Model based health change monitoring in pre-surgical patients
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Model based health change monitoring in pre-surgical
patients
Jan 21, 2014
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Petros Endale
May 18, 1985
B.Sc in computer science(2006)
M.Sc Telemedicine and e-health(2014)
Primary Advisor Prof Gunar H.
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Background• 12,000 annual elective surgery at UNN• Population settlement of Northern Norway• The overall risk of surgery is low in healthy
individuals. Preoperative tests usually lead to false-positive results, unnecessary costs, and a potential delay of surgery. Preoperative tests should not be performed unless there is a clear clinical indication.
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Surgery cancellation
Patient68% (8309)
Hospital non-clinical
24% (2980)
Hospital clinical8% (986)
2001-2002(a Hospital in UK)
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e-Team Surgery…MSc Project
• Exploring if moving the pre-surgical planning out of hospitals and to patients at home through electronic collaboration will improve the quality of care for patients scheduled for surgery
• Developing system for monitoring health changes in pre-surgical patients. The focus will be on the patient model
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Patients
GPs
SurgeonsAnesthesiologist
How can we identify serious changes in the patients health remotely?
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Challenges• Due to the vast scope of pre-operative
assessment, the clinical domain knowledge potentially relevant for assessment is virtually limitless• a comprehensive list of co morbidities, full
history of previous surgery, medication, family history, allergies, previous experiences of clinical adverse events
• Data availability
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Preop
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Detect deviations• Questionnaire(baseline data) + Objective
physiological parameters• Rule Engine(based on guidelines and
expert opinion)• Notification, Recommendation and status • The Health professional decision and
action• The patient status aproved by the HP or in
agreement with the previous model is taken as the new model.
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Patient status
Risk scores
Self Assessment
GuidelinesRules
Models
Inference Engine
Status
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Models• Model can be seen as a simplified high-
level description of a specific patient in XML form.
• The model can inform patients and physicians about the status of the patient, and deviation from expected/normal
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XMLRule
Database
Patient
HealthNet
Server
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• Questionnaire answers and physiological data coming from the patient
• The rule engine and the reasoner compare it with previous models and determines the current state of the patient
• The current state along with the decision of the health professional will be saved as the new model
• Anonimze and save the model the model for future similar cases(case based reasoning)
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Future work• If significant number of Models and their
related decisions are collected then automatic statically population model can be developed.