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Hospital Execs Underestimate QPP Impact

Posted on July 7, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

A new survey by Nuance Communications suggests that hospital finance leaders aren’t prepared to meet the demands of MACRA’s Merit-Based Incentive Payment System (MIPS), and may not understand the extent to which MIPS could impact their bottom line. Worse, survey results suggest that many of those who were convinced they knew what was involved in meeting program demands were dead wrong.

The survey found that many hospital finance leaders weren’t aware that if they don’t participate in the MIPS Quality Payment Program (QPP), they could see a 4% reduction in Medicare reimbursements by 2019.

Not only that, those who were aware of the program didn’t have a great grasp of the details. More than 75% respondents that claimed to be somewhat or very confident about their understanding of QPP got the 4% at-risk number wrong. Meanwhile, 60% of respondents either underestimated the percent of revenue at risk or simply did not know what the number was.

In addition, a significant number of respondents weren’t aware of key QPP reporting requirements. For example, just 35% of finance respondents that felt confident they understood QPP requirements actually knew that they had to submit 90 day of quality data to participate. Meanwhile, 50% either underestimated or did not know how many days of data they needed to provide.

On a broader level, as Nuance noted, the issue is that hospitals aren’t ready to meet QPP demands even if they do know what’s at stake. Too many aren’t prepared to capture complete clinical documentation, develop business processes to support this data capture and raise provider awareness of these issues. In other words, not only are finance leaders unaware of some key QPP requirements, they may not have the infrastructure to meet them.

This is a big deal. Not only will their organizations lose money if they don’t meet QPP requirements, but they’ll miss out on a 5% positive Medicare payment adjustment if they play by the rules.

Lest the respondents sound careless, let’s do a reality check here. Without a doubt, the transition into the world of MIPS isn’t a simple one. Hospitals and medical practices will have to meet deadlines and present quality data in new ways. That would be a hassle in any event, but it’s particularly difficult given how many other quality data reporting requirements they must meet.

That being said, I’d argue that even if they’ve gotten a slow start, hospitals have enough time to meet the basic requirements of QPP compliance. For example, turning over 90 days of quality data by March of next year shouldn’t be a gigantic stretch in contrast to, say, submitting a year’s worth of data under advanced Meaningful Use models. Not to mention the Pick Your Pace option of only 1 measure which avoids all penalties.

Clearly, having the right health IT tools will be important to this process. (Not surprisingly, Nuance is picking its own reporting tools as part of the mix.) But I’m struck by the notion that organizations can’t live on technology alone in this case. As with many problems in healthcare, tech solutions aren’t worth much if the business doesn’t have the right processes in place. Let’s see if finance executives know at least that much.

Thoughts On Innovation In Healthcare

Posted on June 30, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Sure, innovation can be fun and interesting and energizing. But how do you move from innovation as a sport to innovation as a true growth strategy, especially in a conservative business like healthcare? New research by consulting firm PwC might offer some answers.

To conduct its study, P2C surveyed more than 1,200 executives in 44 countries, conducting in-depth interviews with leaders responsible for managing innovation initiatives.

The research, which cut across multiple industries, found that firms that applied customer-engagement strategies leveraging design thinking and user-driven requirements — from idea generation to a product or service launch — saw better results. In fact, they were twice as likely to expect growth of 15% or more over the next five years, PwC found.

In conducting the research, PwC researchers identified five strategies which contribute to effective innovation efforts. They include:

