If there was a graveyard for failed health informatics attempts, it would be littered with the tombstones of Big Tech companies like IBM, Google, and Amazon.
Big tech companies have often been hailed as the saviors who could free the nation from the problems of our dysfunctional, inefficient and archaic healthcare system. The reality is that these organizations continue to fail in their initiatives, ultimately stifling true innovation in healthcare and reducing market confidence in technologies that successfully address real market needs.
After wasting time and money on half-baked solutions, organizations are finally giving up and wondering if Big Tech’s motivations to save healthcare were based more on greed and hubris than real innovation. Oracle’s acquisition of Cerner for $28 billion is Big Tech’s latest investment to make headlines, and it’s a deal that isn’t likely to end well either.
A Quick Review of Notable Big Tech Healthcare Failures
As a refresher, let’s take a look back at some of the most notable Big Tech healthcare debacles of recent years.
- IBM Watson: After billions of investments, IBM’s artificial intelligence data analytics solution Watson failed after years of non-profitability. Watson proved excellent at answering trivial questions on “Jeopardy!” but had little impact on the healthcare industry. Watson’s life at IBM recently ended long ago when she was sold in parts, likely for pennies each of IBM’s investment dollars, marking a “staggering collapseto his once-high expectations.
- Google Health: Google’s personal health record service made headlines when it launched in 2008, but it lasted a short and rather useless life before it ended around the drain just three years later. Suffering from public apathy, low user adoption, and interoperability issues, Google Health was unable to deliver much of the basic functionality that could have made it usable.
- Haven of health: Among the most recent notable failures of Big Tech healthcare were Haven of health, a company formed by Amazon, Berkshire Hathaway and JPMorgan Chase to disrupt healthcare and health insurance. Despite backing some of the most powerful companies in the world and hiring rock star healthcare Atul Gawande as CEO, the company dissolved after just three years once it became clear that she would be far from her grandiose but ultimately presumptuous and condescending ideals at creation.
Why does this keep happening? Dirty health data
It’s easy to see the allure of healthcare for Big Tech. It’s a $4.1 trillion market plagued by disorganization and waste. Despite what we in the industry sometimes like to tell ourselves, the reality is that healthcare in America is horrible. health is more Dear in the United States than anywhere else in the world, but the quality is lower than most developed countries in terms of life expectancy and the results.
Why? American healthcare systems rely on billing codes, meaning financial rather than clinical information. This leads to a host of problems due to the lack of granularity of claims data, which fails to consider many important factors, including the social determinants of patient health.
A recurring theme that runs through all of these missteps is that Big Tech consistently underestimates the problem healthcare has with “dirty data.” Health is not engineering. In healthcare, data is rarely clean or consistent; it’s more like the Tower of Babel. Healthcare is full of industry-specific terminologies, such as ICD-10, SNOMED, RxNorm, and MedDRA.
Indeed, around 30% of the world’s data volume is generated by the health sector, according to RBC Capital Markets. By 2025, the compound annual growth rate of data for healthcare is expected to reach 36%, a faster growth rate than manufacturing, financial services, media and entertainment.
Patient data can be included in medical histories, diagnoses, observations, lab reports, and imaging reports, to name a few of the many disparate sources. In addition, each patient record is unique and medical data can be complex and confusing.
In addition to its complexity, clinical data is often both redundant and incomplete, in confusing and frustrating ways. It’s stored in separate electronic health record systems at various vendors, but full of spelling mistakes and inconsistencies.
Much of the valuable data healthcare organizations need to improve decision-making is also unstructured. Information is trapped in the “notes” sections of EHRs or as PDF files and images, which are difficult for machine learning algorithms to decipher. It is estimated that approximately 80% of health data is unstructured.
Big Tech’s lack of health expertise can create conditions that stifle innovation and reduce market confidence in certain types of technologies. When organizations spend time and money implementing Big Tech solutions that ultimately don’t solve their problems, many simply abandon the technology, even when other innovative solutions are available in the market.
For health care, by those with health care expertise
Listen, I do Google searches and use Big Tech tools like everyone else. For the most part, they’re good at what they do — when they stick to what they do — and that’s not health.
As we’ve seen time and time again, Big Tech just can’t get good healthcare because they can’t overcome the dirty data issues that are endemic to the industry and they’re too proud to integrate the third-party solutions that do. Everyone (meaning: the healthcare industry, Big Tech itself, and, oh yes, patients) would frankly be better off if they stopped trying.
Instead of relying on Big Tech to solve big problems in healthcare, stakeholders would be better served looking at solutions that have been developed for healthcare by experienced experts who know and understand the industry.
Photo: pictafolio, Getty Images