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• 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

Patient68% (8309)

Hospital non-clinical

24% (2980)

Hospital clinical8% (986)

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

Patients

GPs

SurgeonsAnesthesiologist

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

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

Preop

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.

Patient status

Risk scores

Self Assessment

GuidelinesRules

Models

Inference Engine

Status

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

XML

Rule

Database

Patient

HealthNet

Server

• 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.