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Practice’s EMR Implementation Drove Up Costs For Six Months

Posted on September 28, 2018 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.

Everyone knows that providers incur EMR-related costs until well after it is implemented. According to a new study, in fact, one medical incurred higher costs for six months after its implementation.

The study, which appeared recently in The Journal of Bone & Joint Surgery, calculated the impact of an EMR implementation on labor costs and productivity at an outpatient orthopedic clinic. The researchers conducting the study used time-driven activity-based costing to estimate EMR-related expenses.

To conduct the study, the research team timed 143 patients prospectively throughout their clinic visit, both before implementation of the hospital system-wide EMR and then again at two months, six months and two years after the implementation.

The researchers found that after the first two months, total labor costs per patient had shot up from $36.88 to $46.04.

One reason for the higher costs was a growth in the amount of time attending surgeons spent per patient, which went up from 9.38 to 10.97 minutes, increasing surgeon cost from $21 to $27.01. In addition, certified medical assistants for spending what time assessing patients, with the time spent almost tripling from 3.42 to 9.1 minutes.

On top of all of this, providers were spending more than twice as much time documenting patient encounters as they had before, up to 7.6 minutes from 3.3 minutes prior to the implementation.

By the six-month mark, however, labor costs per patient had largely returned to their previous levels, settling at $38.75 compared with $36.88 prior to the installation, and expense which remain at the same level when calculated at two years after the EMR implementation.

However, providers were spending even more time documenting encounters than they had before the rolling, with time climbing to 8.43 minutes or roughly 5 minutes more than prior to the introduction of the EMR. Not only that, providers were spending less time interacting with patients, falling to 10.03 as compared with 14.65 minutes in the past.

Sadly, we might have been able to predict this outcome. Clearly, the clinic’s EMR implementation has burdened its providers and further minimized time the providers spend with their patients. This, unfortunately, is more of a rule than an exception.

So why did the ortho practice even bother? It’s hard to say. The study doesn’t say what the practice hoped to accomplish by putting the EMR in place, or whether it met those goals. Given that the system was still in place after two years one would hope that it was providing some form of value.

Truthfully, I’d much rather have learned about what the clinic actually got for its investment than how long it took to get everyone trained up and using it. To be fair, though, this data might have some relevance to the hospital systems that manage a broad spectrum of medical practices, and that’s worth something.

Hospitals, Doctors And Patients Impacted By Unplanned EHR Downtime

Posted on June 18, 2018 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.

EHRs are going to crash and go offline from time to time. But are physicians and hospitals prepared to deal with the fallout when this happens? The answer seems to be “maybe.”

Of course, physicians and hospitals have plenty of reasons to avoid EHR downtime.

For one thing, EHR crashes can have a major impact on care delivery. After all, without EHRs, physicians may have no access to patient data, which could lead to care complications or adverse events.

Also, downtime adds addition pain (and expense) to the situation. According to one estimate, unplanned system failures can cost $634 per physician per hour. Meanwhile, according to Dean Sitting of the University of Texas, a large hospital may lose as much as $1 million per hour when their EHR is down. Those are scary numbers.

Unfortunately, despite the costs, strain to the hospital operations and consumer complaints arising from downtime, many hospitals refuse to invest in preventive technologies such as a backup data center, arguing that they’re just too expensive. As a result, hospitals can be offline for a long time when their EHR system crashes, which typically has a nasty ripple effect.

One example of how EHR downtime affects hospital operations comes from Sutter Health, the largest health system in northern California, whose EHR went offline for more than 24 hours in May. The crash took place when a fire-suppression system was activated in the system’s data center.

During the shutdown, Sutter hospitals followed a series of steps often used by its peers, such as cutting elective surgeries, transporting patients to other hospitals and discharging patients who weren’t very sick. They also switched over to paper records. But despite these efforts, Sutter still faced some problems that weren’t addressed by its plans.

For one thing, younger doctors were thrown a curve ball, as many had never worked with paper charts. This alone gummed up the works during the downtime episode. There were no signs that these doctors made any mistakes due to using paper records, but the risk was there.

