A northeast Indiana health system has joined the statewide HIE to support public health efforts that lean on interoperability and data exchange.
Parkview Health, a 10-hospital health system in northeast Indiana, has joined the Indiana Health Information Exchange (IHIE) to support statewide, data-driven public health efforts powered by interoperability.
IHIE is the non-profit organization that operates the Indiana Network for Patient Care (INPC), the largest inter-organizational clinical data source in the country. INPC has data on more than 17 million patients from over 117 hospitals, 18,000 practices, and 50,000 providers.
“As Indiana’s statewide health information exchange, IHIE believes it has a responsibility to securely gather, analyze, and communicate information in the best interest of public health, and specifically, in support of the Indiana Department of Health,” John Kansky, chief executive officer of IHIE, said in a press release.
IHIE consolidated with Michiana Health Information Network (MHIN) in 2020 to form a statewide HIE, providing healthcare stakeholders with a comprehensive source of connected patient information.
The consolidation has allowed IHIE to accrue clinical data to improve patient care and support public and population health initiatives, the HIE noted.
Now, the HIE’s clinical database will grow even larger with Parkview as a new partner.
“I believe Parkview’s participation will have a significant positive impact, and we greatly appreciate their participation,” Kansky continued.
Ron Double, chief information officer of Parkview Health, noted that COVID-19 has highlighted the importance of secure data exchange for collaboration and innovation.
“We understand the power of data and its impact on the health of our community,” said Ron Double, chief information officer of Parkview Health. “The pandemic demonstrated the importance of securely sharing information and collaborating with agencies across the state. Parkview is looking forward to seeing the impact of this partnership, especially in research and innovation.”
Parkview’s participation will enhance the statewide asset by increasing interoperability for care coordination and public health research efforts, the organizations said.
“Data are essential to us in our work to protect the health and safety of Hoosiers,” said Kristina Box, MD, FACOG, state health commissioner. “Adding Parkview Health to IHIE will greatly enhance our ability to make data-driven, evidence-based decisions for the whole state.”
IHIE has not only supported statewide interoperability efforts, but national ones, too.
Earlier this year, IHIE and five other major health information exchanges (HIEs) formed the Consortium for State and Regional Interoperability (CSRI).
CSRI aims to boost nationwide patient data exchange by progressing patient data exchange initiatives across the country and promoting state-to-state interoperability for providers, health plans, Medicaid programs, and public health departments.
Additionally, CSRI will form data-driven healthcare insights for federal agencies to advise critical policy decisions and increase health IT innovation.
The consortium is made up of IHIE, Chesapeake Regional Information System for our Patients (CRISP), which covers Maryland, District of Columbia, and West Virginia; CyncHealth in Nebraska and Iowa; Health Current of Arizona; Manifest MedEx of California; and Colorado Regional Health Information Organization (CORHIO).
“CSRI is well-positioned to leverage economies of scale on projects that have the potential to move the interoperability needle in a big way,” Morgan Honea, CEO of CORHIO, told EHRIntelligence.com in a February interview. “I am incredibly excited to be a part of this innovative group and look forward to developing and delivering HIT that can help solve significant data problems.”
In the fight against COVID-19, the six HIEs partnered with their local public health departments to enhance data exchange. The HIEs supported test ordering and scheduling with state and county clinics, as well as the development of dashboards for COVID-19 test results, mortality, and hospitalizations. The HIEs also supported contact tracing efforts, COVID-19 alerts, and predictive analytics to identify high-risk patients.
To recover from COVID-19’s financial downturn and improve patient outcomes, healthcare organizations are prioritizing health IT and EHR optimization.
Healthcare organizations are investing in health IT resources and EHR optimization after a year of COVID-19 financial turbulence, according to the 9th annual Health IT Industry Outlook survey conducted by Stoltenberg Consulting Inc.
The survey collected insights from chief information officers (CIOs) or IT directors at a variety of healthcare facilities.
According to the results, EHR optimization is a big-ticket item for most CIOs in 2021. More than half of respondents (59 percent) said that "getting the most out of existing IT purchases, like the EHR system" is their healthcare organization’s biggest financial goal post-COVID-19.
“In a rapidly evolving environment, technology must adapt to the changing needs of healthcare and the changing preferences of consumers more directly involved in their own care journeys,” the researchers wrote.
Approximately one in three CIOs (31 percent) reported that EHR new version upgrades are the top IT spending priority for their healthcare organizations, while one in four industry leaders reported investment in cybersecurity measures as the top spending priority for 2021.
