Machine learning for surgical time prediction

Martínez, Óscar Sanjuán; Martinez, Carol; Parra, Carlos; Rugéles, Saúl; Suárez, Daniel R.

doi:10.1016/j.cmpb.2021.106220

Cited by 41 publications

(25 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, AIenhanced scheduling systems have been used to identify modifiable risk factors and to stratify patients into highand low-risk groups to optimize preventive measures in advance [1,19,20]. In addition, intelligent digital services have been used to predict the duration of surgery (DOS) [2][3][4][5][6][7] and the postoperative length of stay [2] to optimize resource management with a high degree of accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…The top three ranked statements were: (1) It is important that AI recognizes if the patients are in risk for adverse events during the care; (2) It is important that AI is able to make individual patient profiles based on previous data; and (3) It is important that AI can suggest the best possible timing for a treatment or visit based on patient risks and the predicted patient flow. The ranking within the category as well as overall ranking can be seen in Table 3.…”

Section: Category 2: Relevancy Of Unit-level Recommendations For Oper...mentioning

confidence: 99%

“…Artificial intelligence (AI) and machine learning (ML) are transforming the optimization of clinical and patient workflows in healthcare. The adoption of AI and ML technologies in care pathway planning and scheduling systems can enable early risk assessment [1], provide more accurate schedules [2][3][4][5][6][7], reduce blocking [8], and thus, maximize efficiency [9], minimize unnecessary costs [10], and tackle excessive waiting times [11] throughout the care pathway. However, the current care pathway planning and scheduling systems are mostly manual, time-consuming, and resource intensive [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities

Jansson

Ohtonen

Alalääkkölä

et al. 2022

BMC Health Serv Res

View full text Add to dashboard Cite

Background Artificial intelligence (AI) and machine learning are transforming the optimization of clinical and patient workflows in healthcare. There is a need for research to specify clinical requirements for AI-enhanced care pathway planning and scheduling systems to improve human–AI interaction in machine learning applications. The aim of this study was to assess content validity and prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. Methods A prospective content validity assessment was conducted in five university hospitals in three different countries using an electronic survey. The content of the survey was formed from clinical requirements, which were formulated into generic statements of required AI functionalities. The relevancy of each statement was evaluated using a content validity index. In addition, weighted ranking points were calculated to prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. Results A total of 50 responses were received from clinical professionals from three European countries. An item-level content validity index ranged from 0.42 to 0.96. 45% of the generic statements were considered good. The highest ranked functionalities for an AI-enhanced care pathway planning and scheduling system were related to risk assessment, patient profiling, and resources. The highest ranked functionalities for the user interface were related to the explainability of machine learning models. Conclusion This study provided a comprehensive list of functionalities that can be used to design future AI-enhanced solutions and evaluate the designed solutions against requirements. The relevance of statements concerning the AI functionalities were considered somewhat relevant, which might be due to the low level or organizational readiness for AI in healthcare.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Category 2: Relevancy Of Unit-level Recommendations For Oper...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities

Jansson

Ohtonen

Alalääkkölä

et al. 2022

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

“…The optimization process considers the infrastructure at a coarse level such as number of rooms, number of beds, and is very mechanical in nature. From the mathematical point of view, the optimization algorithm must work with a landscape that has multiple local optimums, described by a very noisy set of parameters: the success of the prediction of how long it takes to complete a medical procedure is very limited because it depends on many unknown factors [28].…”

Section: Heuristic Computer Reasoning Supported By the Digital Twinmentioning

confidence: 99%

Application of Digital Twin and Heuristic Computer Reasoning to Workflow Management: Gastroenterology Outpatient Centers Study

Garbey

Joerger

Furr

2022

Preprint

View full text Add to dashboard Cite

The workflow in a large medical procedural suite is characterized by high variability of input and suboptimum throughput. Today, Electronic Health Record systems do not address the problem of workflow efficiency: there is still high frustration from medical staff who lack real-time awareness and need to act in response of events based on their personal experiences rather than anticipating. In a medical procedural suite, there are many nonlinear coupling mechanisms between individual tasks that could wrong and therefore is difficult for any individual to control the workflow in real-time or optimize it in the long run. We propose a system approach by creating a digital twin of the procedural suite that assimilates Electronic Health Record data and supports the process of making rational, data-driven, decisions to optimize the workflow on a continuous basis. In this paper, we focus on long-term improvements of gastroenterology outpatient centers as a prototype example and use six months of data acquisition in two different clinical sites to validate the artificial intelligence algorithms.

show abstract

“…There is a great effort in place to carefully program the operating times and improving its efficiency. The immediate future will include using machine learning for it [10]. The COVID-19 outbreak taught that lack of accessibility to surgery is a patient safety issue itself [11].…”

Section: Less Is Morementioning

confidence: 99%

Extension of patient safety initiatives to perioperative care

Velasco

Paz-Martín

2022

Current Opinion in Anaesthesiology

View full text Add to dashboard Cite

Purpose of reviewPatient safety has significantly improved during the intraoperative period thanks to the anesthesiologists, surgeons, and nurses. Nowadays, it is within the perioperative period where most of the preventable harm happened to the surgical patient. We aim to highlight the main issues and efforts to improve perioperative patient safety focusing and the relation to intraoperative safety strategies.Recent findingsThere is ongoing research on perioperative safety strategies aiming to initiate multidisciplinary interventions on early stages of the perioperative period as well as an increasing focus on preventing harm from postoperative complications.SummaryAny patient safety strategy to be implemented needs to be framed beyond the operating room and include in the intervention the whole perioperative period.

show abstract

Machine learning for surgical time prediction

Cited by 41 publications

References 8 publications

Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities

Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities

Application of Digital Twin and Heuristic Computer Reasoning to Workflow Management: Gastroenterology Outpatient Centers Study

Extension of patient safety initiatives to perioperative care

Contact Info

Product

Resources

About