant that prompts intensive care unit (ICU) rounding teams to use evidence-based practices based on analysis of their real-time discussions. METHODS We evaluated a novel voice-based digital assistant which audio records and processes the ICU care team's rounding discussions to determine which evidence-based practices are applicable to the patient but have yet to be addressed by the team. The system would then prompt the team to consider indicated but not yet delivered practices, thereby reducing cognitive burden compared to traditional rigid rounding checklists. In a retrospective analysis, we applied automatic transcription, natural language processing, and a rule-based expert system to generate personalized prompts for each patient in 106 audio-recorded ICU rounding discussions. To assess technical feasibility, we compared the system's prompts to those created by experienced critical care nurses who directly observed rounds. To assess potential value, we also compared the system's prompts to a hypothetical paper checklist containing all evidence-based practices. RESULTS The positive predictive value, negative predictive value, true positive rate, and true negative rate of the system's prompts were 0.45 ± 0.06, 0.83 ± 0.04, 0.68 ± 0.07, and 0.66 ± 0.04, respectively. If implemented in lieu of a paper checklist, the system would generate 56% fewer prompts per patient, with 50%±17% greater precision. CONCLUSION A voice-based digital assistant can reduce prompts per patient compared to traditional approaches for improving evidence uptake on ICU rounds. Additional work is needed to evaluate field performance and team acceptance.
Collapse
Affiliation(s)
- Andrew J King
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Derek C Angus
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Gregory F Cooper
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Offices at Baum 4th Floor, 5607 Baum Blvd, Pittsburgh, PA 15206, USA.
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania School of Medicine, Blockley Hall 8th Floor, 423 Guardian Drive, Philadelphia, PA 19104, USA.
| | - Jennifer B Seaman
- Department of Acute & Tertiary Care, University of Pittsburgh School of Nursing, 336 Victoria Building, 3500 Victoria Street, Pittsburgh, PA 15261, USA.
| | - Kelly M Potter
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Leigh A Bukowski
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Ali Al-Khafaji
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Scott R Gunn
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| | - Jeremy M Kahn
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Scaife Hall Suite 600, 3550 Terrace Street, Pittsburgh, PA 15261, USA.
| |
Collapse
|
6
|
MacDonald I, de Goumoëns V, Marston M, Alvarado S, Favre E, Trombert A, Perez MH, Ramelet AS. Effectiveness, quality and implementation of pain, sedation, delirium, and iatrogenic withdrawal syndrome algorithms in pediatric intensive care: a systematic review and meta-analysis. Front Pediatr 2023; 11:1204622. [PMID: 37397149 PMCID: PMC10313131 DOI: 10.3389/fped.2023.1204622] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 05/15/2023] [Indexed: 07/04/2023] Open
Abstract
Background Pain, sedation, delirium, and iatrogenic withdrawal syndrome are conditions that often coexist, algorithms can be used to assist healthcare professionals in decision making. However, a comprehensive review is lacking. This systematic review aimed to assess the effectiveness, quality, and implementation of algorithms for the management of pain, sedation, delirium, and iatrogenic withdrawal syndrome in all pediatric intensive care settings. Methods A literature search was conducted on November 29, 2022, in PubMed, Embase, CINAHL and Cochrane Library, ProQuest Dissertations & Theses, and Google Scholar to identify algorithms implemented in pediatric intensive care and published since 2005. Three reviewers independently screened the records for inclusion, verified and extracted data. Included studies were assessed for risk of bias using the JBI checklists, and algorithm quality was assessed using the PROFILE tool (higher % = higher quality). Meta-analyses were performed to compare algorithms to usual care on various outcomes (length of stay, duration and cumulative dose of analgesics and sedatives, length of mechanical ventilation, and incidence of withdrawal). Results From 6,779 records, 32 studies, including 28 algorithms, were included. The majority of algorithms (68%) focused on sedation in combination with other conditions. Risk of bias was low in 28 studies. The average overall quality score of the algorithm was 54%, with 11 (39%) scoring as high quality. Four algorithms used clinical practice guidelines during development. The use of algorithms was found to be effective in reducing length of stay (intensive care and hospital), length of mechanical ventilation, duration of analgesic and sedative medications, cumulative dose of analgesics and sedatives, and incidence of withdrawal. Implementation strategies included education and distribution of materials (95%). Supportive determinants of algorithm implementation included leadership support and buy-in, staff training, and integration into electronic health records. The fidelity to algorithm varied from 8.2% to 100%. Conclusions The review suggests that algorithm-based management of pain, sedation and withdrawal is more effective than usual care in pediatric intensive care settings. There is a need for more rigorous use of evidence in the development of algorithms and the provision of details on the implementation process. Systematic Review Registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021276053, PROSPERO [CRD42021276053].