  • Use smart metrics to measure innovation success: Whatever you invest, if you track the benefits of innovation by how it boosts revenue and contains cost – along with building sales – you’ve likely got a sustainable model. Sixty-nine percent of respondents named sales growth as the most important way to measure innovation success.
  • Don’t make “blind bets” — build viable business initiatives: Make sure you find a way to square your innovation strategy with your business strategy. And be aware that doing so may be challenging. The PwC report notes that 65% of companies investing 15% or more of their revenue in innovation saw connecting innovation with business goals was their top strategic challenge.
  • Create silo-busting innovation models: To succeed at innovation, break down traditional organization barriers within and outside of your organization, which helps you leverage a wider pool of ideas, insights, talents and technology. Consider more-inclusive operating models like open innovation, design thinking and co-creation with partners, customers and supplies rather than traditional R&D. Thirty-five percent of PwC respondents reported that customers were their most important innovation partners.
  • Leverage a broad base of human experience: See to it that your innovation teams seek input from across a variety of disciplines, rather than letting technology drive your process. For example, while big data may help you know how customers behave, data alone won’t explain why they behave that way. It’s better to bring the right human judgment and intuition to bear on the data rather than sticking strictly with IT experts. Sixty percent of companies surveyed said that internal employees help to drive innovation within their organization.
  • Support technical innovation: While technology is far from the only tool you can use to innovate, it remains a compelling option. Many companies looking to technology to create markets for novel products and services that don’t yet exist, and to meet needs that customers may not even know they have. Half of PwC’s respondents rated technology partners as their most important innovation collaborators.

So, what can the healthcare industry learn from this study? A few things come to mind.

For one thing, I believe that healthcare leaders could do far more to turn silo-busting activities into group innovation projects. In other words, don’t just merge data from different departments into a common database, involve the people in those departments with the process, and ask them how breaking down barriers could change the organization in a positive way.

Another thing that comes to mind that healthcare technology leaders could stand to integrate non-technical opinions into innovation efforts. Right now, health IT organizations are remarkably siloed themselves, and while they may involve clinicians in their process at times, it’s rare for them to take in the opinions of non-medical employees who don’t use advanced IT functions very often. (Yes, a janitorial services worker may have something to offer.)

And what about picking the right metrics to measure innovation success? Of course, existing models emphasizing clinical improvement aren’t misguided, nor are measures of IT performance, but there’s more to consider. Particularly within the ecosystem of a large hospital, as many departments outside IT care delivery which contribute to the organization’s overall health.

Ultimately, what makes innovation valuable is the extent to which it draws upon an organization’s unique strengths.  But it never hurts to take broad principles like these into account, as they may help you extract the full benefits of the innovation process.

The More Hospital IT Changes, The More It Remains The Same

Posted on June 23, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Once every year or two, some technical development leads the HIT buzzword list, and at least at first it’s very hard to tell whether that will stick. But over time, the technologies that actually work well are subsumed into the industry as it exists, lose their buzzworthy quality and just do their job.

Once in a while, the hot new thing sparks real change — such as the use of mobile health applications — but more often the ideas are mined for whatever value they offer and discarded.  That’s because in many cases, the “new thing” isn’t actually novel, but rather a slightly different take on existing technology.

I’d argue that this is particularly true when it comes to hospital IT, given the exceptionally high cost of making large shifts and the industry’s conservative bent. In fact, other than the (admittedly huge) changes fostered by the adoption of EMRs, hospital technology deployments are much the same as they were ten years ago.

Of course, I’d be undercutting my thesis dramatically if I didn’t stipulate that EMR adoption has been a very big deal. Things have certainly changed dramatically since 2007, when an American Hospital Association study reported that 32% percent of hospitals had no EMR in place and 57% had only partially implemented their EMR, with only the remaining 11% having implemented the platform fully.

Today, as we know, virtually every hospital has implemented an EMR integrated it with ancillary systems (some more integrated and some less).  Not only that, some hospitals with more mature deployments in place have used EMRs and connected tools to make major changes in how they deliver care.

That being said, the industry is still struggling with many of the same problems it did in a decade ago.

The most obvious example of this is the extent to which health data interoperability efforts have stagnated. While hospitals within a health system typically share data with their sister facilities, I’d argue that efforts to share data with outside organizations have made little material progress.