However, despite CIOs reporting greater investment in EHR upgrades, 33 percent of respondents said cybersecurity was their organization’s top mission-critical priority compared to 30 percent who reported EHR upgrades as the top mission-critical priority. This is likely due to the uptick in healthcare cybersecurity events in 2021, the report authors noted.
Additionally, after a pause in early 2020, healthcare mergers and acquisitions (M&A) are growing in popularity once again, prompting CIO interest in health IT system integration, the survey authors explained. Approximately 20 percent of CIOs reported that IT integration after system consolidation is a mission-critical priority, indicating the need for high-quality IT support.
However, more than half of respondents (55 percent) reported that as they face decreased revenues from COVID-19, budgeting for qualified IT resources is their organization’s most significant operational burden for the second consecutive year.
The researchers said that enabling a mix of flexible and properly skilled staff is key as CIOs seek to lessen administrative burden and control costs.
When IT support teams are well-versed in both the EHR system and the healthcare organization’s cross-organizational workflow and communication practices, they can better tailor processes to maximize efficiency and system utilization, the researchers explained.
“At a time when the digital experience has become a competitive differentiator for hospitals and health systems, many internally operated help desks cannot handle the crush of inquiries coming their way,” the researchers wrote. “Utilizing IT support resources who can easily flex in and out of project area needs is pivotal for nimble response that better optimizes IT spending without draining resource costs or adding on ramp up and training time.”
Additionally, the researchers called for CIOs to apply analytics to end-user support. By doing so, organizations can determine where further investment is needed. For instance, help desk incident analysis helps underscore large-scale workflow or system education difficulties, the researchers said.
“As a clear view into organization-wide EHR use, this is especially helpful during mission critical events, like crisis management, new system go lives or EHR upgrades to detect areas of concern,” the report authors wrote.
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.
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.”
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.”
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.”
CTHealthLink added Connecticut Children’s and the Connecticut Children’s Care Network to its expanding list of provider connections.
CTHealthLink (CTHL), a physician-led health information exchange (HIE) established in partnership with the Connecticut State Medical Society (CSMS), added its first integrated delivery system to its network, Connecticut Children’s and the Connecticut Children’s Care Network (Care Network).
“We are pleased that Connecticut Children’s and its Care Network are dedicated and committed to a more comprehensive way of sharing patient information,” Richelle deMayo, chief medical information officer at Connecticut Children’s, said in a statement.
The three-year-old HIE enables clinicians, hospitals, and other healthcare providers in the HIE network to exchange patient health records, utilize data analytics tools to improve patient outcomes, and streamline clinical processes. It also grants patients access to their respective health records, CTHL said.
“Being part of CT HealthLink and having access to this type of patient data will allow us to save lives by preventing suicide which is the second leading cause of death in children and young adults starting at 10 years old,” said Steve Rogers, MD, medical director of Emergency Behavioral Health Services.
“Children receiving care in our network often have complex medical needs that require them to see a number of different providers. CTHealthLink will help us to connect information across different providers and EMR systems to improve the care we deliver to our patients,” Rogers continued.
Connecticut Children’s leaders explained that Connecticut’s current mental health crisis was a critical reason to connect to the HIE.
The health system has one of the state’s lone behavioral health units. However, Connecticut Children’s clinicians and patients currently experience difficulty with patient data access and exchange.
“Patients at risk of suicide are seeing their healthcare providers, but their risk is often unrecognized due to the fragmentation of health information throughout the system,” said Rob Aseltine PhD, CTHealthLink Board Chair. “We see access to the integrated data and analytics provided by systems like CTHealthLink as a critical tool in helping pediatricians identify and intervene with children at risk of suicide.”
Connecticut Children’s and Care Network join Yale-New Haven Health, UConn Health, CVS Health and Minute Clinics, the Veterans Administration (VA), DaVita Health, the Department of Defense (DoD), Fresenius Medical Care, and Premise Health on CTHealthLink’s list of connections.
This connection also allows the healthcare organizations to have full access to the state’s public health registries.
These additions, plus Connecticut’s 26 independent pediatrician practices, aim to improve patient data exchange and interoperability across the state, triggering a more effective response to certain health emergencies, including the COVID-19 pandemic.
Clinicians in the Care Network can leverage the HIE to ensure that children with special healthcare needs will receive the COVID-19 vaccine. Connecticut’s immunization registry tracks all COVID-19 vaccinations.
“Children with complex medical conditions are especially at risk for coronavirus infection,” said David Krol, MD, medical director of CT Children’s Care Network.
Additionally, the HIE is connected to the Carequality interoperability network and is a KONZA National Network member, enabling patient data exchange from across the country.
