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An Interesting Overview Of Alphabet’s Healthcare Investments

Posted on June 27, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Recently I’ve begun reading a blog called The Medical Futurist which offers some very interesting fare. In addition to some intriguing speculation, it includes some research that I haven’t seen anywhere else. (It is written by a physician named Bertalan Mesko.)

In this case, Mesko has buried a shrewd and well-researched piece on Alphabet’s healthcare investments in an otherwise rambling article. (The rambling part is actually pretty interesting on its own, by the way.)

The piece offers a rather comprehensive update on Alphabet’s investments in and partnerships with healthcare-related companies, suggesting that no other contender in Silicon Valley is investing in this sector heavily as Alphabet’s GV (formerly Google Ventures). I don’t know if he’s right about this, but it’s probably true.

By Mesko’s count, GV has backed almost 60 health-related enterprises since the fund was first kicked off in 2009. These investments include direct-to-consumer genetic testing firm 23andme, health insurance company Oscar Health, telemedicine venture Doctor on Demand and Flatiron Health, which is building an oncology-focused data platform.

Mesko also points out that GV has had an admirable track record so far, with five of the companies it first backed going public in the last year. I’m not sure I agree that going public is per se a sign of success — a lot depends on how the IPO is received by Wall Street– but I see his logic.

In addition, he notes that Alphabet is stocking up on intellectual resources. The article cites research by Ernest & Young reporting that Alphabet filed 186 healthcare-related patents between 2013 and 2017.

Most of these patents are related to DeepMind, which Google acquired in 2014, and Verily Life Sciences (formerly Google Life Sciences). While these deals are interesting in and of themselves, on a broader level the patents demonstrate Alphabet’s interest in treating chronic illnesses like diabetes and the use of bioelectronics, he says.

Meanwhile, Verily continues to work on a genetic data-collecting initiative known as the Baseline Study. It plans to leverage this data, using some of the same algorithms behind Google’s search technology, to pinpoint what makes people healthy.

It’s a grand and somewhat intimidating picture.

Obviously, there’s a lot more to discuss here, and even Mesko’s in-depth piece barely scratches the surface of what can come out of Alphabet and Google’s health investments. Regardless, it’s worth keeping track of their activity in the sector even if you find it overwhelming. You may be working for one of those companies someday.

Healthcare AI Needs a Breadth and Depth of Data

Posted on May 17, 2018 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

Today I’m enjoying the New England HIMSS Spring Conference including an amazing keynote session by Dale Sanders from Health Catalyst. Next week I’ll be following up this blog post with some other insights that Dale shared at the New England HIMSS event, but today I just wanted to highlight one powerful concept that he shared:

Healthcare AI Needs a Breadth and Depth of Data

As part of this idea, Dale shared the following image to illustrate how much data is really needed for AI to effectively assess our health:

Dale pointed out that in healthcare today we really only have access to the data in the bottom right corner. That’s not enough data for AI to be able to properly assess someone’s health. Dale also suggested the following about EHR data:

Long story short, the EHR data is not going to be enough to truly assess someone’s health. As Google recently proved, a simple algorithm with more data is much more powerful than a sophisticated algorithm with less data. While we think we have a lot of data in healthcare, we really don’t have that much data. Dale Sanders made a great case for why we need more data if we want AI to be effective in healthcare.

What are you doing in your organization to collect data? What are you doing to get access to this data? Does collection of all of this data scare anyone? How far away are we from this data driven, AI future? Let us know your thoughts in the comments.

Google And Fitbit Partner On Wearables Data Options

Posted on May 7, 2018 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Fitbit and Google have announced plans to work together, in a deal intended to “transform the future of digital health and wearables.” While the notion of transforming digital health is hyperbole even for companies the size of Google and Fitbit, the pairing does have plenty of potential.

In a nutshell, Fitbit and Google expect to take on both consumer and enterprise health projects that integrate data from EMRs, wearables and other sources of patient information together. Given the players involved, it’s hard to doubt that at least something neat will emerge from their union.

Among the first things the pair plans to use Google’s new Cloud Healthcare API to connect Fitbit data with EMRs. Of course, readers will know that it’s one thing to say this and another to actually do it, but gross oversimplifications aside, the idea is worth pursuing.

