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Will Chatbots Be Embedded In Health IT Infrastructure Within Five Years?

Posted on December 10, 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.

Brace yourself: The chatbots are coming. In fact, healthcare chatbots could become an important part of healthcare organizations’ IT infrastructure, according to research released by a market analyst firm. I have my doubts but do read on and see what you think.

Jupiter Research is predicting that AI-powered chatbots will become the initial point of contact with healthcare providers for many consumers. As far as I know, this approach is not widespread in the US at present, though there are many vendors developing tools that they could deploy and we’ve seen some success from companies like SimplifiMed and big tech companies like Microsoft that are enabling chatbots as well.

However, Jupiter sees things changing rapidly over the next five years. It predicts that the number of chatbot interactions will shoot up at an average annual growth rate of 167%, from an estimated 21 million per year in 2018 to 2.8 billion per year in 2023.  By that point, healthcare will represent 10% of all chatbot interactions across major verticals, Jupiter says.

According to the market research firm, there are a number of reasons chatbot use in healthcare will grow so rapidly, including consumers’ growing comfort level with using chatbots to discuss their care. Jupiter also expects to see healthcare providers routinely use chatbots for customer experience management, though again, I’ve seen little evidence that this is happening just yet.

The massive growth in patient-chatbot interactions will also be fueled by a rise in the sophistication of conversational AI platforms, a leap so dramatic that consumers will handle a growing percentage of their healthcare business entirely via chatbot, the firm says. This, in turn, will free up medical staff time, saving countries’ healthcare systems around $3.7 billion by 2023.  This would prove to be a relatively modest savings for the giant US healthcare system, but it could be quite meaningful for a smaller country.

As healthcare organizations adopt chatbot platforms, their chief goal will be to see that information collected by chatbots is transferred to EHRs and other important applications, the report says. To make this happen, these organizations will have to make sure to integrate chatbot platforms with both clinical and line-of-business applications. (Vendors like PatientSphere already offer independent platforms designed to address such issues.)

All very interesting, no? Definitely. I share Jupiter’s optimistic view of the chatbot’s role in healthcare delivery and customer service and have little doubt that even today’s relatively primitive bots are capable of handling many routine transactions.

That being said, I’m thinking it will be more like 10 years before chatbots are used widely by providers. If what I’ve seen is any indication, it will probably take that long before conversational AI can truly hold a conversation. If we hope to use AI-based chatbots routinely at the front end of important processes, they’ll just have to be smarter.

Next Steps In Making Healthcare AI Practical

Posted on November 30, 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.

In recent times, AI has joined blockchain on the list of technologies that just sort of crept into the health IT toolkit.

After all, blockchain was borne out of the development of bitcoin, and not so long ago the idea that it was good for anything else wasn’t out there. I doubt its creators ever contemplated using it for secure medical data exchange, though the notion seems obvious in retrospect.

And until fairly recently, artificial intelligence was largely a plaything for advanced computing researchers. I’m sure some AI researchers gave thought to cyborg doctors that could diagnose patients while beating them at chess and serving them lunch, but few practical applications existed.

Today, blockchain is at the core of countless health IT initiatives, many by vendors but an increasing number by providers as well. Healthcare AI projects, for their part, seem likely to represent the next wave of “new stuff” adoption. It’s at the stage blockchain was a year or two ago.

Before AI becomes more widely adopted in healthcare circles, though, the industry needs to tackle some practical issues with AI, and the list of “to-dos” keeps expanding. Only a few months ago, I wrote an item citing a few obstacles to healthcare AI deployment, which included:

  • The need to make sure clinicians understand how the AI draws its conclusions
  • Integrating AI applications with existing clinical workflow
  • Selecting, cleaning and normalizing healthcare data used to “train” the AI

Since then, other tough challenges to the use of healthcare AI have emerged as the healthcare leaders think things over, such as:

Agreeing on best practices

Sure, hospitals would be interested in rolling out machine learning if they could, say, decrease the length of hospital stays for pneumonia and save millions. The thing is, how would they get going? At present, there’s no real playbook as to how these kinds of applications should be conceptualized, developed and maintained. Until healthcare leaders reach a consensus position on how healthcare AI projects should generally work, such projects may be too risky and/or prohibitively expensive for providers to consider.

