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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