7 minutos de lectura 24 abr. 2018
doctor looks screen medical operation

Analytics and intuition: Why health care big data needs both

Por EY Global

Ernst & Young Global Ltd.

7 minutos de lectura 24 abr. 2018

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  • Becoming an analytics driven organization (pdf)

Technology may be advancing, but it’s still human instinct that counts when digging for the insights that can improve performance.

Across every industry, and in all walks of life, we are generating more and more data. This data serves as a potentially rich source of insight that can inform decisions and actions. But only if it is analyzed correctly.

Many organizations have reached a point where their ability to generate data exceeds their ability to consume and analyze that information. They have built capacity for analytics production, but not insight.

Leaders acknowledge this disconnect. In an EY survey of senior executives, 81% of respondents agreed that data should be at the heart of decision-making. But only 31% said they had significantly restructured their operations to incorporate analytics.

Combining analytics and intuition: learning from health care

It is arguable that the sector where data can provide the greatest impact – both in terms of business performance and the benefit to humankind – is health care. Here, the ability to recognize and act upon the right data can genuinely make the difference between life and death.

In recent years, the digitization of medical records by state public health bodies and research by medical institutions and the pharmaceutical industry has begun to pave the way for the widespread use of analytics in health care.

Now, more medical information is being generated and gathered today than ever before: electronic records, health insurance claims, real-time monitoring by connected mobile devices, plus ever more creative ways to gather and combine data. It’s a trend set to continue.

With aging populations putting increasing pressure on health services, and health care costs rising worldwide, working out how to make health care more efficient is a pressing concern.

But analytics – enabling the generation of insights into the way people live their lives – also presents a great opportunity to inject new, creative and effective thinking into health care.

When analytics is not enough

The power of analytics lies in its capacity to find patterns among vast swathes of data quickly. But it’s not always accurate at interpreting those patterns and making the right diagnosis. The binary code of analytical algorithms still lacks the intuitive capability to guess at the motivations driving certain human behaviors.

Perhaps the best-known example of a failure in algorithmic health care analytics is Google Flu Trends. When it first appeared, its ability to spot flu outbreaks weeks ahead of traditional methods was hailed as a breakthrough.

But for the 2013 flu season, the tool’s predictions were widely off the mark. Why? The algorithm didn’t distinguish between someone who had symptoms and someone merely asking about them. With such a vast reservoir of data to draw upon, the volume of misleading or false data became so great as to render the findings almost meaningless.

The idea behind Google Flu Trends was a good one, but its correlation-based insights were too simplistic. Its binary thinking lacked the creative latitude to consider alternative human behaviors that were actually driving the data-set. Today, experiences like this are helping to improve medical analytics.

What it cannot usually do is relate those patterns to real-world scenarios and contexts – the sheer amount of contextual and experiential data this would require is still far beyond even the most advanced Artificial Intelligence algorithms. Although artificial intelligence may be improving, algorithms can only take us so far. Strings of code can help us identify trends in data but still aren’t smart enough to derive true insight on their own.

This is why intuition and creativity remain firmly within the realms of humans – combining quantitative data with qualitative insight that can lead to answers that algorithms could never reach.

Analytics are at their best when they complement these very human traits to help draw more timely conclusions from those patterns, ultimately improving patient outcomes by helping humans reach the right conclusions faster.

Three men looking at a computer screen

Combining human ingenuity and algorithmic analysis

The immense quantitative power health care can now deploy through analytics also lays the groundwork for health care professionals to be more intuitive with how patient diagnoses and treatment is carried out.

But the key is that human analysts are going to remain essential in sense-checking machine findings - spotting spurious results before they lead to misinformed decisions. It’s not enough to get analysts in only at the start – they need to be there to continually monitor results. This ensures the analytics provide genuine insight, and can be adapted to evolve in the face of new data and changing conditions.

Three ways human-centered analytics can improve health care

Intermountain Healthcare has been using analytics for decades: improving operations, driving better health care outcomes, and making a difference in patients’ lives. Today, Intermountain’s leaders see analytics as fundamental to creating value in three key areas:

1. Keeping data at the heart of the organization 

Every business has discussions about how to organize resources. Intermountain made a strategic decision to place its analytics teams close to frontline staff. Today, most of its Clinical Programs have their own data teams. This ensures that Intermountain’s data and analytics experts stay across business problems – ensuring they can ask, and answer, better questions to drive results.

2. Learning loops streamline operations

By embedding analytics into business processes, Intermountain is able to create learning loops. Today, dozens of different data-based decision support tools help employees improve patient care. Cardiology is a great example. Every time doctors treat a heart attack, data on the operation is shared with the treatment team as part of a rapid improvement process. By developing consistent, repeatable processes, this feedback helped reduce the median treatment time from 90 to 57 minutes.

3. Using data to ask smarter, better questions

Intermountain has created an environment where any employee can ask for analytics support. Encouraging them to ask questions – What does the data say about this treatment? What insights can I glean from this result? – has had a positive, lasting impact. To encourage this, Intermountain hired Brent James, a medical doctor with a Master’s in statistics, tasked with championing the use and impact of data to deliver results throughout the organization. He’s been there since 1986.

Getting everyone on board for data success

Strong leadership, as well as the right organizational and business processes, can help a company leverage analytics and align the use of data with organizational strategy. But successful execution still requires individuals to act.

At Intermountain, there are three key factors that help employees leverage analytics for positive impact:

1. Hands-on training in a data-orientated culture

Often when organizations talk about adding analytics capability, they are referring to analytics practitioners. But it’s also needed on the front lines. Identifying how to help employees consume and understand new insights is an important part of creating a data-oriented culture. At Intermountain, that meant improving doctors’ knowledge of data and analytics processes – and encouraging staff to act on the findings.

2. Letting data speak for itself

Intermountain recognized up front that persuading people to change their perceptions can require a coherent engagement strategy. This is why the company doesn’t force those who disagree with analytical findings to fall into line. By letting the data illustrate cases, they are instead able to build loyalty to analytics processes, and increase adoption of more efficient approaches.

3. Incentives to drive positive behaviors

How an organization measures and rewards employee performance matters, and Intermountain’s approach acknowledges the importance of aligning incentives with desired behaviors. This is why they are launching a new insurance product that will make physicians and Intermountain jointly responsible for health care efficiency. Doctors who adopt more efficient methods will earn more income. The company thinks this will help them focus on data-driven decision-making. A commitment to the human dimension can be key to driving return on analytics investments.

An organization can have the best technology, the best analytics and the best insights – and still not create any business value. This is because it still needs a human being to change a business decision or process using the insights that analytics can provide.

As Intermountain Healthcare demonstrates, a commitment to the human dimension can be key to driving return on analytics investments. The combination of data-crunching algorithms with the creative intuition of humans helps to unearth deeper, more impactful insights.


Data has the potential to create a positive impact on a business, but it can only create true value when combined with human insight.

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Por EY Global

Ernst & Young Global Ltd.