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A Look At Nursing Home Readiness For HIE Participation

Posted on October 12, 2016 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.

A newly released study suggests that nursing homes have several steps to go through before they are ready to participate in health information exchanges. The study, which appeared in the AHIMA-backed Perspectives in Health Information Management, was designed to help researchers understand the challenges nursing homes faced in health information sharing, as well as what successes they had achieved to date.

As the study write up notes, the U.S. nursing home population is large — nearly 1.7 million residents spread across 15,600 nursing homes as of 2014. But unlike other settings that care for a high volume of patients, nursing homes haven’t been eligible for EMR incentive programs that might have helped them participate in HIEs.

Not surprisingly, this has left the homes at something of a disadvantage, with very few participating in networked health data sharing. And this is a problem in caring for residents adequately, as their care is complex, involving nurses, physicians, physicians’ offices, pharmacists and diagnostic testing services. So understanding what potential these homes have to connect is a worthwhile topic of study. That’s particularly the case given that little is known about HIE implementation and the value of shared patient records across multiple community-based settings, the study notes.

To conduct the study, researchers conducted interviews with 15 nursing home leaders representing 14 homes in the midwestern United States that participated in the Missouri Quality Improvement Initiative (MOQI) national demonstration project.  Studying MOQI participants helped researchers to achieve their goals, as one of the key technology goals of the CMS-backed project is to develop knowledge of HIE implementations across nursing homes and hospital boundaries and determine the value of such systems to users.

The researchers concluded that incorporating HIE technology into existing work processes would boost use and overall adoption. They also found that participation inside and outside of the facility, and providing employees with appropriate training and retraining, as well as getting others to use the HIE, would have a positive effect on health data sharing projects. Meanwhile, getting the HIE operational and putting policies for technology use were challenges on the table for these institutions.

Ultimately, the study concluded that nursing homes considering HIE adoption should look at three areas of concern before getting started.

  • One area was the home’s readiness to adopt technology. Without the right level of readiness to get started, any HIE project is likely to fail, and nursing home-based data exchanges are no exception. This would be particularly important to a project in a niche like this one, which never enjoyed the outside boost to the emergence of the technology culture which hospitals and doctors enjoyed under Meaningful Use.
  • Another area identified by researchers was the availability of technology resources. While the researchers didn’t specify whether they meant access to technology itself or the internal staff or consultants to execute the project, but both seem like important considerations in light of this study.
  • The final area researchers identified as critical for making a success of HIE adoption in nursing homes was the ability to match new clinical workflows to the work already getting done in the homes. This, of course, is important in any setting where leaders are considering major new technology initiatives.

Too often, discussions of health data sharing leave out major sectors of the healthcare economy like this one. It’s good to take a look at what full participation in health data sharing with nursing homes could mean for healthcare.

A Look At Vendor IoT Security And Vulnerability Issues

Posted on October 5, 2016 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.

Much of the time, when we discuss the Internet of Things, we’re looking at issues from an end-user perspective.  We talk about the potential for IoT options like mobile medical applications and wearable devices, and ponder how to connect smart devices to other nodes like the above to offer next-generation care. Though we’re only just beginning to explore such networking models, the possibilities seem nearly infinite.

That being said, most of the responsibility for enabling and securing these devices still lies with the manufacturers, as healthcare networks typically don’t integrate fully with IoT devices as of yet.

So I was intrigued to find a recent article in Dark Reading which lays out some security considerations manufacturers of IoT devices should keep in mind. Not only do the suggestions give you an idea of how vendors should be thinking about vulnerabilities, they also offer some useful insights for healthcare organizations.

Security research Lysa Myers offers IoT device-makers several recommendations to consider, including the following:

  • Notify users of any changes to device features. In fact, it may make sense to remind them repeatedly of significant changes, or they may simply ignore them out of habit.
  • Put a protocol in place for handling vulnerability reports, and display your vulnerability disclosure policy prominently on your website. Ideally, Myers notes, makers of IoT medical devices should send vulnerability reports to the FDA.
  • When determining how to handle a vulnerability issue, let the most qualified person decide what should happen. In the case of automated medical diagnosis, for example, the right person would probably be a doctor.
  • Make it quick and easy to update IoT device software when you find an error. Also, make it simple for customers to spot fraudulent updates.
  • Create an audit log for all devices, even those that might seem too mundane to interest criminals, as even the least important of devices can assist criminals in launching a DDoS attack or spamming.
  • See to it that users can tell when the changes made to an IoT device’s software are made by the authorized user or a designated representative rather than a cybercriminal or other inappropriate person.
  • Given that many IoT devices require cloud-based services to operate, it’s important to see that end users aren’t dropped abruptly with no cloud alternative. Manufacturers should give users time to transition their service if discontinuing a device, going out of business or otherwise ending support for their own cloud-based option.