Identifying use cases

As an editor, I see a few interesting healthcare AI case studies trickle into my email inbox every week, which keeps me intrigued. The thing is, if I were a healthcare CIO this probably wouldn’t be enough information to help me decide whether it’s time to take up the healthcare AI torch. Until we’ve identified some solid use cases for healthcare AI, almost anything providers do with it is likely to be highly experimental. Yes, there are some organizations that can afford to research new tech but many just don’t have the staff or resources to invest. Until some well-documented standard use cases for healthcare AI emerge, they’re likely to hang back.

The healthcare AI discussion is clearly at a relatively early stage, and more obstacles are likely to show up as providers grapple with the technology. In the meantime, getting these handled is certainly enough of a challenge.

Less Than Half of Healthcare Users Trust Critical Organizational Data

Posted on November 29, 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.

If you’re a healthcare CIO, you must hope that your users trust and feel they can leverage data to do their jobs better. However, some of your colleagues don’t seem to be so sure. A new study has concluded that less than half of users in responding healthcare organizations have a high degree of trust in their clinical, operational or financial data.

The study, which was conducted by Dimensional Insight, surveyed 85 chief information officers and other senior health IT leaders. It asked these leaders how they rated trust in the data leveraged by their various user communities, the percentage of user population they felt was self-service oriented and making data-driven decisions, and whether they planned to increase or decrease their investments in data trust and self-service analytics.

When rating the level of data trust on a 10-point scale, just 40% of respondents rated their trust in financial data at eight or above, followed by 40% of clinical data users and 36% of operational data users.

Perhaps, then, it follows that healthcare organizations responding to the survey had low levels of self-service data use. Clinical data users had a particularly low rate of self-service use, while financial users seemed fairly likely to be accessing and using data independently.

Given these low levels of trust and self-service data usage, it’s not surprising to find out that 76% of respondents said they plan to invest in increasing their investment in improving clinical data trust, 77% their investments in improving operational data trust and 70%  their investment in financial data trust.

Also, 78% said they plan to increase their spending on self-service analytics for clinical data and 73% expect to spend more on self-service analytics for operational data. Meanwhile, while 68% plan to increase spending on financial self-service analytics, 2% actually planned to decrease the spending in this area, suggesting that this category is perhaps a bit healthier.

In summing up, the report included recommendations on creating more trust in organizational data from George Dealy, Dimensional Insight’s vice president of healthcare applications. Dealy’s suggestions include making sure that subject matter experts help to design systems providing information critical to their decision-making process, especially when it comes to clinicians. He also points out that health IT leaders could benefit from keeping key users aware of what data exists and making it easy for them to access it.

Unfortunately, there are still far too many data silos protected by jealous guardians in one department or another. While subject matter experts can design the ideal data sharing platform for their needs, there’s still a lot of control issues to address before everyone gets what they need. In other words, increasing trust is well and good, but the real task is seeing to it that the data is rich and robust when users get it.

MRI Installation Slip Disables Hospital iOS Devices

Posted on November 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.

The following is the story of an MRI installation which took a surprising turn. According to a recent post on Reddit which has since gone viral in the IT press, a problem with the installation managed to shut down and completely disable every iOS-based device in the facility.

A few weeks ago, Erik Wooldridge of  Chicago’s Morris Hospital, a perplexed member of the r/sysadmin subreddit, posted the following:

This is probably the most bizarre issue I’ve had in my career in IT. One of our multi-practice facilities is having a new MRI installed and apparently something went wrong when testing the new machine. We received a call near the end of the day from the campus that none of their cell phones work after testing [the] MRI… After going out there we discovered that this issue only impacted iOS devices. iPads, iPhones, and Apple Watches were all completely disabled.

According to Wooldridge, the outage affected about 40 users. Many of the affected devices were completely dead. Others that could power on seemed to have issues with the cellular radio, though the Wi-Fi connections continued to work. Over time, the affected devices began to recover, but one iPhone had severe service issues after the incident, and while some of the Apple Watches remained on, the touchscreens hadn’t begun working after several days.

At first, Morris and his colleagues feared that the outage could be due to an electromagnetic pulse, a terrifying possibility which could’ve meant very bad things for its data center. Fortunately, that didn’t turn out to be the problem.

Later the vendor, GE, told the poster and his colleagues that the problem was a leakage of liquid helium used for the MRI’s superconducting magnets. GE engineers turned out to be right that the leak was the source of the problems, but couldn’t explain why Android devices were untouched by the phenomenon.