“Health information exchanges have untapped potential and broad reaching benefit for healthcare. In partnership with CTHealthLink, we have an opportunity to further the quadruple aim in enhancing the patient experience, improving population health outcomes, reduce costs, and enhance the clinician experience across Connecticut, supporting our trajectory towards value-based care,” said Jung Park, Chief Information Officer for Connecticut Children’s.
“Implementing this critical infrastructure begins a virtuous cycle, helping to enable future care innovation and accelerating research aims. I’m excited by the prospects and synergies that CTHealthLink will enable,” Park concluded.
Scientifically tracking EHR use measures could improve clinician well-being and ultimately mitigate clinician burden.
To effectively boost EHR optimization and reduce clinician burnout, healthcare stakeholders must develop EHR use measures that are actionable, usable, transparent, and trustworthy, according to a JAMA Network op-ed from American Medical Association and Yale School of Medicine leaders.
Scientific-based methods could effectively track EHR use and performance, op-ed authors Christine Sinsky from the AMA, and Harlan Krumholz and Edward Melnick, both from Yale, wrote.
Extended EHR use, documentation, excessive EHR inbox messages and notifications, and other EHR usability issues can result in clinician burnout. According to a recent study published in the Journal of the American Medical Informatics Association (JAMIA), ambulatory physicians spend more than five hours on the EHR for every eight hours of scheduled patient time.
This EHR use study, plus many more, revealed EHR performance measures and variations that led health IT professionals and developers to increase focus on patient care and clinician burnout.
“Without the capacity to identify targets and evaluate interventions, quality was mired in an era of implicit review without systemic approaches to improvement,” wrote the op-ed authors. “Measures are not sufficient for improvement, but good measures, in the proper context, have a central role in supporting and incentivizing better performance.”
EHR use metrics are vital to measuring EHR design, implementation, and regulation, along with gauging improvement in clinical workflow and teamwork, according to Melnick, Sinski, and Krumholz.
“For a measure to be scientifically sound, its results must be precise, reliable, valid, and adequately risk adjusted,” the authors explained. “Such measures can be used to compare vendors and instances of the same product, identify variation and best practices among clinicians, support training of students and residents, and spark efforts to improve.”
For example, researchers, health systems, and health IT developers could leverage EHR use measures to determine:
- Total EHR time
- Work outside of work
- EHR documentation time
- Prescription orders
- Inbox usage time
- Teamwork for orders
- Undivided attention
EHR audit logs are a common and current way to gauge EHR use measurements. Audit logs automatically capture observational data, such as number of workflows, keystrokes, and mouse clicks.
“EHR audit logs are an appealing data source for measurement,” explained Melnick, Sinski, and Krumholz. “Yet the validity and reliability of their data remain in question due to their unwieldy and subsequent inaccessible nature and lack of standard data definitions. Standard definitions of time-out lengths and work performed outside of scheduled clinical hours across vendor products and better integration of clinician schedules with EHR audit logs could begin to address many of these issues.”
Although audit logs do not always tell the complete story, they can offer critical data and implications. For example, the previously noted JAMIA study authors leveraged audit logs to reveal female physicians spend more time on the EHR than their male counterparts.
Researchers and health systems can utilize this information to conduct further studies or optimize their respective EHR systems. Furthermore, this data could emphasize EHR user experience to add visibility and transparency to the specific problem at hand.
To prove the importance of EHR use data, the authors suggested the Office of the National Coordinator of Health IT (ONC) to require regular vendor EHR use measure reporting to maintain certification. Thus, ONC would place the measurement burden on the developers and not the clinicians.
Reporting EHR use data could also result in accurate, meaningful, and current EHR use measurements, which could trigger policy, regulatory, or workflow changes, Melnick, Sinski, and Krumholz wrote.
EHR use measures should have evidence that links it to a particular outcome, must target a poor performance area, and must produce actionable and usable results that are relevant to healthcare stakeholders, the trio recommended.
“The EHR has the potential for benefit, harm, and burden,” concluded Melnick, Sinski, and Krumholz. “To optimize EHR design, implementation, and regulation, EHR use measures must be developed that are trustworthy, clinically important, scientifically sound, transparent, and feasible for implementation. These measures are needed now.”
Pew Charitable Trusts recommended three items for ONC to improve patient data exchange and public health.
The Office of the National Coordinator for Health Information Technology (ONC) should expand the data required in the United States Core Data for Interoperability (USCDI) to improve public health efforts and patient data exchange, according to Pew Charitable Trusts.
ONC defined USCDI as “a standardized set of health data classes and constituent data elements for nationwide, interoperable health information exchange.”
The agency adopted the first version of USCDI as a standard in the ONC Final Rule. It set a foundation for increased patient data sharing to boost patient care
In January 2021, ONC released USCDI Version 2 to enhance interoperability and patient data exchange between patients, providers, and other users.
