Blog from May, 2021

Jessica Kent 

As part of a six-year study, researchers will collect big data to better understand rural health disparities.

Among the many gaps in care that pervade the medical industry, rural health disparities are some of the most prevalent.

For individuals living in rural parts of the country, geographic isolation, limited access to healthcare, and higher rates of poverty all contribute to worse health outcomes – putting rural residents behind their urban counterparts in terms of health and well-being.

In a 2020 study published in Health Affairs, researchers found that higher rural mortality at the state level is strongly linked to socioeconomic status, patient care access, and lack of health insurance. The results demonstrated that these three variables accounted for 81.8 percent of the total variance of mortality among rural populations.

Now, a new initiative is seeking to collect big data on individuals in rural areas to understand and alleviate these health disparities. The Risk Underlying Rural Areas Longitudinal (RURAL) cohort study is working to address critical gaps in knowledge of heart and lung disorders in rural counties in the southeastern US.

“There are some health disparities that persist among Americans living in rural settings,” Peter Durda, PhD, Faculty Scientist at the Larner College of Medicine at the University of Vermont and co-investigator of the study, told HealthITAnalytics.

“Forty-six million people in the US are living in rural settings – that's one in six. We wanted to look at ten different counties in the rural South. These counties are matched based on socioeconomic status and general health, but their health outcomes are drastically different. Looking at these counties, we're hoping that we can have some insight into what the issues are with rural health, and understand how to help these people live better, longer lives.”

The overall goal of the study is to promote and support the health of rural communities.

“This is a large epidemiological study examining 4,600 people over six years. Rural access to care is always an issue, and our goal is to try to understand these problems,” said Durda.

“By understanding the health issues these individuals have, perhaps we can help encourage those agencies responsible for healthcare to provide increased access in rural areas.”

The study will be led by researchers from 16 different institutions across the US, and will focus on ten rural counties in Alabama, Kentucky, Louisiana, and Mississippi. The research will involve collecting various data points on rural populations, including blood count data, to better understand risk and resilience factors that may be specific to members of rural communities.

“We're looking at a lot of things in this study. We have a mobile examination unit, in which people will participate in questionnaires regarding their health history, behavioral history, and socioeconomic status. Our mobile unit has a CT scanner, and we're doing all the labs on the mobile units. This will help us obtain an overall picture of the health of these individuals,” said Durda.

“Currently, the mobile examination unit is being finished up in California. It's a 53-foot-long trailer containing the labs, an interview room to put participants, and a mobile CT scanner as well. That will be transported to Alabama, and we hope to see the first participants in Alabama in April. We've been pushed back because of the pandemic, so we're about a year behind where we should be, but we should be seeing participants by April.”

The RURAL effort is also committed to include community-based organizations and participants at every stage of the research, with the team working alongside local leaders and community organizers.

“There is a lot of community involvement in this project. Since these individuals are not usually ones that would be in a study and may have questions or concerns about the study, we have partnerships with community healthcare workers and people they trust so that we can engage the participants and get them on board,” said Durda.

“Participants will also get a Fitbit and a cellphone as part of the study so that we can track their data and analyze that kind of information as well. The mobile phone will also be used for questionnaires and future surveys.”

RURAL researchers will share county-specific results with community organizations and other groups in the network, which will help guide future programs to improve health in local areas. As data becomes available throughout the study period, all rural communities will have access to county-level findings.

“It's a really comprehensive study. After Alabama, we'll move on to Mississippi, Louisiana, and Kentucky, over a period of about five years,” said Durda.

“Individually, some of the data we get will be returned to participants, but mainly it's a population-based study. The results from that should encourage greater research into these problems.”

Ultimately, the study will aim to eliminate the health disparities that persist among people living in rural parts of the country.

“Our overall goals are to improve the health of the people in the United States – that's what we hope to do with this study. By focusing on this, we expect to better understand the disparities in health outcomes in these rural populations, and make changes so that these people can have healthier lives,” Durda concluded.

Christopher Jason

Connecticut joined a list of 45 other states to implement and launch a statewide health information exchange.

Following several fruitless attempts over the past decade and a half, Connecticut has launched its statewide health information exchange to streamline patient data exchange across the state.

After signing with 25 healthcare providers in February, the statewide HIE currently has 44 participants. Major health systems, behavioral health providers, community healthcare centers, physician’s groups, medical practices, and Connecticut’s health and human services agencies are signed onto the HIE.

State leaders aimed to have the HIE up and running during the initial COVID-19 surge, but it did not deploy in time. This frustrated state officials because the HIE could have supported providers during the first wave of the pandemic. But, the state is now turning the corner, and the HIE should enhance the state’s reopening plan.

“Today, I’m proud to announce the launch of our statewide health information exchange we call Connie, which provides the technology services to allow healthcare providers to exchange patient data safely and efficiently,” Vicki Veltri, OHS executive director, said in a statement.