Then there were the effects on patients – and some were ugly. For example, when Santa Clara resident Susan Harkema’s father died, she called Sutter Health’s Hospital of the Valley to arrange for removal of his body to a crematorium. According to a story appearing in San Jose Mercury News, Harkema tried a hotline and backup numbers but couldn’t reach anyone due to the outage. It took 8 hours for a hospice nurse to arrive and collect the body, the newspaper reported.

Another patient tweeted that they had to go out of the Sutter system for critical care, which left the treating physicians without care history to review. “It was stressful and scary, and we still aren’t sure we have a successful outcome,” they said.

The net of all of this seems to be that hospital downtime policies could use more than a few tweaks, and more importantly, a better failsafe protecting EHRs from going offline in the first place. Sure, no EHR system is perfect, and crashes are inevitable, but providers can be better prepared.

Patient Demand For Digital Health Tools May Exceed Providers’ Ability To Deliver

Posted on March 15, 2018 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.

It’s taken a long time, but it looks like consumers are getting serious about using digital tools to improve their health. According to a new survey by Accenture, in some cases consumers are actually more interested in using such tools than their providers are, researchers found.

Patients are taking advantage of a wide range of digital health options, including mobile tech (46%), accessing electronic health records (38%), social media (35%), wearable technology (33%), smart scales (27%), remote consultations (16%) and remote monitoring (14%). All of these numbers are up from 2017, notably mobile and access to electronic health records, use of which grew 10% and 9% respectively.

The survey also notes that the number of consumers receiving virtual healthcare services has increased since last year, from 21% in 2017 to 25% this year. Seventy-four percent of those accessing virtual care were satisfied with the encounter. Meanwhile, about three-quarters of consumers said they would use virtual care for after-hours appointments, and about two-thirds would choose this option for follow-up appointments after seeing a doctor in person.

Key takeaways for clinicians, meanwhile, include that while patients agree that in-person visits provide quality care, engage patients in their health care decisions and diagnose problems faster, virtual visits offer some significant advantages too. Virtual care benefits they identified include reducing medical costs to patients, accommodating patient schedules and providing timely care, respondents said.

Clinicians should also note that AI-based virtual doctors may someday become the competition. When asked whether they would use an AI virtual doctor provided by their provider, some were doubtful, with 29% saying they prefer visiting the doctor, 26% that they didn’t understand enough about how AI works, and 23% that they did not want to share their data.

However, 47% said they would choose a virtual doctor because it would be available whenever they needed it. Also, 36% said they’d use a virtual doctor because it would save time by avoiding a trip to the doctor, and 24% said they’d like to access a virtual doctor because the AI would have access to large amounts of relevant information.

Right now, it’s far more likely that hospitals will have the capacity to deliver such services, which may demand a higher level of IT expertise and staff time that many medical practices have available. Nonetheless, it seems likely that at some point, medical practices will need to offer more digital services if they want to remain competitive.

Medical Groups Adopting Telehealth, But Cautiously

Posted on February 5, 2018 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.

Telehealth has gone from a neat idea to an accepted part of the spectrum of care. However, it’s largely been hospitals, not doctors, which have dived into telemedicine wholeheartedly

Recent data suggests that while doctors are gradually adopting telecare, they have many reservations about doing so. A study published last year by Reaction Data found that while 68% of physicians said they were in favor of telemedicine, most were using it only in special situations such as reaching patients in rural areas, visit follow-ups and managing specific patient populations.

A new survey by the Medical Group Management Association has reached a similar conclusion. In a poll conducted last month by the trade group, the MGMA found medical practices’ approaches to telemedicine have changed only marginally since January of last year.

In this year’s Stat poll, which had 1,292 respondents, 26% of respondents said their organization offered telehealth services, and another 15% said they planned to offer them in the future. That’s up only 3% from January 2017 research, which found that 23% of respondents provided such services and 18% planned to add them.

Meanwhile, two key statistics have stayed in place from last year. Thirty-nine percent of respondents to this year’s survey said they didn’t offer telehealth services and 20% weren’t sure if they would, the same percentages found in last year’s research.

