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Using Clinical Decision Support Can Decrease Care Costs

Posted on September 28, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

A study of clinical decision support system use has found that abiding by its recommendations can lower medical costs, adding weight to the notion that they might be worth deploying despite possible pushback from clinicians.

The study, which appeared in The American Journal of Managed Care, looked at the cost of care delivered by providers who adhered to CDS guidelines compared with care by nonadhering providers.

To conduct the study, researchers reviewed 26,424 patient encounters. In the treatment group, the provider adhered to all CDS recommendations, and in the control group, the provider did not do so. The encounters took place over three years.

The data they gathered regarding the encounters included alert status (adherence), provider type, patient demographics, clinical outcomes, Medicare status, and diagnosis information. The research team looked at the extent to which four outcome measures were associated with alert adherence, including encounter length of stay, odds of 30-day readmissions, odds of complications of care and total direct costs.

After conducting their analysis, the researchers found that the total encounter cost was 7.3% higher for nonadherent encounters than adherent ones, and that length of stay was 6.2% longer for nonadherent versus adherent encounters. They also found that the odds ratio for readmission within 30 days increased by 1.14, and the odds ratio for complications by 1.29, for nonadherent encounters versus adherent encounters.

Not surprisingly, given these results, the study’s authors suggest that provider organization should introduce real-time CDS support adherence to evidence-based guidelines.

It is worth noting, however, that the researchers inserted one caveat in their conclusion, warning that because they couldn’t tell what caused the association between CDS interventions and improved clinical and financial outcomes, it would be better to study the issue further.

Besides, getting clinicians on board can be painful, with many clicking through alerts without reading them and largely ignoring their content. In fact, another recent study found that almost 20% of CDS alert dismissals may be inappropriate.

Most of the inappropriate overrides were associated with an increased risk of adverse drug events. Overall, inappropriate overrides were six times as likely to be associated with potential and definite adverse drug events.

Report Champions API Use To Improve Interoperability

Posted on September 26, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

A new research report has taken the not-so-radical position that greater use of APIs to extract and share health data could dramatically improve interoperability. It doesn’t account for the massive business obstacles that still prevent this from happening, though.

The report, which was released by The Pew Charitable Trusts, notes that both the federal government and the private sector are both favoring the development of APIs for health data sharing.

It notes that while the federal government is working to expand the use of open APIs for health data exchange, the private sector has focused on refining existing standards in developing new applications that enhance EHR capabilities.

EHR vendors, for their part, have begun to allow third-party application developers to access to systems using APIs, with some also offering supports such as testing tools and documentation.

While these efforts are worthwhile, it will take more to wrest the most benefit from API-based data sharing, the report suggests. Its recommendations for doing so include:

  • Making all relevant data available via these APIs, not just CCDs
  • Seeing to it that information already coded in health data system stays in that form during data exchange (rather than being transformed into less digestible formats such as PDFs)
  • Standardizing data elements in the health record by using existing terminologies and developing new ones where codes don’t exist
  • Offering access to a patient’s full health record across their lifetime, and holding it in all relevant systems so patients with chronic illnesses and care providers have complete histories of their condition(s)

Of course, some of these steps would be easier to implement than others. For example, while providing a longitudinal patient record would be a great thing, there are major barriers to doing so, including but not limited to inter-provider politics and competition for market share.

Another issue is the need to pick appropriate standards and convince all parties involved to use them. Even a forerunner like FHIR is not yet universally accepted, nor is it completely mature.

The truth is that no matter how you slice it, interoperability efforts have hit the wall. While hospitals, payers, and clinicians pretty much know what needs to happen, their interests don’t converge enough to make interoperability practical as of yet.

While I’m all for organizations like the Pew folks taking a shot at figuring interoperability out, I don’t think we’re likely to get anywhere until we find a way to synchronize everyone’s interests. And good luck with that.

Montefiore Health Makes Big AI Play

Posted on September 24, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

I’ve been doing a lot of research on healthcare AI applications lately. Not surprisingly, while people find the abstract issues involved to be intriguing, most would prefer to hear news of real-life projects, so I’ve been on the lookout for good examples.

One interesting case study, which appeared recently in Health IT Analytics, comes from Montefiore Health System, which has been building up its AI capabilities. Over the past three years, it has created an AI framework leveraging a data lake, infrastructure upgrades and predictive analytics algorithms. The AI is focused on addressing expensive, dangerous health issues, HIA reports.

