Quality Data is Quality Care

8

October

2020

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Big Data has changed the way we manage and analyse data in any industry. The healthcare industry is a promising area where data analytics can be applied as it not only reduces costs, it can prevent diseases, predict epidemic outbreaks and improve overall life quality. The future of healthcare will therefore be driven by data analytics and digital transformation. In this blog post I will address why big data in healthcare is important and in what ways it can be applied.

Big data refers to vast quantities of information created by the digitization of everything that gets analysed by specific technologies. Data collection is critical in the healthcare industry. Doctors need to understand as much as possible about patients, as early as possible. Treating diseases at early stages is simpler and less expensive (Lebied, 2018). For years, health data collection has been very costly and time consuming. With today’s innovative technologies, it becomes easier to collect data and translate it to useful insights for better care. This not only reduces costs; it also makes a patient’s health situation more predictable (Lebied, 2018). This in turn enables insurance companies to tailor their packages based on this information.

Healthcare analytics can provide support in asking critical questions such as ‘What is the probability that this patient will recover within 6 months?’ or ‘How likely is this patient to suffer from complications if we perform this surgery’? Driven by the rise of Internet of Things (IoT) and Artificial Intelligence (AI) such as machine learning and robotics, we now have algorithms that can help us answer these questions (Philips, 2020). According to a 2019 survey, 60% of health executives recognize the benefits of healthcare analytics, and 42% of them have seen improved patient satisfaction (Kent, 2019). Below we see how healthcare organizations are using predictive analytics (Dé, 2019).

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So in what ways do healthcare organizations apply analytics? Here are 3 examples of innovative technologies driven by healthcare analytics.

  1. Electronic Health Records (EHRs)

An EHR is a digital record of a patient’s demographics, medical history, allergies and more. These records are shared via secured systems and are available for providers from the public and private sector (Lebied, 2018). Leading healthcare organizations have integrated next generation analytics platforms into their EHR, such as algorithms and machine learning. This enables predictive, analytics- powered patient risk assessment. EHR can, for example, generate warnings and reminders when a patient should get a new test or when a patient is not following prescriptions.

  1. Precision Medicine

Precision Medicine (PM) is the most common application of machine learning in healthcare. It predicts what treatment protocols are likely to succeed on a patient, based on various attributes and the treatment context (Davenport & Kalakota, 2019). PM requires a training dataset for which the outcome variable is known, which is called supervised learning. Philips, the global leader in healthcare, for example applies PM to the field of oncology. PM will enable treatments to be tailored to genetic changes in each individual’s cancer (Philips, 2020). Cancer patients currently may receive a combination of treatments, while with PM, information about genetics can help doctors decide which treatment is best for each individual patient (Davenport & Kalakota, 2019).

  1. Real-Time Alerting

The traditional way of analysing medical data is facilitated through software that is only used in hospitals (Lebied, 2018). However, as in-house treatments are expensive, doctors want patients to stay away from hospitals as much as possible. To track patient data anytime and anywhere, real-time alerting is applied to wearables. These wearables collect the patient’s data continuously and send this data to the cloud (Knapp, 2018). An example is a blood pressure tracker, which alarms doctors when a patient’s blood pressure is too low or too high, so that appropriate action can be taken. This not only reduces in-house treatment costs; it also makes sure doctors can treat a patient as early as possible. What is more, it allows health executives to access the cloud with collected data to compare data in socioeconomic context and translate the data to useful insights (Lebied, 2018).

Evidently, the opportunities arising from healthcare analytics are very promising. Yet, as predictive analytics can be, their impact eventually depends on their knowledgeable use by health executives. The development of applications empowered by data analytics relies on the expert input. Another important note is that the issue of data privacy arises from the data driven nature of healthcare analytics. What will happen when data is shared seamlessly between different stakeholders? Should patients have control over what data is shared and with whom? The debate of how data can be shared without breaching patients’ trust is still ongoing.

 

References

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94.

Dé, A., (2019). Why Healthcare Analytics Will Deliver More Results In 2019. [online] Biplatform.nl. Available at: <https://biplatform.nl/1826849/why-healthcare-analytics-will-deliver-more-results-in.html> [Accessed 7 October 2020].