“We recognize that these criteria may change each year based on trends within the submissions received, high priority target areas, and other factors,” wrote ONC in January. “We aim to provide relevant details on a given year’s priorities in order to provide greater transparency into the process and ensure continued alignment of USCDI submissions to high priority target areas for health IT and health care.”
However, Kathy Talkington, director of Health Programs at Pew, said USCDI Version 2 is missing valuable information to help combat public health crises, such as COVID-19.
“When finalizing the proposed version, ONC should ensure the USCDI includes data needed for public health and health equity, which can help public health agencies fight the current pandemic—and be better prepared for future crises,” Talkington wrote in a letter to ONC.
According to Pew, over 40 percent of lab results are missing important patient data.
To strengthen USCDI, Pew recommended ONC:
- Require the use of US Postal Service (USPS) standard to boost patient matching
- Include key data elements, such as travel history, employment, and death date
- Accelerate social determinants of health (SDOH) data integration
“Given these existing gaps, ONC should ensure the USCDI includes data needed for public health and health equity, which can help public health agencies fight the current pandemic—and be better prepared for future crises,” Talkington added.
Standardizing data elements, such as phone numbers and addresses, is crucial to patient matching. Talkington said patient matching could improve with the help of USPS formatting. USPS address formatting can increase matching by up to 3 percent, according to a 2019 study published in the Journal of the American Medical Informatics Association.
“Using additional data elements to verify individuals’ identities can help do that,” Talkington explained.
“ONC rightly added more demographic data to the USCDI in version 1, including current and previous addresses; phone number (as well as the type of number, such as a cellphone or home landline); and email address,” she continued.
Pew recommended ONC integrate additional demographic data, such as health plan ID or Medicare Beneficiary ID to provide a standardized way to improve patient matching and link patient records across systems.
However, ONC said integrating this standard would result in provider burden.
“Instead, ONC created Project US@, a multi-stakeholder initiative to create a health care-specific format for address, building off of and removing existing variation in the USPS standard,” Talkington said. “However, this process will take time to develop a more specific standard, and ONC should not delay adoption of the USPS standard in the interim. Even with the variation allowed in the USPS standard, adoption would lead to fewer discrepancies and differences in address depiction than exists today.”
Next, Pew suggested ONC integrate specific public health data elements to boost patient data exchange during a public health situation, such as COVID-19.
Pew said ONC should include an existing “problems” data class, a “specimen” data class, a “travel history” data class, a “work information” data class, an “observations” data class, and also include a “death date” data class.
“Including data needed for public health as part of the USCDI will ensure that all EHRs are able to document and exchange this information in a standard manner, including with public health agencies,” wrote Talkington.
Research shows that identifying and implementing individual SDOH data into the EHR is crucial to finding answers to significant health issues. Studies show this data accounts for 80 to 90 percent of individuals’ health.
Once identified, SDOH data can create opportunities to offer social services and interventions for high-risk individuals.
“The COVID pandemic has also highlighted the importance of using data to improve equity of care, and how missing data can make it harder to target resources, distribute vaccines appropriately, and assess the risks to different communities,” Talkington explained.
“Yet, USCDI fails to include many important SDOH data elements. We encourage ONC to accelerate their inclusion of SDOH in USCDI version 2”
Talkington said providers and patients should engage in conversations about the importance of SDOH data, which could ultimately allow individuals to give providers access to collect and share SDOH data.
“USCDI version 2 is an opportunity to ensure data needed for patient care and public health activities are included within standards for exchange,” concluded Talkington.
“The COVID-19 pandemic has highlighted the existing gaps in current mechanisms for data exchange, both between health care facilities and with public health agencies. A comprehensive USCDI could help close these gaps and ensure complete, standardized data can be seamlessly shared with those who need it.”
HHS awarded New York eHealth Collaborative (NYeC) and a United Way of New York State subsidiary for their social determinants of health (SDOH) data initiative.
The New York eHealth Collaborative (NYeC) and 2-1-1 New York, Inc. (2-1-1 NY), an affiliate of United Way of New York State, will work to promote patient data exchange through social determinants of health (SDOH) data.
This work comes as a part of the organizations’ Social Care Referrals Challenge award granted by the Department of Health & Human Services (HHS).
“We are thrilled to be partnering in this important work that is sure to benefit so many New Yorkers and further the mission of both 2-1-1 and United Way,” said Mary Shaheen, vice president of United Way of New York State (UWNYS) and president of 2-1-1 New York.
The two organizations plan to establish a framework that supports patient data exchange and collaboration between existing networks and users. NYeC and 2-1-1 NY said the framework would create a statewide resource repository of local organizations and services to help exchange SDOH data and improve referrals.
