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Hospital EMR Adoption Divide Widening, With Critical Access Hospitals Lagging

Posted on September 8, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

I don’t know about you, but I was a bit skeptical when HIMSS Analytics rolled out its EMRAM {Electronic Medical Record Adoption Model) research program. As some of you doubtless know, EMRAM breaks EMR adoption into eight stages, from Stage 0 (no health IT ancillaries installed) to Stage 7 (complete EMR installed, with data analytics on board).

From its launch onward, I’ve been skeptical about EMRAM’s value, in part because I’ve never been sure that hospital EMR adoption could be packaged neatly into the EMRAM stages. Perhaps the research model is constructed well, but the presumption that a multivariate process of health IT adoption can be tracked this way is a bit iffy in my opinion.

On the other hand, I like the way the following study breaks things out. New research published in the Journal of the American Medical Informatics Association looks at broader measures of hospital EHR adoption, as well as their level of performance in two key categories.

The study’s main goal was to assess the divide between hospitals using their EHRs in an advanced fashion and those that were not. One of the key steps in their process was to crunch numbers in a manner allowing them to identify hospital characteristics associated with high adoption in each of the advanced use criteria.

To conduct the research, the authors dug into 2008 to 2015 American Hospital Association Information Technology Supplement survey data. Using the data, the researchers measured “basic” and “comprehensive” EHR adoption among hospitals. (The ONC has created definitions for both basic and advanced adoption.)

Next, the research team used new supplement questions to evaluate advanced use of EHRs. As part of this process, they also used EHR data to evaluate performance management and patient engagement functions.

When all was said and done, they drew the following conclusions:

  • 80.5% of hospitals had adopted a basic EHR system, up 5.3% from 2014
  • 37.5% of hospitals had adopted at least 8 (of 10) EHR data sets useful for performance measurement
  • 41.7% of hospitals adopted at least 8 (of 10) EHR functions related to patient engagement

One thing that stood out among all the data was that critical access hospitals were less likely to have adopted at least 8 performance measurement functions and at least eight patient engagement functions. (Notably, HIMSS Analytics research from 2015 had already found that rural hospitals had begun to close this gap.)

“A digital divide appears to be emerging [among hospitals], with critical-access hospitals in particular lagging behind,” the article says. “This is concerning, because EHR-enabled performance measurement and patient engagement are key contributors to improving hospital performance.”

While the results don’t surprise me – and probably won’t surprise you either – it’s a shame to be reminded that critical access hospitals are trailing other facilities. As we all know, they’re always behind the eight ball financially, often understaffed and overloaded.

Given their challenges, it’s predictable that critical access hospitals would continue lag behind in the health IT adoption curve. Unfortunately, this deprives them of feedback which could improve care and perhaps offer a welcome boost to their efficiency as well. It’s a shame the way the poor always get poorer.

An Approach For Privacy – Protecting Big Data

Posted on February 6, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

There’s little doubt that the healthcare industry is zeroing in on some important discoveries as providers and researchers mine collections of clinical and research data. Big data does come with some risks, however, with some observers fearing that aggregated and shared information may breach patient privacy. However, at least one study suggests that patients can be protected without interrupting data collection.

In what it calls a first, a new study appearing in the Journal of the American Medical Informatics Association has demonstrated that protecting the privacy of patients can be done without too much fuss, even when the patient data is pulled into big data stores used for research.

According to the study, a single patient anonymization algorithm can offer a standard level of privacy protection across multiple institutions, even when they are sharing clinical data back and forth. Researchers say that larger clinical datasets can protect patient anonymity without generalizing or suppressing data in a manner which would undermine its use.

To conduct the study, researchers set a privacy adversary out to beat the system. This adversary, who had collected patient diagnoses from a single unspecified clinic visit, was asked to match them to a record in a de-identified research dataset known to include the patient. To conduct the study, researchers used data from Vanderbilt University Medical Center, Northwestern Memorial Hospital in Chicago and Marshfield Clinic.

The researchers knew that according to prior studies, the more data associated with each de-identified record, and the more complex and diverse the patient’s problems, the more likely it was that their information would stick out from the crowd. And that would typically force managers to generalize or suppress data to protect patient anonymity.

In this case, the team hoped to find out how much generalization and suppression would be necessary to protect identities found within the three institutions’ data, and after, whether the protected data would ultimately be of any use to future researchers.

The team processed relatively small datasets from each institution representing patients in a multi-site genotype-disease association study; larger datasets to represent patients in the three institutions’ bank of de-identified DNA samples; and large sets which stood in for each’s EMR population.