Kent, J., (2019). 60% Of Healthcare Execs Say They Use Predictive Analytics. [online] HealthITAnalytics. Available at: <https://healthitanalytics.com/news/60-of-healthcare-execs-say-they-use-predictive-analytics> [Accessed 5 October 2020].

Knapp, J., (2018). Real-Time Healthcare Analytics: Monitor, Predict, Nudge, Act | Vocera. [online] Vocera.com. Available at: <https://www.vocera.com/blog/real-time-healthcare-analytics-monitor-predict-nudge-act> [Accessed 7 October 2020].

Lebied, M., (2018). 12 Examples Of Big Data In Healthcare That Can Save People. [online] BI Blog | Data Visualization & Analytics Blog | Available at: <https://www.datapine.com/blog/big-data-examples-in-healthcare/> [Accessed 5 October 2020].

Philips. (2020). Predictive Analytics In Healthcare: Three Real-World Examples. [online] Available at: <https://www.philips.com/a-w/about/news/archive/features/20200604-predictive-analytics-in-healthcare-three-real-world-examples.html> [Accessed 6 October 2020].

 

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Differential privacy – A sustainable way of anonymizing data?

5

October

2020

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Since a lot of blog contributions mention the increase of data collection, data analytics, and the potential threat to privacy, I thought it would make sense to introduce the technique of differential privacy which is currently on the rise in the US. Apart from the US Consensus Bureau, Apple, and Facebook are in the front row of exploring capabilities and potentials of this technique.

 

What does differential privacy mean?
Differential privacy describes a technique to measure the privacy of a crucial data set.

 

Differential privacy in action
In 2020, the US government is facing a big challenge. It needs to collect data on all of the country’s 330 million residents. At the same time, it must ensure to keep all the identities private. By law, the government needs to ensure that the data collected cannot be traced back to any individual within the data set. The data collected by the US government collects is released in statistical tables for academics and policymakers to analyze when conducting research or writing legislation.

To solve the need for privacy, the US Census Bureau presented a technique, to alter the data collected, making it impossible to trace it back to the individual, without changing the overall information provided through the data set. The Census Bureau technique is a mathematical technique, to inject inaccuracies, or ‘noise’, to the data. That way, some of the individuals within the data might get younger or older, change in ethnicity or religious believes, while keeping the total number of individuals in each group (i.e. age/sex/ethnicity) the same. The more noise injected into the data sets, the harder the activity to de-anonymize the individuals.

This mathematical technique is also used by Apple and Facebook, to collect aggregated data without identifying particular users of products and services.

However, this activity also poses some challenges. Injecting too many inaccuracies can render the data useless. A study of the differentially private data set of the 2010 Census showed households that supposedly had 90 people, which cannot be true. However, since the owner of a data set can decide to which level the ‘noise’ should be injected, that challenge shouldn’t pose too much of a problem. Further, the more noise is included, the harder it gets to see correlations between data attributes and specific characteristics of individuals.

If a further analysis of differentially private data sets proves the technique to ensure required privacy, especially for governmentally created data sets, it is likely that other federal agencies or countries will use the methodology as well.

 

 

From my point of view, differential privacy as used for governmentally created data sets seems to a big step towards getting a clearer view about the status quo of a country, thanks to increased privacy and therefore increased trust by residents as well as probably increased participation in the process of data collection.

However, based on the complexity of the technique, to me it seems unlikely, that differential privacy will be used widely within companies (for the moment). Losing the ability to analyze data in detail due to increased privacy for the user and therefore lost correlations within data sets is a payoff I do not think a lot of companies are willing to take. Especially, since a lot of smaller companies are just starting to analyze the data they are collecting.
Right now, research shows that only big multinationals with high R&D budgets are able to sustainably increase privacy through differential privacy without losing too many insights derived from the data collected.

 

What do you think
Can differential privacy be a step in the right direction? Or should governments limit companies in the collection, aggregation, and analysis of data to increase privacy for the customers?

