Applications for sports and fitness activities were predicted to rise by 63% from 2012 – 2017. With the abundance of fitness and health applications currently available, we have become increasingly health conscious; we can now track our kilometres run, our steps walked, our current heart rate as well as our daily calorie consumption. However, this sheer amount of data is spread out over different platforms and makes us sometimes forget what is really important. In order to change this, the Radboud UMC (Dutch Hospital in Nijmegen) is planning on fully centring around the customer/patient within the health care process. Together with Philips and cloud-software provider Salesforce they have invented a combination of a personal health file and an online community which serves as an connectivity platform for medical equipment, wearables and applications. This electronic platform is called Hereismydata.

The platform Hereismydata focuses on data such as blood pressure and heart rate, but is also able to incorporate weight or daily exercise. Hereismydata is not necessary only for people who have a condition, but is also for people who are just eager to keep track of their own medical data. Individuals themselves can decide who they give access to their data. In most cases this is a family member and a general practitioner. Hereismydata can serve as a preventive measure; if general practitioners or doctors see that your heart rate and blood pressure are suddenly rising, a patient can be contacted to clarify this sudden increase. In this case fatalities can be prevented rather than treated once they have occurred.

The adoption of a platform such as Hereismydata causes a certain paradigm shift. Individuals will no longer be just patients, they will become co-creators of their personal health file to which they have access themselves. Nonetheless, in order to make Hereismydata a success, both sides of the market need to be leveraged. Both doctors/ professional caretakers and individual will need to join the platform to realize its full potential. If, however, hospitals are willing to connect and work with the platform, individuals can truly benefit of their own generated data.

It is predicted that in 2040 the care needs in the Netherlands will double, while the amount of caretakers will diminish. A nationwide electronic patient database could be start to overcome these dreary prospects. By empowering the patient and by giving both the patient and the doctor access to health data, I believe a lot of healthcare costs can be prevented. The scope of this project and the privacy issues could, however, be limiting factors with regard to the implementation of the platform. On the bright side, Arthur Govaert, CIO at Radboud UMC commented that Hereismydata is currently being piloted on a handful of patients in the Netherlands. Will it be a matter of time before everyone has its own electronic patient database?

Sources

http://www.emerce.nl/wire/timmies-the-innovation-awards-2015-anwb-radboud-umc

http://press.ihs.com/press-release/design-supply-chain/sports-and-fitness-app-market-expand-more-60-percent-five-years

http://www.smarthealth.nl/2013/07/24/hereismydata-het-nieuw-elektronisch-patienten-dossier-epd/

Interview with Arthur Govaert (CIO Radboud UMC) on the 10^th of March, 2015

In this blog post I am going to talk about the paper called “Recommendation Systems with Purchase Data” by Anand V. Bodapati (2008).

The majority of firms selling online utilize recommendation systems, which are a decision tool that tries to identify products that the customer is likely to buy, if the product is brought to their attention. They are usually based on analysing the previous purchase behaviour of the user and showing him/her the products that are the most likely to be bought. However, what such a mechanism does not account for is the fact that customers are likely to buy these products even without an explicit recommendation. Therefore, a recommendation may have a higher value if it suggests a product that the customer would not buy unless it is recommended.  Following on that, the main proposition of the author is that recommendation systems should be based not on purchase probabilities but on the sensitivity of the purchase probabilities to the recommendation action.

The methodology of this academic article is highly technical and quantitative. Firstly, the author builds an econometric model that incorporates the role of the recommendation actions of the firm. Secondly, the proposed model is empirically tested using the purchase data of a real life e-commerce company. Then the performance of the proposed model is compared with the one of other benchmark models. The results indicate the superiority of the proposed model.

One of the biggest theoretical contributions of this article is that it suggests the idea that purchasing can be seen as the outcome of two separate factors – awareness and satisfaction. Awareness (A) can be defined as becoming aware of the product and its characteristics. At the same time, Satisfaction (S) involves evaluating the product and buying it, if its utility exceeds a certain threshold level. A consumer buys a product if both A and S occur. The consumer will not buy anything of he/she is unaware of the product, or he/she is aware but decided that the utility is not high enough. Furthermore, the next contribution of the article is showing that these two events can be separated and identified using existing datasets that companies have. These datasets are information about self-initiated purchase data and recommendation response data. Using all of that information, the research paper proposes a decision framework for recommendation system use.

The econometric model that is developed tells firms at what time they should show a product recommendation to the customer in order to optimize the beneficial outcome for the company. The equation takes into consideration the purchasing probability, the timing of the recommendation and the two factors affecting the purchasing decision – awareness (A) and satisfaction (S). The main idea is that recommendations should not increase the purchase probability for products that the customer is already willing to buy, but rather increase the incremental revenue for the company by suggesting items that are otherwise less likely to be bought.

Despite the few limitations of the research, it makes a valuable theoretical and practical contribution.