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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, a free service for matching users and EHRs. For the last dozen years, he’s concentrated on EHR consulting and writing. He spent the 80s and 90s as an itinerant project manger 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.

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, a free service for matching users and EHRs. For the last dozen years, he’s concentrated on EHR consulting and writing. He spent the 80s and 90s as an itinerant project manger 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.

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.

Usability, Interoperability are Political Questions: We Need an EHR Users Group

Posted on October 6, 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, a free service for matching users and EHRs. For the last dozen years, he’s concentrated on EHR consulting and writing. He spent the 80s and 90s as an itinerant project manger 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.

Over the years, writers on blogs such as this and EMRandHIPAA have vented their frustration with lousy EHR usability and interoperability problems. Usability has shown no real progress unless you count all the studies showing that its shortcomings cost both time and money, drives users nuts, and endangers patient lives.

The last administration’s usability approach confused motion with progress with a slew of roadmaps, meetings and committees. It’s policies kowtowed to vendors. The current regime has gone them one better with a sort of faith based approach. They believe they can improve usability as long it doesn’t involve screens or workflow. Interoperability has seen progress, mostly bottom up, but there is still no national solution. Patient matching requires equal parts data, technique and clairvoyance.

I think the solution to these chronic problems isn’t technical, but political. That is, vendors and ONC need to have their feet put to the fire. Otherwise, in another year or five or ten we’ll be going over the same ground again and again with the same results. That is, interop will move ever so slowly and usability will fade even more from sight – if that’s possible.

So, who could bring about this change? The one group that has no organized voice: users. Administrators, hospitals, practioners, nurses and vendors have their lobbyists and associations. Not to mention telemed, app and device makers. EHR users, however, cut across each of these groups without being particularly influential in any. Some groups raise these issues; however, it’s in their context, not for users in general. This means no one speaks for common, day in day out, EHR users. They’re never at the table. They have no voice. That’s not to say there aren’t any EHR user groups. There are scads, but vendors run almost all of them.

What’s needed is a national association that represents EHR users’ interests. Until they organize and earn a place along vendors, etc., these issues won’t move. Creating a group won’t be easy. Users are widely dispersed and play many different roles. Then there is money. Users can’t afford to pony up the way vendors can. An EHR user group or association could take many forms and I don’t pretend to know which will work best. All I can do is say this:

EHR Users Unite! You Have Nothing to Lose, But Your Frustrations!

There’s a New Medicare ID Coming in April – CMS Dumps SSN

Posted on September 26, 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, a free service for matching users and EHRs. For the last dozen years, he’s concentrated on EHR consulting and writing. He spent the 80s and 90s as an itinerant project manger 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.

Following a 2015 Congressional directive, CMS is abandoning its Social Security based Medicare ID for a new randomly generated one. The new card will be hitting beneficiary’s mailboxes in April with everyone covered by a year later.

The old ID is a SSN plus one letter. The letter says if you are a beneficiary, child, widow, etc. The new will have both letters and numbers. It is wholly random and drops the coding for beneficiary, etc. Fortunately, it will exclude S, L, O, I, B and Z, which can look like numbers. You can see the new ID’s details here.

                           New Medicare ID Card

Claimants will have until 2020 to adopt the new IDs, but that’s not the half of it. For the HIT world, this means many difficult, expensive and time consuming changes. CMS sees this as a change in how it tracks claims. However, its impact may make HIT managers wish for the calm and quiet days of Y2K. That’s because adopting the new number for claims is just the start. Their systems use the Medicare ID as a key field for just about everything they do involving Medicare. This means they’ll not only have to cross walk to the new number, but also their systems will have to look back at what was done under the old.

Ideally, beneficiaries will only have to know their new number. Realistically, every practice they see over the next several years will want both IDs. This will add one more iteration to patient matching, which is daunting enough.

With MACRA Congress made a strong case for Medicare no longer relying on SSNs for both privacy and security reasons. Where it failed was seeing it only as a CMS problem and not as a HIT problem with many twists and turns.

Bringing Zen To Healthcare:  Transformation Through The N of 1

Posted on July 21, 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.

The following essay wasn’t easy to understand. I had trouble taking it in at first. But the beauty of these ideas began to shine through for me when I took time to absorb them. Maybe you will struggle with them a bit yourself.

In his essay, the author argues that if providers focus on “N of 1” it could change healthcare permanently. I think he might be right, or at least makes a good case.  It’s a complex argument but worth following to the end. Trust me, the journey is worth taking.

The mysterious @CancerGeek

Before I share his ideas, I’ll start with an introduction to @CancerGeek, the essay’s author. Other than providing a photo as part of his Twitter home page, he’s chosen to be invisible. Despite doing a bunch of skillful GoogleFu, I couldn’t track him down.

@CancerGeek posted a cloud of interests on the Twitter page, including a reference to being global product manager PET-CT; says he develops hospital and cancer centers in the US and China; and describes himself as an associate editor with DesignPatient-MD.

