Machine Learning for Surgical Case Length Prediction With Adaptive Questions
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Solution Overview
Problem
Existing surgical case scheduling systems struggle to accurately predict case lengths due to insufficient information, leading to over- or under-scheduling and inefficient use of operating room resources.
Innovation Solution
A prediction system that uses a machine learning model trained on answers to questions from a flowchart to gather detailed information about surgical cases, guiding medical professionals through a series of questions to enhance input accuracy and predict case lengths, while continuously refining the flowchart based on actual case data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic procedure codes are used for scheduling surgical cases, then scheduling simplicity is improved, but prediction precision of case length deteriorates
Solution Approach 1:
The surgical case information is segmented into multiple hierarchical levels: procedure code (generic), procedure description, and specific case characteristics (e.g., revision status, number of levels). This segmentation allows the system to maintain simplicity at the procedure code level while capturing detailed information needed for accurate prediction through the flowchart questions.
Solution Approach 2:
The system performs preliminary data collection through a flowchart of questions that gathers specific case characteristics before scheduling. By collecting detailed information in advance (preliminary action), the system enables accurate case length prediction without complicating the actual scheduling process, as the data gathering occurs beforehand through structured questions.
2Measurement precision
If detailed information about surgical cases is collected, then prediction precision of case length is improved, but device complexity increases
Solution Approach 1:
The flowchart serves as an intermediary mechanism between the medical professional and the machine learning model. It structures the collection of detailed information through a series of guided questions, transforming complex data gathering into a manageable process. The flowchart mediates between the need for detailed information and the complexity of collecting it, presenting information in an organized, step-by-step manner.
Solution Approach 2:
The system dynamically adjusts which parameters (questions) are presented based on previous answers. For example, if a patient does not require revision, questions about revision levels are not presented. This parameter changes approach reduces the number of questions asked in each specific case while maintaining comprehensive data collection, thereby reducing system complexity without sacrificing prediction precision.
3Loss of information
If flowchart questions are expanded to cover all surgical scenarios, then information completeness is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements partial action by only asking questions that are relevant to the specific surgical case at hand. Based on the answer to each question, the flowchart determines which subsequent questions to present. This means that not all possible questions are asked for every case—only those necessary to capture the relevant characteristics. This partial action approach maintains information completeness for each specific scenario while significantly reducing the overall burden on medical professionals.
Solution Approach 2:
The flowchart is dynamic rather than static; it adapts its structure based on previous answers. Questions are added or skipped depending on the surgical scenario being described. For instance, questions about hardware removal appear only when revision surgery is indicated. This dynamic adaptation ensures information completeness for each specific case type while keeping the interaction concise and relevant, thereby maintaining ease of operation.
Data Source
AI summary
The prediction system accesses a flowchart of questions relating to surgical cases and receives, for each of set of surgical case identifiers, surgical case information and an actual surgical case length. The prediction system trains a machine learning model to predict surgical case lengths using the surgical case information and prunes the flowchart by removing questions associated with a uniform set of answers. The prediction system receives, from a client device, a request to reserve an operating room for a surgical case, and transmits, for display via a user interface of the client device, questions from the flowchart. The prediction system receives a feature vector of answers to the transmitted questions from the client device and inputs a type surgical case and the feature vector to the machine learning model, which outputs a predicted surgical case length. The prediction system reserves an operating room for the predicted surgical case length.