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Using Data to Drive Transparency and Performance in Asthma Care

December 28, 2018
by Barbara P. Yawn, M.D., Industry Voice
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As the healthcare industry moves towards an integrated system, data is becoming available to multiple stakeholders, including healthcare professionals, health plans, pharmacies and other healthcare resources. This will bring much-needed transparency and, eventually, could improve outcomes for patients, including those with conditions such as asthma.

Asthma is the most common chronic disease among children and adolescents. According to statistics published in Personalizing Asthma Management for the Clinician, the disease has become more prevalent over the past 20 years surpassing rates of 8 percent of the population, and this trend is projected to continue. Along with that will likely come increases in the number of asthma exacerbations.

As other chronic diseases have seen improvements in patient outcomes through the use of big data, including population health data, so too could asthma. It’s now possible to use these data to identify patients most likely to benefit from enhanced asthma care and allergy evaluation.

According to a report that appeared in Allergy, the European Journal of Allergy and Clinical Immunology, while allergy trigger testing for high-risk asthma patients requires resources, the cumulative cost of poorly controlled asthma (ED visits, hospital visits, extended stays, etc.) can well exceed the cost of taking this proactive approach. Controlling a patient’s asthma symptoms based on assessment, diagnostic testing and a tailored care plan that follows is likely less costly in the end.

Delivering excellence in value-based asthma care is predicated on the concept that we can simultaneously improve quality while containing costs. This presupposes that we’re using patient data to design and deliver more successful approaches to care, the results of which can then be measured and replicated.

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Opportunities for Improving Population Health

Certain patients will be more likely to benefit from certain treatments, and data is already helping stratify populations. The more data we produce—and share—across payers and systems, the better able we are to predict outcomes and prescribe care plans that ultimately reduce the systemwide costs of managing asthma. Allergy evaluations are a key part of this process.

Using billing and ICD-10 codes, including prescription data, we can identify those with asthma and flag certain high-risk patients, according to asthma guidelines. This would enable providers and plans to focus finite resources on patients who have not had allergy evaluations, rather than intervening with all asthma suffers. This prioritization saves valuable time and resources.

Innovative healthcare organizations are also taking a proactive stance on asthma assessment, embedding tools such as the Asthma Action Plan, according to the American Lung Association; and the Asthma APGAR tool, according to a study that appeared in Annals of Family Medicine, into their electronic health records (EHRs) to make asthma care more efficient and effective. Having these tools easily accessible during an asthma visit—or visits for other health problems—ensures that asthma care isn’t an afterthought in the discussion of a patient’s overall diagnosis and care planning.

Developing Quality Metrics

Quality metrics, based on validated measures, are critical for health systems and plans that adopt value-based care models. For asthma care, metrics fall under processes and outcomes. Both sets of metrics are vital and must be shown to be clinically relevant, have evidence of improving outcomes and be easily measured. If collecting the metrics disrupts clinical workflow, it’s likely the quality program will be unsustainable. 

Currently, the only quality metrics related specifically to asthma are the Healthcare Effectiveness Data and Information Set (HEDIS) measures focused on medication use, according to the National Committee for Quality Assurance. Although measures of medication use are important, other measures in asthma care are critical too, such as testing and management of high-risk asthmatics. This is a good example of a process metric that can be derived from claims data and correlated with outcomes. Aeroallergen assessment through diagnostic testing in high-risk patients should be considered for an updated HEDIS measure or other quality metric to help guide this important but often overlooked aspect of asthma management. 

Regular clinician and team feedback can change practice behavior, especially when care teams are empowered to deploy a workflow model that incorporates tools and resources to support the team, using allergic sensitization test results to teach patients trigger avoidance. When process metrics, such as appropriate trigger-testing rates, are coupled with improvements in outcomes, such as decreased urgent and emergent asthma interventions, and then aligned with payment methodology for enhanced results, significant quality improvement in practice team care patterns will be sustainable.

Understanding and Addressing Disparities

As with most chronic diseases, insurance coverage for basic components, including allergy testing, is excellent. However, disparity in allergy and asthma care is prevalent in the U.S. According to several research initiatives and published reports conducted by the Centers for Disease Control and Prevention (CDC), the Global Initiative for Asthma (GINA), and others, asthma disproportionately affects children, adult women, the poor, African Americans and Americans of Puerto Rican descent; and we know that environmental and genetic influences can also be contributing factors.

