Research shows value of stepwise design in digital health

The longer patients stay in the hospital, the more likely they will suffer from preventable injuries. Hospitals spend $2 billion on avoidable hospital-acquired conditions like pressure sores, falls, and infections every year. A new paradigm in information technology, Ambient Intelligence, holds the potential to transform healthcare for the better. However, digital health teams can unleash such potential only if they include nurses' and patients' voices early on and build their values into the solutions.

The Research

Results of a multi-year design process between Ouva and Radboud University Medical Center (Radboudumc), a 1,000-bed facility in the Netherlands, have shown that an inclusive stepwise approach yields substantial "value by considering user values." The findings were published on January 2022 in the peer-reviewed Value in Health journal on health economics and outcomes research. Merlijn Smits, MSc, design researcher at Radboud University Medical Center, co-authored the scientific look into the design process of the Ouva collaboration.

Ouva team came together with the nurses, physicians and patients to develop the ambient intelligence solution for remotely and continuously monitoring the quality and safety of patient care. A tight feedback loop successfully combined the design and evaluation process through a values-oriented multidisciplinary partnership and created a substantially impactful platform.


Twenty-seven patients volunteered to participate in the study, and nine surgery and ICU nurses, nurse researchers, physicians. The research team worked together with Ouva to identify solution targets in safety, privacy as in freedom from surveillance, and inclusivity by preventing bias in the system.

In all value-oriented targets, the Ouva platform demonstrated significant impact. Patients agreed that the platform increases their sense of safety, and the Ouva team built several high-impact solutions to privacy as requested by the nurses and patients. Consciously selected participant diversity and synthetic data ensured that machine learning models were developed from an unbiased dataset, increasing inclusivity.

While there are many ways that startups and large companies can build digital health solutions successfully, only when hospitals and developers join forces can these solutions indeed be adopted. Furthermore, a multidisciplinary approach reduces costs, time, and research and aligns the outcomes with the needs of the care environments.

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