The inability of physicians to predict the outcome of in-hospital resuscitation

Development and Validation of the Good Outcome Following Attempted Resuscitation (GO-FAR) Score to Predict Neurologically Intact Survival After In-Hospital Cardiopulmonary Resuscitation

“…22,23 Although physicians could provide guidance regarding the likelihood of survival to discharge, a previous study found that the physicians' survival estimates were inaccurate. 24 Our study had several limitations. First, outcomes at the hospitals participating in the GWTG-R may differ from those of nonparticipating hospitals.…”

Development and Validation of the Good Outcome Following Attempted Resuscitation (GO-FAR) Score to Predict Neurologically Intact Survival After In-Hospital Cardiopulmonary Resuscitation

“…22,23 Although physicians could provide guidance regarding the likelihood of survival to discharge, a previous study found that the physicians' survival estimates were inaccurate. 24 Our study had several limitations. First, outcomes at the hospitals participating in the GWTG-R may differ from those of nonparticipating hospitals.…”

Assessing trauma care provider judgement in the prediction of need for life-saving interventions

“…Mean Injury Severity Scores (ISS) were 13.0 (SD 13.2) [interquartile range, [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Injuries included 19% penetrating injuries and 78% blunt injuries (Table 1).…”

Can We Improve Prediction of Adverse Surgical Outcomes? Development of a Surgical Complexity Score Using a Novel Machine Learning Technique

“…Previous studies have demonstrated physician assessment of patient risks and outcomes to be grossly inaccurate and biased. [26][27][28][29][30][31] As such, novel statistical techniques, such as machine learning, may help mitigate this shortcoming by minimizing user input bias. In the current analysis of Medicare beneficiaries undergoing a wide range of elective operations, we developed a novel machine learningderived Complexity Score.…”

“…This is important because previous studies have shown that physicians perform poorly when asked to predict patients at risk for readmission or other unfavorable patient outcomes. [26][27][28][29][30][31] Use of machine learning allows for minimization of this bias and maximizes the overall accuracy of the results. As such, the novel Complexity Score based on machine learning methodology improved upon previous iterations of risk calculators and should be the preferred method to identify and assess perioperative risk in the surgical patient.…”

Development and Validation of the Good Outcome Following Attempted Resuscitation (GO-FAR) Score to Predict Neurologically Intact Survival After In-Hospital Cardiopulmonary Resuscitation

“…22,23 Although physicians could provide guidance regarding the likelihood of survival to discharge, a previous study found that the physicians' survival estimates were inaccurate. 24 Our study had several limitations. First, outcomes at the hospitals participating in the GWTG-R may differ from those of nonparticipating hospitals.…”

Assessing trauma care provider judgement in the prediction of need for life-saving interventions

“…Mean Injury Severity Scores (ISS) were 13.0 (SD 13.2) [interquartile range, [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Injuries included 19% penetrating injuries and 78% blunt injuries (Table 1).…”

Can We Improve Prediction of Adverse Surgical Outcomes? Development of a Surgical Complexity Score Using a Novel Machine Learning Technique

“…Previous studies have demonstrated physician assessment of patient risks and outcomes to be grossly inaccurate and biased. [26][27][28][29][30][31] As such, novel statistical techniques, such as machine learning, may help mitigate this shortcoming by minimizing user input bias. In the current analysis of Medicare beneficiaries undergoing a wide range of elective operations, we developed a novel machine learningderived Complexity Score.…”

“…This is important because previous studies have shown that physicians perform poorly when asked to predict patients at risk for readmission or other unfavorable patient outcomes. [26][27][28][29][30][31] Use of machine learning allows for minimization of this bias and maximizes the overall accuracy of the results. As such, the novel Complexity Score based on machine learning methodology improved upon previous iterations of risk calculators and should be the preferred method to identify and assess perioperative risk in the surgical patient.…”

Development and Validation of the Good Outcome Following Attempted Resuscitation (GO-FAR) Score to Predict Neurologically Intact Survival After In-Hospital Cardiopulmonary Resuscitation

“…22,23 Although physicians could provide guidance regarding the likelihood of survival to discharge, a previous study found that the physicians' survival estimates were inaccurate. 24 Our study had several limitations. First, outcomes at the hospitals participating in the GWTG-R may differ from those of nonparticipating hospitals.…”

Assessing trauma care provider judgement in the prediction of need for life-saving interventions

“…Mean Injury Severity Scores (ISS) were 13.0 (SD 13.2) [interquartile range, [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Injuries included 19% penetrating injuries and 78% blunt injuries (Table 1).…”

Can We Improve Prediction of Adverse Surgical Outcomes? Development of a Surgical Complexity Score Using a Novel Machine Learning Technique

“…Previous studies have demonstrated physician assessment of patient risks and outcomes to be grossly inaccurate and biased. [26][27][28][29][30][31] As such, novel statistical techniques, such as machine learning, may help mitigate this shortcoming by minimizing user input bias. In the current analysis of Medicare beneficiaries undergoing a wide range of elective operations, we developed a novel machine learningderived Complexity Score.…”

“…This is important because previous studies have shown that physicians perform poorly when asked to predict patients at risk for readmission or other unfavorable patient outcomes. [26][27][28][29][30][31] Use of machine learning allows for minimization of this bias and maximizes the overall accuracy of the results. As such, the novel Complexity Score based on machine learning methodology improved upon previous iterations of risk calculators and should be the preferred method to identify and assess perioperative risk in the surgical patient.…”