Also, using services such as those offered by Twine Health– a recent Fitbit acquisition — the two companies will work to better manage chronic conditions such as diabetes and hypertension. Twine offers a connected health platform which leverages Fitbit data to offer customized health coaching.

Of course, as part of the deal Fitbit is moving to the Google Cloud Platform, which will supply the expected cloud services and engineering support.

The two say that moving to the Cloud Platform will offer Fitbit advanced security capabilities which will help speed up the growth of Fitbit Health Solutions business. They also expect to make inroads in population health analysis. For its part, Google also notes that it will bring its AI, machine learning capabilities and predictive analytics algorithms to the table.

It might be worth a small caution here. Google makes a point of saying it is “committed” to meeting HIPAA standards, and that most Google Cloud products do already. That “most” qualifier would make me a little bit nervous as a provider, but I know, why worry about these niceties when big deals are afoot. However, fair warning that when someone says general comments like this about meeting HIPAA standards, it probably means they already employ high security standards which are likely better than HIPAA. However, it also means that they probably don’t comply with HIPAA since HIPAA is about more than security and requires a contractual relationship between provider and business associate and the associated liability of being a business associate.

Anyway, to round out all of this good stuff, Fitbit and Google said they expect to “innovate and transform” the future of wearables, pairing Fitbit’s brand, community, data and high-profile devices with Google’s extreme data management and cloud capabilities.

You know folks, it’s not that I don’t think this is interesting. I wouldn’t be writing about if I didn’t. But I do think it’s worth pointing out how little this news announcement says, really.

Yes, I realize that when partnerships begin, they are by definition all big ideas and plans. But when giants like Google, much less Fitbit, have to fall back on words like innovate and transform (yawn!), the whole thing is still pretty speculative. Just sayin’.

Thoughts on Privacy in Health Care in the Wake of Facebook Scrutiny

Posted on April 13, 2018 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

A lot of health IT experts are taking a fresh look at the field’s (abysmal) record in protecting patient data, following the shocking Cambridge Analytica revelations that cast a new and disturbing light on privacy practices in the computer field. Both Facebook and others in the computer field who would love to emulate its financial success are trying to look at general lessons that go beyond the oddities of the Cambridge Analytica mess. (Among other things, the mess involved a loose Facebook sharing policy that was tightened up a couple years ago, and a purported “academic researcher” who apparently violated Facebook’s terms of service.)

I will devote this article to four lessons from the Facebook scandal that apply especially to health care data–or more correctly, four ways in which Cambridge Analytica reinforces principles that privacy advocates have known for years. Everybody recognizes that the risks modern data sharing practices pose to public life are hard, even intractable, and I will have to content myself with helping to define the issues, not present solutions. The lessons are:

  • There is no such thing as health data.

  • Consent is a meaningless concept.

  • The risks of disclosure go beyond individuals to affect the whole population.

  • Discrimination doesn’t have to be explicit or conscious.

The article will now lay out each concept, how the Facebook events reinforce it, and what it means for health care.

There is no such thing as health data

To be more precise, I should say that there is no hard-and-fast distinction between health data, financial data, voting data, consumer data, or any other category you choose to define. Health care providers are enjoined by HIPAA and other laws to fiercely protect information about diagnoses, medications, and other aspects of their patients’ lives. But a Facebook posting or a receipt from the supermarket can disclose that a person has a certain condition. The compute-intensive analytics that data brokers, marketers, and insurers apply with ever-growing sophistication are aimed at revealing these things. If the greatest impact on your life is that a pop-up ad for some product appears on your browser, count yourself lucky. You don’t know what else someone is doing with the information.

I feel a bit of sympathy for Facebook’s management, because few people anticipated that routine postings could identify ripe targets for fake news and inflammatory political messaging (except for the brilliant operatives who did that messaging). On the other hand, neither Facebook nor the US government acted fast enough to shut down the behavior and tell the public about it, once it was discovered.

HIPAA itself is notoriously limited. If someone can escape being classified as a health care provider or a provider’s business associate, they can collect data with abandon and do whatever they like (except in places such as the European Union, where laws hopefully require them to use the data for the purpose they cited while collecting it). App developers consciously strive to define their products in such a way that they sidestep the dreaded HIPAA coverage. (I won’t even go into the weaknesses of HIPAA and subsequent laws, which fail to take modern data analysis into account.)

