Real impact from meaningful data.

We at Bean Solutions believe that all collected and reported data must have a real-life impact. To have a real and measurable impact, data must be valid and serve a purpose.

Through modern data-stack solutions we can refine collected data into meaningful action and deliver useful insights to healthcare industry.

What do we do?

Healthcare is a complex industry. Data is scattered across multiple systems, making it difficult to find the right information to support timely and accurate decision-making.

As a result, many healthcare organizations are struggling to get a complete picture of their organization and working environment.

This is where Bean Solutions comes in. With a vast experience of healthcare industry, data-analysis, and healthcare industry specific IT-solutions we recognize, collect, validate, and refine your data into meaningful reporting and solutions with real impact.

Full-Stack Data Science and Analysis solutions

Machine Learning & AI

Centralized log management

Software development

Use-case recognition and data discovery

Everything starts with knowing what to ask. When seeking for solutions with real life impact it is crucial to understand the underlying problem and its context. Having a holistic insight into the use-case is a key in delivering an actionable solution with the right answers.

The second step is knowing where to look and what to look for. When we understand the problem, it is time to break the problem in to smaller manageable pieces and clarify the requirements for the data.

Defining the models

Transforming the raw data into valid and comprehendible models that serve the purpose is a necessity in building an impactful solution for the underlying problem. The key here is knowing what metrics in the data depict the situation and problematics accurately in a way which can lead to data driven real life action.
Compressing the data into metrics and indicators that with an appropriate control effect requires that the statistical and mathematical methods used possess real validity and reliability.

Modern data-stack solutions

Modern data-stack turns all the theoretical problematization and planning into reality. With state-of-the-art open-source software we make sure that not only are the used solutions appropriate for the customer they are also adaptable and possess a significantly lesser life cycle cost when compared to licensed software.
Implementation of open-source solutions starts with a customer specific proof of concept after which the customer can make an informed decision concerning the desired solutions for their problem and their data strategy at large.

Continuous improvement

One of the first insights from the data is usually how you could improve your model with broader data sets and additional perceptions. Our philosophy of continuous improvement aspires to get the most out of an organizations data in total with delivering insight about the data itself. Continuous revisiting of the models and the solutions used ensure that the models keep up to date and heed the real-life changes that the customer faces.

When the amount of collected data grows, so do the possibilities. With large enough data sets we can eventually progress towards machine learning solutions and partially or entirely automated decision making that take advantage of the latest ML & AI technologies available.

Want to hear more about our way of working?

Contact us!