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Computer says 2.5 litres--how best to incorporate intelligent software into clinical decision making in the intensive care unit?

ABSTRACT: What will be the role of the intensivist when computer-assisted decision support reaches maturity? Celi's group reports that Bayesian theory can predict a patient's fluid requirement on day 2 in 78% of cases, based on data collected on day 1 and the known associations between those data, based on observations in previous patients in their unit. There are both advantages and limitations to the Bayesian approach, and this test study identifies areas for improvement in future models. Although such models have the potential to improve diagnostic and therapeutic accuracy, they must be introduced judiciously and locally to maximize their effect on patient outcome. Efficacy is thus far undetermined, and these novel approaches to patient management raise new challenges, not least medicolegal ones.

SUBMITTER: Lane K

PROVIDER: S-EPMC2688105 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Computer says 2.5 litres--how best to incorporate intelligent software into clinical decision making in the intensive care unit?

Lane Katie K Boyd Owen O

Critical care (London, England) 20090123 1

What will be the role of the intensivist when computer-assisted decision support reaches maturity? Celi's group reports that Bayesian theory can predict a patient's fluid requirement on day 2 in 78% of cases, based on data collected on day 1 and the known associations between those data, based on observations in previous patients in their unit. There are both advantages and limitations to the Bayesian approach, and this test study identifies areas for improvement in future models. Although such ...[more]

PMID: 19232073

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Project description:Importance:Professional guidelines have identified key communication skills for shared decision-making for critically ill patients, but it is unclear how intensivists interpret and implement them. Objective:To compare the self-evaluations of intensivists reviewing transcripts of their own simulated intensive care unit family meetings with the evaluations of trained expert colleagues. Design, Setting, and Participants:A posttrial web-based survey of intensivists was conducted between January and March 2019. Intensivists reviewed transcripts of simulated intensive care unit family meetings in which they participated in a previous trial from October 2016 to November 2017. In the follow-up survey, participants identified if and how they performed key elements of shared decision-making for an intensive care unit patient at high risk of death. Transcript texts that intensivists self-identified as examples of key communication skills recommended by their professional society's policy on shared decision-making were categorized. Main Outcomes and Measures:Comparison of the evaluations of 2 blinded nonparticipant intensivist colleagues with the self-reported responses of the intensivists. Results:Of 116 eligible intensivists, 76 (66%) completed the follow-up survey (mean [SD] respondent age was 43.1 [8.1] years; 72% were male). Sixty-one of 76 intensivists reported conveying prognosis; however, blinded colleagues who reviewed the deidentified transcripts were less likely to report that prognosis had been conveyed than intensivists reviewing their own transcripts (42 of 61; odds ratio, 0.10; 95% CI, 0.01-0.44; P < .001). When reviewing their own transcript, intensivists reported presenting many choices, with the most common choice being code status. They also provided a variety of recommendations, with the most common being to continue the current treatment plan. Thirty-three participants (43%) reported that they offered care focused on comfort, but blinded colleagues rated only 1 (4%) as explaining this option in a clear manner. Conclusions and Relevance:In this study, guidelines for shared decision-making and end of life care were interpreted by intensivists in disparate ways. In the absence of training or personalized feedback, self-assessment of communication skills may not be interpreted consistently.

Project description:ObjectiveVideo recording and video recognition (VR) with computer vision have become widely used in many aspects of modern life. Hospitals have employed VR technology for security purposes, however, despite the growing number of studies showing the feasibility of VR software for physiologic monitoring or detection of patient movement, its use in the intensive care unit (ICU) in real-time is sparse and the perception of this novel technology is unknown. The objective of this study is to understand the attitudes of providers, patients, and patient's families toward using VR in the ICU.DesignA 10-question survey instrument was used and distributed into two groups of participants: clinicians (MDs, advance practice providers, registered nurses), patients and families (adult patients and patients' relatives). Questions were specifically worded and section for free text-comments created to elicit respondents' thoughts and attitudes on potential issues and barriers toward implementation of VR in the ICU.SettingThe survey was conducted at Mayo Clinic in Minnesota and Florida.ResultsA total of 233 clinicians' and 50 patients' surveys were collected. Both cohorts favored VR under specific circumstances (e.g., invasive intervention and diagnostic manipulation). Acceptable reasons for VR usage according to clinicians were anticipated positive impact on patient safety (70%), and diagnostic suggestions and decision support (51%). A minority of providers was concerned that artificial intelligence (AI) would replace their job (14%) or erode professional skills (28%). The potential use of VR in lawsuits (81% clinicians) and privacy breaches (59% patients) were major areas of concern. Further identified barriers were lack of trust for AI, deterioration of the patient-clinician rapport. Patients agreed with VR unless it does not reduce nursing care or record sensitive scenarios.ConclusionThe survey provides valuable information on the acceptance of VR cameras in the critical care setting including an overview of real concerns and attitudes toward the use of VR technology in the ICU.

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Computer says 2.5 litres--how best to incorporate intelligent software into clinical decision making in the intensive care unit?

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Computer says 2.5 litres--how best to incorporate intelligent software into clinical decision making in the intensive care unit?

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