Exploring, modelling and optimization of home care regions

Title Exploring, modelling and optimization of home care regions
Summary This project is about developing tools and methods for optimization of health care resources using machine learning as the central technology.
Keywords Computational Intelligence, Spatio-Temporal modelling, Clustering, Regression Analysis, Decision Trees, Heteregoenous Data Analysis.
TimeFrame Winter 2016 / Spring 2017
References Madigan, E. A., & Curet, O. L. (2006). A data mining approach in home healthcare: outcomes and service use. BMC health services research, 6(1), 1.

Cheng, B. W., Chang, C. L., & Liu, I. S. (2005). Enhancing care services quality of nursing homes using data mining. Total Quality Management & Business Excellence, 16(5), 575-596.

Hirdes, J. P., Poss, J. W., & Curtin-Telegdi, N. (2008). The Method for Assigning Priority Levels (MAPLe): a new decision-support system for allocating home care resources. BMC medicine, 6(1), 1.

Harrington, C., Zimmerman, D., Karon, S. L., Robinson, J., & Beutel, P. (2000). Nursing home staffing and its relationship to deficiencies. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 55(5), S278-S287.

Prerequisites Courses preferable: Learning Systems, Data Mining. Preferable programming skills: R, Python, Matlab.
Supervisor Jens Lundström, Wagner O. De Morais
Level Master
Status Open

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The world is facing a rapid increase of healthcare costs, parts of the reason is due to the growth of the elderly population. Reports from the UN project that the number of people older than 60 years will near quadruple until the year 2050. In Sweden, the number of elderly receiving home-based healthcare service is as well steadily increasing, one approach to partly meet the increasing demand of home-based healthcare is to understand and assist the design of such healthcare by ICT. This project is about developing tools and methods for optimization of health care resources using machine learning as the central technology.

The home healthcare management in the municipality of Halmstad, Sweden is divided into approximately 20 regions where each region has different resources, customer satisfaction, costs and residents/customer with different levels of burden of care. Currently, it is desired to support the decisions on how these regions are designed using a data-driven approach. Therefore, this project is about how to explore, understand and model health care region characteristics – for the end goal of providing a decision support system.

Data provided in the project comes from heterogeneous sources such as visiting times, decisions, interventions and positions not necessarily in the same resolution, numerical format or context. This is a challenge - the data can not be directly used by traditional machine learning methods. Moreover is it important to focus on models that are transparent and able to provide insight into how health care regions are modelled. Research questions include: How to combine the different heterogeneous sources of information? Which machine learning principles/models should be used for learning models representing health care region characteristics? How to interpret such models?

7 workpackages are suggested:

  • Literature review as well as exploring how the current (not entirely data-driven) model for designing a home care area is adopted.
  • Writing a project plan.
  • Writing a data analysis plan and (possible) data collection.
  • Explorative data analysis.
  • Modelling health care regions (including comparisons of different methods).
  • Exploring/understanding/optimizing models for health care regions.
  • Reporting (thesis, source code, etc.)

The result is expected to include a detailed description of how the current approach is done today in the home care service as well as how the proposed approach could be used for decision support. Moreover is the results expected to include an evaluation of different machine learning methods suitable for the modelling task.