 

Sources:
https://aircloak.com/de/wie-funktioniert-differential-privacy/
https://hci.iwr.uni-heidelberg.de/system/files/private/downloads/182992120/boehme_differential-privacy-report.pdf
https://www.technologyreview.com/10-breakthrough-technologies/2020/#differential-privacy
https://towardsdatascience.com/understanding-differential-privacy-85ce191e198a?gi=9d3ad94ea2e4

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The Moment Your Supermarket Knows You’re Pregnant Before You Do

21

October

2017

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Big data has changed the way most businesses think and work. Instead of looking at only sales numbers as both measure and forecast for performance we can now go in deeper by using analytics. New tools and methods allow retailers to get a whole new perspective and gain consumer insights not seen before. The possibilities to use consumer data it implied some drastic changes by embedding these new insights to boost sales and creating smarter merchants at the same time.  (McKinsey, 2017).

However, can retailers also go too far by means of tracking your spending’s on their products? Is there a line that can be crossed? It certainly felt as if Target went one step too far in 2014 when they started to send a teenage girl coupons of maternity products while she and her parents didn’t even know she was expecting yet. (Hill, 2012)

Target developed a special pregnancy score. By looking at your spending pattern and the kind of products bought an algorithm would calculate a score indicating whether you are pregnant or not. If this was the case, Target could send you personalized coupons with products for pregnancy. (Hill, 2012)

Sending personalized offers is nothing new in the industry, with customer loyalty cards it has become relatively easy to collect consumer data and track spending and product preference. However, Target is not the only supermarket that has been struggling with finding the right balance between the use customer data and the related privacy issues. Tesco was ‘the best practice’ in the retail industry, Tesco was digital when it wasn’t even cool yet. They were far beyond their competition by using analytics and data in their advertising and marketing offers.(Schrage, 2014) Yet, Tesco showed that having a focus on data can also lead to a steep downfall. Because in the end a competitive price and a simpler shopping experience are worth more in the retail environment than gaining insights and having a good loyalty promotion. (Schrage, 2014)

So how bright does the future of retail and analytics looks like? Even though Tesco was brought to a downfall by having a very strong focus on analytics, it does not imply that other retailers will stop using it. However, from bought retailers an important lesson can be learned. You should never forget how to create a competitive advantage in the retail industry and understand why your customers are your customers. (McKinsey, 2017)If data analytics helps to get a better understanding or to lock them in by using loyalty program, this is great idea. However, do not forget the importance of distinguishing your selves from other retailers if you cannot compete on price only. (McKinsey, 2017)Because after all, most people find price the most important factor when doing their groceries.

Sources

Hill, K. 2012.  How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did. [Online] available at: https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/#1e5aa7386668 (Accessed: 6/10/2017)

Marr, B. 2010. Big Data: A Game Changer In The Retail Sector [online] available at: https://www.forbes.com/sites/unicefusa/2017/10/13/when-the-hurricanes-hit-unicef-and-google-joined-forces-to-help/#9d1a2ba3b6b3 (Accessed: 18/10/2017)

McKsinsey. 2017 How Leading Retailers Turn Insights Into profits [online] Available at:https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/how-leading-retailers-turn-insights-into-profits (Accessed: 14/10/2017)

Schrage, M. 2014: Tesco’s Downfall Is a Warning to Data-Driven Retailers. [online] Available at: https://hbr.org/2014/10/tescos-downfall-is-a-warning-to-data-driven-retailers (Accessed: 15/10/2017)

 

 

 

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Technology of the Week – from Public Contribution to Price Discrimination – A Debate over SAS vs. R

29

September

2016

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Screenshot 2016-09-29 23.20.38

The information goods market has experienced a substantial change during the past decade. A lot of new platforms and products have emerged that differ in terms of user interface, cost structure and content. Among the information goods with growing importance are data analysis software programs – platforms that are able to perform complex operations and process tremendous amount of data. The most widely used data analytics tools are SAS and R. The subsequent paragraphs examine and compare differences between the two programs, followed by a prediction for the two products’ prospects.

R Programming Language

R is an open-source programming language used for developing statistical software and data analysis. Since its establishment, it has gathered an entire community of professionals and academics that contribute to its development. Consequentially, the platform is also used by a growing number of data analysts who are part of corporations or academia. One of the greatest advantages of R is that it is open to innovations. Due to its open source nature, the program offers few barriers to entry for new techniques. An individual who develops a new technique can quickly incorporate it, even if the updated version has only niche appeal. This is a great example of the long tail effect in action.