“We have created a system that harvests every piece of data that we can possibly find, from our own EMRs and devices to patient-generated data to socio-economic data from the community,” said Parsa Mirhaji, MD, PhD, director of the Center for Health Data Innovations at Montefiore and the Albert Einstein College of Medicine, who spoke with the publication.

Back in 2015, Mirhaji kicked off a project bringing semantic data lake technology to his organization. The first pilot using the technology was designed to find patients at risk of death or intubation within 48 hours. Now, clinicians can also see red flags for admitted patients with increased risk of mortality 3 to 5 days in advance.

In 2017, the health system also rolled out advanced sepsis detection tools and a respiratory failure detection algorithm called APPROVE, which identifies patients at a raised risk of prolonged ventilation up to 48 hours before onset, HIA reported.

The net result of these efforts was dubbed PALM, the Patient-centered Analytical  Learning Machine. PALM “represents a very new way of interacting with data in healthcare,” Miraji told HIA.

What makes PALM special is that it speeds up the process of collecting, curating, cleaning and accessing metadata which must be conducted before the data can be used to train AI models. In most cases, the process of collecting data for AI use is largely manual, but PALM automates this process, Miraji told the publication.

This is because the data lake and its graph repositories can find relationships between individual data elements on an on-the-fly basis. This automation lets Montefiore cut way down on labor needed to get these results. Miraji noted that ordinarily, it would take a team of data analysts, database administrators and designers to achieve this result.

PALM also benefits from a souped-up hardware architecture, which Montefiore created with help from Intel and other technology partners. The improved architecture includes the capacity for more system memory and processing power.

The final step in optimizing the PALM system was to integrate it into the health system’s clinical workflow. This seems to have been the hardest step. “I will say right away that I don’t think we have completely solved the problem of integrating analytics seamlessly into the workflow,” Miraji admitted to HIA.

Problems We Need To Address Before Healthcare AI Becomes A Thing

Posted on September 7, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Just about everybody who’s anybody in health IT is paying close attention to the emergence of healthcare AI, and the hype cycle is in full swing. It’d be easier to tell you what proposals I haven’t seen for healthcare AI use than those I have.

Of course, just because a technology is hot and people are going crazy over it doesn’t mean they’re wrong about its potential. Enthusiasm doesn’t equal irrational exuberance. That being said, it doesn’t hurt to check in on the realities of healthcare AI adoption. Here are some issues I’m seeing surface over and over again, below.

The black box

It’s hard to argue that healthcare AI can make good “decisions” when presented with the right data in the right volume. In fact, it can make them at lightning speed, taking details into account which might not have seemed important to human eyes. And on a high level, that’s exactly what it’s supposed to do.

The problem with this, though, is that this process may end up bypassing physicians. As things stand, healthcare AI technology is seldom designed to show how it reached its conclusions, and it may be due to completely unexpected factors. If clinical teams want to know how the artificial intelligence engine drew a conclusion, they may have to ask their IT department to dig into the system and find out. Such a lack of transparency won’t work over the long term.

Workflow

Many healthcare organizations have tweaked their EHR workflow into near-perfect shape over time. Clinicians are largely satisfied with work patterns and patient throughput is reasonable. Documentation processes seem to be in shape. Does it make sense to throw an AI monkeywrench into the mix? The answer definitely isn’t an unqualified yes.

In some situations, it may make sense for a provider to run a limited test of AI technology aimed at solving a specific problem, such as assisting radiologists with breast cancer scan interpretations. Taking this approach may create less workflow disruption. However, even a smaller test may call for a big investment of time and effort, as there aren’t exactly a ton of best practices available yet for optimizing AI implementations, so workflow adjustments might not get enough attention. This is no small concern.

Data

Before an AI can do anything, it needs to chew on a lot of relevant clinical data. In theory, this shouldn’t be an issue, as most organizations have all of the digital data they need.  If you need millions of care datapoints or several thousand images, they’re likely to be available. The thing is, they may not be as usable as one might hope.

While healthcare providers may have an embarrassment of data on hand, much of it is difficult to filter and mine. For example, while researchers and some isolated providers are using natural language processing to dig up useful information, critics point out that until more healthcare info is indexed and tagged there’s only so much it can do. It may take a new generation of data processing and indexing tech to prepare the data before we have the right data to feed an AI.

These are just a few practical issues likely to arise as providers begin to use AI technologies; I’m sure there are many others you might be able to name. While I have little doubt we can work our way through such issues, they aren’t trivial, and it could take a while before we have standardized approaches in place for addressing them. In the meantime, it’s probably a good idea to experiment with AI projects and prepare for the day when it becomes more practical.