Another major stagnation point is data analytics. Even organizations that spent hundreds of millions of dollars on their EMR are still struggling to squeeze the full value of this data out of their systems. I’m not suggesting that we’ve made no progress on this issue (certainly, many of the best-funded, most innovative systems are getting there), but such successes are still far from common.

Over the longer-term, I suspect the shifts in consciousness fostered by EMRs and digital health will gradually reshape the industry. But don’t expect those technology lightning bolts to speed up the evolution of hospital IT. It’s going take some time for that giant ship to turn.

We Can’t Afford To Be Vague About Population Health Challenges

Posted on June 19, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Today, I looked over a recent press release from Black Book Research touting its conclusions on the role of EMR vendors in the population health technology market. Buried in the release were some observations by Alan Hutchison, vice president of Connect & Population Health at Epic.

As part of the text, the release observes that “the shift from quantity-based healthcare to quality-based patient-centric care is clearly the impetus” for population health technology demand. This sets up some thoughts from Hutchison.

The Epic exec’s quote rambles a bit, but in summary, he argues that existing systems are geared to tracking units of care under fee-for-service reimbursement schemes, which makes them dinosaurs.

And what’s the solution to this problem? Why, health systems need to invest in new (Epic) technology geared to tracking patients across their path of care. “Single-solution systems and systems built through acquisition [are] less able to effectively understand the total cost of care and where the greatest opportunities are to reduce variation, improve outcomes and lower costs,” Hutchison says.

Yes, I know that press releases generally summarize things in broad terms, but these words are particularly self-serving and empty, mashing together hot air and jargon into an unappetizing patty. Not only that, I see a little bit too much of stating as fact things which are clearly up for grabs.

Let’s break some of these issues down, shall we?

  • First, I call shenanigans on the notion that the shift to “value-based care” means that providers will deliver quality care over quantity. If nothing else, the shifts in our system can’t be described so easily. Yeah, I know, don’t expect much from a press release, but words matter.
  • Second, though I’m not surprised Hutchison made the argument, I challenge the notion that you must invest in entirely new systems to manage population health.
  • Also, nobody is mentioning that while buying a new system to manage pop health data may be cleaner in some respects, it could make it more difficult to integrate existing data. Having to do that undercuts the value of the new system, and may even overshadow those benefits.

I don’t know about you, but I’m pretty tired of reading low-calorie vendor quotes about the misty future of population health technology, particularly when a vendor rep claims to have The Answer.  And I’m done with seeing clichéd generalizations about value-based care pass for insight.

Actually, I get a lot more out of analyses that break down what we *don’t* know about the future of population health management.

I want to know what hasn’t worked in transitioning to value-based reimbursement. I hope to see stories describing how health systems identified their care management weaknesses. And I definitely want to find out what worries senior executives about supporting necessary changes to their care delivery models.

It’s time to admit that we don’t yet know how this population health management thing is going to work and abandon the use of terminally vague generalizations. After all, once we do, we can focus on the answering our toughest questions — and that’s when we’ll begin to make real progress.

Measuring Population Health ROI Is Still Tricky

Posted on May 24, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Over the past few years, health systems have made massive investments in population health management technology. Given the forces driving the investments are still present – or even closer at hand – there’s every reason to believe that they will continue.

That being said, health leaders are beginning to ask more questions about what they’re getting in return.  While systems may have subjected the initial investments to less scrutiny than usual, having accepted that they were critically necessary, many of these organizations are now trying to figure out what kind of return on investment they can expect to realize. In the process, some are finding out that even deciding what to measure is still somewhat tricky.

Many healthcare organizations started out with a sense that while investment returns on pop health management tech would take a while, they were in the knowable future. For example, according to a KPMG survey conducted in early 2015, 20 percent of respondents believed that returns on their investment in population health IT would materialize in one to two years, 36 percent expected to see ROI in three to four years and 29 percent were looking at a five+ year horizon.