“Vulnerable New Yorkers rely on resources and services delivered by community-based organizations, but those needs often go unmet due to the fragmented structure that exists between the healthcare and social services systems,” said Valerie Grey, NYeC CEO.
NYeC runs the Statewide Health Information Network for New York (SHIN-NY), the New York statewide HIE.
One hundred percent of hospitals in New York and over 100,000 healthcare professionals connect to SHIN-NY. The HIE facilitates secure and confidential electronic sharing of patient data across the healthcare system. It connects regional networks, or qualified entities, that allow participating healthcare professionals, with patient consent, to quickly access and share health information and medical records.
2-1-1 NY provides individuals with a repository of health and human resources based on specific needs and locations. The organization said individuals could access these community resources online or by phone.
“Community-based organizations must be supported to assist healthcare providers with resources to improve overall health, reduce disparities, and increase wellbeing of patients and communities,” Grey continued. “While several systems have emerged in recent years to address these types of needs, they are disparate and not interoperable. These are gaps we can fill so stakeholders can continue to innovate within this space for the betterment of our broader community.”
This adds to the investments HHS has been making in health data exchange and interoperability.
Earlier this month, the agency awarded funding to two regional NY HIEs, Bronx RHIO, and HEALTHeLINK, to improve patient data exchange between the HIEs and immunization information systems.
Through this program, HHS plans to help public health agencies track and identify patients who need a second Moderna or Pfizer vaccination and also identify high-risk individuals who need to begin a vaccine regimen.
Bronx RHIO will use the funding to support public health agencies identify and track individuals who need vaccinations in high-risk communities, the HIE explained. The funding will also help the HIE improve COVID-19 vaccination administration, monitor long-term vaccine-related health effects across populations, and measure vaccination patterns based on social determinants of health.
With the funding, HEALTHeLINK intends to develop COVID-19 technologies to assess immunization statuses for individuals in Buffalo and several other western New York counties, the HIE said. The HIE will also deliver patient monitoring to vaccinated individuals and provide clinicians with COVID-19 EHR alert notifications for patient immunization statuses, hospital admissions, and COVID-19 test statuses.
HHS and ONC will distribute roughly $20 million in funds from the Coronavirus Aid, Relief, and Economic Security Act (CARES Act). Among other things, the CARES Act aims to support the country’s COVID-19 vaccination efforts.
The spread of COVID-19 gave health IT experts another reason to implement patient travel history into the EHR.
An EHR extraction system could be the key for translating unstructured text about patient travel history into actionable health data, according to a study published in JMIR Publications.
Without an automatic extraction system, clinicians would have to manually review travel charts, utilize a specific EHR system that imposes travel history documentation, or ignore travel history completely.
The spread of COVID-19 provided urgency to integrate travel history information into the EHR. Implementing travel history into the EHR can help put infectious symptoms into context for clinicians.
If implemented as a vital sign, along with temperature, heart rate, respiratory rate, and blood pressure, travel history can add to detailed patient data, prompt further testing, and spark protective measures for individuals who come into contact with the patient.
EHRs can also integrate with travel history to customize immediate diagnosis for returning travelers, similar to how cardiovascular risk calculators can show the patient a personalized list of potential lifestyle changes.
Although the Department of Veterans Affairs (VA) currently integrates travel history into patient EHRs, the research team evaluated the feasibility of annotating and automatically extracting travel history mentions in clinician notes, which present as unstructured text, across disparate healthcare facilities to respond to public health emergencies.
The researchers created a standard for patient travel history EHR detection through manual patient chart abstraction and developed an automatic text extraction pipeline.
Out of over 4,500 annotated EHRs, 58 percent contained travel history mentions, 34.4 did not contain travel history, and the remaining were undetermined. The research team said automated text processing accuracy and clinician burden levels were acceptable enough to provide rapid screening in the future.
Travel history varied from semi-structured questionnaires, such as “Have you visited a region known for Zika transmission?” to “Has the patient recently returned from Brazil, Mexico, or Miami” to “Went to Europe.”
Several researcher disagreements stemmed from differing attribution of past affirmed travel as opposed to future or hypothetical travel.
For example, one researcher marked “Traveling to visit sister in Hungary in May” as future travel, while another marked this example as past affirmed travel.
Additionally, the study authors expected military deployment locations, but the patient did not always deploy. Some EHRs would display “Service Era: Vietnam” but that does not mean the patient traveled to Vietnam.
