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One Expert’s Insights into Reducing Unwarranted Clinical Variation

December 12, 2016
by Mark Hagland
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Nancy Lakier, R.N., on how hospital leaders can uncover and eliminate unwarranted clinical variation

As the U.S. healthcare system shifts inexorably further away from fee-for-service payment and towards value-based reimbursement, one area of importance that is looming larger than ever before is that of variation in clinical practice and care delivery. Variation in the way that physicians, nurses, and other clinicians deliver care was largely unchallenged under fee-for-service reimbursement, because there was no need to standardize care patterns to produce better patient outcomes or to reduce or curb costs. That underlying landscape is changing now, and the leaders of more and more patient care organizations are expending the time and effort needed to uncover variation and standardize care practices.

Nancy Lakier, R.N. is the founder, CEO and managing principal of Novia Strategies, a San Diego-based healthcare consulting firm that advises hospitals and health systems on improving their operations, quality and financial strength. Lakier sat down recently with Healthcare Informatics Editor-in-Chief Mark Hagland to discuss her and her colleagues’ work in the crucial area of uncovering and eliminating unnecessary clinical variation. Below are excerpts from their interview.

You’ve been in healthcare for a number of years, and have had a broad range of professional experiences. Please share with me some of the experiences that brought you into this type of consulting.

I was the CNO at Scripps Health, and over operations at Scripps-La Jolla, and was recruited down there when I was up in Los Angeles, as they were anticipating a major hit coming from managed care. They had called me back in 1989, and I worked there from 1990 to 1995. And they had been right—very shortly after I arrived, that organization was feeling the strong effects of managed care. It was something of a “perfect storm” of difficult challenges: the economy was in a slump, they were cutting back on military bases, and managed care was taking a big bite in order to save money.

And one thing that I ended up developing was what we called the Lakier Predictable Factor. Essentially, it was a methodology for understanding the trajectories of predictable patients. For example, if you’re 40-something years old and you go in for a total hip replacement, you’ll probably have a relatively smooth and predictable course of care, whereas, for example, the 85-year-old who’s diabetic and breaks their hip, will not have that same predictable trajectory.

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Nancy Lakier, R.N.

And, without realizing it at the time, we were really breaking new ground with that approach. So I partnered with a physician, Dr. Bruce Campbell, and we developed a clinical redesign. We did this across the whole hospital, but started with certain populations, and worked our way through. We were working with a limited database in those days, but we used it and put together teams of physicians, nurses, therapists, etc., to say—for the predictable populations, what were the right protocols? What drugs, supplies, should we use? When you look at implants, instead of 30 vendors, can we reduce that number? So we started looking at changing practice—using lighter anesthetics, decreasing time on ventilator, getting patients up and moving faster—and all those elements were starting to bring down lengths of stay. And working forward in that direction led to a lot of questioning of previously unquestioned practices: for example, why are you ordering a chest x-ray every day? The reality was that physicians practiced based on how they had been trained at the particular medical schools that they had attended, rather than anyone adhering to best demonstrated practices.

So one of the things that we started seeing is that we were reducing the cost, and literally moving the mean line in terms of cost-effectiveness as well as in terms of clinical outcomes. At that time, we were getting one, two, three days’ reductions in lengths of stay—but also improvement in outcomes in every one of the patient populations we addressed. And also, we put in very robust case management to manage the unpredictable. So, for example, what needs to happen with this 84-year-old diabetic who’s broken their hip? We essentially were examining the practice patterns around the care of both predictable and unpredictable patients. And that inevitably led to us directing a team to look at labor issues, because if you’re reducing length of stay, then you also need to look at staffing. So we also ended up creating a productivity task force.

How long did it take to achieve a transformation of your processes at Scripps?

It really took about two years at Scripps-La Jolla, and then we refined the methodology and spread it to the other four hospitals.

Looking at the landscape right now in healthcare, what is your sense of the readiness of clinicians and administrators in hospitals nationwide to pursue the examination and reduction of clinical variation, in the current environment.

It’s still all over the gamut. Leadership is feeling it for sure, in terms of realizing that this kind of work is needed. The rank and file is still a mixed bag. Some physicians are like, ohmygosh, I need to do this; some haven’t even heard about it. Some have heard about it, but they’re planning to retire in five years and think they can just slip out. The younger physicians are ready for it and have been prepared psychologically for it.