At the time, though, many of the execs answering the survey questions were just getting started with pop health. Thirty-eight percent said their population health management capabilities were elementary-stage, 23 percent said they were in their infancy and 15 percent said such capabilities were non-existent, KPMG reported.

Since then, health systems and hospitals have found that measuring – much less realizing – returns generated by these investments can be complicated and uncertain. According to Dennis Weaver, MD, a senior consultant with the Advisory Board, one mistake many organizations make is evaluating ROI based solely on whether they’re doing well in their managed care contracts.

“They are trying to pay for all of the investment – the technology, care managers, operational changes, medical homes—all with the accountable payment bucket,” said Weaver, who spoke with Healthcare Informatics.

Other factors to consider

Dr. Weaver argues that healthcare organizations should take at least two other factors into account when evaluating pop health ROI, specifically reduction of leakage and unwarranted care variation. For example, cutting down on leakage – having patients go out of network – offers a 7 to 10 times greater revenue opportunity than meeting accountable care goals. Meanwhile, by reducing unwarranted variations in care and improving outcomes, organizations can see a 5 percent to 10 percent margin improvement, Weaver told the publication.

Of course, no one approach will hold true for every organization.  Bobbie Brown, senior vice president with HealthCatalyst, suggests taking a big-picture approach and drilling down into how specific technologies net out financially.

She recommends that health organizations start the investment analysis with broad strategic questions like “Does this investment help us grow?” and “Are we balancing risk and reward?” She also proposes that health leaders create a matrix which compares the cost/benefit ratio for individual components of the planned pop health program, such as remote monitoring and care management. Sometimes, putting things into a matrix makes it clear which approaches are likely to pay off, she notes.

Over time, it seems likely that healthcare leaders will probably come to a consensus on what elements to measure when sizing up their pop health investments, as with virtually every other major HIT expense. But in the interim, it seems that figuring out where to look for ROI is going to take more work.

Google’s DeepMind Rolling Out Bitcoin-Like Health Record Tracking To Hospitals

Posted on May 8, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Blockchain technology is gradually becoming part of how we think about healthcare data. Even government entities like the ONC and FDA – typically not early adopters – are throwing their hat into the blockchain ring.

In fact, according to recent research by Deloitte, healthcare and life sciences companies are planning the most aggressive blockchain deployments of any industry. Thirty-five percent of Deloitte’s respondents told the consulting firm that they expected to put blockchain into production this year.

Many companies are tackling the practical uses of blockchain tech in healthcare. But to me, few are more interesting than Google’s DeepMind, a hot new AI firm based in the UK acquired by Google a few years ago.

DeepMind has already signed an agreement with a branch of Britain’s National Health Trust, under which it will access patient data in the development healthcare app named Streams. Now, it’s launching a new project in partnership with the NHS, in which it will use a new technology based on bitcoin to let hospitals, the NHS and over time, patients track what happens to personal health data.

The new technology, known as “Verifiable Data Audit,” will create a specialized digital ledger which automatically records every time someone touches patient data, according to British newspaper The Guardian.

In a blog entry, DeepMind co-founder Mustafa Suleyman notes that the system will track not only that the data was used, but also why. In addition, the ledger supporting the audit will be set to append-only, so once the system records an activity, that record can’t be erased.

The technology differs from existing blockchain models in some important ways, however. For one thing, unlike in other blockchain models, Verifiable Data Audit won’t rely on decentralized ledger verification of a broad set of participants. The developers have assumed that trusted institutions like hospitals can be relied on to verify ledger records.

Another way in which the new technology is different is that it doesn’t use a chain infrastructure. Instead, it’s using a mathematical function known as a Merkle tree. Every time the system adds an entry to the ledger, it generates a cryptographic hash summarizing not only that latest ledger entry, but also the previous ledger values.

DeepMind is also providing a dedicated online interface which participating hospitals can use to review the audit trail compiled by the system, in real-time. In the future, the company hopes to make automated queries which would “sound the alarm” if data appeared to be compromised.