Collapse
Affiliation(s)
- Ibo MacDonald
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
| | - Véronique de Goumoëns
- La Source School of Nursing, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
- Bureau d’Echange des Savoirs pour des praTiques exemplaires de soins (BEST) a JBI Center of Excellence, Lausanne, Switzerland
| | - Mark Marston
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
- Department Woman-Mother-Child, Lausanne University Hospital, Lausanne, Switzerland
| | - Silvia Alvarado
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
- Department Woman-Mother-Child, Lausanne University Hospital, Lausanne, Switzerland
| | - Eva Favre
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
- Department of Adult Intensive Care, Lausanne University Hospital, Lausanne, Switzerland
| | - Alexia Trombert
- Medical Library, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Maria-Helena Perez
- Department Woman-Mother-Child, Lausanne University Hospital, Lausanne, Switzerland
| | - Anne-Sylvie Ramelet
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
- Bureau d’Echange des Savoirs pour des praTiques exemplaires de soins (BEST) a JBI Center of Excellence, Lausanne, Switzerland
- Department Woman-Mother-Child, Lausanne University Hospital, Lausanne, Switzerland
| |
Collapse
|
7
|
Tasker RC. Editor's Choice Articles for May. Pediatr Crit Care Med 2023; 24:353-355. [PMID: 37140330 DOI: 10.1097/pcc.0000000000003269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Affiliation(s)
- Robert C Tasker
- orcid.org/0000-0003-3647-8113
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA
- Selwyn College, Cambridge University, Cambridge, United Kingdom
| |
Collapse
|
8
|
Dahmer M, Jennings A, Parker M, Sanchez-Pinto LN, Thompson A, Traube C, Zimmerman JJ. Pediatric Critical Care in the Twenty-first Century and Beyond. Crit Care Clin 2023; 39:407-425. [PMID: 36898782 DOI: 10.1016/j.ccc.2022.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Pediatric critical care addresses prevention, diagnosis, and treatment of organ dysfunction in the setting of increasingly complex patients, therapies, and environments. Soon burgeoning data science will enable all aspects of intensive care: driving facilitated diagnostics, empowering a learning health-care environment, promoting continuous advancement of care, and informing the continuum of critical care outside the intensive care unit preceding and following critical illness/injury. Although novel technology will progressively objectify personalized critical care, humanism, practiced at the bedside, defines the essence of pediatric critical care now and in the future.
Collapse
Affiliation(s)
- Mary Dahmer
- Division of Critical Care, Department of Pediatrics, University of Michigan, 1500 East Medical Center Drive, F6790/5243, Ann Arbor, MI, USA
| | - Aimee Jennings
- Division of Critical Care Medicine, Advanced Practice, FA.2.112, Seattle Children's Hospital, 4800 Sandpoint Way Northeast, Seattle, WA 98105, USA
| | - Margaret Parker
- Department of Pediatrics, Stony Brook University, 7762 Bloomfield Road, Easton, MD 21601, USA
| | - Lazaro N Sanchez-Pinto
- Department of Pediatrics, Ann and Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, 225 East Chicago Avenue, Box 73, Chicago, IL 60611-2605, USA
| | - Ann Thompson
- Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA
| | - Chani Traube
- Department of Pediatrics, Weill Cornell Medicine, 525 East 68th Street, Box 225, New York, NY 10065, USA
| | - Jerry J Zimmerman
- Department of Pediatrics, FA.2.300B Seattle Children's Hospital, 4800 Sandpoint Way Northeast, Seattle, WA 98105, USA; Pediatric Critical Care Medicine, Seattle Children's Hospital, Harborview Medical Center, University of Washington, School of Medicine, FA.2.300B, Seattle Children's Hospital, 4800 Sand Point Way Northeast, Seattle, WA 98105, USA.
| |
Collapse
|
9
|
Abstract
Clinical informatics can support quality improvement and patient safety in the pediatric intensive care unit (PICU) in several ways including data extraction, analysis, and decision support enabled by electronic health records (EHRs), and databases and registries. Clinical decision support (CDS), embedded in EHRs, now an integral part of the workflow in the PICU, includes several tools and is increasingly leveraging artificial intelligence (AI). Understanding the opportunities and challenges can improve the engagement of clinicians with the design, validation, and implementation of CDS, improve satisfaction with CDS, and improve patient safety, care quality, and value.
Collapse
|