Discriminative Accuracy of Physician and Nurse Predictions for Survival and Functional Outcomes 6 Months After an ICU Admission

Detsky, Michael E.; Harhay, Michael O.; Bayard, Dominique; Delman, Aaron M.; Buehler, Anna E.; Kent, Saida; Ciuffetelli, Isabella V.; Cooney, Elizabeth; Gabler, Nicole B.; Ratcliffe, Sarah J.; Mikkelsen, Mark E.; Halpern, Scott D.

doi:10.1001/jama.2017.4078

Cited by 138 publications

(121 citation statements)

References 37 publications

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“…This result can be considered in agreement with several studies on different medical issues showing that predictor's confidence correlates very well with the correctness of the prediction ( Detsky et al , 2017; Hautz et al , 2015; Kämmer et al , 2017; Kurvers et al , 2016). Indeed, the concordance of different members of a given group (students or runs of the random forest model) can be taken as indicating that the agent is "sure" of the forecast.…”

Section: Discussionsupporting

confidence: 92%

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

Tacchella¹,

Romano

Ferraldeschi

et al. 2017

F1000Res

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Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

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Section: Discussionsupporting

confidence: 92%

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

Tacchella¹,

Romano

Ferraldeschi

et al. 2017

F1000Res

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“…In contrast to the study by Detsky et al [16], clinicians in our study were not explicitly expected to provide prognostic estimates about the patients’ outcomes. We preferred to focus on the intuitive-heuristic more than the analytic-deductive part of the complex ethical decision-making process [26, 27], by asking clinicians whether they felt that the care provided to their patient on a specific day was consistent with the expected outcome in terms of survival and quality of life, and whether this amount of care was in line with the patient’s or relatives’ wishes.…”

Section: Discussionmentioning

confidence: 71%

“…In addition to enhancing trust and cohesion in a team, such a climate may also reduce uncertainty in decision-makers by favoring intra- and interdisciplinary transfer of knowledge, experience and values [14]. Several studies have already shown that concordant prognostic estimates [15, 16] or perceptions of inappropriate [17] or futile care [18] by two clinicians may be considerably more predictive about the patient’s short- and long-term outcomes than usually thought. However, whether the quality of the ethical climate prevailing in a unit further improves the identification of patients receiving excessive care, and impacts on patient outcomes and written treatment-limitation decision (TLD), is unknown.…”

Section: Introductionmentioning

confidence: 99%

Outcome in patients perceived as receiving excessive care across different ethical climates: a prospective study in 68 intensive care units in Europe and the USA

et al. 2018

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PurposeWhether the quality of the ethical climate in the intensive care unit (ICU) improves the identification of patients receiving excessive care and affects patient outcomes is unknown.MethodsIn this prospective observational study, perceptions of excessive care (PECs) by clinicians working in 68 ICUs in Europe and the USA were collected daily during a 28-day period. The quality of the ethical climate in the ICUs was assessed via a validated questionnaire. We compared the combined endpoint (death, not at home or poor quality of life at 1 year) of patients with PECs and the time from PECs until written treatment-limitation decisions (TLDs) and death across the four climates defined via cluster analysis.ResultsOf the 4747 eligible clinicians, 2992 (63%) evaluated the ethical climate in their ICU. Of the 321 and 623 patients not admitted for monitoring only in ICUs with a good (n = 12, 18%) and poor (n = 24, 35%) climate, 36 (11%) and 74 (12%), respectively were identified with PECs by at least two clinicians. Of the 35 and 71 identified patients with an available combined endpoint, 100% (95% CI 90.0–1.00) and 85.9% (75.4–92.0) (P = 0.02) attained that endpoint. The risk of death (HR 1.88, 95% CI 1.20–2.92) or receiving a written TLD (HR 2.32, CI 1.11–4.85) in patients with PECs by at least two clinicians was higher in ICUs with a good climate than in those with a poor one. The differences between ICUs with an average climate, with (n = 12, 18%) or without (n = 20, 29%) nursing involvement at the end of life, and ICUs with a poor climate were less obvious but still in favour of the former.ConclusionEnhancing the quality of the ethical climate in the ICU may improve both the identification of patients receiving excessive care and the decision-making process at the end of life.Electronic supplementary materialThe online version of this article (10.1007/s00134-018-5231-8) contains supplementary material, which is available to authorized users.

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“…Nurses are ideally suited to improve patient and family care at the end of life. Their physical presence at the bedside and training in symptom management and palliation position them to directly attend to and oversee the care of dying patients . In fact, bereaved family members say their loved one's treatment preferences were respected and concerns listened to when nurses were involved in end‐of‐life care and decision making .…”

mentioning

confidence: 99%

“…Their physical presence at the bedside and training in symptom management and palliation position them to directly attend to and oversee the care of dying patients. 17,18 In fact, bereaved family members say their loved one's treatment preferences were respected and concerns listened to when nurses were involved in end-of-life care and decision making. 19 Despite nurses' proximity to patients and their clinical skill set, they remain an untapped resource because of the constraints and demands of their practice environments.…”

mentioning

confidence: 99%

Quality of End‐of‐Life Care and Its Association with Nurse Practice Environments in U.S. Hospitals

Lasater

Sloane

McHugh

et al. 2018

J American Geriatrics Society

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Objectives The purpose of this study is twofold: (1) to describe the quality of end‐of‐life care in US hospitals from the perspective of hospital nurses; and (2) to evaluate the relationship between the nurse practice environment and end‐of‐life care quality. Design Cross‐sectional analysis of multiple linked secondary data sources. PARTICIPANTS and SETTING A total of 12,870 direct care registered nurses in 491 acute‐care hospitals. Measurements Measures of end‐of‐life care quality and nurse practice environment were derived from the 2016 RN4CAST‐US survey of nurses. Nurses reported on end‐of‐life care quality for patients for whom death within a year would not be surprising. Nurse practice environment was measured using the Practice Environment Scale of the Nursing Work Index, a National Quality Forum validated scale. Results Most nurses gave their hospitals an unfavorable evaluation of end‐of‐life care overall (58%) and said patients often experience painful procedures that were unlikely to change their outcome (53%). The best predictor of poor quality was the nurse practice environment. Nurses in the best environments were 55% less likely than nurses in poor environments to rate their hospital's overall end‐of‐life care quality unfavorably (odds ratio = 0.45; 95% confidence interval = 0.39–0.52; P < .001). Conclusion Quality of end‐of‐life care in US hospitals is imperfect and is significantly worse in hospitals with poor nurse practice environments than in hospitals with the best environments. This study reinforces the importance of nurses in providing high‐quality end‐of‐life care. J Am Geriatr Soc 67:302–308, 2019.

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Discriminative Accuracy of Physician and Nurse Predictions for Survival and Functional Outcomes 6 Months After an ICU Admission

Cited by 138 publications

References 37 publications

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

Outcome in patients perceived as receiving excessive care across different ethical climates: a prospective study in 68 intensive care units in Europe and the USA

Quality of End‐of‐Life Care and Its Association with Nurse Practice Environments in U.S. Hospitals

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