When it announced the results, MGMA shared some specific suggestions for planning and implementing a telehealth program. They include:

  • Researching and understanding patient needs
  • Setting clear goals for telehealth and tying them to an existing strategic plan, which demands fewer organizational changes and speeds adoption
  • Understanding how telehealth supports value-based care
  • Researching telehealth vendors and platforms
  • Researching reimbursement and licensure requirements (if any) in the practice environment
  • Engaging and educating practice staff members on telehealth issues and strategies
  • Having doctors reach out to colleagues in their specialty to learn how their telehealth implementation experience has gone
  • Bearing in mind that telehealth implementations typically take an average of one year from plan to rollout

All that being said, it seems likely that some of the practices which are hanging back from telehealth have taken most or even all of the steps outlined above. The thing is, even if a practice has researched the telemedicine market, understands its patients’ needs and knows what issues it will face during a service rollout, these steps still can’t address some of the fundamental realities holding telehealth back today.

The truth is, from what I’ve seen medical practices still face two difficult issues when they consider telehealth seriously: how to make money at it and how to fit it into their workflow. These are major problems and won’t be resolved by advice alone (not that this is MGMA’s fault of course).

Despite medical groups’ concerns, there will doubtless be a tipping point where practices begin to see telehealth services as a routine part of what they provide. However, it seems clear that we’re far from getting there.

Coping With The Loss Of Your Ambulatory EMR

Posted on December 13, 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.

Despite the struggles involved, most practices seem to have settled in with an EMR they can at least tolerate. Their workflows are, well, working, the practice management features seem to connect with the clinical ones and most clinicians are complaining about using it.

Yes, your practice may have had to go through a few systems before you found one everyone liked, wasn’t too expensive and had decent technical support to offer.  By this time, though you may have been a little scarred by the experience, hopefully practice leaders have gotten comfortable with the central role the EMR plays in the practice.

Then, you decide it makes sense to sell your practice to the local health system. It could be because it’s an irresistible deal financially, or you feel you can’t survive without their help and partnership, or any number of additional reasons. Everything looks good, but then you take a hit: your new “partner” wants to dump the EMR you worked so hard to find and customize. They want you to work on the same enterprise system they do.

Now, from a hospital’s perspective that may make sense. Here’s how one consulting firm lays things out:

“[When acquiring a medical practice] one critical issue is how to transition the workflow of these physicians and their staff from the practice-owned ambulatory EMR to the centralized hospital-owned EMR to ensure the efficient and safe delivery of care to patients,” it tells its hospital customers. In other words, it’s a question of when and how, not IF the hospital should require acquired practices to make the switch.

The thing is, while the hospital may have a comparatively large staff dedicated to integrating and managing the data pulled in from your ambulatory EMR, the reverse is probably not true. Unless your practice is particularly large, it probably only includes 5 to 10 doctors. In such practices, having even a single data expert on staff would be unusual. (Not to mention that hiring one part-time or as a consultant wouldn’t be cheap.)

In other words, for a while you may be fishing for your patients’ data as you transition to the larger team to which you will belong. Also, until the hospital health system completes integrating the data from your practice into its enterprise system, you may or may not have access to quality metrics important to running a practice these days, and the effect on your billing practices could turn out to be a disaster too.

At this point, I’m supposed to stop and tell you that all this can be handled efficiently if you take one step or the other. Unfortunately, I’m not sure there is any great happy ending to suggest at this point. If you have to give up your own ambulatory EMR, it’s probably going to be painful.

However, it doesn’t hurt to be prepared. There probably are some strategies, perhaps unique to your practice, that can blunt the impact of some of these problems if you’re prepared. That said, the move to a new EMR is always painful, even if the change ends up being a good one.

Burnout is Overused and Under Defined

Posted on December 8, 2017 I Written By

When Carl Bergman isn't rooting for the Washington Nationals or searching for a Steeler bar, he’s Managing Partner of EHRSelector.com.For the last dozen years, he’s concentrated on EHR consulting and writing. He spent the 80s and 90s as an itinerant project manager doing his small part for the dot com bubble. Prior to that, Bergman served a ten year stretch in the District of Columbia government as a policy and fiscal analyst, a role he recently repeated for a Council member.

