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Case 14.06.2023

The inefficiency of health care is a global problem – Near Real’s AI-assisted remote appointments save time and money

Near Real has received funding for research into remote health care technologies from the Recovery and Resilience Facility (RRF) funding program, which is part of the Sustainable Growth Program for Finland. Secure remote appointments and the efficient use of health and wellbeing data collected automatically from patients save the doctor’s time or even make it possible to avoid medical visits entirely.
Near Real
  • Founded in 2014, employs seven people
  • A growth company in health technology providing services that help health care professionals to treat their patients remotely.
  • Provides secure remote appointment solutions for the private and public sector. The services function either as independent solutions or as part of other health care systems.
  • Investigates the use of artificial intelligence and machine learning for purposes such as improving the efficiency of clinical pathways.
  • Most of the business is currently located in Finland. The aim is to expand to the Middle East and Southeast Asia in particular.
  • Received Tempo funding from Business Finland for international growth, disruption funding during the Covid-19 pandemic for business development, and RRF funding for the promotion of digitalization.

Read more about the Sustainable Growth Program for Finland and the related funding calls


Photos: Near Real


The company is currently investigating how to improve clinical pathways with the help of artificial intelligence and machine learning. The research is centered around the concept of a digital twin whose health and wellbeing develops on the basis of data that is collected from the patient and analyzed with the help of AI.

A digital twin is a technology-based virtual replica of the patient that enables the simulation or modeling of the patient’s health in a digital environment.

The research has been funded in part from the EU’s Recovery and Resilience Facility (RRF) funding program, which is part of the Sustainable Growth Program for Finland. The company received a total of EUR 120,000 in RRF funding for 2022–2023 as part of the funding call for the health and wellbeing sector.

“We have used the RRF funding for purposes such as mapping suitable sources of wellbeing data. By combining data from the Oura Ring or Apple Watch, for example, with other data, we can produce AI-enriched preliminary information about the patient. The funding has also been necessary for interviews with experts, such as doctors, as well as for various experiments, algorithm research, and other practical research,” says Jarmo Mikkola, co-founder of Near Real.

Near Real

Demand surged during the Covid-19 pandemic

Cooperation with Business Finland has played a decisive role in the development of the company.

“The first Tempo project funded by Business Finland was related to internationalization. Through Business Finland’s excellent networks, we were able to present and test our product in locations such as Southeast Asia. By exploring the needs of local hospitals, for example, we realized that inefficiencies in health care are a global phenomenon, for which people are also desperately seeking solutions outside Finland,” Mikkola says. 

As a result of the Tempo funding, the company decided to focus only on health services. From the onset of the Covid-19 pandemic, many operators in the sector could only continue their operations through remote appointments. 

With the help of RRF funding, we have mapped, for example, suitable sources of well-being data. Funding has also been needed for expert interviews, various experiments, algorithm research, and other practical research.

In 2020 and 2021, the company received a total of approximately EUR 22,000 from Business Finland in two separate funding calls for developing its business during the disruption caused by the pandemic.

“The Covid-19 pandemic was an unusual time for the company. On the other hand, demand for our services exploded, but at the same time, we were not fully prepared for the amount of customer support required. The need for customer support grew rapidly with new customers and users. With the help of disruption funding, we automated processes related to the deployment of the service and customer support, enabling us to offer remote service technologies to all operators who needed them. The funding saved many private operators, for example, because their traditional modes of operation were destroyed by Covid-19,” Mikkola says..

The results of the collaboration were convincing

Near Real’s concept was an excellent match with Business Finland’s funding criteria – the company had sufficient expertise and the desire and ability to expand abroad. Moreover, the content of the project fit well with the goals of the RRF funding call for the health and wellbeing sector. The funding call was mainly aimed at research work.

“The RRF funding call for the health and wellbeing sector, which began in 2022, emphasized the promotion of digitalization. The aim of the call is to create digital solutions and operating methods for the health care sector that can also be scaled to export markets,” says Business Finland’s Senior Adviser Arttu Niiles.

Business Finland also requires that the funded projects have novelty value for the industry, that the risks taken in the projects are higher than usual, and that the projects have great market potential if successful.

Through Business Finland’s excellent networks, we were able to present and test our product in locations such as Southeast Asia. 

According to Niiles, more development work is often needed after the end of the project to commercialize the results. The company intends to continue its cooperation with Business Finland, says Mikkola.

“We are quite impressed with the results of the cooperation, and we hope that Business Finland can help us internationalize our products in the future,” Mikkola sums up.

Get to know more about the Sustainable Growth Program for Finland. and the related funding calls


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