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

Background patients

  • 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
Surgery cancellation patients

2001-2002(a Hospital in UK)

E team surgery msc project
e-Team Surgery… patientsMSc 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

GPs patients



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


Challenges patients

  • 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

Preop patients

Detect deviations
Detect deviations patients

  • 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 patients

Patient status

Inference Engine

Self Assessment





Models patients

  • 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

HealthNet patients






  • 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
Future work patient

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