Eventually, a blogger named Kyle Wiens with iFixit.org seems to found an explanation for why iOS devices were hit so hard by the helium leak. Apparently, even Apple admits that exposing iPhones to evaporating liquefied gases such as helium could take them offline.

While no one’s suggesting that liquefied helium is good for any type of microelectronic device, the bottom line seems to be that the iOS devices are more sensitive to this effect than the Android devices. Let’s hope most readers never need to test this solution out.

Hospitals Sharing More Patient Data Than Ever, But Is It Having An Impact On Patient Care?

Posted on November 1, 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.

Brace yourself for more happy talk in a positive interoperability spin, folks. Even if they aren’t exchanging as much health data as they might have hoped, hospitals are sharing more patient health data than they ever have before, according to a new report from the ONC.

The ONC, which recently analyzed 2017 data from the American Hospital Association’s Information Technology Supplement Survey, concluded that 93% of non-federal acute care hospitals have upgraded to the 2015 Edition Health IT Certification Criteria or plan to upgrade. These criteria include new technical capabilities that support health data interoperability.

Today, most hospitals (88%) can send patient summary of care records electronically, and receive them from outside sources (74%), ONC’s analysis concluded. In addition, last year the volume of hospitals reporting that they could query and integrate patient health data significantly increased.

Not only that, the volume of hospitals engaged in four key interoperability activities (electronically sending, receiving, finding and integrating health data) climbed 41% over 2016. On the downside, however, only four in 10 hospitals reported being able to find patient health information, send, receive and integrate patient summary of care records from outside sources into their data.

According to ONC, hospitals that work across these four key interoperability domains tend to be more sophisticated than their peers who don’t.

In fact, in 2017 83% of hospitals able to send, receive, find, and integrate outside health information also had health information electronic available at the point of care. This is a 20% higher level than hospitals engaging in just three domains, and a whopping seven times higher than hospitals that don’t engage in any domain.

Without a doubt, on its face this is good news. What’s not to like? Hospitals seem to be stepping up the interoperability game, and this can only be good for patients over time.

On the other hand, it’s hard for me to measure just how important it is in the near term. Yes, it seems like hospitals are getting more nimble, more motivated and more organized when it comes to data sharing, but it’s not clear what impact this may be having on patient care processes and outcomes.

Over time, most interoperability measures I’ve seen have focused more on receipt and transmission of patient health data far more than integration of that data into EHRs. I’d argue that it’s time to move beyond measuring back and forth of data and put more impact on how often physicians use that data in their work.

There’s certainly a compelling case to be made that health data interoperability matters. I’ve never disputed that. But I think it’s time we measure success a bit more stringently. In other words, if ONC can’t define the clinical benefits of health data exchange clearly, in terms that matter to physicians, it’s time to make it happen.

Hospitals Taking Next-Gen EHR Development Seriously

Posted on October 22, 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.

Physicians have never been terribly happy with EHRs, most of which have done little to meet the lofty clinical goals set forth by healthcare leaders. Despite the fact that EHRs have been a fact of life in medicine for nearly a decade, health IT leaders don’t seem to have figured out how to build a significantly better one — or even what “better” means.

While there has been the occasional project leveraging big data from EHRs to improve care processes, little has been done that makes it simple for physicians to benefit from these insights on a day-to-day basis. Not only that, while EHRs may have become more usable over time, they still don’t present patient data in an intuitive manner.

However, hospital leaders have may be developing a more-focused idea of how a next-gen EHR should work, at least if recent efforts by Stanford Medicine and Penn Medicine are any indication.

For example, Stanford has developed a next-gen EHR model which it argues could be rolled out within the next 10 years. The idea behind the model would be that clinicians and other healthcare professions would simply take care of patients, with information flowing automatically to all relevant parties, including payers, hospitals, physicians and patients. Its vision seems far less superficial than much of the EHR innovation happy talk we’ve seen in the past.

For example, in this model, an automated physician’s assistant would “listen” to interactions between doctors and patients and analyze what was said. The assistant would then record all relevant information in the physical exam section of the chart, sorting it based on what was said in the room and what verbal cues clinicians provided.

