Free Hospital EMR and EHR Newsletter Want to receive the latest news on EMR, Meaningful Use, ARRA and Healthcare IT sent straight to your email? Join thousands of healthcare pros who subscribe to Hospital EMR and EHR for FREE!

Insights from Ted James, MD at the MEDITECH MD & CIO Forum

Posted on October 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.

Over the next couple days, I’m attending the MEDITECH MD and CIO Forum. This is essentially the user conference for the MD and CIO users of MEDITECH software. This morning, they kicked off the event with Ted James, MD, Medical Director at BIDMC/Harvard Medical School. He provided a number of great insights into what’s happening in healthcare and what leaders can do to be more successful.

Below you’ll find a Twitter summary of Ted James, MD’s keynote. You can also watch the live video interviews I’m doing from the event on the Healthcare Scene Facebook page and follow along on Twitter using the hashtag #MDCIO2018.


Healthcare change seems to be an ever ongoing theme. The question really is around the pace of change.


Anyone that’s been through meaningful use understands this experience.


Routine is a powerful idea. So powerful that it prevents change.


Leadership is the key to any change and was a definite theme from Ted James, MD’s keynote.


I love the concept of nudges, but it only works for a subset of use cases in healthcare. Why? Because so many things in healthcare are really complex.


These 3 ideas were really interesting, but I definitely need more time to fully process what they mean. What do you think of these 3 ideas?


This was a really fascinating idea. It illustrates the need to constantly communicate changes so that people get use to the change before the change even occurs. Familiarity with something changes the experience.


Moving an iceberg feels like an apt descrition of healthcare.


This reminds me of when I recently heard that more yoga won’t fix the physician burnout problem.


This is an important lesson for leaders.


This was a refreshing experience to see so many women at a MD and CIO event.

Check back later for more coverage from the MEDITECH MD and CIO Forum.

AI Project Set To Offer Hospital $20 Million In Savings Over Three Years

Posted on October 4, 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.

While they have great potential, healthcare AI technologies are still at the exploration stage in most healthcare organizations. However, here and there AI is already making a concrete difference for hospitals, and the following is one example.

According to an article in Internet Health Management, one community hospital located in St. Augustine, Florida expects to save $20 million dollars over the next the three years thanks to its AI investments.

Not long ago, 335-bed Flagler Hospital kicked off a $75,000 pilot project dedicated to improving the treatment of pneumonia, sepsis and other high mortality conditions, building on AI tools from vendor Ayasdi Inc.

Michael Sanders, a physician who serves as chief medical informatics officer for the hospital, told the publication that the idea was to “let the data guide us.” “Our ability to rapidly construct clinical pathways based on our own data and measure adherence by our staff to those standards provides us with the opportunity to deliver better care at a lower cost to our patients,” Sanders told IHM.

The pilot, which took place over just nine weeks, reviewed records dating back five years. Flagler’s IT team used Ayasdi’s tools to analyze data held in the hospital’s Allscripts EHR, including patient records, billing, and administrative data. Analysts looked at data on patterns of care, lengths of stay and patient outcomes, including the types of medications docs and for prescribing and when doctors were ordering CT scans.

After analyzing the data, Sanders and his colleagues used the AI tools to build guidelines into the Allscripts EHR, which Sanders hoped would make it easy for physicians to use them.

The project generated some impressive results. For example, the publication reported, pathways for pneumonia treatment resulted in $1,336 in administrative savings for a typical hospital stay and cut down lengths of stay by two days. All told, the new approach cut administrative costs for pneumonia treatment by $800,000.

Now, Flagler plans to create pathways to improve care for sepsis, substance abuse, heart attacks, and other heart conditions, gastrointestinal disorders and chronic conditions such as diabetes.

Given the success of the project, the hospital expects to expand the scope of its future efforts. At the outset of the project, Sanders had expected to use AI tools to take on 12 conditions, but given the initial success with rolling out AI-based pathways, Sanders now plans to take on one condition each month, with an eye on meeting a goal of generating $20 million in savings over the new few years, he told IHM.

Flagler is not the first, nor will it be the last, hospital to streamline care using AI. For another example, check out the efforts underway at Montefiore Health, which seems to be transforming its entire data infrastructure to support AI-based analytics efforts.

New MEDITECH EHR API – An Interview with Niraj Chaudhry

Posted on October 2, 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.

The EHR API has been a hot topic lately. Many healthcare organizations and startup companies are looking to EHR vendors in order to connect applications that exist outside the EHR with the EHR. Years ago (2012 to be exact), I wrote that the EHR is the database of healthcare and with all of these APIs coming out, we’re seeing that come to fruition.

The good news is that EHR vendors are finally starting to embrace this viewpoint as well. Many of you have probably read Colin Hung’s article that looks at 2 EHR vendors and their APIs. Many of you probably saw the announcement of MEDITECH’s new app development environment called MEDITECH Greenfield. It’s great to see MEDITECH launching an API for developers who want to engage with their Expanse platform. To learn more about this new platform, we sat down with Niraj Chaudhry, Director of Development Advanced Technology Division from MEDITECH.

