ICT for Healthcare Challenges and Solutions Roberto Montemanni - PowerPoint PPT Presentation

IDSIA University of Lugano Dalle Molle Institute for Artificial Intelligence University of Applied Sciences of Southern Switzerland ICT for Healthcare Challenges and Solutions Roberto Montemanni roberto@idsia.ch

Research Institute in IDSIA Lugano, Switzerland Dalle Molle Institute for since 1988 Artificial Intelligence Research fields:  Optimisation, simulation and decision support systems Optimisation, simulation and decision support systems  Uncertain reasoning, data mining and big data  Machine learning and artificial neural networks  Cognitive and mobile robotics 57 staff members: 7 Professors 19 PHD Students 8 Senior Researchers 3 Master Students 17 Researchers 1 Secretary 2 Research Assistants

IDSIA University of Lugano Dalle Molle Institute for Artificial Intelligence University of Applied Sciences of Southern Switzerland IDSIA Basic Applied Research Research

Contributors to this talk Tomas E. Nordlander Research manager SINTEF Norway Thi Viet Ly Nguyen Jerome Guzzi PhD student PhD student IDSIA-USI IDSIA-USI Giorgio Corani Gianni A. Di Caro Senior researcher Senior researcher Senior researcher IDSIA-SUPSI IDSIA-SUPSI IDSIA-SUPSI Associate professor Carnegie Mellon

Unsustainable Development

Unsustainable Development

Unsustainable Development  More and more patients – population ageing along with the skewed demographic development  Increased quality - Public’s rising expectations of quality treatment  Less funding - Government desire to reduce the health expenditure

Unsustainable Development Proportion of pupils that will need to work in Norwegian healthcare to manage the increase of patients Source: Gunnar Bovim, CEO, Central Norway Regional Health Authority RHF, “ Strategy 2020 ”

Possible Remedies?  Organization changes  Clinical Pathways  Electronic patient journals  More efficient resources utilization  Use of ICT systems – both hardware and software Strategic Software Tactical Operational Hardware

Optimization in Health care?  Health care, a business like any other  Trying to utilize resources (equipment, staff, etc.) efficiently at strategic, tactical, and operational level while involving multiple decision-makers with conflicting goals  Problems are  Too complex to manually find good solutions  Time demanding and repetitive  Strategic and financial heavy  The health care sector lacks Optimization/Operations Research support tools  Why?

Partitioning of Health Care Decision making processes  Initially, small, clean cut subproblems were considered and solved via Optimization tools  Then larger and more complex problem instances have been considered, but always respecting the given partition  This approach never fully convinced Health Care practitioners

Partitions weakness  Simplified  Optimization problems solved today are isolated problems  Resource sharing across the Problem Interrelationship organisation is totally overlooked  Outdated  The problem partition rationale is based on what algorithms and computers could handle decades ago  However, time has passed and the effectiveness of algorithms and computer power have drastically improved

Greater Computational and Algorithmic Power  Greater Processing Power  Moore’s law [Moore, 1975] predicts that processing power* will double approximately every year and its prediction has held true for several decades.  The increase in memory is also beneficial for the algorithms.  Improved Methods  Improved Algorithms  Recent developments in hybrid, parallel methods *More precisely, the number of transistors on integrated circuits doubles approximately every two years, which roughly double the processing power. This prediction has roughly been true until 2013.

Greater Computational and Algorithmic Power Example: Linear Programming solvers During the period 1987 to 2000, Bixby (2002) estimated a speedup increase of six orders of magnitude in solving power , where processing power and memory contributed by half and the remaining three orders of magnitude is due to improved algorithm : "A model that might have taken a year to solve ten years ago, can now solve in less than 30 seconds. ”

Partitions changes needed  When working with the old partitions, we disregard the larger picture and miss chances of really efficient solutions.

Algorithmic changes needed  In recent years, chip manufacturers needed to change the architecture and started to produce multi-core processors to allow them to continue doubling the processor power.  In addition, driven by the computer game race for ever more impressive graphics, more powerful programmable Graphics Processing Units (GPUs) were produced.  Also, we now have Accelerated Processing Units (APUs)  Most classic algorithms still use a sequential optimisation paradigm, that was not an issue while we had an exponential increase of processor clock frequency, now things have changed , and there are great margins of improvements

Robust Optimization for Home Health Care (HHC)  Phases in Home Health Care Planning:  rostering  assignment  routing  scheduling  Current Situation: Phases addressed  Individually and separately  With simplified HHC models  Aims of the project:  Providing an integrated approach to tackle the different phases together  Considering realistic features (e.g., uncertainty, workload balancing, loyalty, etc.) within the model

Methodology: the model MILP model to represent the problem 1 . Minimize the number 2 . Remain a consistent 3 . Minimize the total of unscheduled tasks patient-nurse loyalty of overtime cost 4 . Minimize the total cost of time 5 . Minimize the total waiting time window violation 6 . Maximize the value of the minimum number of tasks for each nurse

Methodology: the model Constraints:  Loyalty  Assignment  Routing  Balance workload  Labor regulations  Hard time window  Visiting  Variables domains 19

Methodology: dealing with uncertainty Solution cost in the best case most robust sol Uncertainty: Unpredictability of problem data compromise sols Robust optimization*: Optimization field aiming at retrieving solutions resistant against uncertainty minimum cost sol Conservativeness degree**: Robustness Desired level of protection against uncertainty of the final solution Example: in robust home health care service the Conservativeness degree 1 is the level of pessimistic thinking of the manager about the budget for a fraction of the number of missing nurses In our approach robustness is embedded within the MILP *Soyster (1973); Ben-Tal and Nemirovski (1997) **Bertsimas and Sim (2003)

Methodology: dealing with large-scale real instances  Robust MILP exact models intractable for real instances  Need for computationally tractable methods  Heuristic algorithms?  Trading optimality for speed  Might generate very suboptimal solutions  Robustness concepts difficult to integrate  MatHeuristic hybridizations!  Inner robust MILP model for subproblems  Outer Genetic Algorithm (population based heuristic algorithm) Mathematical Programming: MILP model Matheuristics Heuristic: Genetic algorithm

Summary

OmniProfiler: Monitoring elderly people at home  Goals :  to monitor elderly people living at home  to track their behavior, detecting anomalous patterns which might show the early stages of a decline of cognitive faculties  Technology involved:  Intelligent home automation systems  Sensors to collect both environmental and biometric data  Monitored variables:  Amount and time of sleep  Physical activity (number of steps during the day)  Body weight  ...  Profiling technique:  Statistical analysis of control charts

Statistical analysis via control charts  By control charts , it is detected whether the past week has shown anomalous patterns  Moreover it is estimated how the monitored variables evolve over time Body weight  Green points: observed values  Red/blue points: anomalous data  Yellow points: estimated trend over time

Statistical analysis via control charts Sleeping hours Heart rate Physical activity

Alma: Ageing without Loosing Mobility and Autonomy  Target: Support the autonomous mobility, navigation, and orientation of the mobility- impaired person (elderly, temporarily or permanently disabled)  Instruments: Realization and combination of a set of advanced hardware and software technologies into an integrated and modular cost-effective system  Validation: Two pilot applications with different scenarios and therapeutic issues for primary (elderly, patients) and secondary (care givers) end-users  Long-term objective: Bring technological results to the market !

Alma: Ageing without Loosing Mobility and Autonomy

IDSIA University of Lugano Dalle Molle Institute for Artificial Intelligence University of Applied Sciences of Southern Switzerland Healthcare is in urgent need of ICT