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. Petros Endale May 18, 1985 B.Sc in computer science(2006) M.Sc Telemedicine and e-health(2014 ) Primary Advisor Prof Gunar H. Background. 12,000 annual elective surgery at UNN

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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

PetrosEndaleMay 18, 1985B.Sc in computer science(2006)M.Sc Telemedicine and e-health(2014)Primary Advisor Prof Gunar H.


  • 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.

Surgery cancellation

2001-2002(a Hospital in UK)

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




How can we identify serious changes in the patients health remotely?



  • 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


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.

Risk scores

Patient status

Inference Engine

Self Assessment






  • 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







  • 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)

Future work

  • If significant number of Models and their related decisions are collected then automatic statically population model can be developed.

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