In the essay, he says that he did clinical rotations from 1998 to 1999 while at the University of Wisconsin-Madison Carbone Comprehensive Cancer Center, working with Dr. Minesh Mehta.

He wears a bow tie.

And that’s all I’ve got. He could be anybody or nobody. All we have is his voice. John assures me he’s a real person that works at a company that everyone knows. He’s just chosen to remain relatively anonymous in his social profiles to separate his social profiles from his day job.

The N of 1 concept

Though we don’t know who @CancerGeek is, or why he is hiding, his ideas matter. Let’s take a closer look at the mysterious author’s N of 1, and decide for ourselves what it means. (To play along, you might want to search Twitter for the #Nof1 hashtag.)

To set the stage, @CancerGeek describes a conversation with Dr. Mehta, a radiation oncologist who served as chair of the department where @CancerGeek got his training. During this encounter, he had an insight which helped to make him who he would be — perhaps a moment of satori.

As the story goes, someone called Dr. Mehta to help set up a patient in radiation oncology, needing help but worried about disturbing the important doctor.

Apparently, when Dr. Mehta arrived, he calmly helped the patient, cheerfully introducing himself to their family and addressing all of their questions despite the fact that others were waiting.

When Dr. Mehta asked @CancerGeek why everyone around him was tense, our author told him that they were worried because patients were waiting, they were behind schedule and they knew that he was busy. In response, Dr. Mehta shared the following words:

No matter what else is going on, the world stops once you enter a room and are face to face with a patient and their family. You can only care for one patient at a time. That patient, in that room, at that moment is the only patient that matters. That is the secret to healthcare.

Apparently, this advice changed @CancerGeek on the spot. From that moment on, he would work to focus exclusively on the patient and tune out all distractions.

His ideas crystallized further when he read an article in the New England Journal of Medicine that gave a name to his approach to medicine. The article introduced him to the concept of N of 1.  All of the pieces began to began to fit together.

The NEJM article was singing his song. It said that no matter what physicians do, nothing else counts when they’re with the patient. Without the patient, it said, little else matters.

Yes, the author conceded, big projects and big processes matter still matter. Creating care models, developing clinical pathways and clinical service lines, building cancer centers, running hospitals, and offering outpatient imaging, radiology and pathology services are still worthwhile. But to practice well, the author said, dedicate yourself to caring for patients at the N of 1. Our author’s fate was sealed.

Why is N of 1 important to healthcare?

Having told his story, @CancerGeek shifts to the present. He begins by noting that at present, the healthcare industry is focused on delivering care at the “we” level. He describes this concept this way:

“The “We” level means that when you go to see a physician today, that the medical care they recommend to you is based on people similar to you…care based on research of populations on the 100,000+ (foot) level.”

But this approach is going to be scrapped over the next 8 to 10 years, @CancerGeek argues. (Actually, he predicts that the process will take exactly eight years.)

Over time, he sees care moving gradually from the managing groups to delivering personalized care through one-to-one interactions. He believes the process will proceed as follows:

  • First, sciences like genomics, proteomics, radionomics, functional imaging and immunotherapies will push the industry into delivering care at a 10,000-foot population level.
  • Next, as ecosystems are built out that support seamless sharing of digital footprints, care will move down to the 1,000-foot level.
  • Eventually, the system will alight at patient level. On that day, the transition will be complete. Healthcare will no longer be driven by hospitals, healthcare systems or insurance companies. Its sole focus will be on people and communities — and what the patient will become over time.

When this era arrives, doctors will know patients far more deeply, he says.

He predicts that by leveraging all of the data available in the digital world, physicians will know the truth of their experiences, including the food they eat, the air they breathe, how much sleep they get, where they work, how they commute to and from work and whether they care for a family member or friend, doctors will finally be able to offer truly personalized care. They’ll focus on the N of 1, the single patient they’re encountering at that moment.

The death of what we know

But we’re still left with questions about the heart of this idea. What, truly, is the N of 1? Perhaps it is the sound of one hand clapping. Or maybe it springs from an often-cited Zen proverb: “When walking, walk. When eating, eat.” Do what you’re doing right now – focus and stay in the present moment. This is treating patients at the N of 1 level, it seems to me.

Like Zen, the N of 1 concept may sound mystical, but it’s entirely practical. As he points out, patients truly want to be treated at the N of 1 – they don’t care about the paint on the walls or Press Ganey scores, they care about being treated as individuals. And providers need to make this happen.

But to meet this challenge, healthcare as we know it must die, he says. I’ll leave you with his conclusion:

“Within the next eight years, healthcare as we know it will end. The new healthcare will begin. Healthcare delivered at the N of 1.”  And those who seek will find.

Google’s DeepMind Runs Afoul Of UK Regulators Over Patient Data Access

Posted on July 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.

