Medication Plans on The Blockchain – Building a Decentralised Application in Healthcare

The theme of this post is easily generalised to other use cases and serves as an example of how blockchain technology can shift power and trust in a well-established system, in this case the one of health care.

TL;DR

Medical prescriptions should be unified and digitalised. They should be resilient and controlled by the real owner of the prescription (and thus of the personal data). This can be achieved by a blockchain-based solution. A system of smart contracts in Solidity is proposed which achieves this and furthermore is modular and update-able. Some general advice on designing a blockchain solution is given.

What’s the problem?

How many of you know what iatrogenic illness means? I confess that prior to writing my Master thesis upon which this post is based, I also had no idea. So, to not keep you waiting, here’s the definition from Merriam-Webster:

ioatrogenic: induced inadvertently by a physician or surgeon or by medical treatment or diagnostic procedures

from the Greek word for physician (iatros). Add an illness to that and you have an illness caused by a physician. Now, it sounds like an oxymoron, but it is in fact more common than we would of course like to be. You can divide the causes for iatrogenic illness into so-called Adverse Drug Events (ADE) and, to be completely MECE*, other reasons. Other reasons would include things such as rough examinations, surgical errors (there’s a reason they draw arrows on the limb to be amputated) and so on. ADE includes all injuries or complications caused be medication, be it the wrong medication, drugs interacting in unintended ways and so on. [1] ADE has shown to be the most common cause of injury to hospitalised patients, and furthermore, the most preventable one.

Where is the problem coming from?

In fact, computer-based prescribing systems have been shown to decrease medication errors by 55% to 80% in a study from 2004. [2] It does not, however guarantee that the most severe of those medication errors are prevented by the usage of an IT solution. Among ADE’s, the most common form of avoidable medication errors are prescribing errors (i.e. an error made somewhere in the process of getting a drug to a patient). There is a list of sixteen classes of these prescribing errors, but basically they boil down to:

  • Knowledge deficiencies – among doctors, patients or pharmacist about drugs, other parties, et c.
  • Mistakes or memory lapses – e.g. a patient forgets what medication he/she is already on
  • Name-related errors – complicated-sounding substance gets mistaken for other complicated-sounding substance
  • Transferring errors – information is missing or incorrect once the order arrives at the pharmacist
  • ID checks – patient, doctor or pharmacist ID isn’t properly verified
  • Illegible handwriting (!)
  • Wrong type of document filled out

These errors all illustrate why prescribing errors are so common, but also why they should, to a large extent, be avoidable. [3] The thing is that, considering the current rate of prescribing errors causing damage or danger to patients being relatively low (ca. 2% [2]), its importance is overshadowed by more clinical research in medicine and is thus being overlooked by the research community and public in general. One reason for this could be the wide-ranging competencies required to implement a system for decreasing the rate of prescribing errors to zero. To do such a thing, one would require technical expertise within security and privacy as well as all the various skills for application development, one would also require medical and pharmacological knowledge, and essentially, one would need to have experience within information systems management.

A step in the right (digital) direction

To combat prescribing errors, many public health systems require or recommend that patients with more than three different prescribed medications have a unified medication plan which should theoretically contain all prescriptions. The effectiveness and quality of medication plans was examined in 2015 by a group of German researchers. The results were scary. 6.5% of all medication plans examined did not contain discrepancies! Where discrepancies means differences in drug names, additional or missing drugs, deviations in dosage, et c. In spite of this, or perhaps to improve the quality of medication plans, a law was passed in Germany three months after the publication of the medication plan review, which makes it mandatory for all patients with three or more medications to have a medication plan. In order to cope with the slowness of technology adoption in healthcare, up until January 2018, there is no requirement that the medication plans should be digital. Thereafter they should be available on an electronic health card (eGK). [4]

Considering the different types of prescribing errors we’ve identified, it is not difficult to translate those into some type of requirements for a system to solve those errors. The resulting requirements happen to fit very well to a blockchain system with smart contracts, therefore we’ll propose a design of a system of smart contracts to function as medication plan. Let’s look at the errors one by one and explain which requirements fit to them:

Knowledge deficiencies

To resolve this error, data regarding patients and their medications needs to be unified, available and guaranteed correct. There shouldn’t be multiple versions with equal or uncertain amounts of validity. Additionally, there should be little chance of the data getting lost or not available when it is needed.

