How ethnography is helping make sense of big data
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How ethnography is helping make sense of big data to improve health care

1) ASKING THE RIGHT QUESTIONS

In 2008, 200,000 residents of the US state of Oregon were invited to enter a lottery with an unusual prize: free medical insurance. The lottery was the result of the state’s budget surplus that year: with only enough budget left over to buy insurance for 10,000 people, a lottery seemed the fairest way to go.

As an unintended bonus, the lottery also represented a uniquely huge randomized trial, allowing researchers to compare the outcomes of those with and without free healthcare. The resulting data took many by surprise.

On the one hand, recipients of free medical insurance experienced improved financial stability and a 30% drop in depression. But on the other hand, they visited the emergency room 40% more often.

To opponents of free medical cover, this apparent inefficiency was a vindication of their position. As economist Amy Finkelstein says, on an episode of Freakonomics: “One of the arguments people make for covering the uninsured, is to get them out of the expensive emergency room and into primary care clinics and other services.” Here the opposite was happening.

But the data only highlights behavior, with little insight into root causes. It doesn’t explain why emergency room visits increased, and therefore what, if anything, could be done about it.

“The data only highlights behaviour, with little insight into root causes.”

So why were people going to the emergency room so often? And was there a way to change that? Applying ethnography to big data holds the key.

2) MAKING SENSE OF BIG DATA 

When New Jersey hospital doctor Jeffrey Brenner noticed that increasing numbers of his patients were victims of violent crime, he decided to join the police reform commission. There, Brenner was shown how the NYPD’s CompStat tool (short for Computer Statistics) records and analyses crime-related data in real time.

The resulting ‘big data’ is used to more effectively map, respond to, and even predict and prevent crime. It also revealed that a relatively small number of people were the cause of a huge amount of crime.

That made Brenner wonder: what would happen if the CompStat method was used to analyse his own hospital data?

“I was just shocked,” Brenner said after finding out, “[by] how often people were going back over and over. The number one reason to go to the hospital was head colds. Number two was viral infection. Number three was sore throat. It can cost $300, $500, $800, $1,000 [a visit]. It’s ridiculous.”

Most significantly, the data showed that just 1% of patients made up 30% of the hospitals’ entire payments.

“So,” Brenner says, “a very small sliver of patients are driving all of the revenues of the system. And [for] this top 1% we use the term ‘super-utilizers’.”

This trend isn’t US-specific. In the UK for example, in 2014 one super-utilizer cost the NHS £40,000, for requesting an ambulance 238 times.

“One ‘super-utilizer’ cost the NHS £40,000 for requesting an ambulance 238 times.”

Now we know who’s coming in to the emergency rooms, we can start to answer why, and what can be done about it. And the best way to do that is by being on the ground, using ethnography at a grassroots level.

3) GETTING TO KNOW PEOPLE

Last year at Close-Up Research, we carried some ethnographic research for the NHS, assessing the impact of ‘personal health budgets’, and the drivers behind recipient behaviours.

Personal health budgets are offered to people with long-term health conditions, allowing them to have more direct control over their care. Many of the recipients could be classed as frequent, and in some cases, super-utilizers.

For example, we met Adrian, a 57-year-old with emphysema and early-on-set dementia, who lives alone. Because Adrian had never joined a GP surgery, and was uncomfortable with strangers looking after him at home, he’d repeatedly call for an ambulance or wind up in A&E for non-urgent issues.

We also met 37-year-old Mark, who, following an assault, is confined to a wheelchair with minimal ability to communicate. Because Mark’s agency-sent carers often turned up with no knowledge of his specific personal needs, and little interest in trying to understand him, he was constantly getting infections and ending up in hospital.

But with personal health budgets, both men are now able to employ members of their community as personal assistants. This has significantly increased the health and wellbeing of both men, and drastically reduced their dependence not only on emergency services, but on non-urgent care too.

This approach to health care is generally called ‘person-centred’ or ‘human-centred’, and reflects the way Finkelstein and Brenner managed their ‘super-utilizers’ too.

“This approach to health care is generally called ‘person-centred’ or ‘human-centred’.”

“For all the stupid, expensive, predictive-modelling software that the big venders sell,” Brenner says, “you just ask the doctors, ‘Who are your most difficult patients?,’ and they can identify them.” Which is what Brenner did, before “hanging out with [a patient] for an hour, hour and a half every day, trying to figure out what makes the guy tick.”

“You see these people,” Finklestein elaborates, “often with unstable housing and family and employment situations, who are coping with really serious illnesses and pages of instructions from having left the hospital, and bags of medication. [So] we spend a whole lot of time really trying to get to know the patient and understand what their needs are, and where they want to see their life go. We then go to their house, go with them to their care appointments, [and] if they’re homeless, help them get housing.”

4) THE RESULTS

Brenner says that a 10 year randomized control trial on precisely this type of ‘person-centred’ health care intervention for the elderly showed that: “they reduced the death rate by 25%. They didn’t just save money, they reduced the death rate just by having a nurse come out to your house every week, or every other week in a highly structured, well organized intervention. That’s a stunning accomplishment.”

“They didn’t just save money, they reduced the death rate by 25%.'”

That’s the value of combining big data, with ethnographic behavioural insights from the field: by seeing first-hand what’s going on, you’re best placed to ask the right questions, make sense of statistics, and come up with practical solutions that actually work.

 

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 This article also appears on HuffPost.

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