What’s the motivation for MEDITECH to launch Greenfield?

Interoperable, open architecture EHR platforms that promote sharing resources for collective growth are critical for driving innovation and progress in today’s healthcare paradigm. By offering a space for mobile app development, MEDITECH is adding more capabilities and value to our customers’ EHRs and driving efficiencies for better community outcomes. Our customers will be able to enhance the EHR experiences of their providers, patients, and consumers with innovative apps available on any mobile device.

Greenfield is a natural extension of what we’ve done with MEDITECH Expanse and reinforces our commitment to a mobile, web-based EHR. We are excited about working with third-party developers and increasing our visibility with the creation of apps to augment Expanse.

What data will developers be able to access through Greenfield?  Is it a read-only environment or will developers be able to write back to MEDITECH using the Greenfield API as well?

Currently, our testing environment includes a list of available Common Clinical Data Set APIs and associated documentation. These APIs support GET methods and so give read-only access to the data. Developers can register now to get started. More APIs will be added to the Greenfield in future which will support other methods such as PUT and POST, and so will allow the ability to write data back to the Greenfield environment.

Will Greenfield only work with Expanse or will it work with other MEDITECH products?

Currently, MEDITECH Greenfield is available to MEDITECH Expanse customers.

Are there costs associated with companies participating in Greenfield (ie. signup and/or usage)?

There are no costs to sign up, access or use Greenfield.

What type of promotion will you do for companies who choose to leverage MEDITECH Greenfield in their application? What are you planning to do so MEDITECH users learn about new partners?

In the future, we plan on highlighting select (or “preferred”) mobile apps that we feel add significant value to the MEDITECH platform. This is still in the very early stages and business models for how we will list or promote apps are being discussed.

Will any company be able to sign up for Greenfield or will you restrict it to a certain number of companies or certain types of companies?

Any interested developers can sign up for access through a secure login process here.

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.

Three Hot Healthcare AI Categories

Posted on 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.

The way people talk about AI, one might be forgiven for thinking that it can achieve magical results overnight. Unfortunately, the reality is that it’s much easier to talk about AI application than execute them.

However, there are a few categories of AI development that seems to be emerging as possible game-changers for the healthcare business. Here’s five that have caught my eye.

Radiology: In theory, we’ve been able to analyze digital radiology images for quite some time. The emergence of AI tools has supercharged the discussion, though. The growing list of vendors competing for business in this nascent market is real.

Examples include Aidence, whose Veye Chest automates analysis and reporting of pulmonary modules, aidoc, which finds acute abnormalities in imaging and adds them to the radiologist’s worklist; CuraCloud, which helps with medical imaging analysis and NLP for medical data and more. (For a more comprehensive list, check out this Medium article.)

I’d be remiss if I didn’t also mention a partnership between Facebook and the NYU School of Medicine focused on speeding up MRI imaging dramatically.

Vendors and industry talking heads have been assuring radiologists that such tools will reduce their workload while leaving diagnostic in clinical decisions in their hands. So far, it seems like they’re telling the truth.

Physician documentation: The notion of using AI to speed up the physician documentation process is very hot right now, and for good reason. The advent of EHRs has added new documentation work to physicians’ already-full plate, and some are burning out. Luckily, new AI applications may be able to de-escalate this crisis.

For example, consider applications like NoteSwift’s Samantha, an EHR virtual assistant which structures transcription content and inputs it directly into the EHR. There’s also Robin, also which “listens” to discussions in the clinic rooms, drafts clinical documentation using Conversational Speech Recognition. After review, Robin also submits final documentation directly to an EMR.

Other emerging companies offering AI-driven documentation products apps including Sophris Health, Saykara, and Suki, all of which offer some isotype of virtual assistant or medical scribe functions. Big players like Nuance and MModal are working in this space as well. If you want to find more vendors – and there’s a ton emerging out there – just Google the terms “virtual physician assistant” or “AI medical scribe.” You’ll be swamped with possibilities.

My takeaway here is that we’re getting steadily closer to a day in which simply approve documentation, click a button and populate the EHR automatically. It’s an exciting possibility.

Medical chatbots: This category is perhaps a little less mature than the previous two, but a lot is going on here. While most deployments are experimental, it’s beginning to look like chatbots will be able to do everything from triage to care management, individual patient screenings and patient education. Microsoft recently highlighted how companies can easily create healthcare chatbots on Microsoft Azure. That should open up a variety of use cases.

The hottest category in medical chatbots seems to be preliminary diagnosis. Examples include Sensely, whose virtual medical assistant avatar uses AI to suggest diagnoses based on patient symptoms, along with competitors like Babylon Health, another chatbot which offing patient screenings and tentative diagnoses and Ada, whose smartphone app offers similar options.

Other medical chatbots are virtual clinicians, such as Florence, which reminds patients to take the medication and tracks key patient health metrics like body weight and mood, while still others focus on specific medical issues. This category includes Cancer Chatbot, a resource for cancer patients,  caregivers, friends and family, and Safedrugbot, which helps doctors who need data about use of drugs during breastfeeding.