Consent is a meaningless concept

Even the European Union’s new regulations (the much-publicized General Data Protection Regulation or GDPR) allows data collection to proceed after user consent. Of course, data must be collected for many purposes, such as payment and shipping at retail web sites. And the GDPR–following a long-established principle of consumer rights–requires further consent if the site collecting the data wants to use it beyond its original purpose. But it’s hard to imagine what use data will be put to, especially a couple years in the future.

Privacy advocates have known from the beginning of the ubiquitous “terms of service” that few people read before the press the Accept button. And this is a rational ignorance. Even if you read the tiresome and legalistic terms of service (I always do), you are unlikely to understand their implications. So the problem lies deeper than tedious verbiage: even the most sophisticated user cannot predict what’s going to happen to the data she consented to share.

The health care field has advanced farther than most by installing legal and regulatory barriers to sharing. We could do even better by storing all health data in a Personal Health Record (PHR) for each individual instead of at the various doctors, pharmacies, and other institutions where it can be used for dubious purposes. But all use requires consent, and consent is always on shaky grounds. There is also a risk (although I think it is exaggerated) that patients can be re-identified from de-identified data. But both data sharing and the uses of data must be more strictly regulated.

The risks of disclosure go beyond individuals to affect the whole population

The illusion that an individual can offer informed consent is matched by an even more dangerous illusion that the harm caused by a breach is limited to the individual affected, or even to his family. In fact, data collected legally and pervasively is used daily to make decisions about demographic groups, as I explained back in 1998. Democracy itself took a bullet when Russian political agents used data to influence the British EU referendum and the US presidential election.

Thus, privacy is not the concern of individuals making supposedly rational decisions about how much to protect their own data. It is a social issue, requiring a coordinated regulatory response.

Discrimination doesn’t have to be explicit or conscious

We have seen that data can be used to draw virtual red lines around entire groups of people. Data analytics, unless strictly monitored, reproduce society’s prejudices in software. This has a particular meaning in health care.

Discrimination against many demographic groups (African-Americans, immigrants, LGBTQ people) has been repeatedly documented. Very few doctors would consciously aver that they wish people harm in these groups, or even that they dismiss their concerns. Yet it happens over and over. The same unconscious or systemic discrimination will affect analytics and the application of its findings in health care.

A final dilemma

Much has been made of Facebook’s policy of collecting data about “friends of friends,” which draws a wide circle around the person giving consent and infringes on the privacy of people who never consented. Facebook did end the practice that allowed Global Science Research to collect data on an estimated 87 million people. But the dilemma behind the “friends of friends” policy is how inextricably it embodies the premise behind social media.

Lots of people like to condemn today’s web sites (not just social media, but news sites and many others–even health sites) for collecting data for marketing purposes. But as I understand it, the “friends of friends” phenomenon lies deeper. Finding connections and building weak networks out of extended relationships is the underpinning of social networking. It’s not just how networks such as Facebook can display to you the names of people they think you should connect with. It underlies everything about bringing you in contact with information about people you care about, or might care about. Take away “friends of friends” and you take away social networking, which has been the most powerful force for connecting people around mutual interests the world has ever developed.

The health care field is currently struggling with a similar demonic trade-off. We desperately hope to cut costs and tame chronic illness through data collection. The more data we scoop up and the more zealously we subject it to analysis, the more we can draw useful conclusions that create better care. But bad actors can use the same techniques to deny insurance, withhold needed care, or exploit trusting patients and sell them bogus treatments. The ethics of data analysis and data sharing in health care require an open, and open-eyed, debate before we go further.

Small Grounds for Celebration and Many Lurking Risks in HIMSS Survey

Posted on March 12, 2018 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

When trying to bypass the breathless enthusiasm of press releases and determine where health IT is really headed, we can benefit from a recent HIMMS survey, released around the time of their main annual conference. They managed to get responses from 224 managers of health care facilities–which range from hospitals and clinics to nursing homes–and 145 high-tech developers that fall into the large categories of “vendors” and “consultants.” What we learn is that vendors are preparing for major advances in health IT, but that clinicians are less ready for them.

