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Ændr 2. linje i overskriften
AARHUSUNIVERSITYAU KAJ GRØNBÆK & JENS PEDER RASMUSSEN
BIG DATA ANALYSIS FOR HOSPITAL LOGISTICSKaj Grønbæk, Professor, PhD
Department of Computer Science, Aarhus University
Jens Peder Rasmussen, Director
Systematic A/S
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
PLANPrevious Galileo & PosLogistics Projects:
u Indoor Positioning and Hospital Service Logistics
u Planning, predicting, and preventing from position tracking data
New DABAI project: Optimized Patient Flows
u Background and visions
u First steps taken
Wrap up
Ændr 2. linje i overskriften
AARHUSUNIVERSITYAU KAJ GRØNBÆK & JENS PEDER RASMUSSEN
PREVIOUS GALILEO & POSLOGISTICS PROJECTS
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
LOGISTICS REQUIRES INDOOR POSITIONINGGalileo Platform for Pervasive Positioning (2007-2011)
u Indoor and outdoor positioning systems and applications
u AU, Alexandra Institute, Terma, Systematic, Danish Agricultural Service, Danish GPS-Center
u Advanced Technology Foundation support
Combining GPS, WiFi, and Dead Reckoning into hybrid positioning
Positioning algorithms running on devices or on server side
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
POSLOGISTICS: SERVICE LOGISTICS FOR HOSPITALSPosLogistics Goals were to:u Minimize waiting time
u Reduce errors and cancelled operations
Planning and managing orderly tasksu Transportation tasks
u Support tasks all over the hospital
Keeping track of patients, equipment, test samples, personnel, etc.u Tracked by smartphones or tags (WiFi, RFID)
Supported by the Innovation Foundation
Partners:
u CS-AU
u Systematic A/S
u Aarhus University Hospital
u Aalborg University Hospital
5
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
WHAT GENERATES DATA ?
6
10.000 employees, 4.500 patients, 100.000 pieces of equipment… to be found (and disappear) in 7.500 rooms over 450.000 m2 of hospital
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
ANALYZING HUMAN ACTIVITY AT HOSPITALu Initial analysis of 10 days data recording:
› 12.000 smartphones detected
› 1 billion WiFi hotspot contacts
u From smartphone position ”heat map” to common routes
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
WHICH ROUTE TO CHOOSE?
8PhD project by Thor Prentow
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
WHEN DOES A PATIENT ARRIVE AT OP?
PhD project by Thor Prentow
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
TRAVEL TIME AND TRANSPORTATION MODEu Indoor Transportation Mode Detection
needed.
u Changes in WiFi signal strength levelsas a rough estimate of speed
Multiple use casesu Travel-time prediction and
analysis
u Task-phase analysis
PhD project by Thor Prentow
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
WHAT IS THE PROGRESS STATUS OF A TASK?
Infer task phases from mobile sensingavoiding waste of time on registration
Benefits
u Automatic detection of phase shifts
u Better overview of task status
u See who needs help from colleagues?
u Provide advice notifications and status in context on
u wearable devices
Idle
Walking to task
Getting Equipment
Prepare
patient
Transporting patient
Ad-Hoc
situation
PatientDeliver
ed
Return Equipment
Idle
PhD project by Allan Stisen
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
STATUSSystematic has brought PosLogistics in operation at several hospitals
u Aarhus University Hospital and Aalborg University Hospital
u now exported to two hospitals in Finland
Research results deployed
u Positioning algorithms and a subset of logistics algorithms in practical use now
u We achieved high accuracy of positioning and logistics methods
Many potentials in applying the data analysis for items beyond service tasks
u Moving objects in general e.g. at airports and manufacturing plants
u Stationary clinical hospital tasks from wearable and ambient sensors
Ændr 2. linje i overskriften
AARHUSUNIVERSITYAU KAJ GRØNBÆK & JENS PEDER RASMUSSEN
DABAI BUSINESS CASE:OPTIMIZED PATIENT FLOWS
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
DABAI CASE: OPTIMIZED PATIENT FLOW
Vision and Objective:
u To use Big Data analysis to gain more efficient and better patient flows within Healthcare
u To support enterprise wide optimization of the entire patient pathway.
u To provide enterprise-wide real-time cockpit of information and insight on both current capacity and the status of active pathways at a hospital.
Partners
u Aarhus University Hospital, Region Midt, Aalborg University Hospital, Systematic
u CS-AU plus other DABAI core partners
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
PATIENT FLOW BACKGROUND AND MODELBackground:u Patient flows related to “complicated diagnosis”
and “treatment of chronic diseases” are to be treated across multiple specialties and departments and cause coordination issues for healthcare providers.
u Approx. 75% of the total healthcare budget is spend on treatment of chronic diseases.
u The Healthcare system has a large amount of unexploited information that, through use of Big Data Analysis, can provide knowledge that can improve efficiency as well as provide improved and optimized patient flows through out the enterprise.
Generic model of a healthcare system with patient flow and care providers interacting and consuming services. The arrows between the patient flow and the care providers illustrates that the patient flow is influenced by and depended on the providers and their interactions
Hospital
A
B
C GP
Municipality Specialists...
Patientflow
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
SYSTEMATICS’ EXPECTED OUTCOMEu To obtain improved capabilities within Big Data Analysis,
visualization and machine learning.u To deploy the capabilities within healthcare to provide improved
quality for our customersu To develop a product/toolset that can support our customers in
improving efficiency and quality in the patient flows across the healthcare enterprise
u Abilities to export the product/toolset
2016KAJ GRØNBÆK & JENS PEDER RASMUSSENAARHUS
UNIVERSITYAU
STATUS
u We are currently setting up the data acquisition procedures to get anonymized patient flow data from two super hospitals
u In collaboration with the hospitals, we are doing empirical studies of analysis needs and central hypothesis to analyze for
u We are preparing the suite of analysis tool to be applied to the data
u We are looking forward to the first results from the exploratory analysis - hoping to find surprising patterns and verify/falsify some central hypothesis
Ændr 2. linje i overskriften
AARHUSUNIVERSITYAU KAJ GRØNBÆK & JENS PEDER RASMUSSEN
THANKS FOR LISTENNING