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Potter KM, Pun BT, Maya K, Young B, Williams S, Schiffman M, Hosie A, Boehm LM. Delirium and Coronavirus Disease 2019: Looking Back, Moving Forward. Crit Care Nurs Clin North Am 2024; 36:415-426. [PMID: 39069360 PMCID: PMC11284274 DOI: 10.1016/j.cnc.2023.12.003] [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] [Indexed: 07/30/2024]
Abstract
During the coronavirus disease 2019 pandemic, crisis changes in clinical care increased rates of delirium in the intensive care unit (ICU). Deep sedation, unfamiliar environments with visitor restrictions, and such factors due to high workload and health system strain contributed to the occurrence of delirium doubling in the ICU. As the pandemic wanes, health care systems and ICU leadership must emphasize post-pandemic recovery, integrating lessons learned about delirium management, evidence-based care, and family involvement. Strategies to empower clinicians, creatively deliver care, and integrate families pave the way forward for a more holistic approach to patient care in the post-pandemic era.
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Affiliation(s)
- Kelly M Potter
- Department of Critical Care Medicine, CRISMA Center, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Brenda T Pun
- Department of Medicine, Pulmonary and Critical Care, Critical Illness, Brain Dysfunction, and Survivorship Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kerri Maya
- Sutter Health System, Sacramento, CA, USA
| | - Bethany Young
- Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Stacey Williams
- Monroe Carrell Jr Children's Hospital at Vanderbilt, Nashville, TN, USA
| | | | - Annmarie Hosie
- School of Nursing & Midwifery Sydney, University of Notre Dame Australia, Sydney, New South Wales, Australia; Cunningham Centre for Palliative Care, St Vincent's Health Network Sydney, Sydney, New South Wales, Australia; IMPACCT- Improving Palliative, Aged and Chronic Care Through Research and Translation, University of Technology Sydney, Sydney, New South Wales, Australia
| | - Leanne M Boehm
- School of Nursing, Vanderbilt University, Nashville, TN, USA; Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Vanderbilt University Medical Center, Nashville, TN, USA
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Zwerwer LR, van der Pol S, Zacharowski K, Postma MJ, Kloka J, Friedrichson B, van Asselt ADI. The value of artificial intelligence for the treatment of mechanically ventilated intensive care unit patients: An early health technology assessment. J Crit Care 2024; 82:154802. [PMID: 38583302 DOI: 10.1016/j.jcrc.2024.154802] [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: 11/29/2023] [Revised: 03/03/2024] [Accepted: 03/23/2024] [Indexed: 04/09/2024]
Abstract
PURPOSE The health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI systems by using available data and simulations. Therefore, we developed a generic health-economic model suitable for early HTA of AI systems for mechanically ventilated patients. MATERIALS AND METHODS Our generic health-economic model simulates mechanically ventilated patients from their hospitalisation until their death. The model simulates two scenarios, care as usual and care with the AI system, and compares these scenarios to estimate their cost-effectiveness. RESULTS The generic health-economic model we developed is suitable for estimating the cost-effectiveness of various AI systems. By varying input parameters and assumptions, the model can examine the cost-effectiveness of AI systems across a wide range of different clinical settings. CONCLUSIONS Using the proposed generic health-economic model, investors and innovators can easily assess whether implementing a certain AI system is likely to be cost-effective before an exact clinical impact is determined. The results of the early HTA can aid investors and innovators in deployment of AI systems by supporting development decisions, informing value-based pricing, clinical trial design, and selection of target patient groups.
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Affiliation(s)
- Leslie R Zwerwer
- Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
| | - Simon van der Pol
- Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Health-Ecore, Zeist, the Netherlands
| | - Kai Zacharowski
- Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Maarten J Postma
- Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Health-Ecore, Zeist, the Netherlands; Department of Economics, Econometrics and Finance, University of Groningen, Faculty of Economics and Business, Groningen, the Netherlands; Center of Excellence for Pharmaceutical Care, Universitas Padjadjaran, Bandung, Indonesia
| | - Jan Kloka
- Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Benjamin Friedrichson
- Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Antoinette D I van Asselt
- Department of Health Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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Younas A, Reynolds SS. Leveraging Artificial Intelligence for Expediting Implementation Efforts. Creat Nurs 2024; 30:111-117. [PMID: 38509712 DOI: 10.1177/10784535241239059] [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] [Indexed: 03/22/2024]
Abstract
Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice and policymaking. Nevertheless, the process of designing and selecting methods and strategies for implementing evidence is complicated because of the complexity of health-care settings where implementation is desired. Artificial intelligence (AI) has revolutionized a range of fields, including genomics, education, drug trials, research, and health care. This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice. Four key aspects of AI use in implementation science are highlighted: (a) AI for implementation planning (e.g., needs assessment, predictive analytics, and data management), (b) AI for developing implementation tools and guidelines, (c) AI for designing and applying implementation strategies, and (d) AI for monitoring and evaluating implementation outcomes. Use of AI along the implementation continuum from planning to delivery and evaluation can enable more precise and accurate implementation of evidence into practice.
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King AJ, Tang L, Davis BS, Preum SM, Bukowski LA, Zimmerman J, Kahn JM. Machine learning-based prediction of low-value care for hospitalized patients. INTELLIGENCE-BASED MEDICINE 2023; 8:100115. [PMID: 38130744 PMCID: PMC10735238 DOI: 10.1016/j.ibmed.2023.100115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Objective Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period; and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system.
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Affiliation(s)
- Andrew J. King
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lu Tang
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA
| | - Billie S. Davis
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sarah M. Preum
- Department of Computer Science, Dartmouth College, Hanover, NH, USA
| | - Leigh A. Bukowski
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - John Zimmerman
- Human-Computer Interaction Institute, Carnegie Mellon University School of Computer Science, Pittsburgh, PA, USA
| | - Jeremy M. Kahn
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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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] [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.
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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.
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