Recently, John hosted a #HITsm chat on using technology to fight physician burnout (Read the full transcript from the chat here). The topic’s certainly timely, and it got me to wondering just what is physician burnout. Now, the simple answer is fatigue. However, when I started to look around for studies and insights, I realized that burnout is neither easily defined nor understood.

The Mayo Clinic, among others, defines it this way:

Job burnout is a special type of job stress — a state of physical, emotional or mental exhaustion combined with doubts about your competence and the value of your work. 

So, it is fatigue plus self doubt. Well, that’s for starters. Burnout has its own literature niche and psychologists have taken several different cracks at a more definitive definition without any consensus other than it’s a form of depression, which doesn’t have to be work related.

Unsurprisingly, burnout is not in the DSM-5. It’s this lack of a clinical definition, which makes it easy to use burnout like catsup to cover a host of issues. I think this is exactly why we have so many references to physician or EHR burnout. You can use burnout to cover whatever you want.

It’s easy to find articles citing EHRs and burnout. For example, a year ago April, The Hospitalist headlined, “Research Shows Link Between EHR and Physician Burnout.” The article then flatly says, “The EHR has been identified as a major contributor to physician burnout.” However, it never cites a study to back this up.

If you track back through its references, you’ll wind up at a 2013 AMA study, “Factors Affecting Physician Professional Satisfaction and Their Implications for Patient Care, Health Systems, and Health Policy.” Developed by the Rand Corporation, it’s an extensive study of physician job satisfaction. Unfortunately, for those who cite it for EHR and burnout, it never links the two. In fact, the article never discusses the two together.

Not surprisingly, burnout has found its way into marketing. For example, DataMatrix says:

Physician burnout can be described as a public health crisis especially with the substantial increase over the last couple of years. The consequences are significant and affect the healthcare system by affecting the quality of care, health care costs and patient safety.

Their solution, of course, is to buy their transcription services.

What’s happened here is that physician work life dissatisfaction has been smushed together with burnout, which does a disservice to both. For example, Medscape recently published a study on burnout, which asked physicians about their experience. Interestingly, the choices it gave, such as low income, too many difficult patients – difficult being undefined — are all over the place.

That’s not to say that all physician burnout studies are useless. A recent study, Electronic Health Record Effects on Work-Life Balance and Burnout Within the I3 Population Collaborative, used a simple, five item scale to ask physicians how they viewed their work life. See Figure 1.

Figure 1 Single-Item Burnout Scale.

Their findings were far more nuanced than many others. EHRs played a role, but so did long hours. They found:

EHR proficiency training has been associated with improved job satisfaction and work-life balance.14 While increasing EHR proficiency may help, there are many potential reasons for physicians to spend after-hours on the EHR, including time management issues, inadequate clinic staffing, patient complexity, lack of scribes, challenges in mastering automatic dictation systems, cosigning resident notes, messaging, and preparing records for the next day. All of these issues and their impact on burnout and work-life balance are potential areas for future research.

There’s a need to back off the burnout rhetoric. Burnout’s overused and under defined. It’s a label for what may be any number of underlying issues. Subsuming these into one general, glitzy term, which lacks clinical definition trivializes serious problems. The next time you see something defined as physician or EHR burnout, you might just ask yourself, what is that again?

EHR-Based Order Prioritization Could Streamline MRI Use

Posted on December 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.

New research suggests that the overuse of STAT requests for MRIs could be trimmed down considerably if criteria for using such requests were integrated into healthcare organizations’ EHRs. The study also suggests, indirectly at least, that adding timing requests for various procedures into EHRs could help with overall workflow in many facilities.

Researchers from Emory University School of Medicine in Atlanta, who presented their findings at the RSNA 2017 show last month, found that the volume of STAT brain MRIs had increased to the point where 60% of all MRI orders were ordered as STAT between 2012 and 2015.

The increasing use of the STAT designation has ended up creating a bottleneck, researchers concluded. They found that the volume of STAT requests for brain MRIs was so high that it actually led to delays in turnarounds for those studies. In fact, they found that the mean turnaround time for STAT brain MRIs was roughly 50% longer than routine brain MRIs (23.43 hours versus 15.46 hours).