Hospitals Struggle To Get Users On Board With Mobile Policies

Posted on August 6, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

A new survey has found that hospitals are having a hard time managing and tracking user compliance with mobile communications policies.

The survey, which was conducted in early 2018 by communications vendor Spok, collected information on mobile device communications strategies from approximately 300 healthcare professionals. Forty-four percent of respondents were clinicians, 10% were IT and telecom staff, 6% were executive leaders, and another 40% had a wide variety of healthcare roles.

Spok found that hospitals who do have a mobile strategy in place have had one for a long time, with 42% having had such a strategy for either 3 to 5 years or more than five years. Another 46% have had a formal mobile strategy for one to three years. Only 12% have had a strategy in place for one year or less.

Reasons they cited for creating mobile device strategies included the launch of a communication initiative (46%); a clinical initiative (25%); or a technology initiative (24%). Five percent of responses were “other.” Top areas of focus for these strategies included mobile management and security (56%), mobile device selection (52%) and integration with the EHR (48%).

Other reasons for mobile initiatives included clinical workflow evaluation (43%), device ownership strategy/BYOD (34%), mobile apps strategy (29%), mobile app catalog (16%), mobile strategy governance (14%) and business intelligence and reporting strategy (12%).

However, there’s little agreement as to which hospital department should monitor compliance. Forty-three percent of respondents said the security team was monitoring policies for the hospital or system, 43% rely on a telecommunications team, 43% said a clinical informatics team played that role, and 26% had monitoring done by a mobile team. Twenty-one percent said individual departments enforce mobile policies and 9% said they don’t have an enforcement method in place. Another 9% of responses fell into the “other” category.

Given the degree to which monitoring varies between institutions, it’s little wonder to learn that policies aren’t enforced effectively in many cases. On the one hand, 39% respondents said the policies were enforced extremely well most of the time, and one-third said they were enforced well most the time. However, 4% said the policies were being enforced poorly and inconsistently, and 44% said they are not sure about how well the policies are being enforced.

Hospitals are aware of this problem, though, and many are taking steps to ensure that users understand and comply with mobile policies. According to the survey, 48% offer educational programs on the subject, 42% use technology or data gathered from devices to measure and track compliance, 37% leverage direct feedback from users and 23% use surveys.

Still, 21% said they don’t have a way to validate compliance — which suggests that hospitals have a lot more work to do.

Switch From Epic To Cerner Comes With Patient Safety Questions

Posted on July 25, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Here’s a story in which no health system hopes to take a lead role — the tale of a Cerner installation that didn’t go well and the blowback the system faced afterward.

On October 1 of last year, Phoenix, Az.-based Banner Health switched its Tucson hospitals from Epic to a Cerner system, a move which reportedly cost the health system $45 million.

No doubt, the hospitals’ staff and physicians were trained up and prepared for a few bumps in the road, particularly given that the rest of its peers had already gone to the process. The Phoenix-based not-for-profit, which owns, leases or manages 28 acute-care hospitals in six states, had already put the Cerner system in place elsewhere, apparently without experiencing any major problems.

But this time it wasn’t so lucky, according to an article in the Arizona Daily Star. According to the news item, there were “numerous” reports of medical errors filed with the Arizona Department of Health Services after Tucson-area hospitals in the Banner chain were cut over to Cerner.

The complaints included claims that errors were creating patient safety and patient harm risks, according to one filing. “Many of the staff are in tears and frustrated because of the lack of support and empathy [for] the consequences [to] patient care,” one stated.

Not only did the conversion lead to patient safety accusations, it also seems to have lowered physician productivity and shrunk revenue as doctors learned to use the Cerner interface. While predictable, this has to have added insult to injury.

Meanwhile, according to the paper, the state seems to come down on the side of the complainants. While hospital leaders denied there were any incidents resulting in a negative outcome for patients, “the hospital’s occurrence log for October 2017 showed numerous incidents of medical errors reported to be a result of the conversion,” state investigators reportedly concluded.

While the state didn’t fine Banner or issue a citation, it did substantiate two allegations about the conversion, the Star reported. The allegations were related to computer/printer glitches impacting patient care and an inability to reliably deliver medications and order tests as part of care for critically ill patients.

The article says that Banner responded by pointing out that it has made more than 100 improvements to the Cerner system, resulting in better workflows and greater information access for physicians and staff. But the damage to its reputation seems to have been done.

