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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Can Brick and Mortar Stores Survive in the Digital Age?

7

October

2020

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Popular stores around the world have closed hundreds of stores in the past few years. In the US, stores like Macy’s, American Apparel and Gap are competing against Amazon. In 2019, there were 59% more store closings compared to 2018 (Reyhle, 2020). This ‘retail apocalypse’ is happening everywhere. In Europe, fast fashion brands like H&M and Inditex (owners of Zara and Bershka) are closing doors to focus on their online offerings (Ho, 2020).

The retail apocalypse has started in 2010 and is continuing onward. The main driver of this phenomenon is the shift to e-commerce, facilitated by the digital age we live in. Online shopping became possible when the internet opened to the public in 1991. Amazon was one of the first e-commerce platforms in the US to disrupt the traditional retail industry. Today Amazon and other online-only e-commerce players are forcing traditional retailers to shift their operations to an online platform. Not only do retailers save rent and labour costs by doing so, they also have to keep up with rising consumer expectations. Consumers want a convenient shopping experience, fast delivery and product availability; things they cannot get from physical stores. E-commerce reduces consumers’ search and retail costs, as they can easily learn and compare prices (Pwc, 2020).

The recent COVID-19 pandemic has stimulated consumers to shift to online stores even further. It has forced consumers to change their beliefs and behavior about many daily activities. For example, a study by McKinsey & Company shows that 15% of consumers in the US tried online grocery shopping for the first time during the pandemic (Charm et al., 2020). They did so in order to limit social contact. The majority was delighted by the experience and says to continue online grocery shopping, even after the pandemic. When consumers are positively surprised about a new experience, they are willing to repeat the behavior. The pandemic has therefore forced consumers to adapt to online shopping.

Convenience, price comparisons, product availability, the global pandemic. All these factors undoubtedly seem to have changed the role of physical, brick and mortar stores. It raises the question of whether physical stores are even necessary today. However, brick and mortar stores do have an advantage over e-commerce: allowing customers to physically see, touch and evaluate products. Research (Reyhle, 2020) shows that customers who go to retail stores become more engaged with the retailer’s brand. If they cannot find the right size, colour or type of product they evaluated in the store, they order it through the online channel. This illustrates the need for both physical and online stores.

It is important for physical retailers to recognize that today’s consumers are omni-channel, meaning that they use physical and online stores interdependently in their purchasing process (Reyhle, 2020). Physical retailers should therefore rethink their strategy in order to provide the most convenient experience to the omni-channel consumers, physically and online. They could, for example, think of their physical stores as showrooms of their digital channels. Only then can they survive in the current state of the digital age we live in.

What do you think: will consumers’ behavior change so that brick and mortar stores become unnecessary in the near future? If so, will they disappear completely?

References:

Charm, T., Dhar, R., Haas, S., Liu, J., Novemsky, N., Teichner, W. (2020) Understanding and shaping consumer behavior in the next normal. [online] Available at: <https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/understanding-and-shaping-consumer-behavior-in-the-next-normal> [Accessed 6 October 2020].

Ho, R. (2020) H&M And Zara Are Closing Retail Stores To Boost E-Commerce. [online] HYPEBAE. Available at: <https://hypebae.com/2019/8/hm-zara-closing-retail-stores-online-shopping-ecommerce> [Accessed 7 October 2020].

Pwc.de. (2020) [online] Available at: <https://www.pwc.de/de/human-resources/studie-surviving-the-retail-apocalypse.pdf> [Accessed 7 October 2020].

Reyhle, N. (2020) Brick And Mortar “Showrooms”? How Stores Can Survive In The Digital Age – Retail Minded. [online] Retail Minded. Available at: <https://retailminded.com/brick-and-mortar-showrooms-how-stores-can-survive-in-the-digital-age/#.X34pNJMzZQJ> [Accessed 6 October 2020].

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