And it’s scary for senior leadership, because their jobs are on the line. Sometimes, that means challenging front-line physicians. And administration has always stayed away from that. And we never told physicians what to do—or nurses. We gave them great data. And today, the data we can give people—our data set is amazing. And the other day, I was looking at a general surgeon who does a lot of bariatric care, and that general surgeon also does appendectomies. And they were using a $381 special bariatric pack for their appendectomies. The question is, do you need that specialized surgical pack? And a lot of times, we hardly finish the sentence, when they say, why are they pulling a special bariatric pack for me? And sometimes, it turns out it’s the mid-levels who are doing the ordering. So a lot of times, the data tells them about potential opportunities.

And when you provide physicians with risk-adjusted morbidity, mortality, and costs—you can’t argue with that. And providing them those scorecards—that brings change just like that. They’re also very competitive souls. And, historically, physicians—it was very much also drilled into them that You, Doctor, are ultimately responsible for the lives of these patients—no team concept. At the end of the day, they had the weight of the world on their shoulders, and that weight is being redistributed.

What should CIOs and CMIOs think about this kind of work?

First of all, I think that they are critical partners in a couple of different ways; and they are instrumental in helping to evaluate and discern systems. The vendors will tell us that every one of them can do everything for you. But from an IT perspective, it’s about partnering with the CMO, CNO, and COO, to help them define what the critical elements are that they need, and then look at the (vendor) system and comparer its capabilities to those needs. At the end of the day, what are those critical elements are needed? Because we know what drives change. And you don’t need everything the vendors offer. And my personal philosophy is simplify, simplify, simplify: get rid of the noise. If you can’t get rid of the noise, frankly, it’s hard to drive change.

I think everyone working in a hospital now is shell-shocked by these waves upon waves of change, to be honest.

Absolutely, they are! So work with the senior leaders to really help define what’s important to them, and to help them define the flexibility needed in the (vendor) system. And then after you have the data system, you need to be able to have a fluid process with the rest of the clinical word, so that you can adjust. So for instance, if you went back and said, OK, we know that sometimes, multiple MRIs are needed. And we went to let every physician know when the last MRI was. Because a patient who’s been admitted may have had an outpatient MRI last week. So you need to have that pop up. Or the protocols. And sometimes, the informatics department will say, we’ll put that on our list and get that to you in about 16 months…! So it’s key for CIOs to say, what are our priorities?

Aligning what IT must do with the top priorities of the organization is important, then, correct?

Yes, absolutely. We do something called operationalizing the strategic plan. Because a lot of organizations have a strategic plan but don’t know how to make it happen. And IT is a key part of that. So we sit down and look at the strategic plan with leaders and ask them, OK, what will it take to do that? And pretty soon you find out things like, OK, it’s going to take five years’ work for the IT department to accomplish something that has to happen in five months. OK, so that’s not going to happen. So we call it having a vision and planning session with senior leaders to help embed the goals into their planning to help make it happen. And for me, the way to operationalize a plan like that is that you’ve got to take it deep into the organization and embed those pieces.

That’s why I believe that it is about clinicians and hospital experts. Because we can walk down and talk the talk with the nurses, the pharmacists, and the therapists, at the patient care level. And that’s what it takes.

Overall, what are the biggest challenges to tackle in eliminating variation, in your view?

The biggest challenge is ensuring you’re using a good data set. That’s critical. The second core element—I talk about core tenets often—it has to be multidisciplinary. You must bring in best demonstrated practices. And then you have to listen to the organizational culture and be respectful—truly understand their patients and their operations and be respectful of that, and integrate the change so you’re in alignment with their vision and their values—it’s really their values. Now, there are many times they want us to drive change. You can drive change and be respectful of their values. That’s key. And then you have to embed it. Often consultants say, here’s what you need to do, now, nurse, do this, doctor do this, etc. But it doesn’t stick, unless you put it into staff accountabilities, unless you embed data collection into your EHR. And you can put in protocols that will never be used. Too often, people think something will happen, and it just won’t.

Do you believe that physicians and nurses are more willing to be data-driven in their clinical practice, if it’s data they can trust?

Yes, absolutely, but the data set has to be valid, the data has to be risk-adjusted, and the people have to be credible. But then, yes. And at the end of the day, they have to make the final decisions; they can’t be told by outsiders what to do.

What would your parting advice be for CIOs and CMIOs, around everything we’ve discussed here?