No, perhaps Banner didn’t do anything particularly wrong when it installed the Cerner platform. However, if its leaders did, in fact, lie to the state about problems it actually had, it was not a smart move. On the other hand, one of the biggest problems you can have during an EHR implementation is users who don’t want to cooperate and make it a success. It’s not hard to see users who were happy with Epic dragging their feet as they shifted to Cerner. Either way, this is an important lesson as hospitals continue to consolidate and they consider switching the EHR of the acquired hospitals.

Clinicians Say They Need Specialized IT To Improve Patient Safety

Posted on July 24, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Hospitals are loaded down with the latest in health IT and have the bills to prove it. But according to a new survey, they need to invest in specialized technologies to meet patient safety goals, as well as providing more resources and greater organizational focus.

Health Catalyst recently conducted a national survey of physicians, nurses and health executives to gather their thoughts on patient safety issues. Among its main findings was that almost 90% of respondents said that their organizations were seeing success in improving patient safety. However, about the same percentage said there was room for improving patient safety in their organization.

The top obstacle they cited as holding them back from the patient safety goals was having effective information technology, as identified by 30% of respondents. The same number named a lack of technologies offering real-time warnings of possible patient harm.

These were followed by lack of staffing and budget resources (27%), organizational structure, culture priorities (19%), a lack of reimbursement for safety initiatives (10%) and changes in patient population practice setting (9%).

Part of the reason clinicians aren’t getting as much as they’d like from health IT is that many healthcare organizations rely largely on manual methods to track and report safety events.

The top sources of data for patient safety initiatives respondents used for safety initiatives voluntary reporting (82%). Hospital-acquired infection surveys (67%), manual audits (58%) and retrospective coding (29%). Such reporting is typically based on data sets which are at least 30 days old, and what’s more, collecting and analyzing the data can be time and resource-consuming.

Not surprisingly, Health Catalyst is launching new technology designed to address these problems. Its Patient Safety Monitor™ Suite: Surveillance Module uses protective and text analytics, along with concurrent critical reviews of data, to find and prevent patient safety threats before they result in harm.

The announcement also falls in line with the organization’s larger strategic plans, as Health Catalyst has applied to the AHRQ to be certified as a Patient Safety Organization.

The company said that he had spent more than $50 million to create the Surveillance module, whose technology includes the use of predictive analytics models and AI. It expects to add new AI and machine learning capabilities to its technology in the future which will be used to propose strategies to eliminate patient safety risks.

And more is on the way. Health Catalyst is working with its clients to add new features to the Suite including risk prediction, improvement tracking and decision support.

I’m not sure if it’s typical for PSOs to bringing their own specialized software to the job, but either way, it should give Health Catalyst a leg up. I have little doubt that doing better predictive analytics and offering process recommendations would be useful.

Despite Risks, Hospitals Connecting A Growing Number Of Medical Devices

Posted on July 20, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Over the past few years, hospitals have gotten closer and closer to connecting all of their medical devices to the Internet — and more importantly, connecting them to each other and to critical health IT systems.

According to a new study by research firm Frost & Sullivan, most hospitals are working to foster interoperability between medical devices and EHRs. By doing so, they can gather, analyze and present data important to care in a more sophisticated way.

“Hospitals are developing connectivity strategies based on early warning scores, automated electronic charting, emergency alert and response, virtual intensive care units, medical device asset management and real-time location solutions,” Frost analysts said in a prepared statement.

Connecting medical devices to other hospital infrastructure has become so important to the future of healthcare that the FDA has taken notice. The agency recently issued guidance on how healthcare organizations can foster interoperability between the devices and other information systems.

Of course, while hospitals would like to see medical devices chat with their EHRs and other health IT systems, it’s just one of many important goals hospitals have for data collection and analysis. Health IT executives are up to the eyebrows supporting big data transformation, predictive analytics and ongoing EHR management, not to mention trying out soon-to-be standard technologies such as blockchain.

More importantly, few medical devices are as secure as they should be. While the average hospital room contains 15 to 20 connected devices, many of them are frighteningly vulnerable. Some of them are still running on obsolete operating systems, many of which haven’t been patched in years, or roughly 1,000 years in IT time. Other systems have embedded passwords in their code, which is one heck of a problem.

While the press plays up the possibility of a hacker stopping someone’s connected pacemaker, the reality is that an EHR hack using a hacked medical device is far more likely. When these devices are vulnerable to outside attacks, attackers are far more likely to tunnel into EHRs and steal patient health data. After all, while playing with a pacemaker might be satisfying to really mean people, thieves can get really good money for patient records on the dark web.

