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For more information on the PAsTAs project, see http://telemed.no/pastas-pasientforloep.5219575-247951.html.
The project taskProblem:
In The focus of this project the focus will be on identifying the most common trajectories patients with chronic diseases go through. The project will involve identifying clusters of actual care trajectory, and write a methodology paper on the summarization of patient trajectories.
Method:
to analyze the patient trajectories of cases pertaining to chronic diseases. Task will include creating a solution for scalable visualization of such data based on different filters or scales. Two such filters could be:
* Relation of group. Meaning you can adjust the visualization based on severity of condition, age or other distinguishable parameters.
* Size of institution. Meaning adjustments will change whether to include a whole hospital as one entity, or split it into different departments or wards, or even specific practices.
The task for the fall project will be to identify which parameters of the data are most relevant to filtering and how to best group clusters of patients.
Scientific goal:
Find a reasonable way to group a cluster of patients based on relevant parameters from their condition.
Method:
Creating a visualization of data will require a good foundation of data. Prepared sets of data will be provided by supervisors of PAsTAs. Analyzing these sets of data will accumulate metadata about each patients appointments, which will need processing in order to yield meaningful information which can be represented within a given set of filters.
Representing patients with an equal or similar diagnosis based on the difference in care and outcomes is a point of focus.
Solution:
Assuming this project is going to continue in the future, perhaps the most important task is to make good data models from the raw sets of data. These data models can then be used to further explore the overall patterns of patient trajectories. Creating the visualization based on these data models will demonstrate the potential of the models and will force the development in a direction of usability.
Make a complexity-index based on the following variables: Diagnoses, GP visits, Hospital Visits, Days in Hospital, Personal Plan, Number of Public Services, etc. (to summarize the patient trajectories).
Another approach is to look at patients with identical diagnoses, but different care and outcomes.
Collaborate with Gry og Ingunn: PAsTAs - Patients Trajectories, Web-interface in order to visualize the clusters.
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