SAS Analytical Tools

The main competitor of R is SAS, a proprietary software suite developed by SAS Institute. By using SAS, users are able to perform a number of different tasks such as report writing, data visualization, operations research, project management and more. This sophisticated data analysis software empowers enterprises by helping them gain valuable insights into their businesses. Another crucial benefit of SAS is that support is provided by experienced master’s- and doctorate-level statisticians who deliver a level of service and knowledge not often found with other software vendors.

SAS vs. R – Pricing Strategies

Besides the above-mentioned dissimilarities, the two programs also differ in their pricing strategies. There is no business model behind R as it was developed for academic purposes, while SAS is a product of a for-profit organization. The incremental total cost of ownership to download, install and use R is zero. It’s completely free which provides a great opportunity for start-ups and companies looking for cost efficiency.

On the contrary, SAS is price discriminating and targeting mostly large companies. SAS achieves higher profits through bundling and generates high marginal revenue from any additional feature a customer buys. SAS Institute has high costs to develop and update the platform but its reproduction costs are modest. For example, the SAS basic package starts from several thousand dollars, a lump sum that customers pay for the first year and every additional year they pay a subscription fee of only 30% of the initial cost. The longer the customers keep the software, the higher amount of the sunk costs they will recover. Most of the products can be divided into countless modules, once the customers configure their bundle, they can request a personal quote on SAS website or negotiate the price with one of the SAS representatives. This approach would not have been possible in the past when customers had to go to a physical store and buy a CD with the software for a fixed price.

Prediction for the Future

Taking into consideration the above-mentioned differences of both programs, it seems that R has the potential to outpace SAS in the future. The open-source platform is more agile to innovation and “cutting-edge” techniques. It keeps gaining more recognition among

academics that directly translates into more graduate students with R programming skills. Due to the fact that SAS Institute offers too many different interfaces, SAS tools tend to be more difficult to integrate. On the other hand, R poses a risk of delivering inconsistent and unverified packages as there is no governing body to assure content quality. Moreover, R is vulnerable to the discretion of the community to contribute. At some point, the community may lose interest and the platform could vanish. This scenario provides an opportunity for SAS, however, SAS needs to keep up with the speed of technology advancement to preserve its market leader position.

Here you can find a link to the video we created on this topic.

 

 

References:

Bansal, Sumeet et al. “SAS Vs R Vs Python – Analytics India Magazine”. Analytics India Magazine. N.p., 2016. Web. 20 Sept. 2016.

Burtch, Linda. “SAS Vs R Vs Python: Which Tool Do Analytics Pros Prefer?”. Kdnuggets.com. N.p., 2016. Web. 19 Sept. 2016.

Dinsmore, Thomas. “2015: Predictions For Big Analytics”. The Big Analytics Blog. N.p., 2015. Web. 18 Sept. 2016.

Dinsmore, Thomas. “SAS Versus R Part Two”. The Big Analytics Blog. N.p., 2014. Web. 19 Sept. 2016.

Python, Infographic:, Infographic: Python, and Manish Saraswat. “Infographic: Quick Guide On SAS Vs R Vs Python”. Analytics Vidhya. N.p., 2015. Web. 18 Sept. 2016.

“R Statistics And SAS Statistics Job Trends | Indeed.Com”. Indeed.com. N.p., 2016. Web. 18 Sept. 2016.

R vs SAS, why is SAS preferred private companies?. “R Vs SAS, Why Is SAS Preferred By Private Companies?”. Stats.stackexchange.com. N.p., 2016. Web. 20 Sept. 2016.

Rita L. Sallam, and Josh Parenteau. “Gartner Reprint”. Gartner.com. N.p., 2016. Web. 17 Sept. 2016.

SAS Institute Inc.,. SAS® Does Data Science: How To Succeed In A Data Science Competition. 2015. Print. Paper SAS2520-2015.

“SAS Vs. R (Vs. Python) – Which Tool Should I Learn?”. Analytics Vidhya. N.p., 2014. Web. 19 Sept. 2016.

“The Popularity Of Data Analysis Software”. r4stats.com. N.p., 2012. Web. 17 Sept. 2016.

“Why Is SAS Insufficient For Me To Become A Data Scientist? Why Do I Need To Learn Python Or R?”. Quora. N.p., 2016. Web. 18 Sept. 2016.

 

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