Though DeepMind does expect to give patients direct oversight over how, where and why their data has been used, they don’t expect that to happen for some time, as it’s not yet clear how to secure such access. In the mean time, participating hospitals are getting a taste of the future, one in which patients will ultimate control access to their health data assets.

E-Patient Update: Before You Call Me A “Frequent Flier,” Check Your EMR

Posted on April 28, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

While there’s some debate about what constitutes an emergency, there’s no doubt I’ve had a bunch of ambiguous, potentially symptoms lately that needed to be addressed promptly. Unfortunately, that’s exposed me to providers brainwashed to believe that anyone who comes to the emergency department regularly is a problem.

Not only is that irritating, and sometimes intimidating, it’s easy to fix. If medical providers were to just dig a bit further into my existing records – or ideally, do a sophisticated analysis of my health history – they’d understand my behavior, and perhaps even provide more effective care.

If they looked at the context their big ‘ol EMR could provide, they wouldn’t waste time wondering whether I’m overreacting or wasting their time.

As I see it, slapping the “frequent flier” label on patients is particularly inappropriate when they have enough data on hand to know better. (Actually, the American College of Emergency Physicians notes that a very small number of frequent ED visitors are actually homeless, drug seekers or mentally ill, all of which is in play when you show up a bit often. But that’s a topic for another time.)

Taking no chances

The truth is, I’ve only been hitting the ED of late because I’ve been responding to issues that are truly concerning, or doing what my primary doctor or HMO nurse line suggests.

For example, my primary care doctor routed me straight to the local emergency department for a Doppler when my calves swelled abruptly, as I had a DVT episode and subsequent pulmonary embolism just six months ago.

More recently, when I had a sudden right-sided facial droop, I wasn’t going to wait around and see if it was caused by a stroke. It turns out that I probably had an atypical onset of Bell’s Palsy, but there was no way I was going to try and sort that out on my own.

And given that I have a very strong history of family members dropping dead of MI, I wasn’t going to fool around when I felt breathless, my heart was racing and I my chest ached. Panic attack, you’re thinking? No, as it turned out that like my mother, I had aFib. Once again, I don’t have a lab or imaging equipment in my apartment – and my PCP doesn’t either – so I think I did the right thing.

The truth is, in each case I’d probably have been OK, but I erred on the side of caution. You know what? I don’t want to die needlessly or sustain major injuries to prove I’m no wimp.

The whole picture

Nonetheless, having been to the ED pretty regularly of late, I still encounter clinicians that wonder if I’m a malingerer, an attention seeker or a hypochondriac. I pick up just a hint of condescension, a sense of being delicately patronized from both clinicians and staffer who think I’m nuts. It’s subtle, but I know it’s there.

Now, if these folks kept up with their industry, they might have read the following, from Health Affairs. The article in question notes that “the overwhelming majority of frequent [ED} users have only episodic periods of high ED use, instead of consistent use over multiple years.” Yup, that’s me.

If they weren’t so prone to judging me and my choices – OK, not everyone but certainly some – it might occur to them to leverage my data. Hey, if I’m being screened but in no deep distress, why not ask what my wearable or health app data has told me of late? More importantly, why haven’t the IT folks at this otherwise excellent hospital equipped providers with even basic filters the ED treatment team can use to spot larger patterns? (Yeah, bringing big data analytics into today’s mix might be a stretch, but still, where are they?)

Don’t get me wrong. I understand that it’s hard to break long-established patterns, change attitudes and integrate any form of analytics into the extremely complex ED workflow. But as I see it, there’s no excuse to just ignore these problems. Soon, the day will come when on-the-spot analytics is the minimum professional requirement for treating ED patients, so confront the problem now.

Oh, and by the way, treat me with more respect, OK?

UCHealth Adds Claims Data To Population Health Dataset

Posted on April 24, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

A Colorado-based health system is implementing a new big data strategy which incorporates not only data from clinics, hospitals and pharmacies, but also a broad base of payer claim data.