Among the sources of this problem, it seems, is that few clinicians were aware of the hospital’s policy for STAT MRIs. In an online survey of 97 providers, only 4% were aware that a STAT imaging study should be initiated within 30 minutes of the order. Instead, many expected that a stat MRI would be completed within the same day for inpatients within 2 to 3 days for outpatients, according to a story appearing in Radiology Business.

To address this problem, the researchers are proposing that hospitals add order prioritization criteria to their EHR.  These criteria will include definitions and clinical examples to help clinicians sort out which category to use when ordering a brain MRI.

This approach would also help clinicians better understand how the institution defines normal versus STAT priority for imaging orders. The researchers are recommending that hospitals include EMR documentation defining both STAT and routine categories, as well as a statement of when they can expect imaging to be completed under each category.

Adding categories and definitions of when imaging orders should be categorized as STAT would actually appeal to clinicians, the study suggests. Researchers found that more than 70% of clinicians said they would find clinical examples of an order prioritization scheme useful. What’s more, 84% of clinicians responding to the study said they would order routine MRIs if they were assured the studies would be completed within 24 hours.

The authors admitted that integrating order prioritization schemes for imaging could be time-consuming for IT departments, which suggests that finding other ways to set these priorities over the short term is probably a good idea. But given how supportive clinicians seem to be the idea of improving order turnaround, it seems likely that the EHR integration work should get done before too long.

EHRs and Keyboarding: Is There an Answer?

Posted on November 28, 2017 I Written By

When Carl Bergman isn't rooting for the Washington Nationals or searching for a Steeler bar, he’s Managing Partner of EHRSelector.com.For the last dozen years, he’s concentrated on EHR consulting and writing. He spent the 80s and 90s as an itinerant project manager doing his small part for the dot com bubble. Prior to that, Bergman served a ten year stretch in the District of Columbia government as a policy and fiscal analyst, a role he recently repeated for a Council member.

One of the givens of EHR life is that users, especially physicians, spend excessive time keying into EHRs. The implication is that much keyboarding is due to excessive data demands, poor usability or general app cussedness. There’s no end of studies that support this. For example, a recent study at the University of Wisconsin-Madison’s Department of Family Medicine and Community Health in the Annals of Family Medicine found that:

Primary care physicians spend more than one-half of their workday, nearly 6 hours, interacting with the EHR during and after clinic hours. The study broke out times spent on various tasks and found, unsurprisingly, that documentation and chart review took up almost half the time.

Figure 1. Percent Physician’s Time on EHR

This study is unique among those looking at practitioners and EHRs. They note:

Although others have suggested work task categories for primary care,13 ours is the first taxonomy proposed to capture routine clinical work in EHR systems. 

They also make the point that they captured physician EHR use not total time spent with patients. Other studies have reached similar EHR use conclusions. The consensus is there too much time keyboarding and not enough time spent one to one with the patient. So, what can be done? Here, I think, are the choices:

  1. Do Nothing. Assume that this is a new world and tough it out.
  2. Use Scribes. Hire scribes to do the keyboarding for physicians.
  3. Make EHRs Easier. Improve EHRs’ usability.
  4. Make EHRs Smarter. Adapt EHRs to physician’s needs through artificial intelligence (AI) solutions.
  5. Offload to Patients. Use patient apps to input data, rather than physician keyboarding.

Examining the Alternatives

 1. Do Nothing. Making no change in either the systems or practioners’ approach means accepting the current state as the new normal. It doesn’t mean that no changes will occur. Rather, that they will continue at an incremental, perhaps glacial, pace. What this says more broadly is that the focus on the keyboard, per se, is wrong. The question is not what’s going in so much as what is coming out compared to old, manual systems. For example, when PCs first became office standards, the amount of keyboarding vs. pen and paper notations went viral. PCs produced great increases in both the volume and quality of office work. This quickly became the new norm. That hasn’t happened with EHRs. There’s an assumption that the old days were better. Doing nothing acknowledges that you can’t go back. Instead, it takes a stoic approach and assumes things will get better eventually, so just hang in there.

2. Scribes. The idea of using a scribe is simple. As a doctor examines a patient, the scribe enters the details. Scribes allow the physician to offload the keyboarding to someone with medical knowledge who understands their documentation style. There is no question that scribes can decrease physician keyboarding. This approach is gaining in popularity and is marketed by various medical societies and scribe services companies.