I think it would be the need to collaborate with the other senior leaders, as we’ve discussed; the need to assist in finding good data sets; and then, to partner with them in driving that change. And that means really looking at their operations to ensure that they have flexibility in their operations, to be responsive. Building IT departments that can be responsive to these clinical change needs. And also, we can take disparate data sets and put in crosswalks. But IT leaders need to be thinking about, what does the future entail? And how can I build those crosswalks for the future? Another tenet is that of pragmatism and simplicity. As you said, don’t boil the ocean. It is so critically important that people simplify and look pragmatically at what can be done, and not try to design the best widget. You mentioned Lean and Six Sigma, and there are elements that can be used, but the key is to think about this pragmatically.

 


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Definitive Healthcare Acquires HIMSS Analytics’ Data Services

January 16, 2019
by Rajiv Leventhal, Managing Editor
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Definitive Healthcare, a data analytics and business intelligence company, has acquired the data services business and assets of HIMSS Analytics, the organizations announced today.

The purchase includes the Logic, Predict, Analyze and custom research products from HIMSS Analytics, which is commonly known as the data and research arm of the Healthcare Information and Management Systems Society.

According to Definitive officials, the acquisition builds on the company’s “articulated growth strategy to deliver the most reliable and consistent view of healthcare data and analytics available in the market.”

Definitive Healthcare will immediately begin integrating the datasets and platform functionality into a single source of truth, their executives attest. The new offering will aim to include improved coverage of IT purchasing intelligence with access to years of proposals and executed contracts, enabling transparency and efficiency in the development of commercial strategies.

Broadly, Definitive Healthcare is a provider of data and intelligence on hospitals, physicians, and other healthcare providers. Its product suite its product suite provides comprehensive data on 8,800 hospitals, 150,000 physician groups, 1 million physicians, 10,000 ambulatory surgery centers, 14,000 imaging centers, 86,000 long-term care facilities, and 1,400 ACOs and HIEs, according to officials.

Together, Definitive Healthcare and HIMSS Analytics have more than 20 years of experience in data collection through exclusive methodologies.

“HIMSS Analytics has developed an extraordinarily powerful dataset including technology install data and purchasing contracts among other leading intelligence that, when combined with Definitive Healthcare’s proprietary healthcare provider data, will create a truly best-in-class solution for our client base,” Jason Krantz, founder and CEO of Definitive Healthcare, said in a statement.

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Machine Learning Survey: Many Organizations Several Years Away from Adoption, Citing Cost

January 10, 2019
by Heather Landi, Associate Editor
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Radiologists and imaging leaders see an important role for machine learning in radiology going forward, however, most organizations are still two to three years away from adopting the technology, and a sizeable minority have no plans to adopt machine learning, according to a recent survey.

A recent study* by Reaction Data sought to examine the hype around artificial intelligence and machine learning, specifically in the area of radiology and imaging, to uncover where AI might be more useful and applicable and in what areas medical imaging professionals are looking to utilize machine learning.

Reaction Data, a market research firm, got feedback from imaging professionals, including directors of radiology, radiologists, chiefs of radiology, imaging techs, PACS administrators and managers of radiology, from 152 healthcare organizations to gauge the industry on machine learning. About 60 percent of respondents were from academic medical centers or community hospitals, while 15 percent were from integrated delivery networks and 12 percent were from imaging centers. The remaining respondents worked at critical access hospitals, specialty clinics, cancer hospitals or children’s hospitals.

Among the survey respondents, there was significant variation in the number of annual radiology studies performed—17 percent performed 100-250 thousand studies each year; 16 percent performed 1 to 2 million studies; 15 percent performed 5 to 25 thousand studies; 13 percent performed 250 to 500 thousand; 10 percent performed more than 2 million studies a year.

More than three quarters of imaging and radiology leaders (77 percent) view machine learning as being important in medical imaging, up from 65 percent in a 2017 survey. Only 11 percent view the technology as not important. However, only 59 percent say they understand machine learning, although that percentage is up from 52 percent in 2017. Twenty percent say they don’t understand the technology, and 20 percent have a partial understanding.

Looking at adoption, only 22 percent of respondents say they are currently using machine learning—either just adopted it or have been using it for some time. Eleven percent say they plan to adopt the technology in the next year.

Half of respondents (51 percent) say their organizations are one to two years away (28 percent) or even more than three years away (23 percent) from adoption. Sixteen percent say their organizations will most likely never utilize machine learning.

