Health care & Digital Innovation: Personalizing Health care

March 18th, 2016, KINTalks with Willem Herter & Wouter Kroese

“The right evidence at the right time” is what the two enthusiastic founders of Pacmed, Willem and Wouter, are aiming for. Pacmed is a tool for general practitioners that provides insights into the extent to which medication worked for patients with similar characteristics. Motivated by sample biases in scientific research, they ventured to make sure patients receive the medical care that best fits their particular case. The proposed solution builds on big data and machine learning algorithms to personalize medical advice.

The start-up by the two alumni of the Nationale Denktank 2014 on Big Data may save both patients and doctors a lot of time, could reveal when medication does not work, and potentially cuts high medical costs. In their talk, however, the founders emphasized that their primary mission is to improve the quality of health care. For instance, Willem and Wouter hope that their tool will facilitate shared decision making: when doctors and patients jointly consider options and choose the approach that best fits the patient’s needs.

The audience engaged in a lively discussion with Wouter and Willem asking questions such as how to choose which diseases to focus on first or where the start-up is headed. The two entrepreneurs are currently facing choices with regard to strategy development, value appropriation, and partnerships. Backed by recent funding, a lot of data, and a great deal of enthusiasm, Willem and Wouter are well on their way to advance health care.


Take-aways

  • Doctors have knowledge that is unlikely to be automated or found in data. Innovations like Pacmed do not necessarily replace doctors but provide additional information.
  • Doctors should be given additional insights: how and why does the tool provide certain advice?
  • There are limits to what variables can (or should) be used to characterize patients. For example, advice based on geographical information may not be considered ethical or make sense to decision makers.