All this being said, connected medical devices are likely to become a key part of hospital IT infrastructure in hospitals over time as the industry solves these problems, Frost predicts that the global market for such devices will climb from $233 million to almost $1 billion by 2022.

It looks like hospital IT executives will have some hard choices to make here. Ignoring the benefits of connecting all medical devices with other data sources just won’t work, but creating thousands of security vulnerabilities isn’t wise either. Ultimately, hospital leaders must find a way to secure these devices ASAP without cratering their budget, and it won’t be easy.

Important Patient Data Questions Hospitals Need To Address

Posted on July 13, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Obviously, managing and protecting patients’ personal health information is very important already.  But with high-profile incidents highlighting questionable uses of consumer data — such as the recent Facebook scandal – patients are more aware of data privacy issues than they had been in the past, says Dr. Oleg Bess, founder and CEO of clinical data exchange company 4medica.

According to Bess, hospitals should prepare to answer four key questions about personal health information that patients, the media and regulators are likely to ask. They include:

  • Who owns the patient’s medical records? While providers and EHR vendors may contend that they own patient data, it actually belongs to the patient, Bess says. What’s more, hospitals need to be sure patients should have a clear idea of what data hospitals have about them. They should also be able to access their health data regardless of where it is stored.
  • What if the patient wants his or her data deleted? Unfortunately, deleting patient data may not be possible in many cases due to legal constraints. For example, CMS demands that Medicare providers retain records for a fixed period, and many states have patient record retention laws as well, Bess notes. However, if nothing else, patients should have the ability to decline having their personally-identifiable data shared with third parties other than providers and payers, he writes.
  • Who is responsible for data integrity? Right now, problems with patient data accuracy are common. For example, particularly when patient matching tools like an enterprise master patient index aren’t in place, health data can end up being mangled. To this point, Bess cites a Black Book Research survey concluding that when records are transmitted between hospitals that don’t use these tools, they had just a 24% match rate. Hospital data stewards need to get on top of this problem, he says.
  • Without a national patient ID in place, how should hospitals verify patient identities? In addition to existing issues regarding patient safety, emerging problems such as the growing opioid abuse epidemic would be better handled with a unique patient identifier, Bess contends. According to Bess, while the federal government may not develop unique patient IDs, commercially developed master patient index technology might offer a solution.

To better address patient matching issues, Bess recommends including historical data which goes back decades in the mix if possible. A master patient index solution should also offer enterprise scalability and real-time matching, he says.

Health Orgs Were In Talks To Collect SDOH Data From Facebook

Posted on April 9, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

These days, virtually everyone in healthcare has concluded that integrating social determinants of health data with existing patient health information can improve care outcomes. However, identifying and collecting useful, appropriately formatted SDOH information can be a very difficult task. After all, in most cases it’s not just lying around somewhere ripe for picking.

Recently, however, Facebook began making the rounds with a proposal that might address the problem. While the research initiative has been put on hold in light of recent controversy over Facebook’s privacy practices, my guess is that the healthcare players involved will be eager to resume talks if the social media giant manages to calm the waters.

According to CNBC, Facebook was talking to healthcare organizations like Stanford Medical School and American College of Cardiology, in addition to several other hospitals, about signing a data-sharing agreement. Under the terms of the agreement, the healthcare organizations would share anonymized patient data, which Facebook planned to match up with user data from its platform.

Facebook’s proposal will sound familiar to readers of this site. It suggested combining what a health system knows about its patients, such as their age, medication list and hospital admission history, with Facebook-available data such as the user’s marital status, primary language and level of community involvement.

The idea would then be to study, with an initial focus on cardiovascular health, whether this combined data could improve patient care, something its prospective partners seem to think possible. The CNBC story included a gushing statement from American College of Cardiology interim CEO Cathleen Gates suggesting that such data sharing could create revolutionary results. According to Gates, the ACC believes that mixing anonymized Facebook data with anonymized ACC data could help greatly in furthering scientific research on how social media can help in preventing and treating heart disease.

As the business site notes, the data would not include personally identifiable information. That being said, Facebook proposed to use hashing to match individuals existing in both data sets. If the project were to have gone forward, Facebook might’ve shared data on roughly 87 million users.

Looked at one way, this arrangement could raise serious privacy questions. After all, healthcare organizations should certainly exercise caution when exchanging even anonymized data with any outside organization, and with questions still lingering on how willing Facebook is to lock data down projects like this become even riskier.

Still, under the right circumstances, Facebook could prove to be an all but ideal source of comprehensive, digitized SDOH data. Well now, arguably, might not be the time to move ahead, hospitals should keep this kind of possibility in mind.