“Connecticut is now officially on the path that forty-five other states have traveled, with a more effective healthcare data delivery model. Information that is accessible in real time is critical for good healthcare; Connie will help providers and patients access information to improve care and lower overall healthcare costs.”

Hospitals and laboratories will have one year from when the state officially considers the HIE operational to connect to Connie. All other healthcare providers will have two years, HIE leaders said.

Connie directors said it could take up to three years to get all providers on board.

“As a practicing physician, I am pleased to support the launch of operations for Connie,” said Allen F. Davis, MD, board member of ProHealth Physicians. “Ensuring physicians have the most up-to-date clinical information on their patients is vital to empowering physicians and their patients to make the best decisions for their health.”

State leaders found statewide HIE implementation is challenging, and as a result, each unsuccessful attempt cost the state millions of dollars.

However, the Connie governing board of directors is enthusiastic about the launch and the many benefits that connected providers will have, HIE leaders said.

“I believe Connie will transform healthcare delivery in the state for the better. Independent hospitals, and providers in particular are going to see tremendous benefits from data sharing and interoperability once connected with Connie,” said Patrick Charmel, president and CEO of Griffin Health Services.

“They’ll have the same advantages as the member hospitals and health care providers of larger health systems that have migrated providers to a shared electronic health record including greater care continuity, better informed clinical decision making and more efficient care delivery resulting from the elimination of care redundancy,” Charmel continued.

The statewide HIE aims to reduce costs and promote patient care by reducing the chances of duplicative testing. It also links providers without going through the tedious process of establishing a connection with each facility.

An HIE also offers financial benefits for the state. Medicaid and Medicare services rely on health outcomes data. Health systems can only receive payments if they can show that they improve care quality and decrease hospital readmissions.

The statewide HIE will utilize an opt-out system, which requires patients to specifically request their data not be included on the HIE. This system ensures high patient participation.

“The launch of Connie provides an incredible benefit to patients and providers in Connecticut,” concluded Jenn Searls, executive director of Connie.

“The capability to instantly share health information among providers in a confidential and secure manner means better and faster care, and fewer unneeded tests and procedures, medical mistakes, and costly medical bills in the health care system. Additionally, exchanging data through Connie creates a more accessible, timely, secure and transparent method for providers to access patient information.”

Jessica Kent

Increased data sharing, improved diversity and inclusion, and expanded use of data analytics technologies will help speed the development of precision medicine.

Enhanced data sharing and increased diversity will help accelerate precision medicine efforts and establish a more equitable healthcare industry, according to a new commentary published in Cell.

In the commentary, Francis S. Collins, MD, PhD, Director of NIH and Joshua C. Denny, MD, MS, CEO of the All of Us Research Program, noted that the healthcare industry is already beginning to realize the promise of data-driven transformation.

“Researchers are routinely using healthcare data for discovery, identifying genomic underpinnings of cancer and many other common and rare diseases, introducing transformative molecularly targeted therapies, and leveraging massive computational capabilities with new machine learning methods. We are beginning to see the fruits of these efforts,” the authors stated.

“There is perhaps no more poignant example than the response to the COVID-19 pandemic. Genomics and molecular technologies were key in identifying the etiologic agent, developing diagnostics and treatments, and creating vaccine candidates. At the same time, COVID-19 has highlighted the need for precision medicine to move further and faster.” 

In order to accelerate equitable precision medicine efforts, Collins and Denny stated that the industry will need to maximize the potential of big data resources.

An open science approach is emerging, which will allow researchers from around the world to access data from national cohorts like the UK Biobank and the All of Us Research Program. However, healthcare leaders will have to take additional steps to enable widespread data sharing, Collins and Denny said.

“The next step is clear: make it easier for researchers to merge data from multiple cohorts. Currently, this requires painstaking manual phenotype adjudication and building large consortia including experts from each cohort. Fortunately, there are efforts underway to improve this process,” the authors wrote.

“Groups such as the Global Alliance for Genomics and Health are working to develop and to coordinate common data models and file formats to facilitate collaboration and interoperability. In recognition of the need for better collaboration, the International Hundred Thousand Plus Cohort Consortium has brought together more than 100 cohorts in 43 countries comprising more than 50 million participants.”

In addition to enhancing data sharing efforts, improving diversity and inclusion in healthcare research will also speed the development of equitable precision medicine.

The authors noted that less than three percent of the participants in published, genome-wide association studies are of African, Hispanic, or Latin American ancestries, while 86 percent of clinical trial participants are white.

This lack of diversity could exacerbate existing health disparities, as well as prevent researchers from making discoveries that could benefit all patient populations.

“With a growing depth of data, we have an opportunity to replace adjustments for race and ethnicity with more specific measures. In particular, ‘race’ conflates a plethora of social, cultural, political, geographic, and biologic factors together and can perpetuate systemic racism,” said Collins and Denny.

“Routine collection of social determinants of health in both research and clinical care in combination with more precise measures of environmental influences, habits, and genetic ancestry can provide more rational, etiology-based adjustments and yield better risk stratifications and treatments.”

