Data’s arrival in Health Care
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Data’s arrival in Health Care

Cerner’s1 Senior Vice President at the 2017 Cerner Health Conference said that “We’re in the middle of an incredible moment in the health care industry, where expectations and standards are shifting”. He was referring to health care’s move away from the old school way of isolated reactive sick care.

The future will target a wide open system which also includes prevention into the chain of treatment and actively involves the patient. In other words, more and better available data is expected to shift the treatment center point away from the doctor to the patient/consumer. Increased availability of data not only improves treatment but also allows increasing efficiency in delivering health care products and services and therefore helps driving down systemic health care cost. This change is largely driven by the increased availability of all sorts of data points flowing in from different sources across many channels (electronic medical records, medical claims data, …).

It will take some time until the industry finds a widely accepted way or standard how to organize, treat and make the vast amount of health related data available. However, one commonly desired outcome driven by the use of data is the so called care-continuum in which all care takers across a full treatment cycle have access to the already existing patient data. One could say that a care continuum is the necessary requirement for a cost efficient way of delivering health care to the masses.

Drivers of change

The arrival of increased use of technology in health care is probably more than a decade behind other industries like transportation, finance and communication. The large delay has mainly been driven by lagging legal and regulatory guidelines regarding digitally based solutions. Another source of the delay certainly is the only relatively recent arrival of tech in most peoples’ daily lives. The daily use of devices like smart phones, tablets and the likes is a precondition for technology making it into the sensitive area of health care. If we think back a few years, smartphones have only made it into our lives after the iPhone was introduced in 2007. It therefore took a few years until people started putting trust in technology and therefore also began accepting the collection of all sorts of health related data. However, politics and regulators around the world have picked up the task of creating the framework needed for technology revolutionizing health care. This trend cannot be stopped anymore and is very large in terms of impact. As a result, the sources of data and the extent to which data is available and will be used in multifaceted ways will increase substantially over coming years.

Components contributing to the transformation

The internet of things (IoT) changes our lives with sensors available in many areas of our daily activities and allowing to quantify many things. This includes more and more consumers wearing fitness trackers which collect data about depth and length of sleep, our heart rate and what we eat. The data collected from fitness trackers already allow, depending on your insurance provider, to reduce the health insurance premium provided one reached a certain amount of steps a day. Even though the data collected from wearables like fitness trackers seem to be very basic at the moment, it already makes it into practitioners’ daily treatment of patients. Cases where a doctor uses the data of a fitness tracker’s heart rate data to asses if a heart rate stabilizing treatment can be stopped are slowly but surely becoming the new normal. This shows that data which seems to be basic already at this early stage has a huge impact in saving and improving peoples’ lives.

Data is the new oil

Andreas Weigend, Amazon’s former Chief Scientist2

Artificial Intelligence (AI), machine- and deep learning as separate technologies have made significant advances forward over the past couple of years. To make big amounts of data useful and get a meaningful output out of it, AI, machine- and deep learning is essential. The use of big data in combination with AI has especially large impact in the early stages of the R&D process for new health care treatments. Clinical studies have two major shortcomings, they are very expensive to run and usually there is only a limited number of participants available. Already today the vast amount of collected data helps to make the clinical study process more cost efficient and allows increasing accuracy of potential outcomes and to assess the risk of any side effects. Every meaningful company active in health care data has a department which applies the so called Real World Evidence (RWE) process. RWE help, based on data, to asses a treatment’s success rate across different patient groups. Since the health care system should only pay for what works, data can contribute big time to the health care cost reduction which is urgently needed.

Challenges

Every time such a monumental transformation is happening, multifaceted challenges emerge and will take some time to be worked out. One of the biggest risk factors for the overall digitalization and increased use of data in health care is the security aspect. In the past few months there were several security breaches and there will be further incidents in the future. It is crucial to keep the impact limited to not damage consumers’ trust in the overall health care system. The overall governance still needs to be defined. One key topic where participants still try to find a common denominator is agreeing on unique data format standards. This however could take a substantial amount of time and usually follows after a first formative period. Defining standards is going to be the key to establish future interoperability. Interoperability refers to several different systems working together. Another sub-component of the governance process is to clearly define who owns the data and who is allowed what to do with it. Here the legal and regulatory framework has in the past not been very supportive. As indicated earlier, regulators and politicians around the world do finally push to put the guidelines in place to solve that issue in the near future.

Conclusion

The amount of data collected from different sources in combination with tools like AI and deep learning will change the way how products are developed and how services are delivered to patients and consumers. Another conclusion is that market power within the industry will be redistributed. Until recently, the leading position has been held by large cap pharma who hoarded proprietary product specific data in their safes in the cellar. The availability of a much broader set of data, which is collected from lots of sources, pulls the leadership to the market participants who collect and reuse that data in combination with the best technology. At the end, more and better data will help the system to increase the quality of managing populations’ health, offer new types of services and help to control increasing cost.

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