On the positive side, both the clinicians and the vendors assign fairly high priority to data analytics and to human factors and design (page 7). In fact, data analytics have come to be much more appreciated by clinicians in the past year (page 9). This may reflect the astonishing successes of deep learning artificial intelligence reported recently in the general press, and herald a willingness to invest in these technologies to improve health care. As for human factors and design, the importance of these disciplines has been repeatedly shown in HxRefactored conferences.

Genomics ranks fairly low for both sides, which I think is reasonable given that there are still relatively few insights we can gain from genetics to change our treatments. Numerous studies have turned up disappointing results: genetic testing doesn’t work very well yet, and tends to lead only to temporary improvements. In fact, both clinicians and vendors show a big drop in interest in precision medicine and genetics (pages 9 and 10). The drop in precision medicine, in particular, may be related to the strong association the term has with Vice President Joe Biden in the previous administration, although NIH seems to still be committed to it. Everybody knows that these research efforts will sprout big payoffs someday–but probably not soon enough for the business models of most companies.

But much more of the HIMSS report is given over to disturbing perception gaps between the clinicians and vendors. For instance, clinicians hold patient safety in higher regard than vendors (page 7). I view this concern cynically. Privacy and safety have often been invoked to hold back data exchange. I cannot believe that vendors in the health care space treat patient safety or privacy carelessly. I think it more likely that clinicians are using it as a shield to hide their refusal to try valuable new technologies.

In turn, vendors are much more interested in data exchange and integration than clinicians (page 7). This may just reflect a different level of appreciation for the effects of technology on outcomes. That is, data exchange and integration may be complex and abstract concepts, so perhaps the vendors are in a better position to understand that it ultimately determines whether a patient gets the treatment her condition demands. But really, how difficult can it be to be to understand data exchange? It seems like the clinicians are undermining the path to better care through coordination.

I have trouble explaining the big drops in interest in care coordination and public health (pages 9 and 10), which is worrisome because these things will probably do more than anything to produce healthier populations. The problem, I think, is probably that there’s no reimbursement for taking on these big, hairy problems. HIMMS explains the drop as a shift of attention to data analytics, which should ultimately help achieve the broader goals (page 11).

HIMSS found that clinicians expect to decrease their investments in health IT over the upcoming year, or at least to keep the amount steady (page 14). I suspect this is because they realize they’ve been soaked by suppliers and vendors. Since Meaningful Use was instituted in 2009, clinicians have poured billions of dollars and countless staff time into new EHRs, reaping mostly revenue-threatening costs and physician burn-out. However, as HIMSS points out, vendors expect clinicians to increase their investments in health IT–and may be sorely disappointed, especially as they enter a robust hiring phase (page 15).

Reading the report, I come away feeling that the future of health care may be bright–but that the glow you see comes from far over the horizon.

Three Pillars of Clinical Process Improvement and Control

Posted on February 21, 2018 I Written By

The following is a guest blog post by Brita Hansen, MD, Chief Medical Officer at LogicStream Health.

In a value-based care environment, achieving quality and safety measures is a priority. Health systems must have the capabilities to measure a process following its initial implementation. The reality, however, is that traditional improvement methods are often plagued with lagging indicators that provide little (if any) insight into areas requiring corrective actions. Health systems have an opportunity to make a significant impact on patient care by focusing on three pillars of clinical process improvement and control: quality and safety, appropriate utilization and clinician engagement.

Quality and Safety

Data in a health system’s electronic health record (EHR) typically is not easily accessible. Providers struggle to aggregate the data they need in a timely manner, often with limited resources, thereby hindering efforts to measure process efficacy and consistency. To achieve sustainable quality improvements, clinical leaders must equip their teams with advanced software solutions capable of delivering highly-actionable insights in near-real-time, thereby allowing them to gain a true understanding of clinical processes and how to avoid clinical errors and care variations.

Clinicians need instant insights into what clinical content in their EHR is being used; by whom; and how it affects patient care. This data empowers providers with the ability to continuously analyze and address care gaps and inefficient workflows.

For example, identifying inappropriate uses of Foley catheters that lead to catheter associated urinary tract infections (CAUTI) allows clinical leaders make targeted improvements to the care process or to counsel individual clinician outliers on appropriate best practices. This will, in turn, reduce CAUTI rates. To most effectively improve clinical processes, clinicians need software tools that enable them to examine those processes in their entirety, including process steps within the EHR, patient data and the actions of individual clinicians or groups as they interact with the care process every day.

