Boosting Surgical Rooms Performance With A Neoteric Data Solution

SPsoft has delivered a solution that improves hospitals’ operational efficiency by turning a mammoth legacy database into a robust BI and decision-making tool.


An American robotic-assisted surgery pioneer, reinventing and advancing what is possible in minimally invasive care. The client produces surgical robots that help practitioners perform operations more precisely and efficiently.




Healthcare, Big Data

Team size:

7 people

Project duration:

September 2021 - Present


Solution architecture design and implementation, UX/UI, DevOps services, Front-end and Back-end development

Tech stack:

  • Ruby on Rails
  • React
  • GraphQL
  • PostgreSQL
  • Terraform
  • Python


Our client’s mission within the project’s framework was to help hospitals acquire a better vision of how surgeries can become more efficient, safer, and cost-efficient. They planned on developing a solution that provides hospitals with a comprehensive account of each surgery, including the timeline, delays, cleaning time, average surgery duration, the best-performing surgeons, and many other business-savvy indexes. The newly-sprung system was meant to gather the data and enable further decoding of each file to establish an ongoing decision-making system for improving surgery units’ efficiency.

The SPsoft team had to deal with two significant bottlenecks. First, there had already been a mammoth unstructured array of JSON data – mainly surgeries timecodes and video annotations – which we had to extract, synthesize, structure, compile, and organize within a new, properly-built database. We had to transform a legacy database worth ten terabytes of poorly-structured JSON files into a neoteric, neatly-structured system enabling advanced analytical capabilities for healthcare institutions.

Second, having no access to our client’s infrastructure, we worked with Terraform, meaning that we coded a whole new infrastructure ourselves from scratch instead of just mouse-clicking its configurations. We ended up with an infrastructure perfectly compatible with our client’s overall system. 3 DevOps engineers engaged in the project provided our client with a stepwise documented Terraform guide on implementing the new infrastructure. Our desire not to disrupt our client’s infrastructure while revamping ten terabytes of crucial data prevailed, as we managed to deliver the solution having “the dirty work” done outside our client’s system.

In addition, it took great effort to adopt the microservices architecture. Since we decided to use such an approach for the new infrastructure, we needed to provide two back-ends: one for web application administration and one for data analytics. 

Value Deliverd

We built a solution that delivers enhanced analytical capabilities, which are the basis for our client’s further stability and growth. Now, our client presents healthcare institutions (particularly their heads and data analysts) with a tool that eradicates guesswork and fuels them with data-based decision-making with predictable outcomes.

Instead of moving blindfolded along the journey of improving surgical efficiency, hospitals can become data-driven. Stable, risk-secured, and profitable operations are the result delivered. No wonder the next phase of scaling the product has already been set in motion.

The solution lets our client’s partnering healthcare institutions analyze duly structured and properly visualized data about surgical operations swiftly and efficiently. An incremental increase in the quality and quantity of operations has become possible. Predictive decision-making – one of the modern business’s compulsory prerequisites – is now an option in the hectic healthcare industry reality – due to the revamped structure and format of the surgery information.

Lastly, the SPsoft team has deprived our client of any turmoil associated with setting up a new infrastructure since our DevOps specialists have enabled a “few mouse clicks” database infrastructure set-up routine. The newly-developed infrastructure was designed to allow for a swift infrastructures merger with no pain points attached.

The process

The project commenced following our client’s referral to SPsoft by another company that had benefited from our software development services. Following fast-paced negotiations, our team completed the discovery stage within weeks, defining the resources and methods for achieving the results expected by the client.

A team of 7, including a back-end tech lead, a front-end developer, a UX/UI designer, a PM, and 3 DevOps engineers, was summoned within a glimpse of an eye to cooperate with the product owner and a tech consultant on the client’s side. The SPsoft team delivered the stipulated MVP within a 2-month active coding phase, followed by two weeks of adaptation and product roll-out.

Our client had their solution locked and loaded for heavy usage in less than three months in the field. As we are currently supporting the platform, the project’s team extension is underway. Our web solution has been proven successful enough to let our client scale the project to a mobile version. Also, by using a do-as-we-go approach, we can demonstrate our flexibility by quickly reacting to stakeholders’ and users’ feedback and implementing necessary changes. 

During the third coding phase, our team of three specialists has substantially extended the platform’s functionality and established an omnichannel experience for anyone using it. We have collected feedback from real users and adopted the product accordingly. Meanwhile, real-life cases also promoted the development of new necessary features. Lastly, the SPsoft team has extended the platform with dynamic filters and added numerous recommendation presets based on data science analysis.

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