Another initiative comes from Penn Medicine, where leaders are working to transform EHRs into more streamlined, interactive tools which make clinical work easier and drive best outcomes. Again, while many hospitals and health centers have talked a good game on this front, Penn seems to be particularly serious about making EHRs valuable. “We are approaching this endeavor as if it were building a new clinical facility, laboratory or training program,” said University of Pennsylvania Health System CEO Ralph Muller in a prepared statement.

Penn hasn’t gone into many specifics as to what its next-gen EHR would look like, but in its recent statement, it provided a few hints. These included the suggestion that they should allow doctors to “subscribe” to patients’ clinical information to get real-time updates when action is required, something along the lines of what social media networks already do with feeds and notifications.

Of course, there’s a huge gap between visions and practical EHR limitations. And there’s obviously a lot of ways in which the same general goals can be met. For example, another way to talk about the same issues comes from HIT superstar Dr. John Halamka, chief information officer of the Beth Israel Deaconess Medical Center and CIO and dean for technology at Harvard Medical School.

In a blog post looking at the shift to EHR 2.0, Halamka argues for the development of a new Care Management Medical Record which enrolls patients in protocols based on conditions then ensures that they get recommended services. He also argues that EHRs should be seen as flexible platforms upon which entrepreneurs can create add-on functionality, something like apps that rest on top of mobile operating systems.

My gut feeling is that all told, we are seeing from real progress here, and that particularly given the emergence of more mature AI tools, a more-flexible EHR demanding far less physician involvement will come together. However, it’s worth noting that the Stanford researchers are looking at a 10-year timeline.  To me, it seems unlikely that things will move along any faster than that.

Hospitals Stumble When Asked To Share Medical Records With Patients

Posted on October 19, 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.

By this point, few would argue that patients are unlikely to be engaged with their medical care if they don’t have free, unfettered access to the medical records. However, unfortunately, research continues to suggest that providers are struggling to meet these goals — and from my point of view, shows signs that they don’t take the entire process that seriously.

Most recently a new study found that not only may hospitals be failing to meet state and federal rules on patient medical record sharing, they may not even be communicating about their own policies consistently.  As a patient with complex medical needs, I found this troubling, though sadly, not so surprising given my past experiences.

The study, which appeared in JAMA Network Open, looked at the way in which 83 US hospitals handled medical record requests by patients. The research team conducted the requests between August 1 and December 7, 2017, tracking what medical information was made requestable, what formats of release were available, costs to receive the information and request processing times. Researchers reviewed hospital processes using medical record release authorization forms and telephone calls with medical records departments.

After analyzing their data, the researchers concluded at least some hospitals weren’t complying with regulations regarding medical information request processing times. Of the 81 hospitals that responded to the researchers with mean times of release for records, seven had ranges extending beyond state requirements before applying the single 30-day extension granted by HIPAA.

In addition, they found that patients obtained different information regarding medical records request processes when they filled out form versus when they communicated directly with medical records departments. For example, just 53% of hospitals gave patients the option to request the entire medical record on their record request forms, while when the medical record department was contacted, all the hospitals said they were able to and release an entire medical record to patients.

Perhaps offering some insight into why patient portals aren’t as muscular as they could be, just 25% of hospital medical record departments said via phone that they were able to release records to online patient portals, and less than half (40%) shared this detail this on their forms.

Another issue highlighted by the study was that the hospitals studied seem to be vague about the costs patients faced in receiving records. Apparently, 22% of hospitals disclosed they would charge patients for such records but did not specify cost, and 43% didn’t specify that there would be a fee.

Having inadvertently walked into a cost backsaw once or twice in my pre-HIT days, I can’t stress enough how disheartening unexpected records fees can be for patients. After all, in some cases patients don’t get the care they need if they don’t have up-to-date-records, and until we have a completely universal interoperability scheme in place patients are on the hook to make this happen.

Getting the records seems to have been pricey. All but one of the hospitals were able to quote the cost for receiving records on paper, at prices which began at zero but went as high as $541.50 for a 200-page record. On the digital side, 59% of the hospital stated a cost of release above the federally-recommended $6.50 flat fee per page for electronically-maintained records.

As the study authors note, it would be helpful if federal regulators keep their eye on issues related to patient medical record access, which is more costly, confusing and time-consuming than it might appear at first glance. In the meantime, hospitals might consider doing a self-audit to see if they are offering patients consistent information on the process when we ask for badly-needed medical data.

 

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.

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.