Mistakes or memory lapses

It is completely human and expectable that a patient taking many different medication can’t remember the details of complicated names of each substance. This can be solved, however, by the unification of medication plans and assurance that all prescriptions are correct and active.

Name-related errors

See point Knowledge deficiencies.

Transferring errors

Through the unification of the various systems available currently, the process of transferring prescriptions would be simplified.

ID checks

Through the digitalisation and implementation of a permissions management system patients would only need some type of identification (could be biometric) to collect their medication.

Illegible handwriting

Assuming the doctor enters the prescription into a digital system and doesn’t write with pen and paper, this problem is practically eliminated.

Wrong type of document filled out

Again, through the unification of the different possibilities to prescribe a medication, there would be no such things as the wrong type of document. At least not inside the system.

Design choices in the solution

So what are the technical details one needs to consider when designing a blockchain-based system for a medication plan? I’ll describe the three most important design choices in this blog post. The three questions are:

  • Who needs to participate in the network?

In this case, the only users are doctors, patients and pharmacies. So to not take on additional risk regarding data exposure, only those who are on-boarded and verified through some separate process should be allowed to participate in the network. There are however some negative aspects of choosing a private or permissioned blockchain, one point being that there might not be enough active nodes to keep the consensus building at an acceptable fault-tolerance level at all times. This can be solve by some type of incentive or requirement that for example doctors keep a running node at all times. Another risk of running a private blockchain is that, when the amount of nodes isn’t very large, and the users consists of a specific group of people (such as doctors in Germany), then the risk of collusion becomes considerable. To combat this, the consensus-making should be well-spread geographically and demographically.

  • What data and functions need to be on the blockchain and what should definitely not be there?

In the case of a medication plan, the data which is required to be on the blockchain consists of three parts; user IDs, prescriptions and doctor/pharmacy permissions to prescribe/sell medications. Naturally, we can’t have plaintext information about patients and their prescriptions, even if it is a private network. Therefore, IDs are formed from a public/private key-pair (similar to bitcoin or ethereum), which should be generated by the user, on a user device. Prescriptions are only ever published on the blockchain as hashes, because even though the users theoretically are anonymous, it has been shown that Bitcoin transactions can be traced back to a person. [5] The permissions of doctors and pharmacies also need to be stored on the blockchain, in a smart contract to ensure that they aren’t manipulated or somehow overruled. Including permissions and sensitive data in smart contract means that extreme caution needs to be taken when programming them, to ensure that no syntactic or logical mistakes are made. The functionality needed on the blockchain is basically complimentary to the data pieces, getters and setters. But additionally, permissions needs to be handled on-chain.

    • How should the smart contracts be written?

There are relatively few resources by experienced smart contracts developers on best practices for building smart contracts, but mostly the general advice for writing good code (failing loudly and as early as possible, commenting, etc.) should be followed. There is however, so much to say about specific smart contract programming that it will be more explained in another blog post. Here, I’ll just talk about architecture of the system of smart contracts briefly.

In order to be able to keep an overview of the smart contracts and functionality used in the application, they should be as small and simple as possible, thus facilitating analysis. Ok, so say that you have a fairly complicated (not in a computational way) functionality to begin with, then you separate it into multiple smart contracts and end up with maybe five to ten of them. How are you supposed to keep track of them and increase the modularity of you system? Enter the contract managing contract. [6] It is basically a contract to keep track of (and manage) the different contracts in your system, it logs the addresses and names of each separate contract and provides another contract, the endpoint of the user-facing application, with the possibility to access them.

Conclusion

Designing an application for managing sensitive personal information needs to be resistant to failure, privacy-preserving and provide accountability so that any changes to the information can be traced. A very relevant use case for such an application is a medication plan. A suitable system for building the application back-end, is a blockchain-based system of smart contracts. Smart contracts programming is a fairly new phenomenon and is based on decentralisation, therefore much thought should be given to how such a system should be designed. A possible solution was drafted above.