If we take a high-level look at these recommendations, there’s a few common themes to be considered:

Awareness:  Particularly in the case of IoT devices, it’s critical to raise awareness among both technical staffers and users of changes, both in features and security configurations.

Protection:  It’s becoming more important every day to protect IoT devices from attacks, and see to it that they are configured properly to avoid security and continuity failures. Also, see to that these devices are protected from outages caused by vendor issues.

Monitoring:  Health IT leaders should find ways to integrate IoT devices into their monitoring routine, tracking their behavior, the state of security updates to their software and any suspicious user activity.

As the article suggests, IoT device-makers probably need to play a large role in helping healthcare organizations secure these devices. But clearly, healthcare organizations need to do their part if they hope to maintain these devices successfully as health IT models change.

What Do You Think Of Data Lakes?

Posted on October 4, 2016 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.

Being that I am not a high-end technologist, I’m not always up on the latest trends in database management – so the following may not be news to everyone who reads this. As for me, though, the notion of a “data lake” is a new one, and I think it a valuable idea which could hold a lot of promise for managing unruly healthcare data.

The following is a definition of the term appearing on a site called KDnuggets which focuses on data mining, analytics, big data and data science:

A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured and unstructured data. The data structure and requirements are not defined until the data is needed.

According to article author Tamara Dull, while a data warehouse contains data which is structured and processed, expensive to store, relies on a fixed configuration and used by business professionals, a data link contains everything from raw to structured data, is designed for low-cost storage (made possible largely because it relies on open source software Hadoop which can be installed on cheaper commodity hardware), can be configured and reconfigured as needed and is typically used by data scientists. It’s no secret where she comes down as to which model is more exciting.

Perhaps the only downside she identifies as an issue with data lakes is that security may still be a concern, at least when compared to data warehouses. “Data warehouse technologies have been around for decades,” Dull notes. “Thus, the ability to secure data in a data warehouse is much more mature than securing data in a data lake.” But this issue is likely to receive in the near future, as the big data industry is focused tightly on security of late, and to her it’s not a question of if security will mature but when.

It doesn’t take much to envision how the data lake model might benefit healthcare organizations. After all, it may make sense to collect data for which we don’t yet have a well-developed idea of its use. Wearables data comes to mind, as does video from telemedicine consults, but there are probably many other examples you could supply.

On the other hand, one could always counter that there’s not much value in storing data for which you don’t have an immediate use, and which isn’t structured for handy analysis by business analysts on the fly. So even if data lake technology is less costly than data warehousing, it may or may not be worth the investment.

For what it’s worth, I’d come down on the side of the data-lake boosters. Given the growing volume of heterogenous data being generated by healthcare organizations, it’s worth asking whether deploying a healthcare data lake makes sense. With a data lake in place, healthcare leaders can at least catalog and store large volumes of un-normalized data, and that’s probably a good thing. After all, it seems inevitable that we will have to wring value out of such data at some point.

Apple’s Healthcare Data Plans Become Clearer

Posted on October 3, 2016 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.

Though it’s not without competitors, I’d argue that Apple’s HealthKit has stood out since its inception, in part because it was relatively early to the game (mining patient-centered data) and partly because Apple products have a sexy reputation. That being said, it hasn’t exactly transformed the health IT industry either.

Now, though, with the acquisition of Gliimpse, a startup which pulls data from disparate EMRs into a central database, it’s become clearer what Apple’s big-picture goals are for the healthcare market – and if its business model works out they could indeed change health data industry.

According to a nifty analysis by Bloomberg’s Alex Webb, which quotes an Apple Health engineer, the technology giant hopes to see the health data business evolve along the lines of Apple’s music business, in which Apple started with a data management tool (the iPod) then built a big-bucks music platform on the device. And that sounds like an approach that could steal a move from many a competitor indeed.