However, using scribes brings a host of questions. How are the implemented? I think the most important question is how a scribe fits into a system’s workflow. For example, how does an attending review a scribe’s notes to determine they convey the attending’s clinical findings, etc. The attending is the responsible party and anything that degrades or muddies that oversight is a danger to patient safety. Then, there are questions about patient privacy and just how passive an actor is a scribe?

If you’re looking for dispositive answers, you’ll have to wait. There are many studies showing scribes improve physician productivity, but few about the quality of the product.

3. Make EHRs Easier. Improving EHR usability is the holy grail of health IT and about as hard to find. ONC’s usability failings are well known and ongoing, but it isn’t alone. Vendors know that usability is something they can claim without having to prove. That doesn’t mean that usability and its good friend productivity aren’t important and are grossly overdue. As AHRQ recently found:

In a review of EHR safety and usability, investigators found that the switch from paper records to EHRs led to decreases in medication errors, improved guideline adherence, and (after initial implementation) enhanced safety attitudes and job satisfaction among physicians. However, the investigators found a number of problems as well.

These included usability issues, such as poor information display, complicated screen sequences and navigation, and the mismatch between user workflow in the EHR and clinical workflow. The latter problems resulted in interruptions and distraction, which can contribute to medical error.

Additional safety hazards included data entry errors created by the use of copy-forward, copy-and-paste, and electronic signatures, lack of clarity in sources and date of information presented, alert fatigue, and other usability problems that can contribute to error. Similar findings were reported in a review of nurses’ experiences with EHR use, which highlighted the altered workflow and communication patterns created by the implementation of EHRs.

Improving EHR usability is not a metaphysical undertaking. What’s wrong and what works have been known for years. What’s lacking is both the regulatory and corporate will to do so. If all EHRs had to show their practical usability users would rejoice. Your best bet here may be to become active in your EHR vendor’s user group. You may not get direct relief, but you’ll have a place, albeit small, at the table. Otherwise, given vendor and regulatory resistance to usability improvements, you’re better off pushing for a new EHR or writing your own EHR front end.

4. Make EHRs Smarter. If Watson can outsmart Kent Jennings, can’t artificial Intelligence make EHRs smarter? As one of my old friends used to tell our city council, “The answer is a qualified yes and a qualified no.”

AI takes on many, many forms and EHRs can and do use it. Primarily, these are dictation – transcription assistant systems. They’re known as Natural Language Processing (NLP). Sort of scribes without bodies. NLP takes a text stream, either live or from a recording, parses it and puts it in the EHR in its proper place. These systems combine the freedom of dictation with AI’s ability to create clinical notes. That allows the theory maintains, a user to maintain patient contact while creating the note, thus solving the keyboarding dilemma.

 The best-known NLP system Nuance’s Dragon Medical One, etc. Several EHR vendors have integrated Dragon or similar systems into their offerings. As with most complex, technical systems, though, NLP implementation requires a full-scale tech effort. Potential barriers are implementation or training shortcuts, workflow integration, and staff commitment. NLP’s ability to quickly gather information and place it is a given. What’s not so certain is its cost-effectiveness or its product quality. In those respects, its quality and efficacy is similar to scribes and subject to much the same scrutiny.

One interesting and wholly unexpected NLP system result occurred in a study by the University of Washington Researchers. The study group used an Android app NLP dictation system, VGEENS, that captured notes at the bedside. Here’s what startled the researchers:

….Intern and resident physicians were averse to creating notes using VGEENS. When asked why this is, their answers were that they have not had experience with dictation and are reluctant to learn a new skill during their busy clinical rotations. They also commented that they are very familiar with creating notes using typing, templates, and copy paste.

The researchers forgot that medical dictation skills are just that, a skill and don’t come without training and practice. It’s a skill of older generations and that keyboarding is today’s given. 

5. Offload to Patients. I hadn’t thought of this one until I saw an article in the Harvard Business Review. In a wide-ranging review, the authors saw physicians as victims of medical overconsumption and information overload:

In our recent studies of how patients responded to the introduction of a portal allowing them to e-mail health concerns to their care team, we found that the e-mail system that was expected to substitute for face-to-face visits actually increased them. Once patients began using the portal, many started sharing health updates and personal news with their care teams.