According to a report published in Critical Care Medicine in 2012, given the complexity of disparity sources and the admixture of American society, healthcare professionals profess incomplete confidence in identifying and tackling these important issues with differing populations in clinical care. 

Research conducted by Dr. Robin Andrew Evans-Agnew, Ph.D., published in Heath Promotion Practice, describes more than 30 evidence-based causes for disparities in asthma and allergy management. For example, according to data from GINA, urban areas, which often have a predominance of African American patients, are heavily concentrated with asthma risk factors such as air pollution. According to a study published in Academic Pediatrics, African Americans are also less likely to receive National Institutes of Health (NIH) guideline-directed care.  

Clinicians can gain considerable disease-management leverage by understanding the determinants of disparity and critically reviewing their own care delivery model. Change starts with clinical recognition that such disparities exist, an obvious fact that is sometimes missed and, according to research published in the Journal of Asthma, when clinicians are trained in cultural competence in addition to asthma care, their confidence in using better counseling and patient-centered approaches are enhanced, compared to standard asthma care training alone.

Data has demonstrated that a care gap exists when it comes to the delivery of non-medication elements of asthma care, as indicated by a clinical assessment published in Mayo Clinic Proceedings. Improved efficiency in allergy testing and management addresses “the gap” intended to improve quality of asthma care, patient satisfaction, value and resource utilization for the entire healthcare system. It’s important for all of us to remember that, at its core, asthma care is about the clinician-patient relationship and personalizing care by not only gathering the relevant data, but also quickly assessing the environmental issues and providing the appropriate counseling.  All are critical to improving outcomes.

 

Barbara P. Yawn, M.D., MSc FAAFP, is a family physician researcher who currently focuses on respiratory diseases, specifically COPD screening/case finding and implementation of new tools to improve asthma outcomes.  She is/was a member of the International Primary Care Respiratory Group; EPR-3 science panel, editor in chief of Respiratory Medicine Case Reviews and Chief Science Officer of the COPD Foundation. She is retired form her position as the director of research at the Olmsted Medical Center and is an Adjunct Professor of Family and Community Health at the University of Minnesota. She serves as a consultant to multiple NIH and PCORI founded studies of asthma and COPD. Dr. Yawn was chair of an Allergy and Asthma Task Force convened and supported by Thermo Fisher Scientific.

 


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AI in Imaging: Where’s the Bang for the Buck?

January 23, 2019
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Over the past year much has been written about the capability of Artificial Intelligence (AI), and what it will mean for imaging services.  At last year’s RSNA, AI was the featured topic and received the lion’s share of publicity. 

The glamorous aspect of AI and Machine Learning has been how AI can assist the radiologist with diagnosis of imaging studies.  A key area of focus has been in chest imaging (https://www.auntminnieeurope.com/index.aspx?sec=sup&sub=aic&pag=dis&ItemID=616828) where there has been some success in triaging abnormal chest images.  The upside of such applications is improved diagnostic efficiency, particularly as healthcare moves toward value-based care.  The downside is that such algorithms require substantial amounts of data to validate, and they will need to go through the FDA approval process, which will take time before they can be fully implemented.

Ultimately, AI imaging applications will pay off.  But, what about the other potentially less-glamorous aspect of applying AI/Machine Learning to the diagnostic process?  By that, I am referring to its use in terms of workflow orchestration.  Aside from interpreting imaging content, AI/Machine Learning applied to workflow orchestration can provide valuable information and assistance in preparing a case for interpretation. 

Take for example Siemens Healthineer’s AI-Rad Companion application (https://www.healthcare.siemens.com/infrastructure-it/artificial-intelligence/ai-rad-companion).  The application provides automated identification, localization, labeling and measurements for anatomies and abnormalities.  Such a capability can improve the radiologist’s efficiency without necessarily employing an algorithm to assess the image.

Other workflow applications can assess the study and mine relevant information from the EHR to present to the radiologist, again with the goal of improving their efficiency and efficacy.  Still other applications match radiologist reading assignments with available studies to improve reading efficiency.  In another twist, one company has demonstrated a capability to further analyze cases, using AI to assign the next appropriate case to a radiologist without the need for a worklist. 