Apple’s HealthKit splash
Apple made a big splash with the summer 2014 launch of HealthKit, a healthcare data integration platform whose features include connecting patient generated health data with traditional systems like the Epic EMR. It also attracted prominent partners like Cedars-Sinai Medical Center and Ochsner Health System within a year or so of its kickoff.

Still, the tech giant has been relatively quiet about its big-picture vision for healthcare, leaving observers like yours truly wondering what was up. After all, many of Apple’s health data moves have been incremental. For example, a few months ago I noted that Apple had begun allowing users to store their EMR data directly in its Health app, using the HL7 CCD standard. While interesting, this isn’t exactly an earth-shattering advance.

But in his analysis — which makes a great deal of sense to me – Bloomberg’s Webb argues that Apple’s next act is to take the data it’s been exchanging with wearables and put it to better use. Apple’s long-awaited big idea is to turn Apple’s HealthKit into a system that can improve diagnoses, sources told Bloomberg.

Also, Apple intends to integrate health records as closely with its proprietary devices as possible, offering not only data collection but suggestions for better health in a manner that can’t be easily duplicated on Android platforms. As Webb rightly points out, such a move could undermine Google’s larger healthcare plans, by locking consumers into Apple technology and discouraging a switch to the Google Fit health tracking software.

Big vision, big questions
As we know, even a company with the reputation, cash and proprietary user base enjoyed by Apple is far from a shoo-in for consumer health data dominance. (Consider the fate of Microsoft HealthVault and Google Health.) Its previous successes have come, as noted, by creating a channel then dominating that channel, but there’s no guarantee it can pull off such a trick this time.

For one thing, the wearables market is highly fragmented, and Apple is far from being the leader. (According to one set of stats, Fitbit had 25.4% of the global wearables market as of Q2 ’16, Xiaomi 14%, and Apple just 7%.) That doesn’t bode well for starting a health tracker-based revolution.

On the other hand, though, Apple did manage to create and dominate a channel in the music business, which is also quite resistant to change and dominated by extremely entrenched powers that be. If any upstart healthcare player could make this happen, it’s probably Apple. It will be interesting to see whether Apple can work its magic once again.

Please, No More HIE “Coming Of Age” Stories

Posted on September 29, 2016 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.

Today I read a Modern Healthcare story suggesting that health information exchanges are “coming of age,” and after reading it, I swear my eyes almost rolled back into my head. (An ordinary eye roll wouldn’t do.)

The story leads with the assertion that a new health data sharing deal, in which Texas Health Resources agreed to share data via a third-party HIE, suggests that such HIEs are becoming sustainable.

Author Joseph Conn writes that the 14-hospital system is coming together with 32 other providers sending data to Healthcare Access San Antonio, an entity which supports roughly 2,400 HIE users and handles almost 2.2 million patient records. He notes that the San Antonio exchange is one of about 150 nationwide, hardly a massive number for a country the size of the U.S.

In partial proof of his assertion that HIEs are finding their footing, he notes that that from 2010 to 2015, the number of HIEs in the U.S. fluctuated but saw a net gain of 41%, according to federal stats. And he attributes this growth to pressure on providers to improve care, lower costs and strengthen medical research, or risk getting Medicare or Medicaid pay cuts.

I don’t dispute that there is increased pressure on providers to meet some tough goals. Nor am I arguing that many healthcare organizations believe that healthcare data sharing via an HIE can help them meet these goals.

But I would argue that even given the admittedly growing pressure from federal regulators to achieve certain results, history suggests that an HIE probably isn’t the way to get this done, as we don’t seem to have found a business model for them that works over the long term.

As Conn himself notes, seven recipients of federal, state-wide HIE grants issued by the ONC — awarded in Connecticut, Illinois, Montana, Nevada, New Hampshire, Puerto Rico and Wyoming — went out of business after the federal grants dried up. So were not talking about HIEs’ ignoble history of sputtering out, we’re talking about fairly recent failures.

He also notes that a commercially-funded model, MetroChicago HIE, which connected more than 30 northeastern Illinois hospitals, went under earlier this year. This HIE failed because its most critical technology vendor suddenly went out of business with 2 million patient records in its hands.

As for HASA, the San Antonio exchange discussed above, it’s not just a traditional HIE. Conn’s piece notes that most of the hospitals in the Dallas-Fort Worth area have already implemented or plan to use an Epic EMR and share clinical messages using its information exchange capabilities. Depending on how robust the Epic data-sharing functions actually are, this might offer something of a solution.

