For decades, technology has relentlessly made phones, laptops, apps and entire industries cheaper and better--while health care has stubbornly loitered in an alternate universe where tech makes everything more expensive and more complex. Now startups are applying artificial intelligence (AI), floods of data and automation in ways that promise to dramatically drive down the costs of health care while increasing effectiveness. If this profound trend plays out, within five to 10 years, Congress won't have to fight about the exploding costs of Medicaid and insurance. Instead, it might battle over what to do with a massive windfall. Today's debate over the repeal of Obamacare would come to seem as backward as a discussion about the merits of leeching. One proof point is in the maelstrom of activity around diabetes, the most expensive disease in the world.

The days of claiming artificial intelligence as a feature that set one startup or company apart from the others are over. These days, one would be hard-pressed to find any technology company attracting venture funding or partnerships that doesn't posit to use some form of machine learning. But for companies trying to innovate in healthcare using artificial intelligence, the stakes are considerably higher, meaning the hype surrounding the buzzword can be deflated far more quickly than in some other industry, where a mistaken algorithm doesn't mean the difference between life and death. Over the past five years, the number of digital health companies employing some form of artificial intelligence has dramatically increased. CB Insights tracked 100 AI-focused healthcare companies just this year, and noted 50 had raised their first equity rounds since January 2015.

We've just added several publicly available healthcare datasets to the collection of public datasets on Google BigQuery (the cloud-native data warehouse for analytics at petabyte scale), including RxNorm (maintained by NLM) and the Healthcare Common Procedure Coding System (HCPCS) Level II. While it's not technically a healthcare dataset, we also added the 2000 and 2010 Decennial census counts broken down by age, gender and zip code tabular areas, which we hope will assist healthcare utilization and population health analysis (as we'll discuss below). Anyone with a Google Cloud Platform (GCP) account can explore these datasets. RxNorm was created by the U.S. National Library of Medicine (NLM) to provide a normalized naming system for clinical drugs and provide structured information such as brand names, ingredients and so on for each drug. Drug information is made available as a single "concepts" table while the relationships that map entities to each other (ingredient to brand name, for example) is made available as a separate "relationships" table.

The days of claiming artificial intelligence as a feature that set one startup or company apart from the others are over. These days, one would be hard-pressed to find any technology company attracting venture funding or partnerships that doesn't posit to use some form of machine learning. But for companies trying to innovate in healthcare using artificial intelligence, the stakes are considerably higher, meaning the hype surrounding the buzzword can be deflated far more quickly than in some other industry, where a mistaken algorithm doesn't mean the difference between life and death. Over the past five years, the number of digital health companies employing some form of artificial intelligence has dramatically increased. CB Insights tracked 100 AI-focused healthcare companies just this year, and noted 50 had raised their first equity rounds since January 2015.

President Trump suggested tonight that it's not fair to compare the Republican health care plan to the Affordable Care Act, because the law is "dying, dying, dying" and won't be around anyway. "They always like to compare -- well, what about [Obamacare]? Obamacare's dead," Trump said at a rally in Harrisburg, PA. "It's gone ... The insurance companies are fleeing." Between the lines: His comments suggested that he might try to use the law's problems -- including the steep premium hikes last year -- to dismiss the comparisons people are making to the GOP replacement plan, which aren't flattering. The biggest criticisms: it would cover 24 million fewer people than the ACA, and under some of the latest changes, it might not give the same protections to people with pre-existing conditions.

Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a high-dimensional, semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill's sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important variables predicting enactment.

When IBM CEO Ginni Rometty delivered the opening keynote at HIMSS17 she effectively set the stage for artificial intelligence, cognitive computing and machine learning to be prevalent themes throughout the rest of the conference. Healthcare IT News asked our readers where they stand in terms of these initiatives. And we threw in a bonus question to figure out what their favorite part of HIMSS17 was. Some 70 percent of respondents are either actively planning or researching artificial intelligence, cognitive computing and machine learning technologies -- while 7 percent are rolling them out and 1 percent have already completed an implementation. A Sunday afternoon session featuring AI startups demonstrated the big promise of such tools as well as the persistent questions, skepticism and even fear when it comes to these emerging technologies.

The Washington fight over the future of Obamacare will have enormous repercussions for our health care system, which now accounts for nearly 18 percent of the U.S. economy. But that impact pales before the transformation already underway due to the rise of artificial intelligence (AI) and machine learning. AI and machine learning are forcing dramatic business model change for all the stakeholders in the health care system. What does AI (and machine learning) mean in the health care context? Simply put, it's the ability to aggregate newly available and vast amounts of disparate data from electronic health records, consumer media and purchasing trends, smart devices, the social sphere, and other sources to make predictions about patient outcomes.

Machine learning coupled with the explosion of data offers the very real possibility of addressing the most intractable problems in healthcare. For the first time, data is helping to answer healthcare's toughest questions. Like Google's Research Director Peter Norvig, I too believe it's not better algorithms that are fueling our advancement. Instead, it's the surge in data sources and the innate ability of machine learning to automatically apply complex calculations to vast stores of data and derive rules that help us to understand the correlations, patterns and predictions within the data. In 2015, our country spent $3.2 trillion a year on healthcare, or $9,990 per person, and the actuaries at the Centers for Medicare & Medicaid Services project the total to rise to nearly $5.6 trillion in 2025.

It can read lips and create new food recipes. It can win at chess, Jeopardy and the game Go. Every major technology company appears to be integrating it into how they organize and operate their business. And it seems like just about every new app in existence claims its software uses some sort of machine learning to make life even better. Artificial intelligence is splashed across headlines like never before.