As healthcare providers consolidate, there is a growing need for improvement in resource utilization across facilities.  Smart worklists that can present cases to individual radiologists across facilities can improve the overall efficiency and efficacy of interpretation.  Rule sets that address radiologist availability, reading sub-specialties, location, etc. can help “level-load” reading resources. 

My point is that while AI applications that manipulate images may hold great promise for the future of diagnosis, areas such as workflow orchestration might offer more immediate results in an environment of changing healthcare.  Providers should take a close look at these applications to assess whether they can achieve a more immediate impact on imaging operations.

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National Library of Medicine Creating Scientific Director Position

January 23, 2019
by David Raths, Contributing Editor
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New position will oversee Lister Hill National Center for Biomedical Communications and National Center for Biotechnology Information

As part of a reorganization of its intramural research activities, the U.S. National Library of Medicine (NLM) has launched a search for a scientific director. The scientific director will oversee a group of 150 scientific personnel, developing new approaches to data science, biomedical informatics, and computational biology.

In a blog post on the library’s website, director Patti Brennan, R.N., Ph.D., called the move a big step in revving up its intramural research operation.

One of the 27 Institutes and Centers of the National Institutes of Health (NIH), NLM creates and hosts major digital resources, tools, and services for biomedical and health literature, data, and standards, sending 115 terabytes of data to five million users and receiving 15 terabytes of data from 3,000 users every weekday.

NLM’s strategic plan for 2017-2027 positions it to become a platform for biomedical discovery and data-powered health. NLM anticipates continued expansion of its intramural research program to keep pace with growing demand for innovative data science and informatics approaches that can be applied to biomedical research and health and growing interest in data science across the NIH.

A Blue Ribbon Panel recently reviewed NLM’s intramural research programs and recommended, among other things, unifying the programs under a single scientific director. That shift also aligns the library with NIH’s other institutes and centers, most of which are guided by one scientific director.

NLM’s  intramural research program includes activities housed in both the Lister Hill National Center for Biomedical Communications (LHC) and the National Center for Biotechnology Information (NCBI). The researchers in these two centers develop and apply computational approaches to a broad range of problems in biomedicine, molecular biology, and health, but LHC focuses on medical and clinical data, while NCBI focuses on biological and genomic data.

But the Blue Ribbon Panel noted that the boundaries between clinical and biological data are dissolving, and the analytical and computational strategies for each are increasingly shared. “As a result, the current research environment calls for a more holistic view of biomedical data, one best served by shared approaches and ongoing collaborations while preserving the two centers’ unique identities, wrote Brennan, who came to NIH in 2016 from the University of Wisconsin-Madison, where she was the Lillian L. Moehlman Bascom Professor at the School of Nursing and College of Engineering.

She added that having a single scientific director should lead to a sharper focus on research priorities, fewer barriers to collaboration, the cross-fertilization of ideas and the optimization of scarce resources.

The new scientific director will be asked to craft a long-range plan that identifies research areas where the NLM can best leverage its unique position and resources. We’ll also look for ways to allocate more resources to fundamental research while streamlining operational support. “Down the road, we’ll expand our research agenda to include high-risk, high-reward endeavors, the kinds of things that raise profound questions and have the potential to yield tremendous impact,” she wrote.

Besides the scientific director, the NLM is also recruiting three investigators to complement its strengths in machine learning and natural language processing.

 

 

 

 

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Survey: Digital, AI Top Priorities in 2019, but EHRs Will Dominate IT Spend

January 22, 2019
by Heather Landi, Associate Editor
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Digital, advanced analytics, and artificial intelligence (AI) are top spending priorities for healthcare executives in 2019, but electronic health record (EHR) systems will dominate technology spending budgets, according to a recent technology-focused healthcare survey.

Damo Consulting, a Chicago-based healthcare growth and digital transformation advisory firm, surveyed technology and service provider executives and healthcare enterprise executives about how the demand environment for healthcare IT is changing and will impact the industry in the coming year. Damo Consulting’s third annual Healthcare IT Demand Survey also analyzes the challenges for healthcare organizations and the perceived impact of macro-level changes.

The report indicates technology vendors will continue to struggle with long sales cycles as they aggressively market digital and AI. For the second year in a row, the rise of non-traditional players such as Amazon and Google will have a strong impact on the competitive environment among technology vendors while EHR vendors grow in dominance.