Reaction Data collected commentary from survey respondents as part of the survey and some respondents indicated that funding was an issue with regard to the lack of plans to adopt the technology. When asked why they don’t ever plan to utilize machine learning, one respondent, a chief of cardiology, said, “Our institution is a late adopter.” Another respondent, an imaging tech, responded: “No talk of machine learning in my facility. To be honest, I had to Google the definition a moment ago.”

Survey responses also indicated that imaging leaders want machine learning tools to be integrated into PACS (picture archiving and communication systems) software, and that cost is an issue.

“We'd like it to be integrated into PACS software so it's free, but we understand there is a cost for everything. We wouldn't want to pay more than $1 per study,” one PACS Administrator responded, according to the survey.

A radiologist who responded to the survey said, “The market has not matured yet since we are in the research phase of development and cost is unknown. I expect the initial cost to be on the high side.”

According to the survey, when asked how much they would be willing to pay for machine learning, one imaging director responded: “As little as possible...but I'm on the hospital administration side. Most radiologists are contracted and want us to buy all the toys. They take about 60 percent of the patient revenue and invest nothing into the hospital/ambulatory systems side.”

And, one director of radiology responded: “Included in PACS contract would be best... very hard to get money for this.”

The survey also indicates that, among organizations that are using machine learning in imaging, there is a shift in how organizations are applying machine learning in imaging. In the 2017 survey, the most common application for machine learning was breast imaging, cited by 36 percent of respondents, and only 12 percent cited lung imaging.

In the 2018 survey, only 22 percent of respondents said they were using machine learning for breast imaging, while there was an increase in other applications. The next most-used application cited by respondents who have adopted and use machine learning was lung imaging (22 percent), cardiovascular imaging (13 percent), chest X-rays (11 percent), bone imaging (7 percent), liver imaging (7 percent), neural imaging (5 percent) and pulmonary imaging (4 percent).

When asked what kind of scans they plan to apply machine learning to once the technology is adopted, one radiologist cited quality control for radiography, CT (computed tomography) and MR (magnetic resonance) imaging.

The survey also examines the vendors being used, among respondents who have adopted machine learning, and the survey findings indicate some differences compared to the 2017 survey results. No one vendor dominates this space, as 19 percent use GE Healthcare and about 16 percent use Hologic, which is down compared to 25 percent of respondents who cited Hologic as their vendor in last year’s survey.

Looking at other vendors being used, 14 percent use Philips, 7 percent use Arterys, 3 percent use Nvidia and Zebra Medical Vision and iCAD were both cited by 5 percent of medical imaging professionals. The percentage of imaging leaders citing Google as their machine learning vendor dropped from 13 percent in 2017 to 3 percent in this latest survey. Interestingly, the number of respondents reporting the use of homegrown machine learning solutions increased to 14 percent from 9 percent in 2017.

 

*Findings were compiled from Reaction Data’s Research Cloud. For additional information, please contact Erik Westerlind at ewesterlind@reactiondata.com.

 

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Drexel University Moves Forward on Leveraging NLP to Improve Clinical and Research Processes

January 8, 2019
by Mark Hagland, Editor-in-Chief
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At Drexel University, Walter Niemczura is helping to lead an ongoing initiative to improve research processes and clinical outcomes through the leveraging of NLP technology

Increasingly, the leaders of patient care organizations are using natural language processing (NLP) technologies to leverage unstructured data, in order to improve patient outcomes and reduce costs. Healthcare IT and clinician leaders are still relatively early in the long journey towards full and robust success in this area; but they are moving forward in healthcare organizations nationwide.

One area in which learnings are accelerating is in medical research—both basic and applied. Numerous medical colleges are moving forward in this area, with strong results. Drexel University in Philadelphia is among that group. There, Walter Niemczura, director of application development, has been helping to lead an initiative that is supporting research and patient care efforts, at the Drexel University College of Medicine, one of the nation’s oldest medical colleges (it was founded in 1848), and across the university. Niemczura and his colleagues have been partnering with the Cambridge, England-based Linguamatics, in order to engage in text mining that can support improved research and patient care delivery.

Recently, Niemczura spoke with Healthcare Informatics Editor-in-Chief Mark Hagland, regarding his team’s current efforts and activities in that area. Below are excerpts from that interview.

Is your initiative moving forward primarily on the clinical side or the research side, at your organization?

We’re making advances that are being utilized across the organization. The College of Medicine used to be a wholly owned subsidiary of Drexel University. About four years ago, we merged with the university, and two years ago we lost our CIO to the College of Medicine. And now the IT group reports to the CIO of the whole university. I had started here 12 years ago, in the College of Medicine.