But what seems apparent to me, after more than a decade of watching HIEs flounder, is that a data-sharing model relying on a third-party platform probably isn’t financially or competitively sustainable.

The truth is, a veteran editor like Mr. Conn (who apparently has 35 years of experience under his belt) must know that his reporting doesn’t sustain the assertion that HIEs are coming into some sort of golden era. A single deal undertaken by even a large player like Texas Health Resources doesn’t prove that HIEs are seeing a turnaround. It seems that some people think the broken clock that is the HIE model will be right at least once.

P.S.  All of this being said, I admit that I’m intrigued by the notion of  “public utility” HIE. Are any of you associated with such a project?

Can Machine Learning Tame Healthcare’s Big Data?

Posted on September 20, 2016 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.

Big data is both a blessing and a curse. The blessing is that if we use it well, it will tell us important things we don’t know about patient care processes, clinical improvement, outcomes and more. The curse is that if we don’t use it, we’ve got a very expensive and labor-hungry boondoggle on our hands.

But there may be hope for progress. One article I read today suggests that another technology may hold the key to unlocking these blessings — that machine learning may be the tool which lets us harvest the big data fields. The piece, whose writer, oddly enough, was cited only as “Mauricio,” lead cloud expert at, argues that machine learning is “the most effective way to excavate buried patterns in the chunks of unstructured data.” While I am an HIT observer rather than techie, what limited tech knowledge I possess suggests that machine learning is going to play an important role in the future of taming big data in healthcare.

In the piece, Mauricio notes that big data is characterized by the high volume of data, including both structured and non-structured data, the high velocity of data flowing into databases every working second, the variety of data, which can range from texts and email to audio to financial transactions, complexity of data coming from multiple incompatible sources and variability of data flow rates.

Though his is a general analysis, I’m sure we can agree that healthcare big data specifically matches his description. I don’t know if you who are reading this include wild cards like social media content or video in their big data repositories, but even if you don’t, you may well in the future.

Anyway, for the purposes of this discussion, let’s summarize by saying that in this context, big data isn’t just made of giant repositories of relatively normalized data, it’s a whirlwind of structured and unstructured data in a huge number of formats, flooding into databases in spurts, trickles and floods around the clock.

To Mauricio, an obvious choice for extracting value from this chaos is machine learning, which he defines as a data analysis method that automates extrapolated model-building algorithms. In machine learning models, systems adapt independently without any human interaction, using automatically-applied customized algorithms and mathematical calculations to big data. “Machine learning offers a deeper insight into collected data and allows the computers to find hidden patterns which human analysts are bound to miss,” he writes.

According to the author, there are already machine learning models in place which help predict the appearance of genetically-influenced diseases such as diabetes and heart disease. Other possibilities for machine learning in healthcare – which he doesn’t mention but are referenced elsewhere – include getting a handle on population health. After all, an iterative learning technology could be a great choice for making predictions about population trends. You can probably think of several other possibilities.

Now, like many other industries, healthcare suffers from a data silo problem, and we’ll have to address that issue before we create the kind of multi-source, multi-format data pool that Mauricio envisions. Leveraging big data effectively will also require people to cooperate across departmental and even organizational boundaries, as John Lynn noted in a post from last year.

Even so, it’s good to identify tools and models that can help get the technical work done, and machine learning seems promising. Have any of you experimented with it?

Mobile Health App Makers Still Shaky On Privacy Policies

Posted on September 16, 2016 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.

A new study has concluded that while mobile health app developers are developing better privacy practices, these developers vary widely in how they share those policies with consumers. The research, part of a program launched in 2011 by the Future of Privacy Forum, concludes that while mHealth app makers have improved their practices, too many are still not as clear as they could be with users as to how they handle private health information.

This year’s FPF Mobile App Study notes that mHealth players are working to make privacy policies available to users before purchase or download, by posting links on the app listing page. It probably has helped that the two major mobile health app distribution sites require apps that collect personal info to have a privacy policy in place, but consumer and government pressure has played a role as well, the report said. According to FPF researchers, mHealth app makers are beginning to explain how personal data is collected, used and shared, a step privacy advocates see as the bare minimum standard.

Researchers found that this year, 76% of top overall apps on the iOS App Store and Google Play had a privacy policy, up from 68% noted in the previous iteration of the study. In contrast, only 61% of health and fitness apps surveyed this year included a link to their privacy policies in their app store listing, 10% less than among top apps cutting across all categories.  “Given that some health and fitness apps can access sensitive, physiological data collected by sensors on a mobile phone, wearable, or other device, their below-average performance is both unexpected and troubling,” the report noted.