*MECE stands for Mutually Exclusive, Collectively Exhaustive

References

1. Tierney LM. Iatrogenic Illness. Western Journal of Medicine. 1989;151(5):536-541.
2. The Epidemiology of Prescribing Errors, The Potential Impact of Computerized Prescriber Order Entry. Anne Bobb; Kristine Gleason; Marla Husch; et al, Arch Intern Med. 2004;164(7):785-792. doi:10.1001/archinte.164.7.785
3. Prescription errors in the National Health Services, time to change practice,
Hamid, Harper and Cushley et al., Scottish Medical Journal. Vol 61, issue 1, pp. 1-6. 21.04.2016
4. Full legal text available at: http://www.bgbl.de/xaver/bgbl/start.xav?startbk=Bundesanzeiger_BGBl&jumpTo=bgbl115s2408.pdf
5. Deanonymisation of Clients in Bitcoin P2P Network. Alex Biryukov, Dmitry Khovratovic, Ivan Pustogarov. Proceeding
CCS ’14, Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Pages 15-29, November 03 – 07, 2014
6. Monax – Solidity tutorials, https://monax.io/docs/solidity/solidity_1_the_five_types_model/, Accessed on 15/05/2017.

Wearable Data Hack Munich 2015

Today, we would like to announce something special. Something we can’t wait to take place and until mid June it’s going to be tough to sit tight. Please, feel invited to our Wearable Data Hack Munich 2015!

The Wearable Data Hack Munich 2015 is the first hack day on wearable tech applications and data. It will take place right after the launch of Apple Watch – the gadget we expect to rise the tide for all wearables. Withe the Wearable Data Hack Munich 2105, we aim to kick-off app development for the emerging smartwatch and wearable tech market. During this weekend you will have the first occasion to share your views and ideas and jointly gather experience with the new data realm.

Apple calls the Apple Watch “Our most personal device ever”. And with good cause: The data from wearable tech, smartphones and smartwatches are really the most personal data ever. Our mobile devices accompany every step we take, every move we make. A plentitude of sensors on the devices draw a multidimensional picture of our daily lives. Applications of wearable data range from fitness to retail, from automotive to health. There is hardly an industry that cannot make direct use of it. And yet, wearable apps still are in their childhood. The Apple Watch will be hitting the street in April and will get the ball rolling.

The Wearable Data Hack Munich 2015 is jointly organized by Stylight and Datarella.

THREADS TO BE PURSUED
Developers, data geeks and artists will pursue one or more of these threads:
– Data-driven business models for wearables
– Data-driven wearables
– Smartphone app (Stand alone / combined with smartphone)
– User Experience
– API
– Open Data
– mHealth / Medical Data

So let’s explore what we can do with this data! Let’s play with the possibilities of our wearable gadgets and mobile sensors.

APPLICATION
To apply for the Wearable Data Hack Munich 2015, please send us an email with
– your name
– your profession
– your take on wearable data
– 3 tags describing yourself best.
Don’t wait for too long – the number of participants is limited.

For more information, please have a look here! See you at Wearable Data Hack Munich 2015!

The Datarella World Map Of Behavior

Every smartphone user produces more than 20 MB of data collected by her phone’s sensors per day. Now, imagine the sensor data of 2 billion smartphone users worldwide, translated into realtime human behavior, shown on a global map. That is the vision of the Datarella World Map of Behavior.

A typical 2015 generation smartphone sports up to 25 sensors, measuring activities as diverse as movements, noise, light, or magnetic flux. Most smartphone users aren’t even aware of the fact that their phone’s camera or microphone never are really „off“ but that they constantly collect data about the noise level or the intensity of light the user is experiencing.

Actions speak louder than words
Actions speak louder than words – if we want to really know a person we have to know how she behaves, and not only what she says. And that’s not only true for politicians. We all form our opinions on others by looking at their actions, more than their words. Many inter-personal problems result from NOT looking at people’s actions, but focusing on other aspects, such as their looks or their words. Behind superficial distinctions such as physical appearances, over time we often realize similarities with other people based on their and our actions.

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Our vision of a World Map of Behavior
At Datarella, our vision is to show the actions of people around the world on a global map. By displaying the actions of people of all continents, we want to tell stories about the differences and similarities of global human behavior – to draw a picture of human co-existence. There already are snapshots of global behavior, provided by data focused companies, such as Jawbone, who map sleep patterns worldwide. From that we know that Russians get up latest, and Japanese get the least sleep in total . And there are different behavior-related maps, showing the world’s most dangerous places, defined by the number and seriousness of crimes or actions of war.