One of their solutions is to offload data collection and monitoring to patient apps:

Mightn’t we delegate some of the screening work to patients themselves? Empowering customers with easy-to-use tools transformed the tax reporting and travel industries. While we don’t expect patients to select what blood-pressure medications to be on, we probably can offload considerable amounts of the monitoring and perhaps even some of the treatment adjustment to them. Diabetes has long been managed this way, using forms of self-care that have advanced as self-monitoring technology has improved.

This may be where we are going; however, it ignores the already crowded app field. Moreover, every app seems to have its own data protocol. Health apps are a good way to capture and incorporate health data. They may be a good way to offload physicians’ keyboarding, but health apps are a tower of protocol Babel right now. This solution is as practical as saying that the way to curb double entering data in EHRs is to just make them interoperable.

What’s an EHR User to Do?

If each current approach to reducing keyboarding has problems, they are not fatal. I think that physician keyboarding is a problem and that it is subject to amelioration, if not solution.

For example, here’s Nordic’s Joel Martin on EHR usability:

… In reality, much of this extra work is a result of expanded documentation and quality measure requirements, security needs, and staffing changes. As the healthcare industry shifts its focus to value-based reimbursement and doing more with less, physician work is increasing. That work often takes place in the EHR, but it isn’t caused by the EHR’s existence.

Blaming the EHR without optimizing its use won’t solve the problem. Instead, we should take a holistic view of the issues causing provider burnout and use the system to create efficiencies, as it’s designed to do.  

The good news is that optimizing the EHR is very doable. There are many things that can be done to make it easier for providers to complete tasks in the EHR, and thereby lower the time spent in the system.

Broadly speaking, these opportunities fall into two categories.

First, many organizations have not implemented all the time-saving features that EHR vendors have created. There are features that dramatically lower the time required to complete EHR tasks for common, simple visits (for instance, upper respiratory infections). We rarely see organizations that have implemented these features at the time of our assessments, and we’re now working with many to implement them.

In addition, individual providers are often not taking advantage of features that could save them time. When we look at provider-level data, we typically see fewer than half of providers using speed and personalization features, such as features that let them rapidly reply to messages. These features could save 20 to 30 minutes a day on their own, but we see fewer than 50 percent of providers using them.

Optimization helps physicians use the EHR the way it was intended – in real-time, alongside patient care, to drive better care, fewer mistakes, and higher engagement. Ultimately, we envision a care environment where the EHR isn’t separate from patient care, but rather another tool to provide it. 

What does that mean for scribes or NLP? Recognize they are not panaceas, but tools. The field is constantly changing. Any effort to address keyboarding should look at a range of independent studies to identify their strengths and pitfalls. Note not only the major findings but also what skills, apps, etc., they required. Then, recognize the level of effort a good implementation always requires. Finally, as UW’s researchers found, surprises are always lurking in major shake-ups.

Join us for this week’s #HITsm chat on Using Technology to Fight EHR Burnout to discuss this topic more.

EHR Data Allows Hospital To Find C. Diff Source

Posted on October 26, 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.

Here’s a kind of story that makes you feel better about your EHR investment. A new journal article is reporting that researchers were able to find a source of Clostridium difficile within a hospital, not with elaborate big data analytics but simply by using basic EHR data.

According to the item, which appeared in JAMA Internal Medicine, a group of researchers examined EHR data on time and location to map roughly 435,000 patient location changes at the University of California San Francisco Medical Center. The effort was led by Russ Cucina, chief health information officer at UCSF.

After analyzing overall data, the researchers found a total of 1,152 cases of laboratory-documented CDI. The data indicated that CDI-positive patients moved through an average of four locations during their hospitalization, but that the CDI events came from a single location.

Researchers concluded that when patients were exposed to C. diff infections in the emergency department’s CT scanner, it was associated with a 4% incidence of CDI. They also noted that the association between CT exposure and CDI was still significant even after adjusting other influences such as antibiotic use and patients’ length of hospital stay. The association also remained significant when their sensitivity analysis extended the incubation period from 24 to 72 hours.