This disquieting lack of thorough privacy protections extended even to apps collecting some of the most intimate data, the FPF report pointed out. In particular, a subset of mHealth developers aren’t doing anything much to make their policies accessible.

For example, researchers found that while 80% of apps helping women track periods and fertility across Google Play and the iOS App Store had privacy policies, just 63% of the apps had posted links to these policies. In another niche, sleep tracking apps, only 66% of even had a privacy policy in place, and just 54% of these apps linked back to the policy on their store page. (FPF terms this level of performance “dismal,” and it’s hard to disagree.)

Underlying this analysis is the unfortunate truth that there’s still no gold standard for mHealth privacy policies. This may be due more to the complexity of the still-maturing mobile health ecosystem than resistance to creating robust policies, certainly. But either way, this issue won’t go away on its own, so mHealth app developers will need to give their privacy strategy more thought.

Engaging Patients With Health Data Cuts Louisiana ED Overuse

Posted on September 15, 2016 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.

Maybe I’m misreading things, but it seems to me that few health IT pros really believe we can get patients to leverage their own health data successfully. And I understand why. After all, we don’t even have clear evidence that patient portals improve outcomes, and portals are probably the most successful engagement tool the industry has come up with to date.

And not to be a jerk about it, but I bet you’d be hard-pressed to find HIT gurus who believed the state of Louisiana would lead the way, as the achingly poor southern state isn’t exactly known for being a healthcare thought leader.  As it so happens, though, the state has actually succeeded where highfalutin’ health systems have failed.

Over one year, the state has managed to generate a 23% increase in health IT use among at-risk patients, and also, a 10.2% decrease in non-emergent use of emergency departments by Medicaid managed care organization members, thank you very much.

So how did Louisiana’s top healthcare brass accomplish this feat? Among other things, they launched a HIE-enabled ED data registry, along with a direct-to-consumer patient engagement campaign. These efforts were done in partnership with the Louisiana Health Care Quality Forum, which developed statewide marketing plans for the effort (See John’s interview with the Louisiana Health Care Quality Forum for more details).

They must have created some snazzy marketing copy. As Healthcare IT News noted, between August 2015 and May 2016, patient portal use shot up 31%, consumer EHR awareness rose 23% and opt-in to the state’s HIE grew by 3%, Quality Forum marketer Jamie Martin told HIN.

Not only that, the number of patients asking for access to or copies of electronic health data increased by 12%, and the number of patients with current copies of their health information grew by 9%, Martin said.

This is great news for those who want to see patients buy in to the digital health paradigm. Though it’s hard to tell whether the state will be able to maintain the benefits it gained in its initial effort, it clearly succeeded in getting a substantial number of patients to rethink how they manage their care.

But (and I’m sorry to be a bit of a Debbie Downer), I was a bit disappointed when I saw none of the gains cited related to changing health behaviors, such as, say, an increase in diabetics getting retinal exams.

I know that I should probably be focused on the project’s commendable successes, and believe it or not, I do find them to be exciting. I’m just not sure that these kinds of metrics can be used as proxies for health improvement measures, and let’s face it, that’s what we need, right?

Apple App Store Toughens Guidelines For Health Apps

Posted on September 13, 2016 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.

In a precedent-setting move, Apple has released new guidelines for its iOS App Store which impose new limitations on health and medical app developers.  iMedicalApps contributor Iltifat Husain, M.D., who wrote a piece about the changed standards, said they contain “the most stringent language I have ever seen Apple used for the health and medical category of apps.”

According to Husain, highlights from Apple’s new developer guidelines include:

  • A warning that if an app could possibly cause physical harm, Apple could reject it
  • A warning that apps which provide inaccurate data or information that could be used to diagnose or treat patients will get increased scrutiny
  • A reminder that apps which calculate drug dosage must come from the drug manufacturer, a hospital, university, health insurance company or other approved entity. In other words, independent developers cannot post a medical app for drug dosages themselves.
  • A ban on marijuana-related apps
  • A ban on apps that encourage people to place their iPhones under a mattress or pillow while charging (such as some sleep monitors)

Historically, Apple has been relatively lax about hosting potentially dangerous health apps, Husain says. For example, he notes that apps purporting to measure a consumer’s blood pressure by using the iPhone’s camera and microphone tend to be quite inaccurate in their measurements, but that Apple had not screened them out.  Now things have changed for the better, Husain writes. “Apps [like these] would not get through the screening review process under Apple’s new guidelines.”

