Managing patients of knee surgeries
This disclosure relates to systems and methods for managing patients of knee surgeries. A pre-operative patient questionnaire user interface is associated with a future knee operation of the patient. Patient input data is indicative of answers of a patient in relation to the pre-operative patient questionnaire. A processor of a computer system evaluates a statistical model to determine a predicted satisfaction value indicative of satisfaction of the patient with the future knee operation. The statistical model comprises nodes stored on data memory representing the patient input data and the predicted satisfaction value, and edges stored on data memory between the nodes representing conditional dependencies between the patient input data and the predicted satisfaction value. The processor then generates an electronic document comprising a surgeon report associated with the future knee operation to indicate to the surgeon the predicted satisfaction value.
1 . A method for managing patients of knee surgeries, the method comprising:
receiving patient input data indicative of activity desires or patient behaviour;
performing a kinematic simulation by a simulator that simulates a result of a future knee operation of a patient to determine kinematic simulation data;
evaluating by a processor of a computer system a statistical model to determine a predicted satisfaction value indicative of satisfaction of the patient with the future knee operation, the statistical model comprising:
nodes stored on data memory representing the patient input data and the predicted satisfaction value and representing the kinematic simulation data and the patient input data indicative of activity desires or patient behaviour, and
edges stored on data memory between the nodes representing conditional dependencies between the patient input data and the predicted satisfaction value,
wherein each stored node is associated with a probability function that is configured to receive as an input of values for parent variables as defined by the stored edges, and is further configured to produce as an output a probability of a variable represented by that stored node; and
generating an indication of the predicted satisfaction value.
2 . The method of claim 1 , further comprising generating an indication of activity level for the patient.
3 . The method of claim 1 , further comprising:
determining, based on the patient input data, quantitative indications of influencing factors.
4 . The method of claim 3 , wherein the influencing factors include one or more factors from a Knee injury and Osteoarthritis Outcome Score (KOOS).
5 . The method of claim 1 , further comprising:
generating an electronic document comprising a surgeon report associated with the future knee operation to indicate the predicted satisfaction value.
6 . The method of claim 5 , wherein the statistical model is a Bayesian Network.
7 . The method of claim 5 , wherein the generating the surgeon report comprises determining a statistical transformation of the predicted satisfaction value.
8 . The method of claim 5 , further comprising:
automatically determining an intervention procedure based on the predicting satisfaction value, wherein generating the surgeon report comprises generating an indication of the intervention procedure.
9 . The method of claim 1 , further comprising:
generating, after a knee operation, a post-operative patient questionnaire user interface associated with the knee operation;
receiving post-operative patient input data indicative of answers of the patient in relation to the post-operative patient questionnaire; and
determining updated conditional dependencies between the patient input data and the predicted satisfaction value based on the post-operative patient input data.
10 . The method of claim 1 , further comprising:
receiving intra-operative data and post-operative data; and
determining, after a knee operation, a revised predicted satisfaction value based on the intra-operative data and the post-operative data.
11 . A non-transitory computer readable medium with program code stored thereon that, when installed on a computer, causes the computer to perform the steps of:
receiving patient input data indicative of activity desires or patient behaviour;
performing a kinematic simulation by a simulator that simulates a result of a future knee operation of a patient to determine kinematic simulation data;
evaluating by a processor of a computer system a statistical model to determine a predicted satisfaction value indicative of satisfaction of the patient with the future knee operation, the statistical model comprising:
nodes stored on data memory representing the patient input data and the predicted satisfaction value and representing the kinematic simulation data and the patient input data indicative of activity desires or patient behaviour, and
edges stored on data memory between the nodes representing conditional dependencies between the patient input data and the predicted satisfaction value,
wherein each stored node is associated with a probability function that is configured to receive as an input of values for parent variables as defined by the stored edges, and is further configured to produce as an output a probability of a variable represented by that stored node; and
generating an indication of the predicted satisfaction value.
12 . The non-transitory computer readable medium of claim 11 , the steps further comprising generating an indication of activity level for the patient.
13 . The non-transitory computer readable medium of claim 11 , the steps further comprising:
determining, based on the patient input data, quantitative indications of influencing factors.
14 . The non-transitory computer readable medium of claim 13 , wherein the influencing factors include one or more factors from a Knee injury and Osteoarthritis Outcome Score (KOOS).
15 . The non-transitory computer readable medium of claim 11 , the steps further comprising:
generating an electronic document comprising a surgeon report associated with the future knee operation to indicate the predicted satisfaction value.
16 . The non-transitory computer readable medium of claim 14 , wherein the statistical model is a Bayesian Network.
17 . The non-transitory computer readable medium of claim 15 , wherein the generating the surgeon report comprises determining a statistical transformation of the predicted satisfaction value.
18 . The non-transitory computer readable medium of claim 15 , the steps further comprising:
automatically determining an intervention procedure based on the predicted satisfaction value, wherein generating the surgeon report comprises generating an indication of the intervention procedure.
19 . The non-transitory computer readable medium of claim 11 , the steps further comprising:
generating, after a knee operation, a post-operative patient questionnaire user interface associated with the knee operation;
receiving post-operative patient input data indicative of answers of the patient in relation to the post-operative patient questionnaire; and
determining updated conditional dependencies between the patient input data and the predicted satisfaction value based on the post-operative patient input data.
20 . The non-transitory computer readable medium of claim 11 , the steps further comprising:
receiving intra-operative data and post-operative data; and
determining, after a knee operation, a revised predicted satisfaction value based on the intra-operative data and the post-operative data.