“Location agreement was calculated for all annotated location text spans and required an exact match of text offset and negation status,” researchers explained. “Any difference in status was counted as a disagreement and any difference in text span was considered as a separate annotation element. Record agreement combined any annotated location status so that each snippet would be assigned a class of either no travel mentioned, negated locations, positive locations, or mixed.”
The research team identified 561 distinct locations over 8,127 location spans.
“Our findings demonstrate that training an accurate model to extract travel mentions is feasible in an automated system,” wrote the study authors. “Both labeled sets and the modeling approaches were chosen to minimize development time and computational resources necessary to continue surveillance in day-to-day operations. The baseline comparison presented here is a simplified evaluation, but it demonstrates that general-purpose geoparsing solutions alone result in lower precision.”
Because the research team developed the technology three years before COVID-19, its use during the spread of the coronavirus was limited because travel was only a relevant risk factor during the early phases of transmission, the study authors wrote. When researchers developed the tool, its capabilities were primarily concerned with individuals bringing infectious disease into the United States.
“The Centers for Disease Control and Prevention (CDC) guidance for Persons Under Investigation on February 12, 2020, included explicit mention for travel to Wuhan or Hubei Province,” the study authors explained. “By March 4, the CDC removed these criteria and instead encouraged clinicians to use best judgment for virus testing. In some surveillance efforts, travel history was deemed to be less important in risk assessment once community acquisition increased.”
Researchers could leverage the method in the future to prevent and contain another COVID-19 spread and the spread of other infectious diseases.
Over half of acute care hospitals reported engagement in all four domains of EHR interoperability in 2019.
EHR interoperability among acute care hospitals increased from 2018 to 2019, according to the 2019 American Hospital Association IT Supplement published by the Office of the National Coordinator (ONC) for Health IT.
Interoperability continues to be a challenge for health systems across the country. Still, the report found that over half of acute care hospitals participated in all four interoperability domains (send, receive, find, and integrate). This number has steadily increased from 26 percent in 2015, to 29 percent in 2016, to 41 percent in 2017, to 46 percent in 2018, and then to 55 percent in 2019.
Roughly 70 percent of hospital respondents integrated data into the EHR, which was a considerable increase from 2018. Furthermore, 75 percent of hospitals reported finding or querying patient data from outside hospitals.
The number of small and medium to large hospitals with 2015 Edition Certified EHR technology increased from 2018 to 2019.
The national average of US non-federal acute care hospitals jumped from 83 percent in 2018 to 91 percent in 2019. Nearly 90 percent of small hospitals had certified EHR technology in 2019, while 95 percent of medium to large hospitals adopted certified EHR technology. The latter was at 67 percent just two years ago.
Health information exchanges (HIEs) are crucial for connecting communities and ensuring patient medical records are available at all times. While interoperability remains a major issue for HIE implementation, HIE connectivity is becoming more prevalent across the country.
According to the survey, there was nearly a 40 percent increase in the proportion of hospitals that used a national network to find patient data between 2018 and 2019.
On the other hand, state, regional, or local HIEs were the most common method utilized by hospitals to find patient data from outside providers. This percentage increased from 46 percent in 2018 to 53 percent in 2019.
A little over four in 10 hospitals utilized an interface connection, such as an HL7 interface, between EHR systems. A similar percentage used provider portals or national networks to find patient data in 2019.
Hospitals reported a 4 percent decrease from using other healthcare organization HER logins credentials. This percentage fell from 31 percent in 2018 to 27 percent in 2019.
National network participation dramatically rose from 2018 to 2019.
Nearly 70 percent of hospitals participated in any national network, and almost 50 percent of hospitals participated in more than one national network. These percentages increased from 57 percent and 33 percent, respectively.
DirectTrust and Sequoia Project’s Carequality connections both increased more than 10 percent.
The report found that 80 percent of medium to large hospitals participated in either a state, regional, or local HIE network. This compared to only 68 percent of small, rural hospitals that participated in HIE networks. Less than 50 percent of small, rural, and critical access hospitals (CAHs) participated in national as well as state, regional, or local HIE networks.
Small, rural, and CAHs reported participating in neither a national HIE, a state, regional, or local HIE compared to larger or more suburban hospitals.
Hospitals reported patient data exchange barriers in 2019. Roughly 70 percent of hospitals noted information blocking barriers, such as exchanging patient data across separate EHR vendor platforms and attempting to exchange patient data with outside providers.
In March 2020, ONC released the next phase of the 21st Century Cures Act, the interoperability rule, which primarily focused on interoperability and patient information blocking. The published rule aims to drive patient access and sharing of patient electronic health information, allowing individuals to coordinate their own healthcare.
