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What’s Involved In Getting To EHR 2.0?

Posted on September 22, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

While the current crop of EHRs have (arguably) served a useful purpose, I think we’d all agree that there’s a ton of room for improvement. The question is, what will it take to move EHRs forward?

Certainly, we face some significant obstacles to progress.

There are environmental factors in play, such as reimbursement issues.

There’s the question of what providers will do with existing EHR infrastructure, which has cost them tens or even hundreds of millions of dollars if next-gen EHRs call for a new technical approach.

Then, of course, there’s the challenge of making the darn things usable by real, human clinicians. So far, we simply haven’t gotten anything that solves that issue yet.

That doesn’t mean people aren’t considering the issue, however. One health IT leader that’s stepped up to the plate is Dr. John Halamka, chief information officer of the Beth Israel Deaconess Medical Center and CIO and dean for technology at Harvard Medical School.

In his Life As Healthcare CIO, Halamka lays out the changes he sees as driving the shift to EHR 2.0. Here are some of his main points:

  • Regulators are shifting their focus from prescribing certain types of EHR functionality to looking at results technology achieves. This supports the healthcare industry’s movement from a data recording focus to an outcomes focus.
  • With doctors being pulled in too many directions, it will take teams to maintain patient health, this calls for a new generation of communication and groupware tools. These tools should include workflow integration, rules-based escalation messages, and routing based on time of day, location, schedules, urgency, and licensure.
  • With value-based purchasing gradually becoming the norm, EHRs need new capabilities. These should include the ability to document care plans and variation from those plans, along with outcomes reported from patient-generated healthcare data. Eventually, this will mean the dawn of the Care Management Medical Record, which enrolls patients and protocols based on their condition then ensures that patients get recommended services.
  • EHRs must be more usable. To accomplish this, it’s helpful to think of EHRs as platforms upon which entrepreneurs can create add-on functionality, along the lines of apps that rest on top of mobile operating systems.
  • Next-gen EHRs need to become more consumer-driven, making patients an equal member of the care team. Although existing EHR models do have patient portals, they aren’t robust enough to connect patients fully with their care, and they don’t include tools helping patients navigate their care system.

As far as I can tell, Dr. Halamka has covered the majority of issues we need to address in transitioning to new EHR models. I was also interested to learn that regulatory bodies have begun to “get it” about the limitations of demanding certain functions be included in an EHR system.

I’m still left with one question, however. How does interoperability fit into this picture? Can we even get to the next generation of EHRs without answering the question of how they share data between one another? To me, it’s clear that the answer is no, we can’t leave this issue aside.

Other than that, though, I found Dr. Halamka’s analysis to be fairly comforting. Nothing he’s described is out of reach, unless, of course, vendors won’t cooperate. I think that as providers reach the conclusions he has, they’ll demand the kind of functionality he’s outlined, and vendors will have no choice but to pony up. In other words, there might actually be light at the end of the EHR tunnel.

The Value Of Pairing Machine Learning With EMRs

Posted on January 5, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

According to Leonard D’Avolio, the healthcare industry has tools at its disposal, known variously as AI, big data, machine learning, data mining and cognitive computing, which can turn the EMR into a platform which supports next-gen value-based care.

Until we drop the fuzzy rhetoric around these tools – which have offered superior predictive performance for two decades, he notes – it’s unlikely we’ll generate full value from using them. But if we take a hard, cold look at the strengths and weaknesses of such approaches, we’ll get further, says D’Avolio, who wrote on this topic recently for The Health Care Blog.

D’Avolio, a PhD who serves as assistant professor at Harvard Medical School, is also CEO and co-founder of AI vendor Cyft, and clearly has a dog in this fight. Still, my instinct is that his points on the pros and cons of machine learning/AI/whatever are reasonable and add to the discussion of EMRs’ future.

According to D’Avolio, some of the benefits of machine learning technologies include:

  • The ability to consider many more data points than traditional risk scoring or rules-based models
  • The fact that machine learning-related approaches don’t require that data be properly formatted or standardized (a big deal given how varied such data inflows are these days)
  • The fact that if you combine machine learning with natural language processing, you can mine free text created by clinicians or case managers to predict which patients may need attention

On the flip side, he notes, this family of technologies comes with a major limitation as well. To date, he points out, such platforms have only been accessible to experts, as interfaces are typically designed for use by specially trained data scientists. As a result, the results of machine learning processes have traditionally been delivered as recommendations, rather than datasets or modules which can be shared around an organization.

While D’Avolio doesn’t say this himself, my guess is that the new world he heralds – in which machine learning, natural language processing and other cutting-edge technologies are common – won’t be arriving for quite some time.

Of course, for healthcare organizations with enough resources, the future is now, and cases like the predictive analytics efforts going on within Paris public hospitals and Geisinger Health System make the point nicely. Clearly, there’s much to be gained in performing advanced, liquidly-flowing analyses of EMR data and related resources. (Geisinger has already seen multiple benefits from its investments, though its data analytics rollout is relatively new.)

On the other hand, independent medical practices, smaller and rural hospitals and ancillary providers may not see much direct impact from these projects for quite a while. So while D’Avolio’s enthusiasm for marrying EMRs and machine learning makes sense, the game is just getting started.

EMR Alert Fatigue Can Have Deadly Consequences

Posted on May 31, 2013 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

A case study published this week in the journal Pediatrics suggests that EMR alert fatigue is becoming a major source of potential medical errors.  According to a piece in iHealthBeat, “a deluge of repetitive, inappropriate alerts” have been generated by EMRs of late, causing clinicians to ignore or override alerts very frequently.

Problems with alerting in medication order entry systems are proving to be a particularly serious safety hazard, it seems.  “It has been well established that clinicians override many drug allergy alerts generated by the electronic health record,” write the authors of the Pediatrics study.

The case study in Pediatrics comes from researchers at Stanford University Biomedical Informatics and Harvard Medical School.  Researchers examined the case of a two-year-old boy who died after clinical staff overrode scores of distracting EMR alerts — more than 100 over the course of one month — and ended up inappropriately administering a diuretic to the patient.

The key to addressing this  problem appears to be zeroing in on approaches to minimize the number of non-evidence based alerts that bedevil physicians during their time with patients. However, implementing these changes can be very complex.

In the PICU researchers were observing for their study, the facility made evidence-based allergy alerting alerts to the hospital’s system. However, that’s just one aspect of a multifaceted problem.  As the authors note, “incorporating clinical evidence in electronic drug allergy alerting systems remains challenging, especially in pediatric settings.”

But given that pediatric patients usually can’t themselves alert doctors or nurses when the wrong drug comes to hand, this seems like it should be a priority when looking at ways to reduce EMR alert fatigue.