As the industry works to promote diversity and inclusivity in research efforts, Collins and Denny pointed out that healthcare should also aim to increase the diversity of the biomedical research workforce.

“A more diverse workforce—in culture, ancestry, beliefs, scientific backgrounds, and methodological approaches—brings increased understanding, innovation, trust, and cultural sensitivity; is more likely to pursue questions relevant to different audiences; and ultimately delivers better research,” the authors stated.

Collins and Denny also emphasized the role big data and artificial intelligence will likely play in the advancement of precision medicine. The technology has transformed areas ranging from language translation to image interpretation, and holds great potential for speeding the development of personalized therapies.

However, the team pointed out that the use of data analytics and AI in healthcare has been limited by the lack of readily available large, commonly structured datasets.

“Looking forward, biomedical datasets will become increasingly ready for analyses. The growth of clinical data (including image, narrative, and real-time monitoring data), molecular technologies (genomics principal among them), and the availability of devices and wearables to provide high-resolution data streams will dramatically expand the availability of detailed phenotype and environmental data not previously available at this scale,” the authors said.

“Applications of machine learning approaches could result in new taxonomies of disease through genomic, phenomic, and environmental predictors.”

The ongoing pandemic has highlighted the need for healthcare research to change in order to serve communities nationwide – especially communities of underserved populations.

“In this time of COVID-19, science has been the answer to an existential medical threat. Yet we are reminded that many of the benefits of medicine’s advancement have not always been available to all. Biomedical approaches, computation algorithms, and the availability of high-resolution data will dramatically increase over the next decade,” Collins and Denny concluded.

“Implementation of a bold plan to collaborate internationally, to engage diverse populations of participants and scientists, to deeply measure our populations, to make clinical and research data broadly available, and to implement this knowledge in clinical practice—in a true learning healthcare system—will allow us to achieve the vision of precision medicine for all populations.”

Christopher Jason

The number of EHR super-user providers increased by 10 percent over a three-year period.

Between 2014 and 2017, healthcare facilities with more significant EHR capabilities had better clinical quality composite measures than other facilities, but healthcare clinics that adopted EHRs during that time period had less significant clinical quality increases, according to a study published in JAMA Network Open.

The latter finding might suggest a longer timeline for seeing clinical quality performance improvements after EHR adoption and implementation.

For over 15 years, healthcare stakeholders have considered the EHR to be vital in achieving quality patient care because of the data storage and analytic capabilities vastly exceeded other alternatives, such as paper records. EHRs also allow for optimization and tool integration to improve quality, such as clinical decision support and registries.

However, a gap remains between high-quality patient care and actual care delivery.

Researchers surveyed 1,141 ambulatory clinics in Minnesota, Washington, and Wisconsin from 2014 to 2017 to gather data on EHR capabilities and dissect clinical quality performance.

Researchers grouped 50 EHR capabilities into seven categories: no functional EHR, EHR under-user, EHR, neither under-user or super-user, EHR super-user, and a standardized composite of ambulatory clinical performance measures, the study authors explained.

In 2014, 51 percent of respondent clinics were EHR super-user and this percentage increased to 54 percent in 2015, 58 percent in 2016, and 61 percent in 2017.

The research revealed ambulatory clinics with more advanced EHR capabilities had higher scores on a composite measure of ambulatory clinical quality than other clinics. This result translated to a roughly 9 percent difference in the clinic’s rank in clinical quality.

The smaller number of clinics that gained advanced EHR capabilities over the three years improved more than other clinics, but the results were not statistically significant.

“This study suggests that ambulatory clinics with advanced EHR capabilities were associated with a better performance on a composite measure of ambulatory clinical quality than clinics with less-advanced EHR capabilities; clinics that adopted advanced EHR capabilities during a 3-year period were not associated with significant increases in ambulatory clinical quality performance,” wrote the study authors.

On average, EHR super-user clinics consistently had better clinical quality performance than clinics that were not super-users, but these results are compatible with either more significant impacts or empty impacts.

Additionally, over time, all clinic respondents improved clinical quality and clinics that transitioned to EHR super-user status had more notable increases in quality than did clinics with static EHR user status.

Although individual performance measure differences might have been small, researchers aggregated the small improvements over 13 measures. The net difference could be more clinically important, the study authors wrote.

For example, a small change in a clinical composite measure is equal to a change in 45 to 75 rank order places if the provider started near the top end of the rank order, or even 85 to 95 places if started in the middle of the rank order.

“These results are consistent with several hypotheses, including that increasing EHR capability is associated with no or, at best, modest improvements in clinical quality; improvements in clinical quality associated with increasing EHR capabilities take several years to be realized,” the study authors concluded.

“Over 2 to 3 years, the largest clinical quality increase was in clinics that already have a functioning EHR that is being underused in terms or its capabilities. As US health care continues to evolve and clinicians gain more EHR capabilities, our results and future studies of the new hypotheses generated will be vital to efforts to improve ambulatory clinical quality.”