Using the algorithm they developed, the team found that most of the data’s value was preserved despite the occasional need for generalization and suppression. On average, 12.8% of diagnosis codes needed generalization; the medium-sized biobank models saw only 4% of codes needing generalization; and among the large databases representing EMR populations, only 0.4% needed generalization and no codes required suppression.

More work like this is clearly needed as the demand for large-scale clinical, genomic and transactional datasets grows. But in the meantime, this seems to be good news for budding big data research efforts.

Physicians Using Paper, Electronic Workarounds To Address EMR Gaps

Posted on March 28, 2013 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 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. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Doctors are frustrated by EMRs, of that there’s little doubt. The question is, how do they cope with the limitations of their particular EMR and get through the day? A new study published in the Journal of the American Medical Informatics Association concludes that doctors and their staff use a combination of paper- and computer-based workarounds to address EMR limitations.

To gather data, the researchers directly observed clinical workflows at several institutions, reports InformationWeek. These included 11 primary care clinics across two VA medical centers, Partners Healthcare in Boston and Indianapolis-based Regenstrief Institute. All told, 120 clinic staff and providers were observed, caring for 118 patients, according to the magazine.

In studying the workarounds, researchers classified them in several ways.

One was dubbed “no correct path,” in which the EMR didn’t provide a way to accomplish a given task and forced clinicians to enter data incorrectly.

Other workarounds involved shortcuts related to efficiency. For example cutting and pasting progress notes, vital signs or health maintenance information was classed as a workaround because it was against hospital policy, InformationWeek notes. Another workaround showed up when a staff member who entered vital signs and health screening responses was absent; patients were asked to fill out paper questionnaires or an interviewer wrote down responses.

Still others involved “memory” — writing reminders on paper — and “awareness” — writing down key patient data so a doctor would have it during an exam.

Not too surprisingly, researchers also noted a class of workarounds related to overcoming design errors in EMRs, such as a case in which a doctor couldn’t enter an order with one EMR without closing the progress note he was working on, because if he made the order with the progress note open, the note would be deleted. The unfortunate physician had to log out then log in again to enter orders.

In perhaps the ultimate workaround, some physicians were observed to keep separate paper notes because they weren’t confident that the EMR would be available when they needed it.  Now that’s a vote of no confidence.

Beware: EMR Installs Could Slow ED Throughput

Posted on November 9, 2011 I Written By

Katherine Rourke 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.

It’s hard to argue that in the wake of an EMR install, some processes are likely to slow down or even break. What makes the following study interesting is that it attempts to do something intriguing —  sorting out how EMRs  affect emergency department throughput they’re implemented.

A new study appearing in the Journal of the American Medical Informatics Association concludes that EMR installs have a distinct impact on ED processes, as well as patient length of stay.

Researchers with the Cincinnati Children’s Hospital Medical Center attempted to track the impact an EMR install had there by looking at how often non-acute patients got routed to alternate sites.  Specifically, they looked at how often potential victims of the H1N1 flu virus were routed to non-acute sites before and after the Center did its implementation.

The hospital phased its EMR rollout in over two years, setting the ED section of the rollout for November 2009, a date which overlapped with the peak of t he H1N1 outbreak in its region. During that period, the hospital set up an overflow clinic — staffed by non-ED providers — to deal with patients who had flu-like illnesses.

The overflow clinic began seeing 50 to 60 patients per day, 10 to 20 percent of the ED’s volume, within two weeks, but plunged to pre-surge levels by November 2009, researchers reported in JAMIA.  While 10 percent or more of patients were being diverted prior to the ED rollout, that total fell to 5 percent afterwards, the study concluded.

Another intriguing finding was that length of stay in the ED went up markedly during the implementation. LOS in the emergency department was 24 to 53 minutes for admissions, and 9 to 19 minutes for discharges prior to the EMR rollout; during EMR implementation, LOS for both groups was greater than the pre-overflow clinic block and the H1N1 overflow clinic block by 32 to 62 minutes for admits and 35 to 44 minutes for discharges.  If reproducible, those are some serious numbers.

Of course, there’s a long ton of confounding factors here, including but not limited to the fact that patient flow in the H1N1 outbreak may be significantly different in important ways than the standard patient population. For all I know, the H1N1 diversions were not too hard to identify, which could mean that the EMR would  have had a worse impact if the virus wasn’t raging.

That being said, the question it asks — what impact the EMR rollout has on the “front door to the  hospital” — is one that deserves more attention. Rest assured that if I get more data on this subject I’ll be reporting it here.