{"id":55004,"date":"2026-08-24T12:05:30","date_gmt":"2026-08-24T10:05:30","guid":{"rendered":"http:\/\/www.turner.nl\/uncategorized\/innovating-with-data-in-healthcare\/"},"modified":"2026-08-24T12:05:33","modified_gmt":"2026-08-24T10:05:33","slug":"innovating-with-data-in-healthcare","status":"publish","type":"post","link":"https:\/\/www.turner.nl\/en\/artificial-intelligence-2\/innovating-with-data-in-healthcare\/","title":{"rendered":"Innovating with Data in Healthcare?"},"content":{"rendered":"\t\t
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Artificial Intelligence<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Innovating with data in Healthcare? Oil the machine first. <\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Turner\u2019s healthcare expert Egbert Clevers is already seeing wonderful examples of data-driven innovation in healthcare. Yet putting it into practice proves to be a challenge. Often, it never goes beyond that single experiment. How do you turn data-driven innovation into a well-oiled machine? <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Innovating with Data in Healthcare?<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Data is all the rage. Even in healthcare. There are some wonderful examples of data innovations in healthcare\u2014from digital triage systems (such as those used in primary care<\/a> or for COVID-19<\/a>) to the use of artificial intelligence (in the interpretation of X-rays<\/a> or colonoscopies<\/a>). <\/p>

Still, implementing new concepts that involve data in the exam room, the lab, or the operating room is no simple matter. There are quite a few objections being raised. \u201cWe\u2019ll have a lot of data (soon), but where should we start?\u201d or \u201cIs big data really worth the investment?\u201d There\u2019s also skepticism and a lack of understanding about how to apply such concepts within their own organization. And even once all members of the department agree, it still takes some time for the Board of Directors to approve the investment budget. As a result, big data remains nothing more than a distant rainbow: beautiful to look at, but never within reach. Yet with the right approach, the pot of gold is within reach. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The Data Machine<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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What\u2019s the difference between data as an illusion and data as a pot of gold? As a data scientist<\/em>, I see data innovations as the result of close collaboration between data experts, managers, and Healthcare professionals, each with their own role: a \u201cdata machine.\u201d A machine that only works if it\u2019s set up properly. And well-oiled. And that\u2019s rarely the case! If the machine\u2019s parts don\u2019t fit together, if a step is skipped, or if you start somewhere in the middle? Then things go awry. <\/p>

There are numerous examples of this. In gastroenterology, for instance, it is a well-known problem that patients respond differently to various treatments because the pathophysiology varies from patient to patient. There is therefore a need for personalized treatment methods. Many startups have capitalized on this opportunity. Some have a clear vision and a concrete, well-developed plan (such as a diary app<\/a> that provides irritable bowel syndrome patients with immediate, data-driven lifestyle advice). Others rushed to start collecting \u201cpromising\u201d big data (microbiome, genetics, blood markers), only to later wonder how\u2014or even if\u2014any valuable insights might ever emerge from it. <\/p>

Data science<\/em> is a process that, just like a machine, has a starting point, a processing stage, and an endpoint. The starting point? It begins with an understanding of the organization (what is the institution\u2019s role and added value in the healthcare chain?), an understanding of the patient, and an understanding of the medical field. Understanding the (sometimes latent) needs of patients, specialists, and the organization. Combine this with data expertise, and the ideas will follow naturally. The end point? A robust application, app, platform, or simply an insight that addresses these needs in a sustainable way. The fuel? Vision and connection! <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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