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And some of the applications of this technology are clinical and some are non-clinical, correct?

Yes, that’s correct. Our data repository is used for clinical and non-clinical research. Clinical: College of Medicine, College of Nursing, School of Public Health. And we’re working with the School of Biomedical Engineering. And college of Arts and Sciences, mostly with the Psychology Department. But we’re using Linguamatics only on the clinical side, with our ambulatory care practices.

Overall, what are you doing?

If you look at our EHR [electronic health record], there are discrete fields that might have diagnosis codes, procedure codes and the like. Let’s break apart from of that. Let’s say our HIV Clinic—they might put down HIV as a diagnosis, but in the notes, might mention hepatitis B, but they’re not putting that down as a co-diagnosis; it’s up to the provider how they document. So here’s a good example: HIV and hepatitis C have frequent comorbidity. So our organization asked a group of residents to go in and look at 5,700 patient charts, with patients with HIV and hepatitis C. Anybody in IT could say, we have 677 patients with both. But doctors know there’s more to the story. So it turns out another 443 had HIV in the code and hep C mentioned in the notes. Another 14 had hep C in the code, and HIV in the notes.

So using Linguamatics, it’s not 5,700 charts that you need to look at, but 1,150. By using Linguamatics, we narrowed it down to 1,150 patients—those who had both codes. But then we found roughly 460 who had the comorbidity mentioned partly in the notes. Before Linguamatics, all residents had to look at all 5,700 charts, in cases like this one.

So this was a huge time-saver?

Yes, it absolutely was a huge time-saver. When you’re looking at hundreds of thousands or millions of patient records, the value might be not the ones you have to look at, but the ones you don’t have to look at. And we’re looking at operationalizing this into day-to-day operations. While we’re billing, we can pull files from that day and say, here’s a common co-morbidity—HIV and hep C, with hep C mentioned in those notes—and is there a missed opportunity to get the discrete fields correct?

Essentially, then, you’re making things far more accurate in a far more efficient way?

Yes, this involves looking at patient trials on the research side, while on the clinical side, we can have better quality of care, and more updated billing, based on more accurate data management.

When did this initiative begin?

Well, we’ve been working with Linguamatics for six or seven years. Initially, our work was around discrete fields. The other type of note we look at has to do with text. We had our rheumatology department, and they wanted to find out which patients had had particular tests done—they’re looking for terms in notes… When a radiologist does a report on your x-ray, it’s not like a test for diabetes, where a blood sugar number comes out; x-rays are read and interpreted. The radiologists gave us key words to search for, sclerosis, erosions, bone edema. There are about 30 words. They’re looking for patients who have particular x-rays or MRIs done, so that instead of looking for everyone who had these x-rays done, roughly 400 had these terms. We reduced the number who were undergoing particular tests. The rheumatology department was looking for patients for patient recruitment who had x-rays done, and had these kinds of findings.

So the rheumatology people needed to identify certain types of patients, and you needed to help them do that?

Yes, that’s correct. Now, you might say, we could do word search in Microsoft Word; but the word “erosion” by itself might not help. You have to structure your query to be more accurate, and exclude certain appearances of words. And Linguamatics is very good at that. I use their ontology, and it helps us understand the appearance of words within structure. I used to be in telecommunications. When all the voice-over IP came along, there was confusion. You hear “buy this stock,” when the message was, “don’t buy this stock.”

So this makes identifying certain elements in text far more efficient, then, correct?

Yes—the big buzzword is unstructured data.

Have there been any particular challenges in doing this work?

One is that this involves an iterative process. For someone in IT, we’re used to writing queries and getting them right the first time. This is a different mindset. You start out with one query and want to get results back. You find ways to mature your query; at each pass, you get better and better at it; it’s an iterative process.

What have your biggest learnings been in all this, so far?

There’s so much promise—there’s a lot of data in the notes. And I use it now for all my preparatory research. And Drexel is part of a consortium here called Partnership In Educational Research—PIER.

What would you say to CIOs, CMIOs, CTOs, and other healthcare IT leaders, about this work?

My recommendation would be to dedicate resources to this effort. We use this not only for queries, but to interface with other systems. And we’re writing applications around this. You can get a data set out and start putting it into your work process. It shouldn’t be considered an ad hoc effort by some of your current people.

 

 


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