UCHealth, which is based in Aurora, includes a network of seven hospitals and more than 100 clinics, caring collectively for more than 1.2 million unique patients in 2016. Its facilities include the University of Colorado Hospital, the principal teaching hospital for the University of Colorado School of Medicine.

Leaders at UCHealth are working to improve their population health efforts by integrating data from seven state insurers, including Anthem Blue Cross and Blue Shield, Cigna, Colorado Access, Colorado Choice Health Plans, Colorado Medicaid, Rocky Mountain Health Plans and United Healthcare.

The health system already has an Epic EMR in place across the system which, as readers might expect, offers a comprehensive view of all patient treatment taking place at the system’s clinics and hospitals.

That being said, the Epic database suffers from the same limitations as any other locally-based EMR. As UCHealth notes, its existing EMR data doesn’t track whether a patient changes insurers, ages into Medicare, changes doctors or moves out of the region.

To close the gaps in its EMR data, UCHealth is using technology from software vendor Stratus, which offers a healthcare data intelligence application. According to the vendor, UCHealth will use Stratus technology to support its accountable care organizations as well as its provider clinical integration strategy.

While health system execs expect to benefit from integrating payer claims data, the effort doesn’t satisfy every item on their wish list. One major challenge they’re facing is that while Epic data is available to all the instant it’s added, the payer data is not. In fact, it can take as much as 90 days before the payer data is available to UCHealth.

That being said, UCHealth’s leaders expect to be able to do a great deal with the new dataset. For example, by using Stratus, physicians may be able to figure out why a patient is visiting emergency departments more than might be expected.

Rather than guessing, the physicians will be able to request the diagnoses associated with those visits. If the doctor concludes that their conditions can be treated in one of the system’s primary care clinics, he or she can reach out to these patients and explain how clinic-based care can keep them in better health.

And of course, the health system will conduct other increasingly standard population health efforts, including spotting health trends across their community and better understanding each patient’s medical needs.

Over the next several months, 36 of UCHealth’s primary care clinics will begin using the Stratus tool. While the system hasn’t announced a formal pilot test of how Stratus works out in a production setting, rolling this technology out to just 36 doctors is clearly a modest start. But if it works, look for other health systems to scoop up claims data too!

Poll: Providers Struggle To Roll Out Big Data Analytics

Posted on April 10, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

A new poll by a health IT publication has concluded that while healthcare organizations would like to roll out big data analytics projects, they lack many of the resources they need to proceed.

The online poll, conducted by HealthITAnalytics.com, found that half of respondents are hoping to recruit data science experts to serve as the backbone of their big analytics efforts. However, many are finding it very difficult to find the right staffers.

What’s more, such hires don’t come cheaply. In fact, one study found that data scientist salaries will range from $116,000 to $163,500 in 2017, a 6.4 percent increase over last year’s levels. (Other research concludes that a data scientist in management leading a team of 10 or more can draw up to $250,000 per year.) And even if the pricetag isn’t an issue, providers are competing for data science talent in a seller’s market, not only against other healthcare providers but also hungry employers in other industries.

Without having the right talent in place, many of providers’ efforts have been stalled, the publication reports. Roughly 31 percent of poll respondents said that without a data science team in place, they didn’t know how to begin implementing data analytics initiatives.

Meanwhile, 57 percent of respondents are still struggling with a range of predictable health IT challenges, including EMR optimization and workflow issues, interoperability issues and siloed data. Not only that, for some getting buy-in is proving difficult, with 34 percent reporting that their clinical end users aren’t convinced that creating analytics tools will pay off.

Interestingly, these results suggest that providers face bigger challenges in implementing health data than last year. In last year’s study by HealthITAnalytics.com, 47 percent said interoperability was a key challenge. What’s more, just 42 percent were having trouble finding analytics staffers for their team.

