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Wednesday, October 1, 2014

More on healthcare data quality

Healthcare organizations struggle with data quality – from serious issues involving fraud, bad debt, billing inefficiencies – to life-threatening issues involving diagnosis and prescribing care and real-time analytics.

As healthcare organizations try to handle increasing amounts of data – coming from disparate entry points with inconsistent data standards, to siloed and/or legacy data – all while working to transition from paper to electronic health records (EHR) – many healthcare organizations are choosing a data warehousing solution that integrates data quality into all of their applications to maintain the accuracy and value of business and critical-care operational information...

The adage “garbage in, garbage out” applies more today than ever before as data-centric systems play an increasing role in supporting healthcare decision making. Data quality issues involving deduplication, incompleteness, and inconsistencies all play a part in undermining the effectiveness of operations…

Today’s healthcare industry is complex, data-driven, and tightly regulated. It’s essential to identify your organization’s data issues and determine the most effective approach to cleaning and updating your data…

Invalid or inaccurate contact data can also enter systems when data entry personnel makes a typo or misspelling during the admission process. And, patients are also responsible for erroneous or even false information entering systems when they fill out registration forms, which leave healthcare organizations susceptible to insurance fraud and bad debt. Sometimes, patients might not provide their most current contact information – either intentionally or unwittingly. Other patients might not give their first name, preferring to list only their nickname or middle name. The most common types of fraud involve false statements or deliberate omissions that are critical to determine healthcare eligibility or for billing purposes...

Duplicate medical records are an alarming and costly threat to the healthcare industry as they can negatively impact patient safety, hospital liability, reimbursement, and administrative efficiencies. Duplicate records can also undermine the integrity of a data warehouse. American Medical Informatics Association (AMIA) cited a report by Fox and Sheridan that stated an average organization’s duplicate rate is between 5 to 10 percent for a single hospital...

The report also estimates that a duplicate pair of records creates $50 in hidden operational costs, so a hospital that generates five duplicates a day could end up spending as much as $78,000 per year as a result of duplicate records. If the hospital is open seven days a week, costs rise to over $91,000 per year according to the AMIA.

Duplicates are Dangerous
Not only are duplicates costly, they can be harmful to patients. A provider could mistake one patient for another patient with a similar name, especially if there are duplicate records of the patient in the system. For example, a “Beth Smith” might be recorded as “Smith, Elizabeth” in another database, but both names are the same person. Or, the provider might associate “Beth Smith” and a “Beth Smithe” as the same patient, but they are actually two different people...
Not bad. Highly artsy rendering. Free, with registration.

I searched for "interoperability."

Pretty severe oversight, in my view, given the broad heterogeneity of healthcare data sources and widely variant attention to data quality. Two words "Error Propagation."
The entire point of searching, locating, linking, retrieving, merging, reordering, indexing, and analyzing data originating in various data repositories (digital or otherwise) is to reduce uncertainty in order to make accurate, value-adding decisions. To the extent that data are "dirty" (riddled with errors), this objective is thwarted. Worse, the resulting datasets borne of such problematic inquiry then themselves frequently become source data for subsequent query, iteratively, recursively. Should you be on the receiving end of bad data manipulation, the consequences can range from the irritatingly trivial to the catastrophic. We all have our hair-pulling stories regarding the mistakes bequeathed us by those who sloppily muck about in our information and misinformation.


From The Daily Beast:
Peter Thiel’s Radical Political Vision
The right-leaning tech billionaire offers a look into how Silicon Valley could transform the way we think about politics.

...First and foremost, Thiel thinks innovation is the key to mankind’s ills—and he isn’t happy with Washington’s apparent lack of interest in technological progress.

“I don’t think we can solve any of our problems without technological progress,” said Thiel. “That is, in my mind, the single most important issue. It’s one that’s not particularly high on the political agenda of any of our leaders in Washington, most of whom are fairly scientifically illiterate and an uninterested or hostile to technology.”...

Much of Thiel’s startup-advice book makes the case that capitalism is a game of Monopoly. He advises young entrepreneurs that the entire goal of any good businessman is to completely own their market. Google, he claims, is a “good monopoly” because it keeps pumping out fresh ideas. But were it to sit idle, and prevent a new crop of entrepreneurs from innovating, it would be acceptable for the government to step in and break it up.

From this vantage point, government is not so much the harbinger of evil as an ineffective nuisance, only to be invoked when businesses lose their way in advancing society...
Having now closely studied his book, I don't find him "right leaning" at all. I find him an eclectic, clear thinker, one with socially beneficent motives. His debunking of myriad sacred cows was a joy to read.

...[B]oth George H. W. Bush and Bill Clinton preached the gospel of free trade: since every person has a relative strength at some particular job, in theory the economy maximizes wealth when people specialize according to their advantages and then trade with each other. In practice, it’s not unambiguously clear how well free trade has worked, for many workers at least. Gains from trade are greatest when there’s a big discrepancy in comparative advantage, but the global supply of workers willing to do repetitive tasks for an extremely small wage is extremely large...

Now think about the prospect of competition from computers instead of competition from human workers. On the supply side, computers are far more different from people than any two people are different from each other: men and machines are good at fundamentally different things. People have intentionality— we form plans and make decisions in complicated situations. We’re less good at making sense of enormous amounts of data. Computers are exactly the opposite: they excel at efficient data processing, but they struggle to make basic judgments that would be simple for any human.
The stark differences between man and machine mean that gains from working with computers are much higher than gains from trade with other people. We don’t trade with computers any more than we trade with livestock or lamps. And that’s the point: computers are tools, not rivals.