Among the key findings from the survey, IT budgets are expected to grow by 20 percent or more, with healthcare executives indicating they are more upbeat about IT spend growth than vendors. All the healthcare executives who participated in the survey said digital transformation initiatives are gaining momentum in their enterprises.

However, the majority (75 percent) agree that rapid change in the healthcare IT landscape makes technology decisions harder and only 58 percent believe there are plenty of viable and ready-to-deploy solutions available today in emerging technologies such as AI and digital health solutions. Seventy-one percent agree that federal government policies have provided a boost to healthcare IT spend this past year.

Top IT priorities for healthcare enterprise executives in 2019 are digital, advanced analytics and AI. Of the survey respondents, 79 percent said accelerating digital health initiatives was a top priority and 58 percent cited investing in advanced analytics and AI capabilities as top priorities. However, modernizing IT infrastructure (25 percent) and optimizing EHRs (21 percent) are also significant priorities.

Technology vendors also see AI, advanced analytics and digital transformation as top areas of focus for next year, as those areas were cited by 75 percent and 70 percent of technology and service provider executives, respectively. Thirty-three percent of those respondents cited EHR optimization and 25 percent cited cybersecurity and ransomware. Thirteen percent cited M&A integration as a top area of focus in 2019.

However, EHR systems will dominate technology spending budgets, even as the focus turns to digital analytics, the survey found. Technology and service provider executives who participated in the survey identified EHR system optimization and cybersecurity as significant drivers of technology spend in 2019. Sixty percent of respondents said enterprise digital transformation and advanced analytics and AI would drive technology spend this year, but 38 percent also cited EHR optimization and cybersecurity/ransomware. One executive survey respondent said, “For best of breed solutions, (the challenge is) attracting enough mindshare and budget vs. EHR spends.”

When asked what digital transformation means, close to half of healthcare executives cited reimaging patient and caregiver experiences, while one quarter cited analytics and AI and 17 percent cited automation. As one executive said, “The biggest challenge for healthcare in 2019 will be navigating tightening margins and limited incentives to invest in care design.”

Healthcare executives are divided on whether digital is primarily an IT-led initiative, and are also divided on whether technology-led innovation is dependent on the startup ecosystem.

The CIO remains the most important buyer for technology vendors, however IT budgets are now sitting with multiple stakeholders, the survey found, as respondents also cited the CFO, the CTO, the CMIO and the chief digital officer.

“Digital and AI are emerging as critical areas for technology spend among healthcare enterprises in 2019. However, healthcare executives are realistic around their technology needs vs. their need to improve care delivery. They find the currently available digital health solutions in the market are not very mature,” Paddy Padmanabhan, CEO of Damo Consulting, said in a statement. “However, they are also more upbeat about the overall IT spend growth than their technology vendors.”

Looking at the technology market, healthcare executives perceive a lack of maturity in technology solution choices for digital initiatives, as well as a lack of internal capabilities for managing digital transformation. In the survey report, one executive said, “HIT architecture needs to substantially change from large monolithic code sets to an API-driven environment with multiple competing apps.”

A majority of healthcare enterprise executives view data silos and lack of interoperability as the biggest challenges to digital transformation. And, 63 percent believe the fee-for-service reimbursement model will remain the dominant payment model for the foreseeable future.

In addition, cybersecurity issues will continue to be a challenge for the healthcare sector in 2019, but not the biggest driver of technology spending or the top area of focus for health systems in the coming year, according to the survey.

Healthcare executives continue to be confused by the buzz around AI and digital and struggle to make sense of the changing landscape of who is playing what role and the blurred lines of capabilities and competition, according to the survey report. When asked who their primary choice is when looking for potential partners to help with digital transformation, 46 percent of healthcare executives cited their own internal IT and innovation teams, 17 percent cited their EHR vendor and 8 percent cited boutique consulting firms. A quarter of respondents cited “other.”

For technology vendors, the biggest challenge is long cycles, along with product/service differentiation and brand visibility.

The rise of non-traditional players, such as Amazon, Apple, and Google, will have a strong impact on the competitive healthcare technology environment, the survey responses indicated. At the same time, deeply entrenched EHR vendors such as Epic and Cerner will grow in dominance.

 

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