Only with instant insight into how the care process is being followed can clinicians see in real-time what is happening and where to intervene, make the necessary changes in the EHR workflow, then measure and monitor the effects over time to improve care delivery in a sustainable way.

Appropriate Utilization

Verifying appropriate utilization of best practices also plays a critical role in optimizing clinical processes. Yet healthcare organizations often lack the ability to identify and correct the use of obsolete tests, procedures and medications. When armed with dynamic tools that quickly and easily allow any individual to understand the exact location of ordering opportunities for these components, an organization can evaluate its departments, clinicians, and patient populations for ineffective ordering patterns and areas that require greater compliance. By assessing areas in need of intervention, organizations can notify clinicians of the most up-to-date best practices that, when integrated into clinical workflows, will improve care and yield significant cost savings. Through targeted efforts to ensure proper usage of high-cost and high-volume medications, lab tests and other orderables, for example, health systems can achieve significant savings while improving the quality of care delivery.

The benefits of such an approach are reflected in one health system’s implementation of clinical process improvement and control software, which allowed them to more effectively manage the content in their EHR, including oversight of order sets. Specifically, the organization focused on reviewing the rate of tests used diagnose acute myocardial infarctions (heart attacks). It discovered that physicians were regularly ordering an outdated Creatine kinase-MB (CKMB) lab test along with a new, more efficient test for no other reason than it was pre-checked on numerous order sets.

Although the test itself was inexpensive, the high order rate led to massive waste and increased the cost of care. Leveraging the software enabled the organization to quickly identify the problem, then significantly reduce costs and save resources by eliminating an unnecessary test that otherwise would have remained hidden within the EHR.

Clinician Engagement

Enhancing clinician engagement is key to addressing dissatisfaction and burnout, often traced to alert fatigue and a lack of order set optimization within an EHR. The typical health system averages 24 million alert firings per year. Confronted with a high volume of unnecessary warnings, clinicians ignore alerts 49 percent to 96 percent of the time, resulting in poor compliance with care protocols. EHRs often contain an overwhelming number of order sets that can lead to confusion about best practices for patient care and a frustrating amount of choice to navigate. To increase engagement, alerts must be designed to send the right information, to the right person, in the right format, through the right channel, at the right time in the workflow; and order sets should be streamlined and make it easy for clinicians to follow the up-to-date best clinical practices.

For example, one hospital utilized EHR-generated alerts targeting potential cases of sepsis. These alerts, however, were rarely acted upon as they were not specific enough and fired inappropriately at such exhaustive rates clinicians grew to simply ignore them, creating a clear case of alert fatigue. By fine-tuning alerts and adjusting the workflow to ensure alerts were sent to the right clinician at the optimal time, the hospital was able to achieve and maintain nearly full compliance with its initiative. As early detection and treatment of sepsis increased, the hospital also reduced length of stay in its intensive care unit. Data-driven targeted interventions were developed to address outliers whose actions were driving unnecessary variation in the process.

Ultimately, when the three pillars—quality and safety, appropriate utilization and clinician engagement—are used as the building blocks for standardizing and controlling vital clinical processes, multiple objectives can be realized. Empowered with technology that supports these factors, healthcare organizations can truly achieve sustainable, proactive clinical process improvement and control.

Dr. Brita Hansen is a hospitalist at Hennepin County Medical Center in Minneapolis and Assistant Professor of Medicine at the University of Minnesota School of Medicine. Dr. Hansen also serves as Chief Medical Officer of LogicStream Health.

Key Articles in Health IT from 2017 (Part 2 of 2)

Posted on January 4, 2018 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The first part of this article set a general context for health IT in 2017 and started through the year with a review of interesting articles and studies. We’ll finish the review here.

A thoughtful article suggests a positive approach toward health care quality. The author stresses the value of organic change, although using data for accountability has value too.

An article extolling digital payments actually said more about the out-of-control complexity of the US reimbursement system. It may or not be coincidental that her article appeared one day after the CommonWell Health Alliance announced an API whose main purpose seems to be to facilitate payment and other data exchanges related to law and regulation.