“ONC is working to improve the flow of EHI between patients, health care providers, and health information networks,” concluded ONC.
eHealth Exchange users reported high customer satisfaction across multiple areas, including patient data exchange and interoperability.
eHealth Exchange customers said the health information exchange (HIE) enables patient data exchange across state and regional HIEs and public health agencies, according to a recent KLAS First Look report.
Although a few respondents reported a lack of consistent support and platform navigation, all respondents said they would purchase the product again in the future.
eHealth Exchange, the nation’s largest HIE, connects to 75 percent of all US hospitals, over 60 regional or state HIEs, and four government agencies, including Veterans Affairs (VA) and Department of Defense (DoD). KLAS interviewed 19 individuals from 19 unique organizations, made up of HIEs, clinics, hospitals, and various health systems to assess client satisfaction and use.
Nearly every respondent said the HIE supported integration goals, promoted needed functionality, and would recommend the service to a friend.
Sixty-seven percent of respondents said they were highly satisfied with the HIE overall, and 28 percent said they were satisfied. Meanwhile, only 5 percent of eHealth Exchange customers reported dissatisfaction with the HIE.
Following integration, 46 percent of respondents said they saw immediate results and an equal percentage saw results within six months. Less than 10 percent said they saw results between six and 12 months.
Over 60 percent of respondents said eHealth Exchange is easy to scale, while only 7 percent said it was not scalable. A little over 30 percent of customers reported it was scalable with effort.
When it comes to connectivity, 92 percent of respondents said they could connect with VA and DoD, 69 percent said they could connect with the Social Security Administration (SSA), 69 percent reported connectivity with 60 state and regional HIEs. In comparison, only 38 percent of respondents reported connectivity with public health agencies, and 23 percent said they could connect with the Indian Health Service (IHS).
The respondents reported several strengths, but most agreed that interoperability, especially with SSA and VA connections, was most beneficial. Customers also reported seeing value with the organization and most said the HIE works as well as expected and promoted.
“We wanted to be able to exchange the information and utilize the system to exchange with our state reporting agency,” an anonymous manager told KLAS. “The system allows us to do that work easily and successfully. With the SSA, we have been hugely successful with our information exchange. There is a fast turnaround on the disability claims because the SSA can electronically obtain the information from us quickly.”
However, some respondents said the HIE sometimes lacks health IT support and the platform can be difficult to navigate.
“eHealth Exchange’s support is confusing. Sometimes when I try to seek out information, I feel like there are a couple of different steps to take before I can find out whether I have all of the information that I need,” described an anonymous application manager.
Mike Davis of the KLAS Research Arch Collaborative said eHealth Exchange could address a few key interoperability areas.
“Land mines for interoperability include the ability to provide positive patient identification when exchanging patient information,” Davis said. “How many Tom Smiths are there? The other challenge is creating an effective minimum discrete data set that can be used to improve care management and analytics. CDA information helps, but discrete patient data would be more useful.”
Overall, Davis said eHealth Exchange has long-term viability across the healthcare sector.
“Promoting interoperability is a key focus of CMS to drive higher levels of patient care quality and safety,” Davis explained. “The pandemic exposed the need for better information sharing between care providers. eHealth Exchange’s ability to provide an interoperability solution that can be quickly implemented with standard exchange protocols and partners sets them up for long-term success.”
Researchers found a 10.1 percent transmission risk percentage with EHR data, which was on par with traditional contact tracing methods.
Extracting household patient EHR data proved to be as effective at tracking transmission as COVID-19 contact tracing, according to a research letter published in JAMA Network Open.
Because COVID-19 is primarily transferred by person-to-person contact through respiratory droplets in households, researchers aimed to find out if healthcare professionals could leverage EHR home address data to identify COVID-19 risk factors and estimate transmission risk.
Researchers analyzed EHR COVID-19 data between exposed children and adults from Mass General Brigham between March and May 2020. Researchers compiled data from all patients registered at the addresses of index cases but excluded patients who did not have at least one health system visit within the last 60 months.
Overall, researchers evaluated 7,762 index cases between 17,917 at-risk individuals. Using EHR data, researchers found a 10.1 percent overall household infection risk, or 1,809 COVID diagnoses. This transmission risk percentage was consistent with traditional contact tracing, the study authors wrote.
“Independent factors significantly associated with higher transmission risk included age greater than 18 years and multiple comorbid conditions,” the study authors wrote. “In sensitivity analyses limiting the maximum size of the household to as small as 2 persons, the calculated transmission risk increased to only 13.8%.”
Although EHRs proved to be useful to track COVID-19 patients, relying on home address EHR data was also a major limitation, wrote the research team. The study authors said leveraging home address data could lead to undercounting and overcounting household members.
There currently isn’t a great fix for that issue, but nevertheless, the researchers contended the EHR-based strategy was effective.
