Realisticknowledge-basedwaitingtimesforradiotherapypatients

Addressing the pain of waiting

Ackeem JosephAQPMC Student Day

December 4, 2015

Realistic knowledge-based waiting times for radiotherapy patients – addressing the pain of waitingWinners of Q+ Challenge 2014

Patient & Doctor App

Personalized DataServicingawayforpatientsto

knowabout theirhealth;allinonecentrallocation

CommunicationLeveragefeedbackbetweenpatient

anddoctor

InnovativeApplyingmoderntechnology tomedicine

Waiting Time EstimatesReducewaitinguncertaintiestoimproveworkflowandincreasepatientsatisfaction

Waiting...…when there are other things that can be done.

Patients experience...... 3 different types of waiting in radiation oncology

1. Treatmentplanning• Waitingathomebythephone• Canlastdaystoweeks

2. Daily-fractionatedtreatments• Waitinginthewaitingroom• Canlastminutestohours

3. Consultations withphysician• Waitinginthewaitingroom• Canlastminutestohours

• Difficult forstafftopredict.

• Onlyroughestimatesaregivenbasedonexperience.

The problem?

Canwebuildanalgorithmtoaccuratelypredicthowlongapatientisexpectedtowait?

Solution: Machine learning

• Goal: Toprovideradiotherapypatientswithpersonalizedpredictions regardinghowlongtheywillwaitfortheprovisionofcareintheDepartmentofRadiationOncologyattheMUHC

• How: Learndatafrompreviouspatientstomakepredictionsforfuturepatients.

What is machine learning?

• SubfieldofArtificialIntelligence

• Learning:Anyprocessbywhichasystemimprovesfromexperience

• MachineLearning: Writtencomputerprogramsthatautomaticallyimprovetheirperformancethroughexperience

• Theyareprogramsthatcanlearn fromdata

Why machine learning?

• Developsystemsthatcanautomaticallyadaptthemselvestoindividualusers• Personalizedinformation

• Discovernewknowledgefromlargedatabases• Datamining,correlations(ex:beeranddiapers)

• Mimichumanthought-processtoreplacemonotonous/laborioustasks

• Tacklesystemsthataretoocomplextoconstructanalytically• Dynamicprograminstructions(ex:humanbrain)

1.Definetheproblem• Notknowinghowlongtowait.

2.Defineyourdataset• Puttinginhistoricalpatientinformationsuchas:• Timeoftheappointment,doctor,diagnosis,etc.

• Gettingoutthedurationofanappointmenttoinferawaitingestimate.

3.Choosingtherightalgorithm• Thereisnoperfectmodel;onlyamodelthatisgoodenough.

4.Validateyouralgorithm• Divideyourexistingdatasetintotrainingandtestingsets.• Cross-validate.

How does ML work?

On a typical treatment day for a particular resource

Appointment Timeline

START IMRT SRS 3D IMRT RA ELECTRON ... END

PatientarrivesChecks in Scheduled start

oftreatment

Actualstartoftreatment

-- Delayed by 10 mins

-- Expected 5 min delay

-- No delay expected

-- Expected 10 min delay

Waiting Time

3D

IMRT

RA

Grey: Past durations (definite / already happened)

Red: Delays

Yellow: ML predictions (duration)

Green: Total wait

Blue: Treatment type

Traits that can explain appointment delays

Defining features

Patient #1

• -----------• -----------• -----------

Patient #2

• -----------• -----------• -----------

Patient #3

• -----------• -----------• -----------

ML Model

15 minsduration

12 minsduration

23 minsduration

Input(Vector)

Output(Real Number)

Patient • Diagnosis• Oncologist• Treatment machine• Age• Day of the week• Hour of the day• Month• Plan• # of treatment fields• Fraction number

Machine Learning Model

• MLrelatescloselytomathematicaloptimizationtheory

• Costfunctionforbuildingamodel

• Trainingmeanssolving:

• Innon-linearspace,kernelfunctionsareappliedtotransformfeaturespacetolinearspace(Kerneltrick)

• Replacexi withϕ(xi)– Polynomial,Gaussian,Hyperbolic

Results

Residual histogramResults

--- Mean error: 0.25 mins--- Median error: 0.5 mins --- Standard deviation: ~8 mins

Conclusion

• MachinelearningcanbesuccessfullyappliedtowaitingtimesinRadiationOncology.

• Futurework• Featureanalysis(correlations,patterns)• Algorithmtuning(optimizationparameters)• Exploringthecode(Pythonscripts)• Communicatewaitingtimestopatients(patientapp)• Gatherfeedbackfrompatients

• Thiscanhaveasignificantimpactonpatientlivesandstaffworkflow!

Thanks!

AJ acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council

(Grant number: 432290)

FundingprovidedbyFinancépar