A well-organised data infrastructure
FysioHolland
- Customer case
- Data Engineering
- Data Analytics
- Data projects
FysioHolland is an umbrella organisation for physiotherapists in the Netherlands. A central service team relieves therapists of additional work, so that they can mainly focus on providing the best care. In addition to organic growth, FysioHolland is connecting new practices to the organisation. Each of these has its own systems, work processes and treatment codes. This has made FysioHolland's data management large and complex.
There were already some dashboards built in Google Data Studio, but they thought it was time to hit the reset button. That's why they asked us to help centrally store all data, standardise reports, tighten definitions and streamline insights from data. The aim here was to give each employee insight into the performance that is relevant for their job.
Our approach
We started by building a data infrastructure. The data is retrieved from various data sources. While it was not possible for all data sources to automate data entry, we have streamlined this process. For example, there were several Google Sheets used by finance professionals to track treatment prices. We have standardised and centralised these so that FysioHolland can automatically read and process data with a much higher quality. We documented definitions for such data sources, which we refined during the process.
In addition to Google Sheets, data uploaded to Google Cloud Storage via scripts all comes together in Google Big Query. This data is combined with static tables and processed automatically.
FysioHolland wanted insight at four levels: at board level, regional level, practice level and at individual level per physiotherapist. We sketched a dashboard for each level based on the old data. We visualised the data in consultation with the customer. Then we built them in Google Data Studio. This was already being worked with, so it was easy to integrate within the organisation. We first rolled out the dashboards for a test group with a number of stakeholders. After processing their feedback, we made them available to the entire organisation.
With regard to the four levels, it was also important to properly handle access to the data. For this we implemented row-level access in Google Big Query so that, for example, a physiotherapist does not see data from another region in his dashboard. We have also made improvements in the processing of data into a format that can be displayed in the dashboards. By rewriting the processing and removing redundant queries, we have not only optimised but also reduced the overview.
The result
The required data is retrieved and stored in a data lake. From the data lake, the data is exported daily to the data warehouse, where the data is cleaned, supplemented and processed to ultimately serve as a reliable source for the dashboard.
Every FysioHolland employee now has insight into the data that is relevant to the work they do. For example, a physiotherapist can see how many hours have been booked in advance and which administrative actions still require action. These insights are available on a daily basis and can be easily viewed in Datastudio.
Future
In the future we will help FysioHolland to gain even more insights. We do this by adding extra sources to the data warehouse and further expanding the dashboard. In addition, we use our Data Science expertise to predict future planning of flexible schedules and potential wait times for clients at the therapist level.
Want to know more?
Reimer will be happy to talk to you about what we can do for you and your organisation as a data partner.
Business Manager+31(0)20 308 43 90+31(0)6 83 69 07 78reimer.vandepol@digital-power.com
Receive data insights, use cases and behind-the-scenes peeks once a month?
Sign up for our email list and stay 'up to data':
You might find this interesting too
Your Data Engineering partner
Generate reliable and meaningful insights from a solid, secure and scalable infrastructure. Our team of 25+ Data Engineers is ready to implement, maintain and optimise your data products and infrastructure end-to-end.
Central data storage with a new data infrastructure
Dedimo is a collaboration of five mental healthcare initiatives. In order to continuously enhance the quality of their care, they organize internal processes more efficiently. Therefore, they use perceptions from the data that is internally available. Previously, they acquired the data themselves from different source systems with ad hoc scripts. They requested our help to make this process more robust, efficient and to further professionalise it. They asked us to facilitate the central storage of their data, located in a cloud data warehouse. The goal was to set up the data infrastructure within this environment, since they were already used to working with Google Cloud Platform (GCP).
Your Data Engineering partner
Generate reliable and meaningful insights from a solid, secure and scalable infrastructure. Our team of 25+ Data Engineers is ready to implement, maintain and optimise your data products and infrastructure end-to-end.
A scalable machine-learning platform for predicting billboard impressions
The Neuron provides a programmatic bidding platform to plan, buy and manage digital Out-Of-Home ads in real-time. They asked us to predict the number of expected impressions for digital advertising on billboards in a scalable and efficient way.