Co-operation & Participation
To create the World Map of Behavior is our ambitious project for 2015 that we won’t complete alone. We need your support: if you are an expert in the field of mobile sensor data or if your company already focuses on collecting and interpreting mobile sensor data in the fields of mobility, finance, health or transport and travel. If you are interested to play a role in this project, please send us an email with a brief description of how you would like to contribute. We are looking forward to hearing from you!

BYOD – Bring your own Data. Self-Tracking for Medical Practice and Research

„Facebook would never change their advertsing relying on a sample size as small as we do medical research on.“
(David Wilbanks)

People want to learn about themselves and get their lives soundly supported by data. Parents record the height of their children. When we feel ill, we measure our temperature. And many people own a bathroom scales. But without context, data is little meaningful. Thus we try to compare owr measurements with those of other people.

Data that we track just for us alone

Self-tracking has been trending for years. Fitness tracker like Fitbit count our steps, training apps like Runtustic deliver to us analysis and benchmark us with others. Since 2008, a movement has been around that has put self-tracking into its center: The Quantified Self.

Self-tracking has been tending for years. In this picture you see a wristband that already made it into a museum and is now on display in the London Science Museum.
Self-tracking has been tending for years. In this picture you see a wristband that already made it into a museum and is now on display in the London Science Museum.

However it is not just self-optimizer and fitness junkies who measure themselves. Essential drive to self-tracking originated from self-caring chronically ill.

Data for the physician, for family members, and for nursing staff

In the US like in many countries lacking strong public health-care, it becomes increasingly common to bring self-measured data to the physician. With many examinations this saves significant consts and speeds up the treatment. With Quantified Self, many people have been able to get good laboratory analytics about their health for the first time ever. One example is kits for blood analysis that sends the measurement via mobile to the lab and then displays the results. Such kits are e.g. widely in use in India.
Also for family members and nursing staff, self-tracked data of the pations is useful. They draw a realistic picture of our conditions to those who care for us. Even automatic emergency calls based on data measured at site are possible today.

The image at the top is taken from the blog of Sara Riggere, who suffers from Parkinson. Sara tracks her medication and the syptoms of her Parkinson’s desease with her smartphone. Her story is worth reading in any case, and it shows all facettes that make the topic „own data“ so fascinating:
http://www.riggare.se/ and
http://quantifiedself.com


Mood-tracking – a mood diary. People suffering from bipolar disorder try to help themselves by recording their mood and other influences of their lives. By doing so, they are able to counteract, when they approach a depression, and they are able to finetune their medication much better, than it would be possible by the rare visits to their psychiatrist. (Shown here is soundfeelings.com)

Data for research

Self-recorded data for the first time maps people’s actions and condition into an uninterupted image. For research, these data are significantly richer than the snap-shots made by classic clinical research – regarding case numbers as well as by making possible for the first time to include the multivariate influences of all kinds of behavior and environment. Even if only a small fraction of self-trackers is willing to share their data with researchers, it is hardly to imagine the huge value the findings will have for medicine, enabled by this.

Privacy

The difficulty with these data: they are so rich and so personal, that it is always possible to get down on the single individual. Anonymization, e.g. by deleting the user id or the IP adress is not possible. Like fingerprints, the trace we leave in the data can always identify us. This problem cannot be solved by even more privacy regulation. Already today, the mandatory committment to informed consent and to data avoidance impede research with medical data to such extent, it is hardly worthwhile to work with it, at all. The only remedy would be comprehensive legal protection. Every person sharing their data with research has to be sure that no disadvantages will come from their cooperation. Insurance companies and employers must not take advantage from the openness of people. This could be shaped similar to anti-discrimination laws. Today, e.g. insurance companies are not allowed to differenciate their rates by the insurant’s gender.

Algorithm ethics

Another issue lies within the data itself. First, arbitrary, technical differences like hardware defects, compression algorithms, or samling rates make the data hard to match. Second, it is hardly the raw data itself, but rather mathematical abstractions derived from the data, that gets further processed. Fitbit or Jawbone UP don’t store the three-dimensional measurements of the gyroscope, but the steps, calculated from it. However, what would be regarded as a step, and what would be another kind of movement, is an arbitrary decision of the author of the algorithm programmed for this task. Here it is important to open the black boxes of the algorithms. As the EU commission demands Google to open its search algorithms, because they suspect (probably with good reasons) that Google would discriminate against obnoxious content in a clandistine way, we have to demand to see behind the tracking-devices from their makers.
Data is generated by the users. The users have to be heared what is made from it.