Having identified the CT as a potential vector of infection, the hospital next looked at how the that happened. It found that cleaning practices for the device didn’t meet the standards set for other radiology suites, and took steps to address the problem.

While healthcare leaders will ultimately use EHR data to make broad process changes, addressing day-to-day problems that impact care is also valuable. After all, finding the source of CDI is no trivial manner.

For example, a study recently concluded that ambulatory care organizations can do a pretty good job of analyzing their workflow by using EHR timestamp data.

Researchers had developed the study, a write up of which appeared in the Journal of the American Medical Informatics Association, to look at how such data be could be used in outpatient settings. Aware that many outpatient organizations don’t have the resources to conduct workflow studies, the researchers looked for alternatives.

During the research process, the team began by studying the workflow at four outpatient ophthalmology clinics associated with the Oregon Health and Science University, timing each workflow step. They then mapped the EHR timestamps to the workflow timings to see how they compared.

As it turned out, the workflow times generated by analyzing EHR timestamps were within three minutes of observed times for more than 80% of the clinics’ appointments. The study offers evidence that outpatient organizations can examine their workflow without spending a fortune, using data they already collect automatically.

Of course, hospitals will continue to do more in-depth workflow analyses using higher-end tools like big data analytics software. These efforts will provide a multidimensional picture that wouldn’t be available using only timestamp analysis.  But for hospitals and clinics with fewer resources, timestamp analysis may be a starting point for some useful research.

Mercy Shares De-Identified Data With Medtronic

Posted on October 20, 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.

Medtronic has always performed controlled clinical trials to check out the safety and performance of its medical devices. But this time, it’s doing something more.

Dublin-based Medtronic has signed a data-sharing agreement with Mercy, the fifth largest Catholic health system in the U.S.  Under the terms of the agreement, the two are establishing a new data sharing and analysis network intended to help gather clinical evidence for medical device innovation, the company said.

Working with Mercy Technology Services, Medtronic will capture de-identified data from about 80,000 Mercy patients with heart failure. The device maker will use that data to explore real-world factors governing their response to Cardiac Resynchronization Therapy, a heart failure treatment option which helps some patients.

Medtronic believes that the de-identified patient data Mercy supplies could help improve device performance, according to Dr. Rick Kuntz, senior vice president of strategic scientific operations with Medtronic. “Having the ability to study patient care pathways and conditions before and after exposure to a medical device is crucial to understanding how those devices perform outside of controlled clinical trial setting,” said Kuntz in a prepared statement.

Mercy’s agreement with Medtronic is not unique. In fact, academic medical centers, pharmaceutical companies, health insurers and increasingly, broad-based technology giants are getting into the health data sharing game.

For example, earlier this year Google announced that it was expanding its partnerships with three high-profile academic medical centers under which they work to better analyze clinical data. According to Healthcare IT News, the partners will examine how machine learning can be used in clinical settings to sift through EMR data and find ways to improve outcomes.

“Advanced machine learning is mature enough to start accurately predicting medical events – such as whether patients will be hospitalized, how long they will stay, and whether the health is deteriorating despite treatment for conditions such as urinary tract infections, pneumonia, or heart failure,” said Google Brain Team researcher Katherine Chou in a blog post.

As with Mercy, the academic medical centers are sharing de-identified data. Chou says that offers plenty of information. “Machine learning can discover patterns in de-identified medical records to predict what is likely to happen next, and thus, anticipate the needs of the patients before they arise,” she wrote.

It’s worth pointing out that “de-identification” refers to a group of techniques for patient data protection which, according to NIST, include suppression of personal identifiers, replacing personal identifiers with an average value for the entire group of data, reporting personal identifiers as being within a given range, exchanging personal identifiers other information and swapping data between records.

It may someday become an issue when someone mixes up de-identification (which makes it quite difficult to define specific patients) and anonymization, a subcategory of de-identification whereby data can never be re-identified. Such confusion would, in short, be bad, as the difference between “de-identified” and “anonymized” matters.

In the meantime, though, de-identified data seems likely to help a wide variety of healthcare organizations do better work. As long as patient data stays private, much good can come of partnerships like the one underway at Mercy.