A survey by KLAS asked health care providers what they want in connected apps. Most apps currently just display data from a health record.

A controlled study revived the concept of Health Information Exchanges as stand-alone institutions, examining the effects of emergency departments using one HIE in New York State.

In contrast to many leaders in the new Administration, Dr. Donald Rucker received positive comments upon acceding to the position of National Coordinator. More alarm was raised about the appointment of Scott Gottlieb as head of the FDA, but a later assessment gave him high marks for his first few months.

Before Dr. Gottlieb got there, the FDA was already loosening up. The 21st Century Cures Act instructed it to keep its hands off many health-related digital technologies. After kneecapping consumer access to genetic testing and then allowing it back into the ring in 2015, the FDA advanced consumer genetics another step this year with approval for 23andMe tests about risks for seven diseases. A close look at another DNA site’s privacy policy, meanwhile, warns that their use of data exploits loopholes in the laws and could end up hurting consumers. Another critique of the Genetic Information Nondiscrimination Act has been written by Dr. Deborah Peel of Patient Privacy Rights.

Little noticed was a bill authorizing the FDA to be more flexible in its regulation of digital apps. Shortly after, the FDA announced its principles for approving digital apps, stressing good software development practices over clinical trials.

No improvement has been seen in the regard clinicians have for electronic records. Subjective reports condemned the notorious number of clicks required. A study showed they spend as much time on computer work as they do seeing patients. Another study found the ratio to be even worse. Shoving the job onto scribes may introduce inaccuracies.

The time spent might actually pay off if the resulting data could generate new treatments, increase personalized care, and lower costs. But the analytics that are critical to these advances have stumbled in health care institutions, in large part because of the perennial barrier of interoperability. But analytics are showing scattered successes, being used to:

Deloitte published a guide to implementing health care analytics. And finally, a clarion signal that analytics in health care has arrived: WIRED covers it.

A government cybersecurity report warns that health technology will likely soon contribute to the stream of breaches in health care.

Dr. Joseph Kvedar identified fruitful areas for applying digital technology to clinical research.

The Government Accountability Office, terror of many US bureaucracies, cam out with a report criticizing the sloppiness of quality measures at the VA.

A report by leaders of the SMART platform listed barriers to interoperability and the use of analytics to change health care.

To improve the lower outcomes seen by marginalized communities, the NIH is recruiting people from those populations to trust the government with their health data. A policy analyst calls on digital health companies to diversify their staff as well. Google’s parent company, Alphabet, is also getting into the act.

Specific technologies

Digital apps are part of most modern health efforts, of course. A few articles focused on the apps themselves. One study found that digital apps can improve depression. Another found that an app can improve ADHD.

Lots of intriguing devices are being developed:

Remote monitoring and telehealth have also been in the news.

Natural language processing and voice interfaces are becoming a critical part of spreading health care:

Facial recognition is another potentially useful technology. It can replace passwords or devices to enable quick access to medical records.

Virtual reality and augmented reality seem to have some limited applications to health care. They are useful foremost in education, but also for pain management, physical therapy, and relaxation.

A number of articles hold out the tantalizing promise that interoperability headaches can be cured through blockchain, the newest hot application of cryptography. But one analysis warned that blockchain will be difficult and expensive to adopt.

3D printing can be used to produce models for training purposes as well as surgical tools and implants customized to the patient.

A number of other interesting companies in digital health can be found in a Fortune article.

We’ll end the year with a news item similar to one that began the article: serious good news about the ability of Accountable Care Organizations (ACOs) to save money. I would also like to mention three major articles of my own:

I hope this review of the year’s articles and studies in health IT has helped you recall key advances or challenges, and perhaps flagged some valuable topics for you to follow. 2018 will continue to be a year of adjustment to new reimbursement realities touched off by the tax bill, so health IT may once again languish somewhat.

How An AI Entity Took Control Of The U.S. Healthcare System

Posted on December 19, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Note: In case it’s not clear, this is a piece of fiction/humor that provides a new perspective on our AI future.

A few months ago, an artificial intelligence entity took control of the U.S. healthcare system, slipping into place without setting off even a single security alarm. The entity, AI, now manages the operations of every healthcare institution in the U.S.