While many of these apps are in beta or still sorting out their role, they’re becoming more capable by the day and should soon be able to provide patients with meaningful medical advice. They may also be capable of helping ACOs and health systems manage entire populations by digging into patient records, digesting patient histories and using this data to monitor conditions and send specialized care reminders.

This list is far from comprehensive. These are just a few categories of AI-driven healthcare applications poised to foster big changes in healthcare – especially the nature of the health IT infrastructure. There’s a great deal more to learn about what works. Still, we’re just steps away from seeing AI-based technologies hit the industry hard. In the meantime, it might be smart to consider taking some of these for a test run.

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.

Do We Need Another Interoperability Group?

Posted on September 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 last few years, industry groups dedicated to interoperability have been popping up like mushrooms after a hard rain. All seem to be dedicated to solving the same set of intractable data sharing problems.

The latest interoperability initiative on my radar, known as the Da Vinci Project, is focused on supporting value-based care.

The Da Vinci Project, which brings together more than 20 healthcare companies, is using HL7 FHIR to foster VBC (Value Based Care). Members include technology vendors, providers, and payers, including Allscripts, Anthem Blue Cross and Blue Shield, Cerner, Epic, Rush University Medical Center, Surescripts, UnitedHealthcare, Humana and Optum. The initiative is hosted by HL7 International.

Da Vinci project members plan to develop a common set of standards for data exchange that can be used nationally. The idea is to help partner organizations avoid spending money on one-off data sharing development projects.

The members are already at work on two test cases, one addressing 30-day medication reconciliation and the other coverage requirements discovery. Next, members will begin work on test cases for document templates and coverage rules, along with eHealth record exchange in support of HEDIS/STARS and clinician exchange.

Of course, these goals sound good in theory. Making it simpler for health plans, vendors and providers to create data sharing standards in common is probably smart.

The question is, is this effort really different from others fronted by Epic, Cerner and the like? Or perhaps more importantly, does its approach suffer from limitations that seem to have crippled other attempts at fostering interoperability?

As my colleague John Lynn notes, it’s probably not wise to be too ambitious when it comes to solving interoperability problems. “One of the major failures of most interoperability efforts is that they’re too ambitious,” he wrote earlier this year. “They try to do everything and since that’s not achievable, they end up doing nothing.”

John’s belief – which I share — is that it makes more sense to address “slices of interoperability” rather than attempt to share everything with everyone.

It’s possible that the Da Vinci Project may actually be taking such a practical approach. Enabling partners to create point-to-point data sharing solutions easily sounds very worthwhile, and could conceivably save money and improve care quality. That’s what we’re all after, right?

Still, the fact that they’re packaging this as a VBC initiative gives me pause. Hey, I know that fee-for-service reimbursement is on its way out and that it will take new technology to support new payment models, but is this really what happening here? I have to wonder.

Bottom line, if the giants involved are still slapping buzzwords on the project, I’m not sure they know what they’re doing yet. I guess we’ll just have to wait and see where they go with it.

CMIOs Say Medication Management Is Improving, But Still Needs Work

Posted on September 14, 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 suggests that chief medical information officers are optimistic about the progress they’ve seen in medication management processes though they still see some obstacles that need to be tackled. Their top concerns seem to be related to the sharing of prescription information and a lack of faith in the medication lists as they’re currently generated.

According to research conducted by the Association of Medical Directors of Information Systems (AMDIS) and vendor DrFirst, medication management improvement efforts have made a positive impact on the rate of adverse drug events over the past five years.

About half of the CMIOs said they were satisfied with their existing medication management process, while 12% said they were dissatisfied.

The CMIOs reported that the biggest gaps in the medication management process were incomplete patient medication histories (cited by 80%) and misaligned medication reconciliation and care transition cycles (75%). Respondents said that this kind of misalignment sometimes lead to misinformed decisions by care teams.

Another vulnerability respondents identified was lack of visibility into patients’ medication adherence levels, with 91% calling it the biggest gap in medication history adherence and monitoring. They didn’t name any particular solution that could address the problem, though existing medication management apps for consumers might at some point address this issue.

Eight-five percent of responding CMIOs said that when patients don’t participate in the medication reconciliation process it leads to gaps in the patient medication history. They didn’t specify the point in the process at which it might be most helpful to involve patients.

In addition, 95% of respondents said that it would help matters to cut down on the order entry and data validation tasks pharmacists and clinical staffers had to perform, arguing that it would enhance patient safety and improve efficiency.

Other patient safety concerns they cited included a lack of process buy-in and/or process compliance (77%), a lack of process ownership (73%) and workflow variations across departments (91%).

As part of the discussion, the surveyed CMIOs noted that the right technology approach could help them address the opioid epidemic.

As things stand, they told AMDIS, it’s not clear the providers are able to prevent opioid abuse since at times they can’t easily distinguish between drug “shoppers” and other patients.

However, 65% of CMIOs said that if providers could access an integrated clinician workflow including e-prescribing of controlled substances, access to state Prescription Drug Monitoring Programs to track patients’ opioid histories and access lists of other prescriptions, it would be easier for them to avoid potentially harmful drug combinations.

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.