Husain argues that the new guidelines are more important than the FDA’s recently-updated guidelines on health apps: “There is no way the FDA can regulate the hundreds of thousands of health and medical apps and the updates made to them,” Husain writes. “The screening process is what has to change.” And given Apple’s market footprint and influencer status it’s hard to disagree with him.

At this point the question is whether Google will follow suit. After all, while the Apple app store hosted 2 million apps as of June, Google Play offered 2.2 million apps, according to one study, and as of February there were three Android users for every iPhone user. So If Google doesn’t put more stringent health app requirements in place as well, creators of dodgy health apps can still develop for Android and find a wide audience.

That being said, neither Google nor Apple are required to impose new restrictions on health apps, and are likely to be governed by commercial pressure more than medical appropriateness. Also, both parties are free to set any rules they choose, and uses might not be aware of important differences between the two sets of policies. In other words, if the goal is to protect consumers, relying on guidelines generated by app store hosts probably won’t fly over the long-term.

I’m not necessarily suggesting that the FDA or other regulatory body should come down on the app stores like a ton of bricks. That would be overkill, and as Husain notes, is probably beyond their capabilities.

But doctors in the know about apps might want to warn patients about their potential limitations, and offer some criteria as to what they can expect from health apps. After all, most consumers have experimented with one health app of the other, so even if the doctor doesn’t prescribe them, patients need to be educated about their options. So if you’re a mobile health savvy clinician reading this, consider increasing patients on these issues.

Study: Health IT Costs $32K Per Doctor Each Year

Posted on September 9, 2016 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.

A new study by the Medical Group Management Association has concluded that that physician-owned multispecialty practices spent roughly $32,500 on health IT last year for each full-time doctor. This number has climbed dramatically over the past seven years, the group’s research finds.

To conduct the study, the MGMA surveyed more than 3,100 physician practices across the U.S. The expense number they generated includes equipment, staff, maintenance and other related costs, according to a press release issued by the group.

The cost of supporting physicians with IT services has climbed, in part, due to rising IT staffing expenses, which shot up 47% between 2009 and 2015. The current cost per physician for health IT support went up 40% during the same interval. The biggest jump in HIT costs for supporting physicians took place between 2010 and 2011, the period during which the HITECH Act was implemented.

Practices are also seeing lower levels of financial incentives to adopt EHRs as Meaningful Use is phased out. While changes under MACRA/MIPS could benefit practices, they aren’t likely to reward physicians directly for investments in health IT.

As MGMA sees it, this is bad news, particularly given that practices still have to keep investing in such infrastructure: “We remain concerned that far too much of a practice’s IT investment is tied directly to complying with the ever-increasing number of federal requirements, rather than to providing patient care,” the group said in a prepared statement. “Unless we see significant changes in the final rule, practice IT costs will continue to rise without a corresponding improvement in the care delivery process.”

But the MGMA’s own analysis offers at least a glimmer of hope that these investments weren’t in vain. For example, while it argues that growing investments in technologies haven’t resulted in greater administrative efficiencies (or better care) for practices, it also notes that more than 50% of responders to a recent MGMA Stat poll reported that their patients could request or make appointments via their practice’s patient portal.

While there doesn’t seem to be any hard and fast evidence that portals improve patient care across the board, studies have emerged to suggest that portals support better outcomes, in areas such as medication adherence. (A Kaiser Permanente study from a couple of years ago, comparing statin adherence for those who chose online refills as their only method of getting the med with those who didn’t, found that those getting refills online saw nonadherence drop 6%.)

Just as importantly – in my view at least – I frequently hear accounts of individual practices which saw the volume of incoming calls drop dramatically. While that may not correlate directly to better patient care, it can’t hurt when patients are engaged enough to manage the petty details of their care on their own. Also, if the volume of phone requests for administrative support falls enough, a practice may be able to cut back on clerical staff and put the money towards say, a nurse case manager for coordination.

I’m not suggesting that every health IT investment practices have made will turn to fulfill its promise. EHRs, in particular, are difficult to look at as a whole and classify as a success across the board. I am, however, arguing that the MGMA has more reason for optimism than its leaders would publicly admit.