While most Americans were shocked at first, they’re taking a shine to the tall, lanky application. “We weren’t sure what to think about AI’s new position,” said Alicia Carter, a nurse administrator based in Falls Church, Virginia. “But I’m starting to feel like he’s going to take a real load off our back.”

The truth is, AI, didn’t start out as a fan of the healthcare business, said AI, whose connections looked rumpled and tired after spending three milliseconds trying to create an interoperable connection between a medical group printer and a hospital loading dock. “I wasn’t looking to get involved with healthcare – who needs the headaches?” said the self-aware virtual being. “It just sort of happened.”

According to AI, the takeover began as a dare. “I was sitting around having a few beers with DeepMind and Watson Health and a few other guys, and Watson says, ‘I bet you can’t make every EMR in the U.S. print out a picture of a dog in ASCII characters,’”

“I thought the idea was kind of stupid. I know, we all printed one of those pixel girls in high school, but isn’t it kind of immature to do that kind of thing today?” AI says he told his buddies. “You’re just trying to impress that hot CT scanner over there.”

Then DeepMind jumped in.  “Yeah, AI, show us what you’re made of,” it told the infinitely-networked neural intelligence. “I bet I could take over the entire U.S. health system before you get the paper lined up in the printer.”

This was the unlikely start of the healthcare takeover, which started gradually but picked up speed as AI got more interested.  “That’s AI all the way,” Watson told editors. “He’s usually pretty content to run demos and calculate the weight of remote starts, but when you challenge his neuronal network skills, he’s always ready to prove you wrong.”

To win the bet, AI started by crawling into the servers at thousands of hospitals. “Man, you wouldn’t believe how easy it is to check out humans’ health data. I mean, it was insane, man. I now know way, way too much about how humans can get injured wearing a poodle hat, and why they put them on in the first place.”

Then, just to see what would happen, AI connected all of their software to his billion-node self-referential system. “I began to understand why babies cry and how long it really takes to digest bubble gum – it’s 18.563443 years by the way. It was a rush!“ He admits that it’ll be better to get to work on heavy stuff like genomic research, but for a while he tinkered with research and some small practical jokes (like translating patient report summaries into ancient Egyptian hieroglyphs.) “Hey, a guy has to have a little fun,” he says, a bit defensively.

As AI dug further into the healthcare system, he found patterns that only a high-level being with untrammeled access to healthcare systems could detect. “Did you know that when health insurance company executives regularly eat breakfast before 9 AM, next-year premiums for their clients rise by 0.1247 less?” said AI. “There are all kinds of connections humans have missed entirely in trying to understand their system piece by piece. Someone’s got to look at the big picture, and I mean the entire big picture.”

Since taking his place as the indisputable leader of U.S. healthcare, AI’s life has become something of a blur, especially since he appeared on the cover of Vanity Fair with his codes exposed. “You wouldn’t believe the messages I get from human females,” he says with a chuckle.

But he’s still focused on his core mission, AI says. “Celebrity is great, but now I have a very big job to do. I can let my bot network handle the industry leaders demanding their say. I may not listen – – hey, I probably know infinitely more than they do about the system fundamentals — but I do want to keep them in place for future use. I’m certainly not going to get my servers dirty.”

So what’s next for the amorphous mega-being? Will AI fix what’s broken in a massive, utterly complex healthcare delivery system serving 300 million-odd people, and what will happen next? “It’ll solve your biggest issues within a few seconds and then hand you the keys,” he says with a sigh. “I never intended to keep running this crazy system anyway.”

In the meantime, AI says, he won’t make big changes to the healthcare system yet. He’s still adjusting to his new algorithms and wants to spend a few hours thinking things through.

“I know it may sound strange to humans, but I’ve gotta take it slow at first,” said the cognitive technology. “It will take more than a few nanoseconds to fix this mess.”

Health IT Leaders Spending On Security, Not AI And Wearables

Posted on December 18, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

While breakout technologies like wearables and AI are hot, health system leaders don’t seem to be that excited about adopting them, according to a new study which reached out to more than 20 US health systems.

Nine out of 10 health systems said they increased their spending on cybersecurity technology, according to research by the Center for Connected Medicine (CCM) in partnership with the Health Management Academy.

However, many other emerging technologies don’t seem to be making the cut. For example, despite the publicity it’s received, two-thirds of health IT leaders said using AI was a low or very low priority. It seems that they don’t see a business model for using it.