The differences are even deeper on the demand side. Unlike people in industrializing countries, computers don’t yearn for more luxurious foods or beachfront villas in Cap Ferrat; all they require is a nominal amount of electricity, which they’re not even smart enough to want. When we design new computer technology to help solve problems, we get all the efficiency gains of a hyperspecialized trading partner without having to compete with it for resources. Properly understood, technology is the one way for us to escape competition in a globalizing world. As computers become more and more powerful, they won’t be substitutes for humans: they’ll be complements...
The other buzzword that epitomizes a bias toward substitution is “big data.” Today’s companies have an insatiable appetite for data, mistakenly believing that more data always creates more value. But big data is usually dumb data. Computers can find patterns that elude humans, but they don’t know how to compare patterns from different sources or how to interpret complex behaviors. Actionable insights can only come from a human analyst (or the kind of generalized artificial intelligence that exists only in science fiction).
We have let ourselves become enchanted by big data only because we exoticize technology. We’re impressed with small feats accomplished by computers alone, but we ignore big achievements from complementarity because the human contribution makes them less uncanny . Watson, Deep Blue, and ever-better machine learning algorithms are cool. But the most valuable companies in the future won’t ask what problems can be solved with computers alone. Instead, they’ll ask: how can computers help humans solve hard problems?

Thiel, Peter; Masters, Blake (2014-09-16). Zero to One: Notes on Startups, or How to Build the Future (Kindle Locations 1641-1736). Crown Publishing Group. Kindle Edition.
If you are considering doing a tech startup, particularly in the Health 2.0 arena, this book is a must-read.
AS MATURE INDUSTRIES stagnate, information technology has advanced so rapidly that it has now become synonymous with “technology” itself. [ibid, location 1616]
Nothwithstanding all of this effusive optimism, a bit of cautionary leavening is always prudent.

Silicon Valley is guilty of many sins, but lack of ambition is not one of them. If you listen to its loudest apostles, Silicon Valley is all about solving problems that someone else—perhaps the greedy bankers on Wall Street or the lazy know-nothings in Washington—have created.

“Technology is not really about hardware and software any more. It’s really about the mining and use of this enormous data to make the world a better place,” Eric Schmidt, Google’s executive chairman, told an audience of MIT students in 2011. Facebook’s Mark Zuckerberg, who argues that his company’s mission is to “make the world more open and connected,” concurs . “We don’t wake up in the morning with the primary goal of making money,” he proclaimed just a few months before his company’s rapidly plummeting stock convinced all but its most die-hard fans that Facebook and making money had parted ways long ago. What, then, gets Mr. Zuckerberg out of bed? As he told the audience of the South by Southwest festival in 2008, it’s the desire to solve global problems. “There are a lot of really big issues for the world to get solved and, as a company, what we are trying to do is to build an infrastructure on top of which to solve some of these problems,” announced Zuckerberg.

In the last few years, Silicon Valley’s favorite slogan has quietly changed from “Innovate or Die!” to “Ameliorate or Die!” In the grand scheme of things, what exactly is being improved is not very important; being able to change things, to get humans to behave in more responsible and sustainable ways, to maximize efficiency, is all that matters. Half-baked ideas that might seem too big even for the naïfs at TED Conferences —that Woodstock of the intellectual effete— sit rather comfortably on Silicon Valley’s business plans. “Fitter, happier, more productive”— the refreshingly depressive motto of the popular Radiohead song from the mid-1990s— would make for an apt welcome sign in the corporate headquarters of its many digital mavens. Technology can make us better—and technology will make us better. Or, as the geeks would say, given enough apps, all of humanity’s bugs are shallow.

California, of course, has never suffered from a deficit of optimism or bluster. And yet, the possibilities opened up by latest innovations make even the most pragmatic and down-to-earth venture capitalists reach for their wallets. After all, when else will they get a chance to get rich by saving the world? What else would give them the thrill of working in a humanitarian agency (minus all the bureaucracy and hectic travel, plus a much better compensation package)?

How will this amelioration orgy end? Will it actually accomplish anything? One way to find out is to push some of these nascent improvement efforts to their ultimate conclusions. If Silicon Valley had a designated futurist, her bright vision of the near future— say, around 2020 or so— would itself be easy to predict. It would go something like this: Humanity, equipped with powerful self-tracking devices, finally conquers obesity, insomnia, and global warming as everyone eats less, sleeps better, and emits more appropriately. The fallibility of human memory is conquered too, as the very same tracking devices record and store everything we do. Car keys, faces, factoids: we will never forget them again. No need to feel nostalgic, Proust-style, about the petite madeleines you devoured as a child; since that moment is surely stored somewhere in your smartphone— or, more likely, your smart, all-recording glasses— you can stop fantasizing and simply rewind to it directly. In any event, you can count on Siri, Apple’s trusted voice assistant, to tell you the truth you never wanted to face back then: all those madeleines dramatically raise your blood glucose levels and ought to be avoided. Sorry, Marcel!

Morozov, Evgeny (2013-03-05). To Save Everything, Click Here: The Folly of Technological Solutionism. Locations 85 - 101. Public Affairs. Kindle Edition.
Yet another worthy read.

More to come...

1 comment:

  1. Data-driven decision making will be one of the key factors in changing the future of business, especially in the business of health care. There is so much great work being done with data analysis and data quality tools in various industries such as health care. It will be interesting to see the impact of these changes down the road.

    Linda Boudreau