Back in February, I recounted the tale of DeepMind, a standout AI startup acquired by Google a few years ago. In the story, I noted that DeepMind had announced that it would be working with the Royal Free London NHS Foundation Trust, which oversees three hospitals, to test out its healthcare app

DeepMind’s healthcare app, Streams, is designed to help providers kick out patient status updates to physicians and nurses working with them. Under the terms of the deal, which was to span five years, DeepMind was supposed to gain access to 1.6 million patient records managed by the hospitals.

Now, the agreement seems to have collapsed under regulatory scrutiny. The UK’s data protection watchdog has ruled that DeepMind’s deal with the Trust “failed to comply with data protection law,” according to a story in Business Insider. The watchdog, known as the Information Commissioner’s Office (ICO), has spent a year investigating the deal, BI reports.

As it turns out, the agreement empowered the Trust hospitals to share the data without the patients’ prior knowledge, something that presumably wouldn’t fly in the U.S. either. This includes data, intended for use in developing the Streams’ app kidney monitoring technology, which includes info on whether people are HIV-positive, along with details of drug overdoses and abortions.

In its defense, DeepMind and the Royal Free Trust argued that patients had provided “implied consent” for such data sharing, given that the app was delivering “direct care” to patients using it. (Nice try. Got any other bridges you wanna sell?) Not surprisngly, that didn’t satisfy the ICO, which found several other shortcomings and how the data was handled as well.

While the ICO has concluded that the DeepMind/Royal Free Trust deal was illegal, it doesn’t plan to sanction either party, despite having the power to hand out fines of up to £500,000, BI said. But DeepMind, which set up his own independent review panel to oversee its data sharing agreements, privacy and security measures and product roadmaps last year, is taking a closer look at this deal. Way to self-police, guys! (Or maybe not.)

Not to be provincial, but what worries me about this is less the politics of UK patient protection laws, and bore the potential for Google subsidiaries to engage in other questionable data sharing activities. DeepMind has always said that they do not share patient data with its corporate parent, but while this might be true now, Google could do incalculable harm to patient privacy if they don’t maintain this firewall.

Hey, just consider that even for an entity the size of Google, healthcare data is an incredibly valuable asset. Reportedly, even street-level data thieves pay 10x for healthcare data as they do for, say, credit card numbers. It’s hard to even imagine what an entity the size of Google could do with such data if crunched in incredibly advanced ways. Let’s just say I don’t want to find out.

Unfortunately, as far as I know U.S. law hasn’t caught up with the idea of crime-by-analytics, which could be an issue even if an entity has legal possession of healthcare data. But I hope it does soon. The amount of harm this kind of data manipulation could do is immense.

Dogged By Privacy Concerns, Consumers Wonder If Using HIT Is Worthwhile

Posted on May 17, 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.

I just came across a survey suggesting that while we in the health IT world see a world of possibilities in emerging technologies, consumers aren’t so sure. The researchers found that consumers question the value of many tech platforms popular with health execs, apparently because they don’t trust providers to keep their personal health data secure.

The study, which was conducted between September and December 2016, was done by technology research firm Black Book. To conduct the survey, Black Book reached out to 12,090 adult consumers across the United States.

The topline conclusion from the study was that 57 percent of consumers who had been exposed to HIT through physicians, hospitals or ancillary providers doubted its benefits. Their concerns extended not only to EHRs, but also to many commonly-deployed solutions such as patient portals and mobile apps. The survey also concluded that 70 percent of Americans distrusted HIT, up sharply from just 10 percent in 2014.

Black Book researchers tied consumers’ skepticism to their very substantial  privacy concerns. Survey data indicated that 87 percent of respondents weren’t willing to divulge all of their personal health data, even if it improved their care.

Some categories of health information were especially sensitive for consumers. Ninety-nine percent were worried about providers sharing their mental health data with anyone but payers, 90 percent didn’t want their prescription data shared and 81 percent didn’t want information on their chronic conditions shared.

And their data security worries go beyond clinical data. A full 93 percent responding said they were concerned about the security of their personal financial information, particularly as banking and credit card data are increasingly shared among providers.

As a result, at least some consumers said they weren’t disclosing all of their health information. Also, 69 percent of patients admitted that they were holding back information from their current primary care physicians because they doubted the PCPs knew enough about technology to protect patient data effectively.

One of the reason patients are so protective of their data is because many don’t understand health IT, the survey suggested. For example, Black Book found that 92 percent of nurse leaders in hospital under 200 beds said they had no time during the discharge process to improve patient tech literacy. (In contrast, only 55 percent of nurse leaders working in large hospitals had this complaint, one of the few bright spots in Black Book’s data.)

When it comes to tech training, medical practices aren’t much help either. A whopping 96 percent of patients said that physicians and staff didn’t do a good job of explaining how to use the patient portal. About 40 percent of patients tried to use their medical practice’s portal, but 83 percent said they had trouble using it when they were at home.

All that being said, consumers seemed to feel much differently about data they generate on their own. In fact, 91 percent of consumers with wearables reported that they’d like to see their physician practice’s medical record system store any health data they request. In fact, 91 percent of patients who feel that their apps and devices were important to improving their health were disappointed when providers wouldn’t store their personal data.