Access Smartphone Data With our new API

Datarella now provides an API for our app ‚explore‘, that allows every user to access the data collected and stored by the app.

An Application Programming Interface, in short API, is an interface for accessing software or databases externally. Web-APIs giving us access via the internet, have become the principle condition for most businesses in the web. Whenever we pay something online with our credit card, the shop system accesses our account via the API of the card issuing company. Ebay, Amazon, PayPal -they all provide us with their APIs to automatize their whole functionality to be included in our own website’s services. Most social networks offer APIs, too. Through these we can post automatic messages, analyze data about usage and reach, or control ad campaigns.

The ‚explore‘ app was developed by Datarella to access the smartphones internal sensors (or probes), and to store the data. It is however not just about standard data like location, widely known because of Google Maps. ‚explore‘ reads all movements in three dimensions via the gyroscope, accelleration, magnetic fields in the environment. Mobile network providers and Wifis in reception are also tracked. From these data we can learn many interesting things about ourself, our surroundings and environment, and about our behavior. To set the data in context, the API also gives out data from other users. For the sake of privacy and information self-determination, this is aggregated and averaged over several users, so that identification of a specific person is not possible.

With our API, Datarella commits to open data: We are convinced, that data has to be available for users.

➜ Here is our API’s documentation: explore.datarella.com/data_1.0.html

➜ Here the download-link for ‚explore‘: play.google.com

We are excited to learn, what you will make from the data.

Download the 'explore' app here.
Download the ‚explore‘ app here.

Call for Data Fiction

DATA FICTION – THE STORIES BEHIND THE DATA

Do you read science fiction? Can you make data interesting? Can you tell the story behind a pool of data? Are you a data fictionista? Submit your data fiction.

People, animals, plants and things produce data – a lot of data. The data itself is the basic resource – like words are the basis for language. If you put words together to sentences and you combine sentences to chapters and aggregate several chapters – you write a story, you create fiction. Same with data: if you combine different data sources to data pools and aggregate them – you write the story behind the data, you create data fiction.

[Strong narrative] augments the available data by way of context, and extends the patience of the audience by sustaining their interest as well.

Does that sound like you?

We’d love to see and discuss your applications, analyses, case studies and models with you and help you make your data fiction become reality.

DATA, APP & COMPLEX EVENT PROCESSING ENGINE
The Data
We will provide you with sample data resulting from the usage of our explore app.

The App
The data has been created by users of the explore app. In explore, the user interacts by answering surveys, attending tasks and heeding valuable recommendations based on her behavior. She immediately sees the results of her interactions in the feedback area. Second, explore tracks several sensors of the user’s phone, which can be set on and off by the user herself (see full list of sensors below). explore connects both areas, interactions and the sensor tracking area, with the integrated Complex Event Processing Engine CEPE.

datarella explore app

The Complex Event Processing Engine (CEPE)
The CEPE is a mechanism to target an efficient processing of continuous event streams in sensor networks. It enables rapid development of applications that process large volumes of incoming messages or events, regardless of whether incoming messages are historical or real-time in nature.
Our CEPE is based on ESPER and Event Processing Language EPL

List of Sensors
– GPS location data
– Network location data
– Accelerometer
– Gyroscope
– Wifi
– Magnetic field
– Battery status
– Mobile Network

REQUIRED
– Overview and extended description or representation of your main idea, any subtopics and a conclusion
– Use or integration of at least 1 (one) category of sensor data (e.g. Gyroscope). If you use GPS location, you should use or integrate at least 1 (one) additional category of sensor data beside GPS location data.

DATA FICTION TYPES
– Presentation
– Video
– Installation

RESULTS
We will reward fascinating data fiction with preferred access to our data, a post on the QS Blog and the possibility of making data fiction come true.

Yes, I am a data fictionista and want to submit my data fiction!

The Analytics Advantage – eine Studie von Deloitte Touche Tohmatsu

Basically, analytics is about making good business decisions. Just giving reports with numbers doesn’t help. We must provide information in a way that best suits our decision-makers.„, ein Zitat von einem HR Officer eines US Unternehmens.