But at the same time, it seems like provider executives are throwing their weight behind these initiatives. The survey found that just 17 percent faced problems with getting executive buy-in and budget constraints this year, while more than half faced these issues in last year’s survey.

This squares with research released a few months ago by IT staffing firm TEKSystems, which found that 63 percent of respondents expected to see their 2017 budgets increase this year, a big change from the 41 percent who expected to see bigger budgets last year.

Meanwhile, despite their concerns, providers are coping well with at least some health IT challenges, the survey noted. In particular, almost 90 percent of respondents reported that they are live on an EMR and 65 percent are using a business intelligence or analytics solution.

And they’re also looking at the future. Three-quarters said they were already using or expect to enhance clinical decision making, along with more than 50 percent also focusing laboratory data, data gathered from partners and socioeconomic or community data. Also, using pharmacy data, patient safety data and post-acute care records were on the horizon for about 20 percent of respondents. In addition, 62 percent said that they were interested in patient-generated health data.

Taken together, this data suggests that as providers have shifted their focus to big data analytics– and supporting population health efforts – they’ve hit more speed bumps than expected. That being said, over the next few years, I predict that the supply of data scientists and demand for their talents should fall into alignment. For providers’ sake, we’d better hope so!

EMRs Can Improve Outcomes For Weekend Hospital Surgeries

Posted on April 7, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Unfortunately, it’s well documented that people often have worse outcomes when they’re treated in hospitals over the weekend. For example, one recent study from the Association of Academic Physiatrists found that older adults admitted with head trauma over the weekend have a 14 percent increased risk of dying versus those admitted on a weekday.

But if a hospital makes good use of its EMR, these grim stats can be improved, according to a new study published in JAMA Surgery. In the study, researchers found that use of EMRs can significantly improve outcomes for hospital patients who have surgeries over the weekend.

To conduct the study, which was done by Loyola Medicine, a group of researchers identified some EMR characteristics which they felt could overcome the “weekend effect.” The factors they chose included using electronic systems designed to schedule surgeries seamlessly as well as move patients in and out of hospital rooms efficiently.

Their theories were based on existing research suggesting that patients at hospitals with electronic operating room scheduling were 33 percent less likely to experience a weekend effect than hospitals using paper-based scheduling. In addition, studies concluded patients at hospitals with electronic bed-management systems were 35 percent less likely to experience the weekend effect.

To learn more about the weekend effect, researchers analyzed the records provided by the AHRQ’s Healthcare Cost and Utilization Project State Inpatient Database.  Researchers looked at treatment and outcomes for 2,979 patients admitted to Florida hospitals for appendectomies, acute hernia repairs and gallbladder removals.

The research team found that 32 percent (946) of patients experienced the weekend effect, which they defined as having longer hospital stays than expected. Meanwhile, it concluded that patients at hospitals with high-speed EMR connectivity, EMR in the operating room, electronic operating room scheduling, CPOE systems and electronic bed management did better. (The analysis was conducted with the help of Loyola’s predictive analytics program.)

Their research follows on a 2015 Loyola study, published in Annals of Surgery, which named five factors that reduced the impact of the weekend effect. These included full adoption of electronic medical records, home health programs, pain management programs, increased registered nurse-to-bed ratios and inpatient physical rehabilitation.

Generally speaking, the study results offer good news, as they fulfill some the key hopes hospitals had when installing their EMR in the first place. But I was left wondering whether the study conflated cause and effect. Specifically, I found myself wondering whether hospitals with these various systems boosted their outcomes with technology, or whether hospitals that invested in these technologies could afford to provide better overall care.

It’s also worth noting that several of the improvement factors cited in the 2015 study did not involve technology at all. Even if a hospital has excellent IT systems in place, putting home health, pain management and physical rehabilitation in place – not to mention higher nurse-to-patient ratios – calls for different thinking and a different source of funding.

Still, it’s always good to know that health IT can generate beneficial results, especially high-ticket items like EMRs. Even incremental progress is still progress.