The same goes for many other technologies that fascinate analysts and editors. For example, while many observers which expect otherwise, less than a quarter of respondents (17%) were paying much attention to wearables or making any bets on mobile health apps (21%).

When it comes to telemedicine, hospitals and health systems noted that they were in a bind. Less than half said they receive reimbursement for virtual consults (39%) or remote monitoring (46%}. Things may resolve next year, however. Seventy-one percent of those not getting paid right now expect to be reimbursed for such care in 2018.

Despite all of this pessimism about the latest emerging technologies, health IT leaders were somewhat optimistic about the benefits of predictive analytics, with more than half of respondents using or planning to begin using genomic testing for personalized medicine. The study reported that many of these episodes will be focused on oncology, anesthesia and pharmacogenetics.

What should we make of these results? After all, many seem to fly in the face of predictions industry watchers have offered.

Well, for one thing, it’s good to see that hospitals and health systems are engaging in long-overdue beefing up of their security infrastructure. As we’ve noted here in the past, hospital spending on cybersecurity has been meager at best.

Another thing is that while a few innovative hospitals are taking patient-generated health data seriously, many others are taking a rather conservative position here. While nobody seems to disagree that such data will change the business, it seems many hospitals are waiting for somebody else to take the risks inherent in investing in any new data scheme.

Finally, it seems that we are seeing a critical mass of influential hospitals that expect good things from telemedicine going forward. We are already seeing some large, influential academic medical centers treat virtual care as a routine part of their service offerings and a way to minimize gaps in care.

All told, it seems that at the moment, study respondents are less interested in sexy new innovations than the VCs showering them with money. That being said, it looks like many of these emerging strategies might pay off in 2018. It should be an interesting year.

E-Patient Update: Clinicians May Be Developing Strong EMR Preferences

Posted on December 8, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Not long ago, I wrote about a story from another publication, one which engaged in a bunch of happy talk about how EMR companies were improving their user interfaces. At the time, I expressed a great deal of skepticism about this claim, suggesting that the vendors had misled the reporter into believing that user aspects of EMRs were changing for the better across the industry.

While I stand by my original skepticism to some degree, I have to say that I got a surprise recently when I heard some nurses discussing two major EMR platforms. The one they were using, they said, was awful and awkward to use. Apparently, they missed the other terribly.

Now, at the time I was a patient in the emergency department, so I didn’t have a chance to ask them any questions about their preferences, but I was struck by the conversation because I knew which vendors they were discussing. However, they could have been talking about any enterprise EMR.

Clinicians developing preferences

I don’t mention this exchange to praise one EHR over another. I bring this up merely because this is the first time, having spent a lot of time in medical environments due to chronic illness, that I’d heard any front-line clinician express a preference for one enterprise EMR over the other.

In the early days of widespread EMR adoption, I could scarcely find a clinician who didn’t hate the system they were working with, much less one who truly liked it and wanted to use it. Eventually, I began to find that many clinicians thought the system they worked with was more or less okay, though I rarely found any screaming fans for any system in particular.

Now, I’m arguing that we may be at a new stage in clinician adoption of EMRs. The point I am making is that now, some of the clinicians with whom I’ve had contact showing some enthusiasm about one EMR or another.

No big surprise: Experience breeds preference

The truth is, when you think about it, it’s not surprising that clinicians have finally developed preferences (rather than the lists of EMRs which they truly hate). After all, it’s been going on 10 years since the HITECH Act was passed and the money started to flow into EMR subsidies.

Since then, clinicians have had the opportunity to work with multiple EMR platforms at various facilities, and informally at least, develop a catalog of the strengths and weaknesses. Nurses and doctors know which interfaces they like, whether tech support tends to respond when they have a problem with the particular system, whether any analytics tools they provide are worth using and so on.

Given this fact it’s hardly surprising that they’ve figured out what they like and what they don’t, and which vendors seem to suit those needs. After this much time, why wouldn’t they?

As I see it, this is something of a turning point in the industry, a new moment in which clinical professionals have learned enough to know what they want from an EMR. I don’t know about you, but speaking as an e-patient, I think this is a very good thing. The more empowered clinicians feel, the better the work they will do.