“Although we acknowledge that contact investigations are the standard approach for estimating household transmission risk, we believe that the consistency of our results with these approaches suggests that our approach may provide a more efficient method for risk estimation and household contact identification,” the study authors explained. “Moreover, our sensitivity analysis indicated that the results were qualitatively similar when restricted to smaller households.”
Overall, EHR data could support COVID-19 control efforts, so as long as adequate infrastructure is in place to put this to scale.
Developing, implementing, and assessing a plan for EHR systems and public health information systems require a boost in health IT, governance, and overall strategy, according to a separate study published in The Journal of the American Medical Informatics Association (JAMIA).
COVID-19 response efforts have included the collection and analysis of individual and community EHR data from healthcare organizations, public health departments, and socioeconomic indicators. But those resources haven’t been deployed the same way in all healthcare organizations, the researchers stated.
An analysis of COVID-19 response efforts from 15 healthcare organizations that saw delays in correctly understanding, predicting, and mitigating the COVID-19 spread highlighted some pitfalls.
The research team determined a number of steps that could help organizations in the current and future steps to mitigate the pandemic. The researchers’ recommendations may also help in future public health crises.
Health IT infrastructure needs to support public health that leverages EHR systems and associated patient data, but it cannot be developed and implemented right away, the researchers wrote.
Additionally, having better control of the timeliness of data analysis will be essential. Because analytic methods do not always give real-time results, it is easy to overlook or underuse EHR data.
Researchers also found public health information infrastructure does not currently support larger-scale integration. Due to this issue, health organizations have been largely unable to gather information during the pandemic because it requires multiple data submissions to a number of agencies.
Implementing a clinical decision support tool into the EHR could decrease hospital readmissions and boost patient care in the home care setting.
An EHR-implemented clinical decision support tool can influence the standardization and customization of home care nurse decision making and also improve patient care in the home care setting by decreasing hospital readmissions, according to a study published in JMIR Publications.
Although home care provides care to over 5 million patients a year, it remains an understudied healthcare setting. According to the study authors, roughly one in five home care patients are readmitted to the hospital during home care and nearly two-thirds are hospitalized during the first two weeks of home care services.
The research team said the timing of the first home care visit is crucial to prevent hospital readmissions. However, home care nurses have limited or inaccurate patient data, meaning they don’t have a lot of information off which they can base decisions about when that first home care visit should take place.
Researchers developed an EHR-integrated CDS tool, Priority for the First Nursing Visit Tool (PREVENT), to help nurses flag which patients they should visit in the home first. Complex patients might get a home care visit sooner than patients with fewer medical complications, for example.
The research team aims to evaluate if patients would receive more-timely initial home care visits and if the tool could reduce hospitalization and hospital readmissions within 60 days. Throughout the data collection period, researchers will assess reach, adoption, and implementation by interviewing home care nurses and analyzing relevant data.
PREVENT was approved in October 2019 and it is currently being integrated into both home care and hospital EHR systems. The research team said data collection will begin in early 2021 once patients are selected for research.
The researchers said the EHR implementation will consist of three phases: preintervention, intervention, and postintervention.
In the preintervention phase, the researchers said they will identify CDS users and conduct training with users. The research team will evaluate the existing health IT infrastructure and the EHR system to develop a plan for EHR integration.
In the intervention phase, the trained clinicians will utilize PREVENT in a practice setting. The researchers will monitor the clinicians and recommend usability changes to the users. The research team would also measure the PREVENT usability and optimize the infrastructure and EHR workflow, if needed.
For the third phase, the research team will evaluate the barriers and facilitators for CDS implementation and user effectiveness. To ensure a successful implementation for field use, the research team will conduct interviews, simulations, and assessments. Furthermore, the team will evaluate appropriate resource access and both EHR workflow and health IT adjustments.
“In this study, we introduced a rigorous methodology for evaluating the implementation of an innovative CDSS, PREVENT, which was developed to assist in determining which patients should be prioritized for the first homecare nursing visit,” the study authors wrote. “This methodology was built on the RE-AIM framework and mixed methods approaches, incorporating homecare admission staff interviews, think-aloud simulations, and analysis of staffing and other relevant data.”
The research team said these steps present the outline of a study that aims to boost patient care at home care settings.
“We strongly encourage other researchers who study the effects of CDSS in clinical practice to apply similar mixed qualitative and quantitative methodologies in their studies,” concluded the study authors. “The application of mixed methods can enable researchers to gain an in-depth understanding of the complex socio-technological aspects of CDSS use in clinical practice. In turn, such comprehensive understanding can improve long-term effective use of CDSS in clinical settings.”