Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.

Supplemental Digital Content is available in the text.

Background Although there is considerable interest in machine learning (ML) and artificial intelligence (AI) in critical care, the implementation of effective algorithms into practice has been limited. Objective We sought to understand physician perspectives of a novel intubation prediction tool. Further, we sought to understand health care provider and nonprovider perspectives on the use of ML in health care. We aim to use the data gathered to elucidate implementation barriers and determinants of this intubation prediction tool, as well as ML/AI-based algorithms in critical care and health care in general. Methods We developed 2 anonymous surveys in Qualtrics, 1 single-center survey distributed to 99 critical care physicians via email, and 1 social media survey distributed via Facebook and Twitter with branching logic to tailor questions for providers and nonproviders. The surveys included a mixture of categorical, Likert scale, and free-text items. Likert scale means with SD were reported from 1 to 5. We used student t tests to examine the differences between groups. In addition, Likert scale responses were converted into 3 categories, and percentage values were reported in order to demonstrate the distribution of responses. Qualitative free-text responses were reviewed by a member of the study team to determine validity, and content analysis was performed to determine common themes in responses. Results Out of 99 critical care physicians, 47 (48%) completed the single-center survey. Perceived knowledge of ML was low with a mean Likert score of 2.4 out of 5 (SD 0.96), with 7.5% of respondents rating their knowledge as a 4 or 5. The willingness to use the ML-based algorithm was 3.32 out of 5 (SD 0.95), with 75% of respondents answering 3 out of 5. The social media survey had 770 total responses with 605 (79%) providers and 165 (21%) nonproviders. We found no difference in providers’ perceived knowledge based on level of experience in either survey. We found that nonproviders had significantly less perceived knowledge of ML (mean 3.04 out of 5, SD 1.53 vs mean 3.43, SD 0.941; P<.001) and comfort with ML (mean 3.28 out of 5, SD 1.02 vs mean 3.53, SD 0.935; P=.004) than providers. Free-text responses revealed multiple shared concerns, including accuracy/reliability, data bias, patient safety, and privacy/security risks. Conclusions These data suggest that providers and nonproviders have positive perceptions of ML-based tools, and that a tool to predict the need for intubation would be of interest to critical care providers. There were many shared concerns about ML/AI in health care elucidated by the surveys. These results provide a baseline evaluation of implementation barriers and determinants of ML/AI-based tools that will be important in their optimal implementation and adoption in the critical care setting and health care in general.

No abstract

BACKGROUND Although there is considerable interest in machine learning (ML) and artificial intelligence (AI) in critical care, implementation of effective algorithms into practice has been limited. OBJECTIVE We sought to understand physician perspectives of a novel intubation prediction tool as well as provider and patient perspectives on the use of ML in healthcare to elucidate implementation determinants of ML/AI-based algorithms in critical care. METHODS We developed two anonymous surveys in Qualtrics, one single-center survey distributed to 99 critical care physicians via email, and one social media survey distributed via Facebook and Twitter with branching logic to tailor questions for providers and patients. The surveys included a mixture of categorical, Likert scale, and free-text items. Likert scale means with standard deviations were reported from 1-5. We used student t-tests to examine differences between groups. RESULTS Forty-seven critical care physicians completed the initial survey (47/99, 47.5%). Willingness to use the ML-based algorithm was 3.32 (0.95). The social media survey had 770 total responses (provider n=605 (78.6%), patient n=165 (21.4%)). We found no difference in providers’ knowledge based on level of experience in either survey. We found that patients had significantly less knowledge of ML (3.04 (1.53) vs 3.43 (0.941), p<0.001) and comfort with ML (3.28 (1.02) vs 3.53 (0.935), p= 0.0038) than providers. Free text responses revealed multiple shared concerns, including workflow interruptions and data bias. CONCLUSIONS These data suggest that providers and patients have positive perceptions of ML-based tools, and that a tool to predict need for intubation would be of interest to critical care providers. There were shared concerns regarding workflow interruption and data bias. These survey results provide a baseline evaluation of implementation determinants of ML/AI-based tools that will be important in their optimal implementation and adoption in the critical care setting.