Immer mehr Unternehmen gerade in den USA sehen die Relevanz von Data Analytics bei der Vorbereitung von strategischen Entscheidungen. Wo stehen die Unternehmen in Hinblick auf Data Analytics? Diese Frage stellte sich Deloitte und hat mehr als 100 Unternehmen in den USA, Kanada, China und UK befragt. Das Ergebnis ist nicht überraschend: Data Analytics ist ein großes Thema für viele Unternehmen und gewinnt immer mehr an Bedeutung.

Bildschirmfoto 2013-06-27 um 17.38.13
Quelle: Deloitte „The Analytics Advantage“, 2013

Ein guter Manager zeichnet sich dadurch aus, dass er Entscheidungen trifft. Wie zu erwarten erhoffen sich Unternehmen eine bessere Entscheidungsgrundlage mit Hilfe von Data Analytics. Für die Pflege von Kundenbeziehungen und bei der besseren Einschätzung von Geschäftsrisiken werden Daten immer mehr eingesetzt. Trefflich von einem Teilnehmer der Befragung formuliert:

There are now enough reasons for us to prove beyond all doubt that what we’ve always done, based on intuition, isn’t the best way to go.

Bildschirmfoto 2013-06-27 um 17.38.25
Quelle: Deloitte „The Analytics Advantage“, 2013

Der Großteil der Befragten sind überzeugt davon, dass Analytics die Wettbewerbsfähigkeit ihres Unternehmens verbessern. Nur 3% gaben an, dass sie keinen Mehrwert darin sehen. Wenn Daten so eine große Rolle in Unternehmen spielen, stellt sich die Frage, wer sich damit in der Organisation beschäftigt.

Bildschirmfoto 2013-06-27 um 17.38.35

Quelle: Deloitte „The Analytics Advantage“, 2013

In der Regel befasst sich das Management Board (CEO, CFO, COO, usw.) mit aufbereiteten Daten. Kaum verwunderlich, denn diese Institution trifft bekanntlich die strategischen Entscheidungen einer Firma. Nicht überraschend dass auch Manager in der 2. Ebene, also Abteilungsleiter oder Geschäftsbereichsleiter sich auf Daten Analytics stützen.

Nun zur Kernfrage: wie fortgeschritten ist Data Analytics in den Unternehmen?

Bildschirmfoto 2013-06-27 um 17.38.54
Quelle: Deloitte „The Analytics Advantage“, 2013

Das Ergebnis ist ernüchternd. In vielen Unternehmen herrscht keine einheitliche Strategie beim Umgang mit Daten. Daten werden nicht zentral verwaltet und analysiert, auch die Verantwortung für diesen Bereich ist in vielen Unternehmen nicht eindeutig geregelt. Oft mangelt es an der passenden Technologie, um Data Analytics zu betreiben.

Fazit:

  • Data Analytics muss in Unternehmen gelebt werden und von den Führungspersonen voll unterstützt werden
  • Analytics soll Teil der strategischen Entscheidungsgrundlage werden
  • Analytics soll erweitert werden für Marketing und Kundenbetreuung
  • Analytics soll zentral verwaltet werden
  • Auch Analytics braucht eine klar ausformulierte Strategie

Hier können Sie eine Zusammenfassung der Studie vorgetragen von Tom Davenport, Professor von Harvard Business School und Berater von Deloitte Analytics ansehen.

Selbst die Franzosen werden offener in Hinblick auf Daten-Sharing

Wer hätte das gesagt, die prüden Franzosen gehen immer offener mit ihren Daten um. Laut einer Studie von IPG Mediabrands und Microsoft wären knapp 45% der Befragten in Frankreich bereit, Daten über ihr Kaufverhalten mit anderen zu teilen. Im Gegenzug erwarten sie nützliche Tipps und Empfehlungen. Auch für gezielte Werbung wären sie nicht abgeneigt.

48% der Befragten gaben an, dass sie einen Vorteil bei Kaufentscheidungen sehen, wenn sie ihre Datenidentität freigeben. Jedoch wären nur 36% der Konsumenten bereit, sich von Brands „tracken“ zu lassen, wenn ihnen ein besseres Kauferlebnis in Aussicht gestellt wird.

Diese Studie belegt wieder, dass Menschen offen für das Daten Sharing sind, wenn sie im Gegenzug einen Mehrwert sehen. Dies kann in Form eines nützlichen Feedbacks erfolgen aber auch durch gezielte Tipps und Empfehlungen für zukünftige Handlungen. Könnte diese Art der Incentivierung die Zukunft der Marktforschung sein?